The 2025 technology predictions bonanza

AI/ML

1. Rise of Virtual Co-Worker: The Autonomous AI Agents: In 2024, we saw organisations exploring generative AI, but adoption is not widespread other than adopting it for personal productivity. AI agents will be most important in 2025 where every vendor, companies including startups, are investing heavily in. These “virtual co-workers” will augment humans to manage multi-step workflows.
   
2. Building Foundation of Trust and Governance in AI: Given the rise of the new AI era, building a foundation of trusted, high-quality and secure data is imperative. Bad data leads to bad AI – organisations will be at risk if data is incomplete, scattered or rely on inaccurate data for decision making. The need for AI oversight and accountability is important as organisations place emphasis on data democratisation, data stewardship and AI data pipelines as topmost important use cases. Organisations will need a strong data governance framework to manage their AI systems and ensure AI is transparent and used responsibly, while complying with AI regulations and policies.
   
3. AI Upskilling: Legacy infrastructure and outdated tools, accompanied by widespread fragmented siloed data make data democratisation across organisation difficult and challenging – hindering business users from harnessing trusted insights from their AI-powered solutions. Developing talents and competency in AI and data across enterprise-level, along with a metadata-driven intelligence layer to support AI-driven development and ability to do rapid prototyping, will be crucial in making sure trusted data is democratised and being used responsibly, thus delivering successful outcomes for the organisation.
   
4. Returns on AI investment: Organisations experimenting with generative AI models for productivity and tasks automation must now quantify their returns on AI investments. Companies will need clear metrics aligned with their business objectives to measure the true value and impact of their generative AI applications. Till they are able to demonstrate tangible outcomes, they will then be able to diversify and scale their AI usage across their business practice for greater business outcomes.
   
5. Vertical AI: While there are multiple use cases among major sectors including financial services, healthcare, and retail, current exploration is often limited to reducing operational costs and improving productivity. Broadening the usage of AI to for e.g. optimising supply chain management, combating fraud and risks and improving customer experience through hyper-personalisation, will drive significant value for organisations looking to innovate and drive competitive advantage. Vertical AI models that utilise enterprise data and are tailored to specific industries are designed to deliver more meaningful insights and can provide self-service options to tackle workforce shortages and enhance work agility, thereby ensuring a quicker return on investment.

– Steven Seah, Vice President for Informatica, ASEAN, India & Korea, Informatica

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AI adoption and increased security scrutiny

In 2025 and beyond, we’ll see more organisations incorporating AI into their infrastructure and products as the technology becomes more accessible. This widespread adoption will lead to data being distributed across a more complex landscape of locations, accounts and applications, creating new security and infrastructure challenges. In response, CISOs will prioritise the development of AI-specific policies and security measures tailored to these evolving needs. Expect heightened scrutiny over vendor practices, with a focus on responsible and secure AI usage that aligns with organisational security standards. As AI adoption accelerates, ensuring secure, compliant implementation will become a top priority for all industries.

AI-powered attacks will outpace traditional security measures

Despite the best efforts of companies like OpenAI, Google and Microsoft to implement robust security protocols, cybercriminals now have powerful tools at their disposal, including AI-driven virtual assistants that can streamline and amplify their attacks. As data volumes continue to surge and become more accessible, the appeal and ease of targeting sensitive information will grow. This convergence of advanced attack tools and abundant data will make it increasingly difficult for organisations to stay ahead of evolving cyberthreats.

– Tenable

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Leaders must harness curiosity in an AI-driven world
To thrive in the AI era, leaders must embrace endless curiosity. CEOs should actively explore AI tools for personal use and organisational transformation. Experimenting with capabilities like ChatGPT or Gemini reveals AI’s strengths, limitations, and long-term potential. Familiarity with AI models empowers leaders to make better decisions and prepare for the challenges ahead. Curiosity is the secret weapon to navigating and innovating in an AI-driven world.

From AI gold rush to clear-eyed utility
After 2024’s excitement around AI adoption, 2025 will focus on achieving tangible value. Enterprises must move beyond simply using AI and define specific goals, such as speeding decision-making or improving productivity. Not all applications will be AI-powered, but those integrating language models, human input, and data repositories will mature and find surprising use cases. Boards and CEOs must prioritise investments that deliver the highest returns, transforming AI into a practical, strategic asset.

Redefining employee incentives for AI adoption
AI’s integration requires rethinking performance metrics and incentives. For example, if engineers are evaluated based on manual coding output, they may avoid using AI copilots, even if it enhances their productivity. To accelerate adoption, leaders must align incentives with AI-driven efficiencies and foster a culture of innovation. Upskilling employees and rewarding strategic outputs ensures AI is embraced as a tool for growth and collaboration, not seen as a threat to individual contributions.

Enterprise data as the next frontier for AI
Early AI applications often relied on public data sets, but 2025 will see enterprises leveraging their massive private data troves. As reliable AI systems using structured data emerge, organisations will demand practical applications that deliver consistent value. Meeting these expectations will require AI solutions to prioritise reliability over flashy demos, setting a new standard for enterprise AI.

Balancing innovation with smart regulation
AI advancements will challenge existing regulatory frameworks, particularly as misuse of generative tools grows. Governments and industry players must collaborate to establish thoughtful regulation, ensuring both safety and innovation. While overregulation risks stifling progress, a lack of oversight could harm individuals and organisations alike. Striking this balance will be critical as AI’s influence expands.

Protecting intellectual property to sustain AI development
As publishers grow wary of unauthorised data use, AI companies face increasing restrictions. Without access to high-quality data sets, innovation could stall. The solution lies in licensing agreements that fairly compensate content providers, ensuring mutual growth. Companies must act quickly to avoid legal disputes and foster trust with data providers, securing the resources essential to refining AI technologies.

– Sridhar Ramaswamy, CEO, Snowflake

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The Rise of AI Agents

In 2025, AI agents will outnumber human workers. These aren’t your garden-variety chatbots, think of them as autonomous digital co-workers, programs capable of working independently, learning from data, and making decisions in real time. They’ll transform everything from onboarding to enterprise systems. But with their rise comes a critical need for oversight—without it, these digital workers could turn from assets to liabilities.

AI agents can be as unpredictable as they are transformative. We’ve already seen the issues that can arise from allowing technology to run without monitoring or oversight, such as with the Robodebt scandal. But, while The Government spent much of 2024 drafting guardrails and issuing guidelines for ethical AI use, many organisations are lagging, underestimating the sheer scale of governance required to ensure these systems don’t go off-script.

AI is only as effective as the data it’s trained on, yet in Australia, 68% of company data remains unused, according to the Seagate study, “Rethink Data: Put More of Your Data to Work — From Edge to Cloud.” This is like asking a chef to prepare a feast with mere crumbs. Compounding the issue are data silos, outdated systems, and inadequate quality control, creating a perfect storm that leaves businesses increasingly vulnerable to failure.

The tech is there but the oversight isn’t, which is why we can expect governance systems to be where businesses set their sights: monitoring every decision, tracking every data point, and ensuring AI plays fair. But, creating these systems goes beyond a simple tick-the-box exercise—it’s about breaking the fear, uncertainty, and doubt mould surrounding AI, ensuring AI agents are seen as trusted collaborators rather than unpredictable disruptors.

2025 won’t simply be about adopting AI — it will be about creating resilient systems that can drive results while inspiring confidence in both the technology and the organisations that use it.

– David Irecki, Chief Technology Officer, APJ at Boomi

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AI will move from thought to action as the age of agentic AI dawns

2025 will mark the rise of agentic AI, where AI agents move beyond content generation to autonomous understanding, planning, and execution. These agents, powered by advanced AI and machine learning, will augment human workers by responding to prompts, solving complex problems, and executing action plans to achieve business goals.

Organisations are optimistic about agentic AI’s potential, with 70% of APAC businesses planning or considering its integration. Early applications include financial advisors using AI agents for data analysis and reporting, with agents offering either direct support to human representatives or acting autonomously to deliver services.

To enable these capabilities, enterprises will focus on building orchestration systems to coordinate tasks, manage workflows, and integrate decisions from multiple agents into cohesive processes. Orchestration will become a critical foundation for scaling agentic AI, ensuring seamless collaboration between technologies while maintaining efficiency and control.

Humans will be sharing jobs with machines: The great work reallocation begins

Agentic AI will drive a reallocation of roles, reshaping workflows to leverage the unique strengths of humans and machines. Globally, job skill sets have shifted by 25% since 2015, with projections reaching 65% by 2030. In Singapore, a 50% job transformation rate is targeted by 2025.

This shift will require enterprises to redesign operating models, retrain workers, and redistribute tasks. The C-suite, supported by consultants and operations experts, will lead these changes, focusing on implementing AI-driven systems and managing large-scale transformations.

Handoffs between humans and machines will become increasingly complex, making orchestration capabilities vital to maintaining clear roles, systems, and processes. Without a strong infrastructure, scaling agentic AI sustainably will be impossible, leading to inefficiencies and missed opportunities.

Built-in AI is set to soar in 2025: New tools and approaches are taming the data deluge

The scalability challenges of generative AI are driving demand for integrated AI solutions. A survey reveals that 64% of APAC organisations prefer vendors that offer generative AI features embedded in their products, reflecting a growing need for accessible and effective AI.

Emerging tools like knowledge graphs, retrieval-augmented generation (RAG), and internal large language models (LLMs) are addressing these challenges. Knowledge graphs connect fragmented data to enhance insights, while RAG improves AI performance by incorporating real-world data. Internal LLMs, trained on proprietary enterprise data, offer secure and tailored applications.

Organisations are increasingly focusing on ethical AI adoption, embedding governance and transparency into their strategies to ensure compliance with escalating regulations. Enterprises that successfully scale agentic AI will achieve significant business value while maintaining control and addressing security concerns.

– Jess O’Reilly, Area Vice President, Asia, UiPath

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AI will not just be LLMs

In 2025, APAC organisations will strategically deploy AI for measurable outcomes, prioritising investments in AI-powered observability solutions. AI has become crucial for enhancing security and optimising productivity. Our latest Observability Report reveals that AI-driven practices, such as AIOps, are reducing alert fatigue by resolving false positives and automating tasks, boosting productivity and preventing talent burnout.

Moreover, organisations will move beyond just traditional Large Language Models (LLMs) for AI. Domain-specific AI, particularly Small Language Models (SLMs), is expected to gain widespread popularity due to their superior accuracy, reliability, and efficiency in specialised tasks, significantly reducing operating costs and environmental impact.

This balance of performance and sustainability makes SLMs an appealing choice for organisations seeking to align innovation with responsible business practices and greater operational efficiency.

– Simon Davies, Senior Vice President and General Manager, APAC, Splunk

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Southeast Asia’s AI gold rush

Generative AI has reshaped industries, with Southeast Asia leading the charge. The region drove US$30 billion in AI infrastructure investment in early 2024, supported by high digital literacy, widespread smartphone adoption, and a digital economy with over 400 million internet users.

In 2025, AI agents will transform workflows, improve efficiency, and deliver value across industries, from customer-facing chatbots to internal employee tools. This shift, powered by AI’s potential to generate US$2.6 trillion in annual value, will demand robust infrastructure and trusted cloud solutions to manage increasingly dispersed data.

The quantum-AI revolution

Quantum computing, the next frontier, is poised to solve complex problems beyond classical computing’s reach. While still emerging, significant strides in hardware and software are paving the way for faster innovation.

Asia-Pacific leads the quantum race, bolstered by investments, a strong research ecosystem, and collaborations. Singapore’s SG$396 million National Quantum Strategy aims to position the nation as a global quantum research hub.

As quantum technology matures, its integration with AI into quantum AI could revolutionise industries, optimising financial models, accelerating drug discovery, and advancing renewable energy technologies. This convergence promises transformative impacts across sectors.

– Terry Maiolo, Vice President & General Manager, Asia Pacific, OVHcloud

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AI for Sustainability

As cities accelerate their net-zero efforts, AI is becoming a critical tool for achieving ambitious sustainability goals. From data centres to hospitals and airports, building owners are leveraging AI-driven insights to make smarter, more impactful decisions on energy consumption and resource management.

The IDC FutureScape report highlights the growing importance of sustainable AI, predicting enterprises will prioritise energy-efficient infrastructure and resource optimisation to minimise environmental impacts, including e-waste, while driving digital transformation. Organisations will increasingly invest in AI technologies designed with sustainability in mind, focusing on energy efficiency, resource optimisation, and waste reduction.

This convergence of AI and sustainability marks a pivotal shift, as businesses move from experimentation to embedding AI into operations. By adopting decarbonised AI infrastructure and predictive analytics, organisations can meet growing digital demands while contributing to global net-zero goals.

– Anu Rathninde, President, Asia Pacific, Johnson Controls

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AI goes beyond flashy buzzwords

While we’re seeing fatigue from business leaders on the AI hype over the past months, in 2025, they will begin to realise the true power and value it brings as AI technology gains more widespread familiarity across business functions and at the executive level. As perspectives on generative AI matures, we will see more practical applications of the technology that solves actual business problems. This increased adoption will further break through traditional narrow AI capabilities to drive true automation within business processes. We can also expect a rise in novel use cases previously unimagined, particularly as agentic AI capabilities – that is, AI-powered systems that act as intelligent agents with a certain degree of autonomy – take shape.

Ecosystem partners rise the ranks in fuelling AI adoption

Limited resources, inadequate data governance frameworks, and a lack of in-house expertise, are common barriers that companies face in implementing AI for their business. For example, 37% of midmarket businesses report lack of quality data, as well as data silos and disparate systems, as challenges hindering their AI adoption and ability to deliver actionable insights. 

Channel partners have been pivotal in bridging this gap, providing pre-built frameworks, industry expertise, solution add-ons, and end-to-end support to augment and integrate AI capabilities into essential business processes. SAP is already witnessing the growth of partner-led territories in markets like Australia and New Zealand, India, Indonesia, as well as many parts of Southeast Asia, where partners combine their local business expertise with unique intellectual property (IP), on top of the complete suite of SAP solutions, to offer customers tailored AI solutions that drive meaningful business outcomes.

In 2025, we will see increased AI collaboration and technology alliances in the partner ecosystem, and a significant expansion and evolution in the role that channel partners play in helping businesses of all sizes make the promise of Business AI a reality.

– Utkarsh Maheshwari, Chief Partner Officer and Head of Midmarket, SAP Asia Pacific Japan (APJ)

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AI Clouds Redefining the Infrastructure Behind Tomorrow’s Breakthroughs

These AI clouds will provide critical resources for innovation and democratise access to cutting-edge AI technologies. In 2025, accelerated investments in AI clouds will address the growing need for infrastructure that supports machine learning algorithms, genAI, and data-intensive operations.

By offering scalable, flexible, and cost-efficient solutions, they will enable businesses of all sizes to harness AI’s transformative potential. For enterprises, AI clouds go beyond a technical resource. From enhancing predictive analytics and automating workflows to personalising customer experiences, organisations will increasingly rely on these ecosystems to drive efficiencies and foster innovation. However, to maximise impact, businesses must navigate challenges such as interoperability, data security, and regulatory compliance while partnering with providers offering seamless integration and support.

– Amitabh Sarkar, Vice President & Head of Asia Pacific and Japan – Enterprise at Tata Communications

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AI’s transformative journey continues amid challenges

As AI adoption accelerates, businesses will face a harsh reality: AI requires significant investments in infrastructure, training, and governance. While generative AI tools hold promise, their real-world application is hindered by infrastructure gaps and scepticism around ROI. Companies must move beyond experimentation, investing in sustainable AI solutions to realise its transformative potential.

Guardrails and governance become critical for AI adoption

With AI becoming more accessible, businesses will face increasing scrutiny around data privacy and responsible AI usage. Governance frameworks and compliance will be key to ensuring trust. These measures are not just about risk mitigation but fostering confidence in AI-driven business strategies.

Balancing sustainability with AI-driven growth

AI’s growing footprint poses challenges for energy consumption and sustainability goals. Companies will need to integrate AI responsibly, focusing on energy efficiency and sustainable practices. This balance is critical to achieving long-term growth without compromising environmental responsibilities.

– Tay Bee Kheng, President, ASEAN, Cisco

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AI and large language models (LLMs) are reshaping industries, with AI agents driving automation across workflows. In 2025, their adoption will vary by sector, with industries like healthcare advancing cautiously, while finance and legal accelerate adoption to boost efficiency and innovation.

The Power of Large Language Models

Unlike rule-based automation, LLMs interpret and act on ambiguous instructions, eliminating complex programming. Tasks like standardising data are streamlined by instructing LLMs, enabling scalable outcomes with less technical effort.

The Era of Autonomous AI Agents

AI agents perform multi-step tasks with minimal input, adapting to changing conditions. These systems learn from feedback, requiring less oversight over time. Finance and legal industries will leverage AI agents to transform workflows, while high-risk sectors like healthcare rely on AI as a collaborative tool for decision-making.

Sector-Specific AI Impacts

AI’s impact varies globally and by industry. In low-risk areas like business analytics, AI adoption will expand, automating processes such as generating reports and reviewing documents. High-risk sectors will focus on using AI to augment human expertise, ensuring precision and mitigating errors.

Trust and AI Adoption

Building trust in AI systems is critical for broader adoption. IT leaders must implement trust frameworks to manage compliance, risks, and uncertainties. Once these layers are in place, high-stakes industries, including healthcare, will embrace AI more confidently.

AI in Observability

AI-driven automation will revolutionise observability, shifting from human-led analysis to automated problem-solving. By leveraging historical data, AI can predict issues and take corrective actions, streamlining system management and reducing reliance on intuition.

Evolving Roles in AI Development

As AI advances, new roles will emerge to support its development. Specialists will craft prompts, curate data sets, and fine-tune AI models to address unwanted behaviours. These roles demand a blend of domain expertise and technical skills, bridging human knowledge with machine learning. Investing in this workforce will enhance AI’s potential and improve its reliability.

– Peter Marelas, Field Chief Technology Officer, APJ New Relic

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In 2025, purpose-driven AI agents tailored to specific workflow needs will help organisations achieve tangible results. Despite the initial excitement over generative AI over the last years, few organisations have moved beyond proofs of concept (POCs) and limited trials to full-scale implementation. We’ve seen cases of generative AI being disconnected from workflows and failing to deliver reliable outputs due to incomplete data. AI agents represent a significant advancement in enterprise AI by autonomously performing tasks and making real-time decisions within workflows.

AI agents will disrupt traditional service models in ASEAN with scalable capacity, intelligence and personalised experiences. In ASEAN, businesses often rely on increasing service staff to improve customer experience, but this doesn’t always improve problem resolution or customer satisfaction. AI agents offer a better alternative by autonomously managing customer interactions and delivering high-quality, context-aware support using real-time data. By integrating AI agents into workflows, businesses can provide faster, more accurate, and personalised service, transforming customer experiences without added complexity or extensive training.

Agents building agents, and agents talking to other agents will become commonplace. AI agents, each with a defined function, will work alongside human employees, communicate with other agents, and create new agents as business needs evolve. In this network, meta-agents will be crucial, coordinating actions across agents to keep workflows seamless. For instance, an orchestration agent would assess user needs and route requests to the appropriate agent, ensuring tasks are managed effectively. This new era of agents will redefine collaboration, creating a blended environment where humans and agents work side by side and interact as a unified team, enhancing responsiveness and coordination.

As AI agents become central to business operations, there is a greater need for professionals with new, specialised AI skills to guide these systems effectively. These skills will include being able to define agent instructions, craft prompts, and set guardrails. While prompt engineering for LLMs is common, writing instructions and setting guardrails for reasoning engines to ensure an AI model performs as intended will require expertise and become critical skills.

New types of AI models will push the boundaries of what AI can deliver. We’ll see new, highly specialised AI models that go beyond text generation to drive complex, autonomous actions. They can operate across multi-agent networks and add a new dimension to CRM, enabling AI agents to handle tasks like function calling, reasoning, planning, and adapting actions to fit real-world business contexts. We’ll also see a proliferation of specialised small language models trained on targeted, reliable data sets that offer cost-effective, accurate solutions for industry-specific tasks.

Beyond agents, robotics – the fourth wave of AI – will see interactions evolve from text and voice systems to immersive experiences with physical robots and virtual avatars with lifelike, dynamic, and highly interactive engagements.

– Gavin Barfield, Vice President & Chief Technology Officer, Solutions, Salesforce ASEAN

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Distributed computing will be the go-to for greater efficiency, flexibility, and Responsiveness: By 2025, distributed computing will emerge as the solution to our overstretched cloud infrastructure. As technologies like AI, spatial computing, and smart urban infrastructure demand more resources, organisations will shift from rigid centralised models to dynamic, distributed architectures. This transformation will empower platform engineering teams to strategically align processes with user locations, resource costs, compliance needs, and sustainability goals, unlocking unprecedented efficiency and adaptability. In the diverse markets of the APJ region, this flexibility will be vital for innovation and competitiveness, enabling companies to tailor solutions to local demands while minimising their carbon footprints.

Optimising AI workloads will be crucial in unlocking cost savings and performance gains:  As businesses in the APJ region face soaring AI workload costs, 2025 will mark a critical turning point. Leaders will prioritise optimising the inference phase—where AI generates actionable insights—to streamline operations and boost speed and accuracy. This focus on optimisation will not only cut computational expenses but also enhance performance, allowing organisations to redirect resources toward growth and innovation. The result will be a powerful cycle where smarter AI translates into improved profitability and continuous advancement in AI capabilities.

AI agents will fundamentally change the way people interact with the web: When ChatGPT first exploded onto the scene back in late-2022, it drastically changed the technology landscape and setting us on a course to rethink the world wide web. Looking ahead to the next decade, I anticipate a future where AI agents will play an active role and assist in tasks like scheduling appointments, making purchases, and paying bills, allowing us to step away from our screens. By 2025, we will start to witness the initial phases of this transformation. The chatbots we’ve become familiar with will develop into basic AI agents capable of performing simple tasks instead of merely guiding users through menus. For example, rather than just helping you navigate the process of booking an appointment with your healthcare provider, these agents could potentially handle it directly, offering you available time slots without any extra effort on your part. This shift will not only redefine convenience but also free us to focus on what truly matters, heralding a new era of effortless living powered by AI.

The rising popularity of Small language models among enterprises: Small language models (SLMs) are poised to gain significant traction among enterprises by 2025. Their ability to deliver tailored insights while reducing dependence on high-end GPUs makes them an appealing option for businesses looking to efficiently leverage large language models to enhance their products and services. In addition, the increasing focus on data privacy will drive enterprises to adopt SLMs that are more suitable for on-premises deployment, ensuring easier protection of sensitive information. The modular design and scalability of SLMs will further enable organisations to customise these models to meet their specific requirements, allowing for seamless adaptation to changing business needs.

– Jay Jenkins, Chief Technology Officer, Akamai Technologies APJ

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2025: The AI tipping point for the financial sector

Singapore’s financial sector stands at a crossroads of transformation, driven by the convergence of regulatory evolution and technological advancement. Broader regulation will mark a turning point for organisations, especially for financial institutions that are traditionally hesitant to adopt AI, opening the floodgates for a wide variety of uses.

As regulatory frameworks mature, the industry is set to accelerate its adoption of artificial intelligence (AI), positioning Singapore as a global leader in embracing these transformative technologies. According to a recent research, 51% of firms are leveraging AI, and 57% are exploring generative AI solutions, indicating the sector’s eagerness to harness these technologies for competitive advantage.

This momentum is fuelled by the proactive stance of the Monetary Authority of Singapore (MAS), with initiatives such as the S$100 million Financial Sector Technology and Innovation Grant Scheme (FSTI 3.0) laying the groundwork for a robust AI ecosystem. By funding the establishment of AI innovation centres, development of industry-wide AI platforms, and enhancements to risk management frameworks, MAS is enabling financial institutions to deploy AI safely and effectively. These investments not only signal the government’s commitment to AI adoption, but also its strategic aim to future-proof the sector.

As more business leaders view innovation as a critical strategy to mitigate financial risks, it is more critical than before for organisations to embrace transformative technologies, including AI. This sentiment reflects a growing recognition of AI’s potential to enhance operational efficiencies, improve customer experiences, and strengthen risk management practices. However, as financial institutions expand their AI deployments, challenges loom.

The rapid evolution of AI demands that regulatory standards keep pace. In 2025, there’s an imperative need for financial institutions to strike the right balance between fostering innovation and ensuring compliance.

Workforce readiness is another pivotal factor. Equipping employees with technical skills as well as a comprehensive understanding of the ethical and regulatory dimensions of AI will reshape roles and workflows in the coming year. Banks in Singapore are already addressing this need by launching AI courses for fresh graduates and existing employees, fostering an ‘AI-first’ culture. These efforts are not merely about upskilling; they represent a strategic alignment with demands of an AI-driven future.

The turning point will be governed by GenAI deployments safeguarded against compliance risks. Clear and robust regulations will be essential to safeguard against compliance risks and foster growth, encouraging hesitant adopters to embrace AI. These frameworks will unlock high-impact innovations, ensuring trust, governance, and the full realisation of AI’s potential in the financial sector.

This tipping point is not just a technological evolution. It’s a redefinition of the financial landscape, where trust, innovation, and governance converge to unlock AI’s full potential.

– Andy Ng, Vice President and Managing Director for Asia South and Pacific Region, Veritas Technologies

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Prediction: AI will firmly establish itself as a co-pilot, but true automation remains on the horizon 

The AI conversation over the past couple of years has been plagued with the notion that AI will soon fully take over. However, 2025 will be characterised by the widespread adoption of AI as an intelligent assistant rather than a fully autonomous decision-maker. While AI will become deeply integrated into workflows and decision-making processes, human oversight and intervention will remain crucial, particularly in business operations. The industry will move beyond the hype of full automation to focus on practical, collaborative human-AI partnerships.

Advice: organisations should focus their AI strategies on augmenting human capabilities rather than pursuing full automation. This means designing workflows that leverage AI’s strengths in processing data and generating insights while maintaining human judgment for critical decisions. While AI shows promise in automating workflows, both technological limitations and regulatory considerations will keep full automation at bay through 2025. Companies should invest in training programs that help employees effectively collaborate with AI tools, focusing on areas where human expertise and AI capabilities can create synergistic outcomes. 

Prediction: AI at the edge will enable hyper-personalised, preventative solutions

2025 will mark a significant shift from reactive to preventative AI solutions, driven by the convergence of edge computing and language models. By bringing AI capabilities directly to edge devices, organisations will be able to process and analyse in-session data in real-time, creating highly contextual and personalised experiences. Current remote connectivity solutions are largely reactive, addressing problems after they occur. However, the combination of edge AI and in-session data analysis will enable a more sophisticated approach.

Advice: organisations should begin mapping their in-session data opportunities and developing strategies for edge AI deployment. Success will require understanding the unique contexts of different users and use cases, as well as investing in edge computing infrastructure that can support AI workloads. Companies should focus on building preventative capabilities that leverage real-time insights while maintaining user privacy through local processing.

Prediction: Business AI will evolve from text-only to visual experiences 

While current AI business applications primarily focus on text-based output, 2025 will see a dramatic shift toward rich, visual AI experiences. Instead of receiving text-based instructions or reports, users will interact with AI through dynamic visual content, including real-time video generations, interactive demonstrations and visual problem-solving guides. This evolution will fundamentally change how information is communicated and consumed in business settings.

We’re already seeing early indicators of this shift, with AI art gaining prominence and selling for more than a million dollars, for example. However, the real transformation will come as these capabilities are applied to business contexts, such as AI generating 30-second tutorial videos for technical support or creating visual simulations for training. 

Advice: organisations should begin preparing for this visual AI revolution by identifying areas where visual communication could enhance current text-based processes. Companies should invest in tools and platforms that support visual AI generation while developing guidelines for effective visual content creation.

– Mei Dent, Chief Product & Technology Officer, TeamViewer

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Industrial AI will take off as the next AI wave in 2025

In 2025, we will see the next wave in this current AI revolution. Market observers estimate that most GPUs deployed are currently severely underutilised. Additionally, the majority of GPUs are deployed in a handful of companies, including the hyperscalers, with very few in private enterprises. This will shift in 2025, as enterprises bring much of the AI capability in-house to extract even more value out of their data and “industrialise” AI. We call this Industrial AI, which brings its own set of challenges including governance – specifically around how to train the models with proprietary data that needs to be kept confidential even between departments. Agentic AI and Large Quantitative Models (LQMs) will play a key role in this wave.

Machine Learning & Agentic AI will transform decision making in enterprises in 2025

While we expect Agentic AI to only become mainstream from 2026 onwards, Agentic systems will change the way AI is used for decision making in enterprises next year. Additionally, enterprises will unlock more value from machine learning in allowing them to analyse complex datasets, identify patterns, and act with velocity. Streamlining laborious and manual tasks such as data modelling will allow enterprises to solve more challenges with greater speed, while scaling and enabling faster iteration and product evolution. Use cases will be more internally focused than GenAI, with interest coming from large IT organisations in companies such as banks and telcos with complex infrastructure environments. With machine learning and Agentic AI, seamless and rapid access to the right decision-making data becomes increasingly critical.

Enterprise spend on AI will rise dramatically in 2025; pivot towards grounded approaches such as RAG

Paradoxically, in dollar terms, enterprise investments in AI will increase in 2025 while the total number of GenAI proof of concepts (POCs) and pilots will decline. In 2024, the failure rate for POCs was higher than anticipated as they failed to deliver on expectations, or were not economically viable when scaling from the training phase to the inference phase.

Rather than an AI reckoning, enterprises will move toward a renewed focus on fundamental business values and practical AI. Generic, off-the-shelf AI solutions like ChatGPT are set to decline in enterprise use as trust concerns over output reliability increase. In 2025, organisations will increasingly pivot to grounded approaches leveraging techniques like Retrieval-Augmented Generation (RAG).

This shift will reflect a deeper commitment to AI transparency and ethics, with a preference for context-aware systems that mitigate data biases and inaccuracies. The demand for RAG will surge, particularly in fields like healthcare and financial services, where real-time data integration and contextually accurate responses are critical for nuanced understanding and decision-making.

– Nathan Hall, Vice President and General Manager, Asia Pacific and Japan, Pure Storage

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1. By 2026, 40% of new applications in Asia-Pacific (APAC) will integrate Generative AI, fundamentally transforming user experiences and solving challenges that traditional systems cannot address. The real breakthrough, however, will be the seamless integration of AI into low-code platforms. AI will evolve from task-focused assistants into strategic, context-aware orchestrators. These systems won’t just manage tasks but will optimise workflows, forecast outcomes, and dynamically adapt to real-time user behavior. AI-enhanced low-code platforms have the potential to redefine operational excellence and generate new avenues for digital innovation within businesses.

2. In APAC, over 60% of business-critical applications still run on legacy systems, which are expensive to maintain and slow to adapt. We firmly believe that AI-powered low-code platforms will revolutionise this landscape next year. Such platforms will go beyond extending the life of legacy systems by integrating advanced AI-driven capabilities without costly infrastructure overhauls. Processes such as predictive maintenance, code refactoring, and system optimisation will become automated, allowing businesses to proactively address system limitations and innovate faster. This hybrid approach will combine the stability of legacy systems with the agility of modern applications, empowering traditional industries like shipping and logistics to overcome growth bottlenecks, deliver superior user experiences, and unlock new business models.

3. Generative AI is redefining the role of developers, marking a fundamental shift in the software development landscape. Research indicates that 80% of developers will transition from routine coding to becoming strategic AI facilitators. Rather than writing lines of code, developers will design frameworks and prompts that guide AI systems, enabling faster, more precise, and adaptive application outputs. This evolution will halve development cycles, making applications more resilient and user-centric. To capitalise on this transformation, businesses must act now to upskill their teams, preparing them to lead in this new era of AI-driven application development.

– Leonard Tan, Regional Director, Singapore, Malaysia, Brunei, and Greater China Region, OutSystems

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• Agentic AI architecture will revolutionise human-computer interaction, driving a shift from passive tools to autonomous agents.

• Enterprises will scale AI implementation strategically, focusing on high-impact areas and building reusable AI foundations.

• Sovereign AI initiatives will accelerate global AI adoption, empowering nations to leverage their own data and infrastructure.

• AI’s convergence with emerging technologies like quantum computing, intelligent edge, and Zero Trust security will unlock unprecedented innovation.

• AI fluency will become an essential skill, transforming the job market and requiring workforce development.

– John Roese, Global Chief Technology Officer and Chief AI Officer, Dell Technologies

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In 2025, AI-driven network management is set to revolutionise telecommunications through advancements in autonomous planning and intent-driven design, enabling predictive network optimisation. By leveraging real-time data analysis through AI and machine learning, operators will enhance efficiency, reliability, and scalability across their networks. 

Additionally, digital twins will emerge as a transformative tool, allowing operators to simulate and optimise network configurations in virtual environments. This approach will accelerate service development and reduce time-to-market, setting new benchmarks for operational precision.

Broadly, AI will continue to drive innovation across industries, reshaping how businesses operate and make decisions. While 2024 was a year of experimentation, 2025 will be the year where more businesses will transition from experimenting to scaling their AI capabilities, enabling businesses to unlock new opportunities for innovation, competitive advantage, and growth.

– Geraldine Kor, Managing Director, Head of Global Enterprise Business, Telstra International

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Data quality will be key to AI-enabled service roles

Rapid AI tool innovation and adoption are transforming service-based roles, with the most immediate impact visible in customer support, IT support and marketing functions. As organizations move from experimentation to more established processes, these service roles will evolve and move to more solidified and usable processes in 2025. IT organisations will see wider implementation and standardisation of AI-enabled services, and IT support roles will expand beyond help desk functions into AI tool deployment and optimisation.

Success in these evolved service roles will depend heavily on high-quality data. A focus on maintaining well-organised and clean data will be critical to ensuring AI tools can effectively assist with customer inquiries and service delivery.

AI premium pricing model will eventually collapse as features become table stakes

The current model of charging premium prices for AI features as add-ons will face increasing pushback from enterprise customers in 2025 and beyond. With AI becoming a standard in tech stacks, AI processing must become more cost-efficient. This shift and customer expectations that AI should enhance offerings and not raise software costs will force vendors to make AI capabilities a standard and integrate them into core product pricing. 

Tech democratisation will reduce business users’ dependence on IT

The democratisation of technology is accelerating, with user-friendly AI and no-code tools making it possible for business users to take on more technical responsibilities. With business users handling more technical tasks independently, we’ll see IT teams shift to more governance and oversight roles in some areas. 

With business or less technical users within IT able to use AI tools, it will accelerate delivery of systems functionality. These users will increasingly function as an extension of the IT team and eventually be able to implement their own workflows with IT ensuring compliance and helping to manage any performance issues. While this transformation is already underway in some organisations, increasing AI adoption and user trust will accelerate the trend through 2025. The question that remains is about where governance responsibilities will ultimately reside—whether in IT, operations or elsewhere.

– Julie Irish, SVP & CIO, Couchbase

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The AI revolution will hinge on edge computing. 

To unlock AI’s true potential, edge computing must bring the compute power closer to where it’s actually needed.  Edge computing represents a paradigm shift, dramatically reducing latency and enabling a new generation of sophisticated, responsive applications. Imagine autonomous vehicles making split-second decisions, interactive gaming with zero perceptible delay, and real-time video processing that responds instantaneously. These innovations become possible when compute resources are strategically positioned near their point of use. That’s why the future of AI is not just about raw computational power, but about smart, distributed computing that brings intelligence closer to where it’s most impactful. 

AI is the double-edged sword of cybersecurity. 

On the one hand, it powers advanced threat detection, anomaly detection, and automated response systems, enabling defenders to stay ahead of emerging threats. On the other, it is being weaponized by attackers to create more sophisticated and adaptive exploits. We are entering an era where AI systems will battle AI systems, with human security teams orchestrating strategies to maintain the upper hand. This shift underscores the need for continuous innovation in AI-driven security solutions, as static defenses become increasingly inadequate.

AI will transform the user experience and how we interact with our favorite technologies. 

Imagine retail platforms that intuitively understand your preferences before you articulate them, or educational tools that dynamically adjust to your unique learning style in real time. These experiences are made possible by sophisticated AI algorithms that leverage comprehensive yet ethically sourced data. Critical to this transformation is robust infrastructure that ensures seamless, consistent experiences across devices and locations. Emerging technologies like edge computing are key to this vision, bringing computational resources closer to users and enabling faster, more responsive interactions. The future of user experience is not just about technology—it’s about creating intelligent, intuitive connections that feel almost magical in their precision and personalisation.

– John Engates, Field Chief Technology Officer, Cloudflare

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2024 was the year of AI-powered applications making intellectual contributions towards business and life. As we move towards 2025 (and beyond), we are excited and bullish about how AI computing will unlock empowering knowledge and automate actions through intuitive interfaces and powerful agents.

2025 Predictions:

Ensuring Governed AI Deployments

As AI becomes increasingly integrated into government and business operations for 2025, AI governance will take center stage as a critical focus. The industry is turning towards building robust governance frameworks for ethical, transparent, and accountable AI. Governments and businesses will need to invest into developing policies and practices that will address AI data risks and security, allowing them to embed AI data observability and AI regulatory capabilities right into the heart of their operations. This would reduce lag times in security response and introduce more dynamic ways of ensuring compliance with regulatory requirements.

Defining Solid Data Foundation for AI

At the core of solid AI governance is robust data governance. An organization is only as good as its data, so an innovative, agile AI Data Foundation that enables business self-service of trusted enterprise data is critical if organizations want to optimally manage their data for Departmental AI-driven projects (whether in Fraud Management, Customer Engagement, ESG, etc.). This way, organisations can ensure regulatory compliance while democratising data access for productivity in a best-of-both-worlds solution, circumventing the potential hazards of departments barreling ahead with AI projects without IT’s compliance check. 

Decentralised Data Management

Traditional data management approaches are not keeping up with the modern needs of AI. We in Denodo have seen a steady trend of organizations moving away from monolithic, centralised data management towards logical data architectures (like Logical Data Fabric and Data Products) that enable them to handle data by business-critical domains for a more flexible, scalable and thereby efficient way of managing enterprise data. This shift will support the growing trend of decentralised AI Governance, allowing for more localised and context-specific oversight of AI systems. We predict this trend will continue strongly into 2025 and beyond.

Hybrid Cloud Architectures is the Future

Privacy regulations, IT risk mitigation and cost optimisation are pushing organizations to adopt a hybrid cloud approach in their data and system architecture. They complement existing on-premise legacy systems with a mix of public and private cloud environments. This hybrid trend, which will have steady growth from 2025 onwards, provides organisations with greater flexibility and control over their data and AI workloads, enhancing their ability to meet regulatory requirements and optimise performance, while also supporting the integration of diverse data sources/classes for more comprehensive and accurate AI insights.

To navigate the complexities of AI governance and decentralised data management, industry collaboration and standardization will be essential. Companies that embrace data infrastructural agility will be better positioned to leverage AI’s full potential for a competitive business edge, while also complying with stringent compliance regulations for responsible AI deployment and innovation.

– Shanmuga Sunthar Muniandy, Director of Architecture & Chief Evangelist, APAC, Denodo

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Balancing Intelligence with Transparency

As financial institutions increasingly rely on artificial intelligence (AI), transparency and accountability are essential. In Asia-Pacific, the AI market in financial services is expected to reach US$190.33 billion by 2030, growing at a rate of 30.6% annually.

A study found that 70% of consumers in the region are concerned about the opacity of AI algorithms. Explainable AI (XAI) can help by providing clear insights into AI-driven outcomes, ensuring fairness in lending and risk management.

With XAI, banks can improve credit risk assessments, enhance fraud detection, and expedite loan approvals, all while maintaining customer trust. Ethical AI practices help institutions meet regulatory standards and maintain operational integrity.

– Pramod Kumar, Head of Business (APAC), Newgen Software

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GenAI leadership is open, small and global. We will see a lot of open permissible models that can do as well, if not better than closed models. We will also see open reasoning models too. In addition, models will also get compressed by learning from bigger or more powerful models. 

AI Inference will become even more important with the growth of reasoning models and agents. However, inference scaling or scaling computation during inference time will dramatically increase the cost of AI inference. Enterprises will need to revisit their infrastructure and power investments due to this new technology trend.

Multi-agent AI or Systems of cooperating Agents will take centre stage. This will involve a collection of AI agents working cooperatively and will need new people, processes and technology to enable this in the enterprise. I also anticipate Agents will negotiate with other Agents. 

Cloud robotics will become a reality and enter the enterprise. This will lead to shared services and data infrastructure spending, especially in the Manufacturing sector. The same design pattern will also be true for managing software multi-agents in the enterprise. 

New technology and architecture will emerge to fuel the next-gen models and multi-agent systems:

• ARM will proliferate.

• To overcome the memory wall, in-memory compute will get popular (similar to how Apple M4 style converged architectures with very high memory bandwidth). 

• Smart NICs will run entire storage controllers.

– Debojyoti Dutta, Vice President of Engineering, Nutanix

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The Era of Agentics

The most significant shift ahead is what’s becoming known as the Era of Agentics. Unlike traditional AI systems that follow prescribed steps, AI agents are autonomous systems capable of understanding contexts, making decisions, and taking actions independently. These agents — similar to but far more sophisticated than today’s chatbots — use generative, training-based approaches rather than deterministic programming. By 2025, we’ll see these agents emerging across different products and services, from video analytics to automated security responses.

Think of AI agents as digital colleagues that can handle complex tasks without constant human direction. They can wait for specific conditions, respond to prompts, or act on their own initiative when they detect relevant situations. Most importantly, they learn from their actions and adapt to new scenarios, much like human operators do. In security applications, this means systems that can automatically identify potential threats, coordinate responses, and even predict incidents before they occur.

The real power of these agents lies in their ability to reason and adapt. Unlike traditional software that needs explicit programming for every scenario, these systems can understand context and make nuanced decisions. This capability will transform everything from access control to emergency response, creating more intelligent and responsive security environments.

Beyond Thinking: The Age of AI That Acts

In the evolution of artificial intelligence, we’re witnessing a pivotal shift from systems that merely analyse to those that take decisive action. While traditional metrics like IQ measure cognitive ability and EQ gauges emotional awareness, a new capability is emerging: the power to act intelligently and autonomously — AQ (Action Quotient). Think of Tesla’s self-driving cars, which don’t just process road conditions, they smoothly navigate complex traffic scenarios in real time.

This shift toward action intelligence is particularly relevant in security operations. Traditional monitoring systems alert operators to potential issues, requiring human intervention for every response. In contrast, high-AQ systems can assess situations, initiate appropriate responses, and adjust their actions based on changing conditions. This capability will transform how we approach security management, making systems more proactive and less dependent on constant human oversight.

The implications extend far beyond simple automation. These systems will be able to coordinate complex responses across multiple subsystems, from access control to emergency communications, creating more comprehensive and effective security solutions. The key is that these actions aren’t just pre-programmed responses — they’re intelligent decisions based on real-time analysis and learned patterns.

– Rahul Yadav, Chief Technical Officer, Milestone Systems

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The hype around Gen AI will wane, and businesses will take a pragmatic approach to AI

2025 will see two camps emerging – the first includes businesses that have found successful use cases for GenAI and are reaping the fruits. According to McKinsey, 65% of organisations report regular use of GenAI, with meaningful cost reductions in HR and revenue increases in supply chain management. Financial service institutions, for example, are early adopters of GenAI, and at Cloudera we’re witnessing a notable shift rippling through the industry as more banks move from rule-based to model-based systems for fraud detection. The real value of GenAI lies in gaining knowledge and insights at scale – without good data, AI models will not be able to run successfully. Thus, businesses that are most likely to benefit are from sectors with large pools of trusted data that they can tap into for actionable insights. The second group of companies do not traditionally have large scale databases to scale nor benefit as much from GenAI, and they will turn to traditional AI or deterministic Machine Learning models instead to drive efficiency and productivity. Ultimately, we foresee that businesses will cease buying into the hype and shine of Gen AI, and instead focus on mapping their technology investment roadmap to their broader organisation goals.

Businesses will favor private LLMs over public LLMs

With enterprise AI innovation taking center stage in the year ahead, businesses will eschew public large language models (LLMs) in favor of enterprise-grade or private LLMs that can deliver accurate insights informed by the organisational context. According to a McKinsey study, less than half (47%) of companies are significantly customising and developing their own models currently and we believe that this is set to change in 2025 as businesses develop AI-driven chatbots, virtual assistants, and agentic applications tailored to the individual business and industry. As more businesses deploy enterprise-grade LLMs, they will require the support of GPUs for faster performance over traditional CPUs, and robust data governance systems with improved security and privacy. In the same vein, businesses will also ramp up their use of retrieval-augmented generation in a bid to transform generic LLMs into industry-specific or organisation-specific data repositories that are more accurate and reliable for end users working in field support, HR, or supply chain.

– Remus Lim, Senior Vice President, Asia Pacific and Japan, Cloudera

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Verticalisation of LLMs will shift the industrial AI landscape in 2025

Verticalisation, the process of tailoring LLMs to specific industries or domains will begin to transform and revolutionise various sectors in the year ahead across Asia Pacific. By focusing on specific domains, verticalised LLMs will potentially delve deeper into industry-specific nuances, regulations, and best practices, enabling industries and companies to generate more accurate and relevant output, tailored to the unique needs of each domain. These customised LLMs’ ability to analyse vast amounts of industry data to identify patterns and trends, will enable industry specialised data-driven decision-making. Moreover, verticalisation of LLMs will set the stage for these models to accelerate innovation by automating tasks, streamlining workflows, and uncovering new opportunities, thereby possibly adding more value in human resources for strategic initiatives. With Asia accounting for more than half of global manufacturing value-add in 2022, according to McKinsey, this region will be a hotspot for the deployment of verticalisation of LLMs. By leveraging the power of AI, these specialised models will drive innovation, improve efficiency, and ultimately reshape the future of industries worldwide.

AI will become a true partner or companion, enabling true user-centricity

Agentic AI, or AI agents, capable of independent action and decision-making, are set to make waves over the next year and drive not just personalisation, but complete individualisation. For the first time, AI is no longer just a generative knowledge base or chat interface, it is both reactive and proactive—a true partner. Gartner estimates that nearly 15% of day-to-day work decisions will be taken autonomously through agentic AI by 2028. AI agents will leverage local LLMs enabling real-time interaction with a user’s personal knowledge base without relying on cloud processing. This offers enhanced data privacy, as all interactions remain locally stored on the device, and increased productivity, as the agent helps to automate and simplify a wide range of tasks, from document management, meeting summaries to content generation. We will also see the emergence of personal digital twins, which are clusters of agents that capture many different aspects of our personalities and act on many different facets of need. For example, a digital twin might comprise a grocery buying agent, a language translation agent, a travel agent, etc. This cluster of agents become a digital twin when all of them work together, in sync with the individual’s data and needs. 

– Sumir Bhatia, President, Asia Pacific, Infrastructure Solutions Group, Lenovo

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Automation

Businesses to Focus on ‘Small Changes to Win Big’

2025 will be the year businesses swap the sledgehammer for a scalpel. Instead of top-down mega-projects that bleed budgets and overpromise, companies are embracing bottom-up innovation. Business units are being handed the reins to tackle inefficiencies head-on. AI and bots are stepping in like detectives, uncovering hidden, repetitive tasks that no one realised were eating time and resources. These small, high-impact wins are snowballing into massive organisational gains—big results without the big price tag.

For CIOs and CFOs, this isn’t just about cost-cutting; it’s about surviving. With talent shortages and pressure to trim tech bloat, automation is shifting from a “nice to have” to a lifeline. This coming year, it’s not the big transformations that will win — it will be the relentless, incremental ones.

– Keith Payne, Vice President, APAC at Nintex

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The Hyperconnected Economy Will Usher in the Era of Autonomous Enterprises

By 2025, the integration of AI, IoT, and blockchain will enable businesses to evolve into autonomous enterprises capable of self-optimisation. Deloitte anticipates that 25% of enterprises utilising generative AI will deploy AI agents by 2025, with this figure rising to 50% by 2027. These AI agents will enhance operational efficiency by performing tasks with minimal human intervention. Additionally, Gartner predicts that by 2026, 20% of organisations will reduce labour costs by leveraging AI, potentially eliminating over half of current middle management positions. These developments signal a significant shift towards autonomous operations powered by advanced technologies.

– Theo Scherman, Chief Strategy Officer, Hitachi Asia

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Board priorities

As the Asia Pacific region navigates in an increasingly volatile and uncertain business and technology landscape in 2025, CIOs need to be forward-looking and continuously innovate while adopting the right tools to help them to meet regulatory requirements. In the upcoming Year of the Snake, which symbolises wisdom, focus and adaptability, CIOs should embody these qualities to successfully navigate the region’s complex regulatory and operational landscape: 

1. Align with Evolving Data Privacy Regulations (CSRD, GDPR, AI Act) 

The region is home to stringent data privacy and sustainability regulations, such as Personal Data Protection Act (PDPA), and the upcoming AI guidelines. CIOs must ensure compliance by enhancing data governance practices, particularly around data sourcing, usage transparency, and ethical AI practices. Building compliant data pipelines and audit trails can ensure adherence and help organisations avoid penalties. 

Recommendation: Invest in regulatory technology (RegTech) solutions that automate compliance and offer real-time monitoring of data practices. Establish dedicated compliance teams to stay updated on regional regulatory shifts, including cross-border data sharing requirements. 

2. Embrace Cross-Border Data Infrastructure Optimisation 

With the Asia Pacific region experiencing diverse data residency laws, optimising cross-border data infrastructure is key. CIOs should strategically consider hybrid cloud and edge computing solutions that allow compliance with local data residency laws while providing scalability for AI applications. 

Recommendation: Work with cloud providers that offer localised data centres in the region and edge computing solutions to minimise latency. Partner with multi-cloud vendors that understand the regulatory landscape and support data localisation needs in countries with strict data requirements.

3. Enhance AI Readiness with Language and Cultural Diversity in Data 

The Asia Pacific region spans numerous languages and cultures, requiring AI models to be trained on localised, culturally relevant data. CIOs should focus on creating language-specific datasets and ensuring that AI solutions are inclusive and adapted for local markets, especially for customer-facing applications. 

Recommendation: Collaborate with regional data providers to source diverse datasets and prioritise AI models with multilingual capabilities. Invest in data annotation and translation services to train models in languages relevant to the Asia Pacific markets. 

4. Focus on Data Sovereignty and Ethical AI Practices 

Public and government sentiment favours data sovereignty and ethical AI. CIOs should prioritise transparency in AI-driven decisions, especially in sectors like finance, healthcare, and public services. 

Recommendation: Develop a regional ethics board or governance framework specifically for AI and data initiatives. Engage with local stakeholders to communicate data handling practices clearly, particularly for AI applications involving sensitive or personally identifiable information.

5. Invest in Data Literacy and Upskilling Programs for a Diverse Workforce 

The region’s diverse workforce benefits from data literacy initiatives tailored to different cultural and educational backgrounds. CIOs should support data literacy training and upskilling programs to foster AI readiness across the organisation, with an emphasis on data interpretation, ethical considerations, and regulatory compliance. 

Recommendation: Develop regionally tailored data literacy programs that incorporate multilingual materials and contextualise data ethics based on specific regulations by various countries. Collaborate with universities and technical institutes to build a talent pipeline that supports the region’s evolving data and AI needs.

– Hemanta Banerjee, Vice President of Public Cloud Data Services, Rackspace Technology

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Cloud

Cloud-first approach accelerates AI innovation

For AI to reach its transformative potential, it must be built in the cloud. As Asia-Pacific (APAC) becomes a hotspot for AI development, cloud infrastructure investments are skyrocketing, with hyperscalers pouring billions into expanding their data centre footprint, especially across emerging digital markets such as Indonesia and Vietnam.

In the APAC region, SMEs make up a significant majority of businesses, and 92% of midsized businesses already consider generative AI a priority. In the coming year, as companies in the region increasingly recognise AI as the springboard for innovation, we can expect to see the cloud boom continue, with companies doubling down on cloud transformation.

Companies that have already invested early in building a resilient, scalable cloud infrastructure will see the biggest return from their AI investments, while those that haven’t will need to leapfrog with a cloud-first approach to remain competitive.

– Utkarsh Maheshwari, Chief Partner Officer and Head of Midmarket, SAP Asia Pacific Japan (APJ)

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The Everything-as-a-Service Model Will Dominate Across Industries

The everything-as-a-service (XaaS) model is projected to become prevalent by 2025, enabling consumers to access products and services on a subscription basis. The global XaaS market is expected to grow at a compound annual growth rate of 23.3%, reaching approximately US$1.2 trillion by 2030. This growth is driven by the adoption of cloud technologies and demand for agile solutions. Gartner predicts IT spending will rise to US$1.8 trillion by 2025, with a significant portion allocated to cloud-based services. This reflects a broad shift towards service-based consumption models across industries.

– Theo Scherman, Chief Strategy Officer, Hitachi Asia

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On cloud-native architecture

While we see cloud-native architecture rapidly becoming the new standard for building and deploying applications, organisations in the APJ and SEA region still face unique challenges in their cloud-native journey.

One major hurdle is the cost-benefit analysis of migrating to the cloud. While cloud-native architectures offer significant advantages, such as reduced infrastructure costs and improved scalability, the initial investment and ongoing operational expenses can be substantial. This has led many organisations to maintain on-premise deployments, especially for workloads that are not highly scalable or have specific security and compliance requirements.

Another key challenge is the complex decision-making process involved in selecting the right cloud provider. With a plethora of options available, including global giants like AWS, Azure, and GCP, as well as regional players like Alibaba Cloud and Tencent Cloud, organisations must carefully evaluate factors such as cost, performance, security, and compliance to make informed choices to enable their businesses to achieve greater agility, scalability, and efficiency.

– Philip Madgwick, Regional Vice President for Asia and ANZ, Alteryx

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2025 will see enterprises prioritise AI-optimised hybrid IT infrastructure and data management

While the past two years have seen an explosion in AI innovation, much of it has remained at the testing and experimentation stage. Heading into 2025, we will see organisations in Asia Pacific (APAC) take a more practical, outcome-driven approach towards AI, as business leaders look to translate AI’s potential into business value and optimise the return on their AI investments. In fact, IDC has called 2025 the year of the AI Pivot, where organisations will move beyond AI experimentation to comprehensive AI integration into their operations to accelerate innovation, efficiency and growth.

This shift towards AI integration at scale will bring to the forefront the urgent need for AI-optimised IT infrastructure and data management. To make the most out their AI, which is an inherently data intensive and hybrid workload, more organisations will need to invest in native AI systems that optimise everything across the AI lifecycle, regardless of whether the workload is on-premises, in a colocation facility, the public cloud, or at the edge.

A hybrid cloud strategy will no longer be just an option, but a prevailing operating model of choice because it’s ideal for unlocking the value of organisational data and accelerating AI deployment. A hybrid-by-design operation model, rather than a hybrid by accident model where hybrid model planning is an afterthought, will be key to success. Furthermore, robust and efficient hybrid cloud infrastructure that has been designed for AI will enable organisations to have better data visibility, enhanced control and protection, and streamlined data management across environments. This also helps them mitigate unplanned costs caused by unexpected challenges around operational complexities, security risks and inefficient use of resources.

– Mohan Krishnan, Vice President & General Manager, HPE GreenLake Cloud Services, APAC, HPE

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The Future of Cloud Security in 2025: Unified, Intelligent, and AI-Driven

The rapid adoption of AI and multi-cloud environments is fundamentally reshaping the future of cloud security. Traditional security models, which often rely on manual intervention and static rules, are no longer sufficient to keep pace with the growing sophistication of threats like phishing, ransomware, and deepfakes—many of which now leverage generative AI to bypass static defenses. 

By 2025, businesses will need to transition from reactive defenses to predictive, unified approaches powered by AI. It will enable businesses to detect zero-day vulnerabilities, automate response mechanisms, and neutralise threats before they materialise. Solutions powered by large language models (LLMs) will bring unprecedented accuracy and efficiency to threat detection, alert evaluation, and incident response. For instance, Alibaba Cloud’s AI-driven Security Center, already in use in China, covers 99% of alert events while improving detection precision and reducing false positives.

Equally critical is the integration of security across increasingly complex cloud ecosystems. Businesses are embracing multi-cloud and hybrid environments, but fragmented security systems lead to inefficiencies, data silos, and higher risks. Alibaba Cloud’s three-dimensional strategy for unified security addresses this by consolidating security management across public and private clouds, unifying technology domains through centralised data lakes, and bridging office and production environments for seamless operations.

In 2025, unified platforms and AI will converge to redefine how businesses protect their cloud environments. With tools that integrate advanced threat intelligence, centralized management, and predictive AI capabilities, organisations will be empowered to confidently secure their digital infrastructure in a fast-evolving threat landscape. At Alibaba Cloud, we are committed to leading this transformation, helping enterprises stay ahead of tomorrow’s challenges with smarter, unified, and AI-driven security solutions.

– Ouyang Xin, General Manager of Security Products, Alibaba Cloud Intelligence

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Customer experience

Customer experience (CX) will undergo a profound transformation. There will be a rise in the “everything customer,” where they expect to be recognised and have their needs met across all touchpoints. At the same time, delivering personalised CX will shift from being the responsibility of contact centres alone to an AI-first ‘total experience’ approach that involves every department shaping and delivering CX. This shared accountability across departments will help foster a supportive environment for agents, balancing AI-driven efficiency with the human touch to deliver personalised CX that meets rising customer expectations. Together with enhanced agent performance and hyper-personalised anticipatory service,  these changes will fuel a long-awaited rebound in customer satisfaction. Proactive outreach, once a nice-to-have, will become a baseline expectation, enhancing satisfaction and reducing churn across every touchpoint.

– Ricky Kapur, Head of Asia Pacific, Zoom

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1. The future of brand relationships will be built on an ‘Ecosystem of Trust’.

Trust has always been the cornerstone of brand loyalty, but in 2025, consumer trust is poised to hit an all-time low. According to Twilio’s 2024 Consumer Preferences Report, 56% of consumers in APAC say they will not purchase from a brand they don’t trust.

With consumers growing increasingly cynical and scrutinising every interaction, brands must demonstrate greater respect by putting themselves in their customers’ shoes. 

It’s time to return to the basics in 2025—keeping promises, following through, and delivering reliable and supportive experiences, during key moments of truth. As the focus shifts towards cultivating an ‘Ecosystem of Trust’, more brands will turn to Chief Trust Officers, to help redefine customer experience, marketing, and technology strategies for building lasting customer relationships. 

AI will continue to be a key driver in solidifying this ‘Ecosystem of Trust. Brands will increasingly bet on predictive AI as they strive to eliminate guesswork, refine recommendations and improve communications ultimately strengthening brand-customer interactions.

– Robert Woolfrey, VP for Communications in APJ, Twilio

2. Consumers are giving retailers a run for their loyalty. More brands will turn away from traditional “earn-and-burn” models.

Customer loyalty programmes driven mainly by discounts and cashbacks are on their way out. Instead, brands will shift towards creating experiences that surprise and delight customers, making them feel valued as their individual preferences and behaviour are taken into account. For instance, instead of offering financial incentives, brands might surprise loyal customers with preferential access to new products, or exclusive invites to previews – which cost retailers little, but provide genuine value to loyal customers.

A key priority will be around strengthening ‘phygital’ loyalty programmes, combining in-store activations with special offers for loyalty members. Brands will become more intentional about leveraging data to test, iterate, and quickly refine their loyalty strategies. In Singapore, for example, 45% of surveyed retailers are already leveraging data to personalise experiences, according to a recent survey conducted by Twilio at the National Retail Federation’s 2024 Retail’s Big Show Asia Pacific. 

– Ben Chamlet, Senior Director for Solutions Engineering, APJ, Twilio

3. Bots will move away from deflecting, towards genuinely connecting and engaging. 

Brands are already adopting conversational AI systems capable of better identifying user intent and generating sentences that mimic the nuances of human conversations. Conversational AI can engage with customers, reference previous interactions, and respond in a way that feels more dynamic and natural.

Beyond conversational AI, brands are also realising the potential of intelligent AI agents that can offer/ upsell products and services, take action on customer issues, make decisions within constraints, and operate across communication channels. These agents—which are not confined to chat windows—have an in-depth understanding of consumer preferences collected over time, past purchases, and previous customer interactions such as login issues or unresolved concerns. They can tailor their communication style to a specific context across channels. AI agents can even be triggered based on an event in the customer journey.

– Chris Connolly, Solutions Engineering Lead for Communications, APJ, Twilio

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Data

The growth of distributed data will be a boon for cybercriminals

As data volumes grow and become more distributed across multi-cloud environments, the risk of data breaches will rise significantly. With AI tools relying on vast amounts of customer data, cybercriminals will have more opportunities to target these systems, making data exfiltration and unauthorised access easier. Organisations will face an escalating risk as attackers exploit these expanding data environments to achieve malicious goals.

Data is business fuel but secure AI adoption is critical
These predictions should not deter organisations from embracing AI. Instead, they underscore the importance of developing robust strategies for secure and responsible AI adoption. Organisations must focus on integrating AI into their systems securely rather than viewing it as a risky proposition.

“Organisations must understand that data is the fuel driving their business — it enables insights, fosters collaboration, and powers innovation,” said Liat Hayun, VP of Product Management and Cloud Security Research at Tenable. “As AI adoption skyrockets and data storage demands grow, safeguarding distributed data has never been more critical. As we head into 2025, business leaders and security teams must strike a careful balance between innovation and security, ensuring that AI initiatives do not inadvertently open new doors for cyberattackers.”

– Tenable

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Overcoming Data Barriers in 2025: The Future of AI in Industrial Applications 

As industries across the globe grapple with the transformative potential of artificial intelligence (AI), one critical hurdle looms large: the lack of relevant data. For many organisations, the delay in gathering and processing this data to train AI models translates into a slow and often frustrating journey toward realising the full value of enterprise AI. This impediment remains a significant barrier to adoption, stalling innovation and efficiency gains that would otherwise expedite decision-making processes and operational optimisation. 

However, as we look ahead to 2025, the landscape is set to change dramatically. The technological advancements on the horizon will address the data scarcity problem head-on, setting the stage for a more seamless integration of AI in the industrial sector. Companies that have struggled with data acquisition and preparation will find respite in emerging solutions designed to streamline these processes. 

Among the frontrunners of this technological evolution are Large Vision Models (LVMs) and other foundation models. These groundbreaking computer vision models redefine how AI interprets images and videos. LVMs quickly process visual data, making it exceptionally fast and efficient for tasks like object detection. By deploying LVMs, organizations can shorten the time it takes to realise the value of AI dramatically. With their ability to detect and classify multiple objects within a single frame swiftly, LVMs stand to enhance the ways in which industries monitor, assess, and respond to their operational environments. 

The introduction of enhanced computer vision technologies, particularly variations of LVMs tailored for specific industrial needs, will empower enterprises to overcome the data bottlenecks that have historically hindered their AI initiatives. By leveraging cameras and sensors deployed throughout industrial facilities, organisations can capture vast amounts of visual data. This data can then be processed and analysed, yielding insights that drive actionable outcomes. 

As we approach 2025, we can expect innovative AI companies to enter the fray with their variations of LVMs and other next-generation computer vision models, as well as others that will develop platforms and tools that facilitate the operationalisation of these models. These startups will expedite the deployment of AI by offering customisable solutions that address specific industry challenges. For instance, in manufacturing, AI-driven camera systems could monitor assembly lines, improving productivity and quality. In logistics, these technologies could enhance inventory management by instantly recognising products and components and tracking their movement throughout the supply chain. 

While the lack of relevant data has posed a significant barrier to the widespread adoption of AI in the industrial sector, 2025 heralds a new chapter in overcoming this challenge. With the deployment of advanced computer vision technologies, industries are set to witness a drastic reduction in the time it takes to realise the full value of AI. As innovative companies introduce tailored solutions to meet the unique demands of their sectors, the era of intelligent, data-driven operations is poised to flourish, reshaping the industrial landscape in profound ways.

– Robert Young, Vice President of Strategic Innovation, Aicadium

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Data growth is driving network infrastructure modernisation, further driven by AI-powered applications and connected Internet of Things (IoT) devices. With greater demands from business and consumers, the network is more than ever, a more important component of a modern IT infrastructure. In 2025, networks are expected to be even more scalable, agile, AI-ready, flexible, intelligent and secure; and Wi-Fi 7 acting as a major propellant of modern networking needs.

From a business’ perspective, as Wi-Fi 7 adoption increases, the greatest benefit will likely be improved efficiencies in new technologies like AR/VR, 8K streaming, and the growing IoT ecosystem. These enhanced capabilities will be especially critical for industries such as education, hospitality and tourism, healthcare, retail and gaming, where timely and experiential services are now the expected baseline.

As the networking needs of tomorrow’s organizations evolve, so will AI’s role. The explosion of data creation and consumption is continuing to drive momentum towards distributed edge computing, where organisations operating on edge networks will demand real-time network processing that minimises latency and reduces bandwidth usage, while also ensuring security.

– Kho Teck Meng, CommScope’s Regional Sales Director, RUCKUS Networks ASEAN

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At Teradata, we believe AI is only as good as its data. For example, a highly sophisticated Generative AI (GenAI) model with bad data can only deliver poor results. AI models and data inherently also have biases, which means inaccurate information can be delivered without indicating biases present. For example, we are seeing that AI bias is starting to creep into company strategies in Asia when analysis comes from an AI system that was trained on a slanted collection of data, with an AI-based job recommendation system in Indonesia inadvertently excluding women from certain job opportunities due to historical biases in the hiring data. 

It is thus critical that AI users in Asia start to understand the lineage of the data behind the AI systems they use, especially now that we are in an AI-driven explosion of data. By 2025, there will be 120 zetabytes of data created, captured, or used this year. However, this ocean of data is mostly unusable as it is generally unrefined, duplicate, or inaccessible. Finding the valuable information in the sea of raw data means filtering through a lot of unusable or polluted content. While technology can do some of this, we still need human discernment to find the rivers of clean and reliable data that should inform AI training models.   

Hence enabling explainable AI has become even more urgent and necessary. Organizations can enable explainable AI by offering complete visibility into the data behind decisions, including how a model uses data and complies with all the proper regulations. It should also be clear to employees and users why the decision is both fair and equitable, and this could be done by ensuring the data sources are validated and proved trustworthy before any AI implementation occurs, and the reasoning behind the model’s output must be digestible and accountable to a human decision-maker. 

– Praveen Thakur, SVP for Asia, Teradata

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Data centres

1. Power and cooling infrastructure innovates to keep pace with computing densification: In 2025, the impact of compute-intense workloads will intensify, with the industry managing the sudden change in a variety of ways. Advanced computing will continue to shift from CPU to GPU to leverage the latter’s parallel computing power and the higher thermal design point of modern chips. This will further stress existing power and cooling systems and push data centre operators toward cold-plate and immersion cooling solutions that remove heat at the rack level. Enterprise data centres will be impacted by this trend, as AI use expands beyond early cloud and colocation providers.
   
   • AI racks will require UPS systems, batteries, power distribution equipment and switchgear with higher power densities to handle AI loads that can fluctuate from a 10% idle to a 150% overload in a flash.
     
   • Liquid cooling systems will increasingly be paired with their own dedicated, high-density UPS systems to provide continuous operation.
     
   • Servers will increasingly be integrated with the infrastructure needed to support them, including factory-integrated liquid cooling, ultimately making manufacturing and assembly more efficient, deployment faster, equipment footprint smaller, and increasing system energy efficiency.
     
2. Data centres prioritise energy availability challenges: Overextended grids and skyrocketing power demands are changing how data centres consume power. Globally, data centres use an average of 1-2% of the world’s power, but AI is driving increases in consumption that are likely to push that to 3-4% by 2030. Expected increases may place demands on the grid that many utilities can’t handle, attracting regulatory attention from governments around the globe – including potential restrictions on data centre builds and energy use – and spiking costs and carbon emissions that data centre organisations are racing to control. These pressures are forcing organisations to prioritise energy efficiency and sustainability even more than they have in the past.
   
3. Government and industry regulators tackle AI applications and energy use: While our 2023 predictions focused on government regulations for energy usage, in 2025, we expect the potential for regulations to increasingly address the use of AI itself. Governments and regulatory bodies around the world are racing to assess the implications of AI and develop governance for its use.

“AI adoption is accelerating across the Asia-Pacific region, with the likes of Singapore, Malaysia, and Australia enterprises leading the way in harnessing AI for transformation, unlocking new levels of efficiencies, enhancing customer experience, and solving complex challenges with agility. Our predicted trends for 2025 suggest a strong need for investment in energy-efficient and innovative digital infrastructure to unlock AI’s full potential.”

– Paul Churchill, Vice President and General Manager, Vertiv Asia.

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The looming energy crisis in data centres

Data centres account for 1-1.5% of global electricity consumption, with AI and quantum workloads driving demand higher. SEA’s cooling systems consume 40% of data centre energy, surpassing the global average by 10%. Yet, over 95% of facilities rely on less efficient air-cooling systems.

Singapore, a key SEA player, has set standards for energy-efficient operations and plans to introduce requirements for IT equipment and water cooling by 2025. Globally, governments are exploring cleaner energy sources, such as hydrogen fuel cells, to reduce environmental impacts.

As environmental concerns rise on business agendas, green data centres — focusing on energy efficiency, scalability, and sustainability — will be essential. Advanced cooling technologies like water cooling can significantly lower energy and water consumption. Businesses adopting these eco-friendly practices will ensure digital ambitions align with sustainability goals.

– Terry Maiolo, Vice President & General Manager, Asia Pacific, OVHcloud

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The Rise of Intelligent and Sustainable Data Centres

As Asia-Pacific pushes towards its position as a global hub for digital transformation, data centres are evolving from mere infrastructure providers to strategic enablers of technological innovation. With the rise of cloud computing, edge applications, and AI-driven workloads, these facilities now account for a significant share of global energy consumption. As sustainability mandates tighten and operational demands increase, the push is on to reimagine data centres as hubs of efficiency and resilience. Those embracing AI-driven solutions will lead the way to a greener, more efficient digital ecosystem, securing a competitive edge amid growing environmental and operational pressures.

A Forrester Consulting report, commissioned by Johnson Controls, reveals that 93% of data centre leaders admit their building systems are not fully integrated, resulting in decreased efficiencies (65%), diminished customer loyalty (64%), and increased regulatory penalties (60%). These inefficiencies highlight the need for smarter solutions to harmonise operations, reduce environmental impact, and enhance customer satisfaction. By integrating AI-powered platforms, operators can unlock real-time insights to optimise energy use, adjust cooling systems dynamically, and predict maintenance needs before issues arise. These capabilities improve efficiency, reduce downtime, extend asset lifecycles, and lower carbon emissions. As a result, operators are not only meeting digital demands but also creating a more sustainable and resilient ecosystem for the future.

– Anu Rathninde, President, Asia Pacific, Johnson Controls

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Digital transformation

Businesses will continue to rely on technology to develop resilience against headwinds in the coming years, while navigating the challenges and complexities around emerging technologies such as generative AI. The increase in data and concerns about data security will drive the need for cloud strategies to be collaborative and efficient in ensuring that data and workloads are open, simplified and integrated. In response, businesses need to work towards progressing in their digital maturity to derive the greatest value and maximise their technological investments. Looking ahead, here are three key points that businesses need to keep in mind:

• The evolution of digitalisation, from operational to strategic
  Digitalisation has evolved significantly in the past few years and, in turn, processes and security challenges have become more sophisticated. Businesses see unprecedented opportunities with digitalisation, but will require a well-defined digital road map and deeper insights into processes and culture to invest strategically.

• Advancing human-machine integration
  The year ahead will see digital applications more purposefully designed to be optimised to complement the user or improve a function, which we sometimes call human-machine integration. Coupled with the advancements in AI, data, cloud and low-code technologies, these implementations will help workers across industries such as healthcare, insurance or defence to enhance their workflows and boost their productivity so they can focus on higher value tasks.

• Putting humanity back in technology
  As the digital landscape continues to evolve, there is a greater need for human-centric design that prioritises the needs of people. There is a shift towards more personalised and practical design that is tailored to individual patterns and preferences, considering the consumer touchpoints to form connection and loyalty.

The ever increasingly competitive landscape and rate of rapid technological advancement are important reminders to rethink existing IT processes to identify gaps and opportunities within the organisation. As businesses mature in their digital transformation journey, they should look at deeper technology integration to transform and become more agile and prepared for volatile times.

– Wu Chun Wei, Managing Director, Technology, Temus

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How can enterprises prepare for the next wave of disruptive technologies, such as quantum computing and advanced automation?

Every project should have a compelling business case, with measurable success metrics and strong buy-in from leadership and employees. A strategic, business-focused approach ensures organisations can adapt and thrive in the face of technological disruption, be that quantum computing, advanced automation or any other up and coming technologies.

While some of these advancements are on the near horizon, there are steps business can take today that will both prepare them for these future technologies while also delivering immediate benefits. For example, eliminating data silos. Connected, structured data is not
only vital to the new wave of advancement, but even now it enables business leaders to unlock insights that drive competitive advantages.

What do you believe will define success in digital transformation efforts 2-3 years from now?

Perhaps the most influential factor will be how effectively businesses can transition their critical systems to the cloud. Cloud adoption enables seamless access to data and fosters connected, integrated systems, empowering organisations to make informed, real-time
decisions. Businesses that embrace this shift will gain agility, scalability, and a competitive edge, leveraging the cloud’s capabilities to innovate and adapt rapidly. The ability to harness cloud-driven insights, break down data silos, and create interconnected ecosystems will
define market leaders, while those that lag in cloud adoption risk falling out of step with the pace of digital evolution.

How should organisations approach the integration of legacy systems with modern technologies?

First, assess legacy solutions for integration capabilities, ensuring they support modern tools like APIs. If these mechanisms are lacking, modernisation may be necessary. Next, evaluate whether legacy systems can be effectively migrated to the cloud, considering their
architecture and compatibility with cloud benefits. Collaborating with an Integration Platform as a Service (iPaaS) provider is essential but can be challenging. Seek guidance from your ERP or technology partner to identify a solution that bridges legacy systems with modern extensions seamlessly. A thoughtful approach ensures smooth integration and long-term success.

– Andy Coussins, EVP, International at Epicor

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Future of work

In 2025, we will see new paradigms of work emerge. AI in the workplace will shift from one-size-fits-all solutions and surface-level automation to highly customised experiences that address the specific needs of businesses and employees. As organisations adopt increasingly complex tech stacks, critical information often becomes dispersed across various platforms. Custom AI will play a pivotal role by integrating with internal systems and knowledge collections, quickly resurfacing relevant information, and enabling employees to focus on high-impact tasks. For example, AI can retrieve an employee’s relevant project updates, automatically summarise key discussions, and assign actionable tasks, seamlessly managing workflows across multiple platforms.

At the same time, organisations must also prepare for a workforce of AI natives – incoming employees who have grown up with AI and now expect it to be seamlessly integrated into their daily workflows. The best talent will naturally gravitate toward organisations that fully embrace AI and empower their teams to use them effectively. Looking ahead, AI is no longer just a tool, but a critical driver of competitive advantage and long-term business success.

– Ricky Kapur, Head of Asia Pacific, Zoom

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On workforce trends changing with technology

The rapid advancement of AI is fundamentally transforming the nature of work and businesses looking to harness the full potential of AI should prioritise workforce upskilling and data management. While there is a common misconception that AI can be immediately leveraged for workplace transformation, organisations often need more time to undergo a foundational process of data cleansing, cataloguing, and understanding their data landscape. This groundwork is crucial for effectively plugging into AI and extracting valuable insights.

As we move into 2025, this focus on data preparation will continue to be a priority for many organisations. Additionally, developing AI-specific skills is essential. While general AI can provide basic responses, businesses often require more tailored solutions to address specific business unit needs and consider wider economic implications. By investing in training programs, organisations can empower their employees to adapt to the evolving technological landscape and acquire the necessary skills to work effectively alongside AI systems, making the most of this transformative technology.

– Philip Madgwick, Regional Vice President for Asia and ANZ, Alteryx

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Humans and AI shape the next-generation workforce

AI is redefining the workforce, blending human creativity with machine efficiency. Companies will focus on upskilling employees to collaborate with AI tools, creating a workforce that thrives on innovation while addressing concerns around job displacement.

The office as a hub of trust and innovation

The role of the office will continue to evolve, with businesses prioritising trust and collaboration. Thriving workplaces will leverage physical spaces as hubs for innovation and community-building, fostering employee engagement and organisational growth.

– Tay Bee Kheng, President, ASEAN, Cisco

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Hiring for tech skills will matter less than emotional intelligence

In 2025, the demand for IT professionals will not slow down – the nature of living in an AI-driven world calls for those experts. But a significant shift in desirable skills is on the horizon as we move towards roles that blend business acumen with tech skills and novel AI technologies. Technical roles will no longer be traditional because they aren’t necessarily the tech users moving the business forward. Organisations will start prioritising high emotional intelligence and strong people skills over pure technical expertise, which are essential to training teams at the business level on how to use the advanced technologies available to them. This shift will be paramount to driving complex digital transformation, and innovation, and facilitating the integration of technology, like AI and automation, into business processes.

CIOs need to prepare for agentic AI to flip workplaces on its head 

As businesses integrate AI into everyday processes, organisations must prioritise communication and reskilling their workforce now, and continue education throughout 2025. CIOs know technologies like AI agents are poised to change the workplace, but they need to get ahead of workers’ fears that it is coming in to replace them. AI’s role is to augment their jobs, not take them. Businesses that fail to proactively address employee concerns around agentic AI, risk resistance and inefficiency in implementing these technologies. We’ve seen the data and it’s clear: early adopters of generative AI and their employees are the current winners, and the early adopters of AI agents are sure to follow a similar course.

AI initiatives will be as “unsuccessful” as business leaders make them

A question every business leader is asking is whether or not the investments they have made in AI have produced anything of value. They are asking this question too soon. In 2024, many organizations threw money at AI with the mindset that they would see immediate and meaningful results, without thinking critically about what those key indicators are. Now that we have experimented with AI in 2024, next year, we will see leaders determine the key metrics for evaluating success and thinking about long-term measurement. 

Gen Zs will need to embrace collaboration and communication in the world of hybrid work

Gen Zs have no idea about life without a smart phone and the internet, and their critical social learning years were impacted by the pandemic. The way they communicate is very different from millennials and Gen X.  As more employers are mandating back-to-office as the new norm,  Gen Zs need  to embrace more face-to-face communication.  After all, business is done by building relationships with your peers and customers. Those relationships are built on trust, and trust happens when you look people in the eye.      Gen Z will be the generation that leverages AI the most and the more they embrace old school collaboration and communication, the better they will be able to own AI initiatives at their company.  

– Carter Busse, Chief Information Officer, Workato

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IoT

2025: The Year of IoT Trust

Security in IoT has always been a pressing concern, but in 2025, the focus will evolve to something even more critical: trust. With billions of IoT devices generating and transmitting sensitive data and with AI making decisions for us, ensuring device identity and network integrity will take centre stage. Zero-trust architectures, which assume that no device or user is trustworthy by default, will underpin IoT systems globally.

Traditional security measures like data encryption and network firewalls are no longer sufficient in a world where IoT devices are transmitting gigabytes of critical data every second. The next leap is trust—ensuring that every device on the network can identify itself reliably and be trusted to perform its role effectively.

Innovations such as blockchain and AI-based anomaly detection will help create this ecosystem of trust. Anomaly detection tools, which continuously monitor IoT networks for vulnerabilities and strange traffic patterns, will be instrumental. 

Intelligence Moves to the Edge

AI has already transformed industries, but its true potential will unfold when paired with IoT at the edge. As 5G enables low-latency, high-speed connectivity, IoT devices will gain the capability to analyse data and make decisions locally, without depending on centralised data centres.

Imagine a world where irrigation systems adjust themselves in real time based on precise soil conditions or where traffic lights respond dynamically to congestion, all without human intervention. That’s the power of AI at the edge.

IoT Bridges the Connectivity Divide

While urban centres have leveraged IoT to enhance convenience and productivity, the next frontier lies in rural and underserved regions. In 2025, satellite-enabled IoT will emerge as a game-changer, connecting devices in areas beyond the reach of traditional networks.

Asia’s rural economies, particularly in agriculture and resource management, are ripe for IoT transformation. Satellite connectivity will enable these areas to adopt IoT solutions for precision farming, disaster management, and even remote healthcare.

By enabling IoT devices to seamlessly roam between terrestrial and satellite networks, solutions can now be deployed in challenging environments—from dense jungles to vast agricultural landscapes. 2025 will mark the tipping point where IoT becomes truly inclusive, democratising access to technology and its benefits.

IoT Accelerates Transformation Across Key Sectors

As IoT matures, its impact will deepen in specific verticals where the stakes are highest. Agriculture and healthcare, in particular, will undergo profound transformations driven by IoT.

Precision agriculture will help us grow more food with fewer resources. By deploying IoT sensors and automation in fields, farmers can monitor and adjust irrigation, fertilisation, and pest control in real time. This not only boosts yields but also conserves water and reduces environmental impact. 

In healthcare, IoT will bring personalised care to new heights. Wearable devices will monitor patient vitals continuously, enabling preventive care and personalized insurance policies. IoT will empower patients to manage their health more proactively, while insurers can design better, data-driven policies. These innovations will address some of the world’s most pressing challenges—feeding a growing population and ensuring quality healthcare for all.

– Simon Trend, Managing Director of Americas, APAC and MENA, Wireless Logic

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IT strategy

Outcome-driven IT

Modern-day enterprises are powered by IT, which now occupies a place at the top of the management table, not as a back-office function. Any failure that results in services being unavailable or disrupted can result in huge business implications. Yet, in some quarters, IT is still considered a cost centre rather than a contributor to business profits.

To change this perception, IT leaders must clearly articulate the value IT brings to the business or risk shrinking budgets. While dashboards provide metrics that point to the operational performance of a technology, they don’t always present a clear case for the business benefits derived. That clarity can be gained by aligning IT with operational efficiency, business velocity, and opportunity costs.

In 2025, CIOs need to focus on KPIs and metrics that provide a direct link to the business outcomes that depend on them. For instance, in the healthcare industry where there is constant focus on safeguarding data and compliance management, metrics that track user behaviour and anomalies are most vital since they all affect business operations.

– Rajesh Ganesan, President of ManageEngine

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Observability Data will influence Product Road Maps

Downtime can set off a slew of severe repercussions, encompassing not only financial losses but also reputational damage to the organisation. To stave off these consequences, observability will evolve from reactive troubleshooting and performance analytics to proactive influence on product road maps, guiding software developers’ focus towards enhancing customer experiences.

Against an increasingly complex macro environment in 2025, teams will be expected to measure ROI more critically, considering the impact on digital experience, customer satisfaction, and retention rates. Observability practices will shift towards viewing performance through the lens of the customer.

Understanding relationships, such as site speed’s impact on conversion rates, will allow companies to leverage observability data early in the software development cycle. This proactive approach will inform and optimise application code and features, impacting product road maps, and ultimately transforming observability practices.

– Simon Davies, Senior Vice President and General Manager, APAC, Splunk

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How Sustainability Will be the Driving Force Behind Organisational Excellence

The workforce is evolving, and leaders are increasingly tasked with embedding both technology and sustainability into core strategies. Digital-savvy executives will drive this transformation, aligning innovation with environmental responsibility to ensure lasting success.

Employees will increasingly use advanced tools like AI and analytics to streamline processes while advancing sustainability goals. Training programmes in sustainable practices will upskill workers to tackle challenges such as mandatory reporting and energy efficiency, empowering teams to contribute to environmental efforts actively.

As organisations embrace the convergence of sustainability and technology, those leading the way will benefit from improved resilience, higher employee engagement, and a competitive edge in a market where eco-consciousness is key. By aligning workforce strategies with sustainability goals, businesses will thrive in the growing green economy.

– Anu Rathninde, President, Asia Pacific, Johnson Controls

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Uncomplicate and Simplify

In a hyperconnected world, companies must address the complexities of integrating communications, technology and cloud services. Those who build and manage hyperconnected ecosystems will be better positioned to lead in their industries by leveraging synergies between AI, cloud, and other technologies to deliver enhanced customer experience, improve operational efficiencies, and increase agility. Enterprises will need partners to simplify and manage the complexity of these systems to realise their potential.

– Amitabh Sarkar, Vice President & Head of Asia Pacific and Japan – Enterprise at Tata Communications

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What did companies get wrong / what were the biggest misses in 2024? (e.g. investments,

priorities)

• Cybersecurity issues – not diversifying the risk. Too much was placed on a few deeply embedded players (i.e. CrowdStrike).

What did companies get right / what were the biggest wins in 2024?

• We knew AI would move fast, and many companies are investing. They are also accelerating their move to the cloud in order to take advantage of AI.
• Many companies are also choosing industry-specific cloud ERP, which is a smart move for the future – as more AI is introduced, it will need more and more specific data structures to create the most value and accurate outputs.
• We saw a pivot back to sustainability and innovating in new sustainable technologies.
• Focus on people with connected worker technologies, and focus on resilient supply chains with inventory optimisation, route optimisation, collaboration, visibility, and forecasting technologies.

What still needs addressing from the past year as we head into 2025?

• Most AI vendors are not helping companies enough with their data quality. This continues to be an issue holding many back from the promise of AI.
• Deeper focus on security – as quantum computing continues to evolve, we need to always be proactively and relentlessly prioritising new security measures.

What lessons or experiences from 2024 will be most useful for leadership and decision-makers in 2025?

• AI is moving extremely rapidly. It needs investment and you should not take it on alone – you need an expert partner to advise.
• Fundamentals still matter – even though technology is moving so fast, it’s important to still do the business case, define measures of success, communicate your vision, and track metrics.
• Starting to see a resurgence in sustainable technologies after a dip during and post- COVID. There is a window of opportunity in electrification, new partnerships, collaborations, data, etc.
• The value is in the application – the technology is well defined, and there has been some application, but we’ve only just skimmed the surface.

– Kerrie Jordan, Group Vice President, Product Management at Epicor

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Trend 1: Embracing a hybrid approach to AI deployment, incorporating private AI infrastructure

Much of the initial boom in AI service deployment has been facilitated by the availability of Large Language Models (LLMs) available in the public cloud. However, we are seeing digital enterprises increasingly recognising that alternative infrastructure approaches might be better suited for some AI workloads – particularly those involving private data. 

Rather than sending user queries and their associated data to be processed by models in the public cloud – an approach we can term ‘Data To The Model’ – many organisations are now using a ‘Model To The Data’ approach. This involves deploying AI models on private computing infrastructure, adjacent to the organisation’s private data storage, typically in physical locations which are close to the end-users of the model.  This approach can potentially offer benefits from the perspectives of privacy, speed and cost.

Trend 2: Strengthening cybersecurity with the growing prowess of AI and quantum

Cyber threats are on the rise in the Asia-Pacific region, with cybersecurity spending expected to reach $36 billion in 2024[1]. Quantum computing will become another key accelerating threat to cybersecurity. It presents serious risks to critical elements of today’s public key infrastructure and is expected to be able to break today’s encryption in minutes. In fact, nation-state actors are already harvesting encrypted sensitive data with the intention to decrypt it later when the technology is available, so-called “harvest now, decrypt later” attacks.

Trend 3: Leveraging edge computing to enhance data sovereignty

Governments’ increasing focus on data sovereignty, coupled with the rise of Internet of Things (IoT), generative AI, and real-time applications, necessitates robust IT infrastructure at the edge. Edge computing allows for localised data processing, reducing transfer risks and ensuring compliance with national data sovereignty laws, which vary widely across Asia-Pacific.

Trend 4: Advancing business applications with hybrid multicloud

Hybrid multicloud will continue to be the standard for enterprises seeking to optimise their IT infrastructure, balancing the benefits of public and private clouds. Equinix’s global digital infrastructure platform offers high-speed connectivity to an extensive ecosystem of cloud and network providers, enabling seamless integration and efficient data movement. This approach allows businesses to become more agile, adapting to the ever-evolving business environment while maintaining control over their critical workloads.

– Yee May Leong, Managing Director, Equinix Singapore

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1. The Workforce of Tomorrow is Mission-Driven: Millennials and Gen Z are reshaping the workforce, preferring purpose-driven roles that create positive societal impact. Companies that align their missions with this shift will attract and retain top talent while driving long-term success.

2. A New Era of Energy Efficiency Drives Innovation: Advances in renewable energy and nuclear solutions like Small Modular Reactors (SMRs) are poised to revolutionize energy systems. Coupled with more efficient hyperscale data centers, these innovations will power the next generation of technological progress sustainably.

3. Technology Tips the Scales in the Discovery of Truth: AI-powered tools are helping journalists and everyday citizens combat misinformation, democratizing access to fact-checking and investigative capabilities for a more informed and resilient society.

4. Open Data Drives Decentralised Disaster Preparedness: Hyperlocal, community-driven data systems are transforming disaster management, enabling faster, more effective responses while empowering communities to take ownership of their resilience.

5. Intention-Driven Consumer Technology Takes Hold: A shift toward mindfulness-focused devices and practices is redefining how we engage with technology, emphasising deep thinking and intentional usage over constant distractions.

– Dr Werner Vogels, CTO, Amazon

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1. Generative AI Will Become a Cornerstone of Business Operations

Generative AI is reshaping how organisations operate, offering unprecedented capabilities to streamline tasks and deliver value at scale. However, deploying generative AI effectively requires organisations to focus on foundational enablers: robust governance frameworks, clear ethical guidelines, and extensive workforce training. By aligning AI strategies with broader business goals and customer expectations and enabling enterprise-wide adoption, forward-thinking organisations can fully realise AI’s transformative potential. 

2. First-Party Data and Collaboration Will Replace Third-Party Cookies

With third-party cookies being phased out of Chrome by early 2025, businesses must shift to first-party data strategies to maintain effective customer personalisation. Customer Data Platforms (CDPs) that can unify and activate data in real-time will become a key piece of the equation, as they will pave the way for actionable insights that translate into better segmentation, personalisation, and activation across channels. In fact, CDPs were the top priority for technology investments by senior executives in APJ, and this trend will continue to 2025 and beyond.

Beyond internal efforts, data collaboration with trusted partners is emerging as a vital strategy to expand insights without compromising privacy. This shift demands a strong commitment to transparency, responsible data use, and robust security measures. Companies that build consumer trust through clear communication and ethical data practices will not only weather the transition but also strengthen customer loyalty and market leadership.

3. GenAI-Driven Personalisation at Scale Will Redefine Customer Experiences

The digital age has ushered in an era where consumers expect highly personalized interactions across all touchpoints. 86% of consumers in the APJ region expect brands to provide personalized product recommendations based on their interests and past purchases, according to Adobe’s APJ Digital Trends Report.

However, businesses must also be able to understand the nuances of personalisation across different markets. While most consumers are comfortable with brands using AI for personalisation, preferences vary. Common tactics used in ANZ, India, and Asia could be seen as too direct in Japan. Brands must hence tailor their approach accordingly instead of adopting a one-size-fits-all strategy.

Generative AI is a game-changer here, enabling businesses to create, adapt, and personalise content with unprecedented speed and precision.

4. Responsible Innovation Will Define Competitive Advantage

Adobe found that 64% of consumers in APJ are worried about how much data brands hold about them, and 64% would be more open to granting permission to their data if brands were more transparent. 

As organisations increasingly rely on advanced technologies, the need for responsible innovation has never been greater. This means embedding ethical considerations into AI adoption, ensuring data privacy and security, and developing transparent practices that build consumer trust. Responsible innovation also requires thoughtful leadership—senior executives must champion AI strategies that align with organisational values and customer expectations while empowering teams with the training and governance needed for effective implementation. 

– Shashank Sharma, Senior Director, Digital Experience, Korea and SEA, Adobe

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Cloud-Native Observability: A Cornerstone of Digital Resilience

Customers in cloud-native environments often struggle with identifying and resolving issues quickly due to a lack of end-to-end visibility across their infrastructure and application stack, impacting service reliability and availability. In 2025, we can expect customers to double down on cloud-native observability so that they are able to visualise and correlate events and metrics in real-time, from the infrastructure to the application across their hybrid and multicloud environments. This will enable them to analyse data across their entire environment, and identify patterns, anomalies and potential issues before they escalate into incidents.

Strengthening Digital Resilience Against Outages

In 2024, we witnessed major tech outages at the regional and global stage e.g. the Crowdstrike global outage. The outage trend is expected to continue and this will lead to companies putting in place IT strategies to withstand, adapt and recover from such outages. Examples of such strategies may include mitigating software concentration and vendor lock-in by diversifying the IT stack, building alternative tech stacks for failover scenarios, and adopting a multivendor approach that provides choice for critical infrastructure software, such as operating systems and Kubernetes.

Zero-Trust Security: A Shield Against Ransomware

Ransomware attacks have increased dramatically in 2024 and we can only anticipate this trend will persist as bad actors employ increasingly sophisticated attacks powered by AI. The availability of Ransomware-as-a-Service (RaaS) operators exacerbates the situation. In 2025, more companies are expected to invest in zero-trust security solutions capable of detecting and preventing attacks such as ransomware as well as other zero-day exploits.

Secure and Private GenAI: Powering Innovation Responsibly

Companies adopting AI technologies face numerous security challenges, including GenAI privacy and data protection concerns, as well as vulnerabilities within the AI supply chain. To address these risks, the adoption of private AI platforms is set to grow. These platforms can empower companies with full control over their data, safeguarding their operations against increasing threats such as unauthorised data sharing, regulatory non-compliance, and the proliferation of “shadow AI” usage.

AI Observability: Optimising Performance and Reducing Carbon Footprint

AI, particularly large language models, requires substantial amounts of energy to operate. This has a significant impact on CO2 emissions. To optimise the energy consumption of their AI workloads and reduce CO2 emissions, companies will be adopting a range of strategies in the coming years. These include fine-tuning and optimising LLMs, eliminating performance bottlenecks in LLMs, selecting energy-efficient cloud providers, and scaling AI resources responsibly. Observability tools will be instrumental in enabling and guiding these efforts.

A Standard Operating Environment for AI: Driving Efficiency and Innovation

There are multiple options available today to run GenAI and other types of AI workloads. Over time, we can expect most companies to create a standard operating environment for AI use cases. Such an environment will comprise a common AI platform that is highly scalable and provides common modules and services required by AI workloads such as a curated set of LLMs, data privacy and security, observability etc. 

– Vishal Ghariwala, Chief Technology Officer, SUSE Asia Pacific

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The “need for speed” will drive enterprises to rethink their platform strategy

Every engineering leader knows about Conway’s Law and strives not to ship their organisation chart – however, as an industry, we have instead been shipping our customers’ organisation charts. This has resulted in platforms that work in isolation for NetOps, CloudOps, and SecOps.  However, this inevitably creates delays in workflows as teams pass tickets via ITSM systems between one another to accomplish tasks on a daily basis.  

To address this, enterprises will begin to rethink their platform strategy.  Increasingly demanding horizontally integrated platforms in addition to the vertically integrated platforms we have seen traditional vendors pursue. In fact, many leading edge customers have already started to converge their organisations to reflect this need, with NetOps and SecOps members rotating into CloudOps teams. You can see this happening in some areas of the market like the move from Secure Service Edge (SSE) to Secure Access Service Edge (SASE), where a NetOps function (branch routing) is converging with SecOps or the addition of Digital Experience Monitoring (DEM) capabilities to SecOps platforms. 

– Scott Harrell, CEO, Infoblox

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The rise of AI-focused leadership roles

The continuous proliferation of data into 2025 will see the introduction of new AI-focused roles. Chief AI Officers (CAIOs) are responsible for overseeing the ethical, responsible and effective use of AI, and bridge the gap between technical teams and key stakeholders. This will mark the increasing integration of data resilience in the boardroom, as CAIOs work alongside Chief Data Officers and Data Privacy Officers to ensure data integrity, recoverability, and alignment with AI initiatives.

AI middleware companies to bridge the gap in AI adoption

More businesses will engage AI middleware companies to accelerate the adoption of secure, responsible and efficient AI solutions. Middleware simplifies the adoption process by allowing different systems to communicate seamlessly, improving time to market, and reducing the need for in-house AI expertise as well as the risks associated with AI development. 

Middleware also helps maintain ethical standards, which is significant as governments are imposing stricter regulations around the use of AI. With this, businesses will see a marked increase in the volume and complexity of data they need to handle, driving the need for robust data management practices.

Repatriation of data from the public cloud to on-premise

More businesses are expected to shift workloads from the public cloud to on-premises data centers to manage costs and improve efficiencies. Organisations that previously shifted to the public cloud are realising that a hybrid approach is more advantageous for achieving cloud economics. While the public cloud offers benefits, local infrastructure can provide superior control and performance in specific cases. 

Factors like market uncertainty, evolving licensing, and regulatory considerations also drive this trend, as businesses seek adaptable strategies that optimize access and ensure data sovereignty. As a result, demand for flexible, scalable solutions like multi-cloud and hybrid-cloud approaches is growing.

An increasingly sophisticated AI-powered threat landscape

Bad actors will continue exploiting AI through complex attacks like deepfakes and AI-powered phishing. Consequently, the adoption of proactive cybersecurity strategies and advanced identity verification methods is expected to accelerate.

The use of AI-powered solutions to prevent and prepare for cyberattacks is also set to rise. AI-driven threat detection alerts security teams to high-priority vulnerabilities in real time, enabling faster responses and efficient attack containment. This underscores the view that data resilience needs to be championed at the executive level and align with business resilience goals.

Preparing for the unknown: a year of realisation, regulations and resilience

2025 will be a year of realisation as many organisations remain underprepared to recover efficiently from cyber incidents. However, with the growing risk of AI-powered cyber threats, more businesses will adopt proactive resiliency measures. This will go beyond basic cybersecurity training to include AI-driven threat detection and robust backup and data recovery strategies. 

– Anthony Spiteri, Regional CTO APJ, Veeam

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It looks like we might be hitting the hype saturation point with generative artificial intelligence (AI). After two years of hype, there are clear signals that we’re probably headed into the low that follows the high—where public perception of AI shifts as the gap between expectation and reality becomes more apparent.

This isn’t new. History is full of what seem like false starts, where new technology burns bright and fizzles just as quickly. Predicting the timing and impact of technological advancements is challenging, but anticipating which technologies will truly capture public imagination is even harder. These technologies set expectations and influence how society adapts.

Sometimes, “technology failures” lay the groundwork for transformative ideas, though their full impact may emerge years later. Thanks to a convergence of societal changes and new capabilities, I foresee that 2025 will set the stage for a dynamic decade of technological advancement.

Agentic AI: From Insights to Action

While generative AI might be nearing its saturation point, AI capable of acting autonomously hasn’t had its moment yet. Technologists have long envisioned computers acting as personal assistants. Now, the world might finally be ready for agentic AI—AI that doesn’t just provide insights but takes action.

AI chatbots currently already excel at generating and summarising information. But agentic AI could transform businesses by functioning as “digital employees”, independently performing tasks like fraud detection, stock ordering, or customer support.

Self-driving cars are early examples of AI making autonomous decisions without human intervention. In business, similar applications could unlock new opportunities, though balancing ethical risks will be crucial.

Ambient Computing: From Tiny Screens to Vast Vistas

We’re surrounded by devices that are always connected to the cloud. This year, Meta’s partnership with Ray-Ban introduced smart glasses that allow users to interact with AI seamlessly. Ambient computing can leverage such devices to respond naturally to the context of their environment, transforming how we interact with technology.

As battery and display technologies improve, these devices could integrate augmented reality features, fundamentally changing how businesses operate. For instance, shop owners could use smart glasses to visualise inventory needs or a virtual assistant that provides them real-time sales insights.

Crypto and Blockchain: From Experimentation to Digital Gold

While critics often dismiss blockchain technologies as solutions in search of problems, their progress and adoption continues. Today, central banks are experimenting with digital currencies like China’s Digital Yuan and The Bahamas’ Sand Dollar for faster payments. Stablecoins pegged to commodities like gold offer predictable value, and decentralised digital identity systems are gaining traction.

For small businesses, these advancements could secure digital assets, protect intellectual property, and combat counterfeit goods. While widespread adoption may still be some distance away, its potential is undeniable.

– James Bergin, Executive General Manager – Technology Research & Advocacy, Xero

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Low code/ No code

The low-code/no-code (LCNC) revolution is reshaping the way organisations approach application development. With advancements like Generate AI (GenAI) deeply integrated into LCNC platforms, the stage is set for transformative possibilities that promise to democratise technology and accelerate digital innovation. 

Here are key insights and predictions shaping the LCNC domain and the broader impact it will have on industries worldwide:

The Surge in LCNC Adoption

For a long time, low-code/no-code (LCNC) platforms were considered just one option among many in an organisation’s tech stack. However, they are now evolving into a primary choice for new enterprise implementations.

The adoption of LCNC platforms has significantly increased in recent years. Analysts at Gartner project that by 2025, 70% of new applications developed by organisations will utilise LCNC technologies, a substantial rise from less than 25% in 2020. 

Non-technical employees are also increasingly empowered to create applications using LCNC platforms. Gartner forecasts that by 2026, 80% of users of low-code development tools will be outside traditional IT departments.

Industries Embracing LCNC

LCNC platforms are rapidly becoming the go-to choice for business operations applications due to their faster time-to-value compared to traditional methods. Growth opportunities include expanding industry-specific solutions and integrating emerging technologies like Gen AI. LCNC adoption among SMEs and enterprise-wide implementations with governance frameworks are driving large-scale digital transformation.

Furthermore, LCNC platforms will be widely used across various industries, with significant adoption in IT, BFSI, Retail, and E-commerce. 

LCNC + AI: Unlocking Citizen Development

LCNC+AI will finally make citizen development a reality. While LCNC platforms have reduced the learning curve and cognitive load, business users still need to undergo a significant transformational journey to solve their own problems using these platforms. However, the addition of AI eliminates this barrier by enabling intuitive, natural language-based interactions, empowering users to build solutions with minimal effort and expertise.

Emerging Trends

Natural Language-Driven Interactions: Forms and traditional interfaces are being replaced by conversational chat, enabling users to interact through natural language. IT is embedding Gen AI-powered chat interfaces to deliver seamless conversational experiences .

Multi-Modal User Interactions: Interactions are moving beyond text to include audio, video, and images, offering more intuitive options. Products are adopting voice recognition, video inputs, and computer vision to support diverse inputs.

Composable UI for Personalisation: Applications are embracing modular, composable UI components to create highly personalised experiences. IT is leveraging design systems and micro frontends to dynamically assemble UIs based on user preferences and context.

– Dinesh Varadharajan, Chief Product Officer, Kissflow

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Network

Role of Edge Computing in Enabling Hyperconnectivity

According to IDC, by 2026, 60% of enterprises will leverage enhanced edge security, AI-enabled automation, and optimised operational efficiency by adopting “as-a-service” models for SD-WAN and security. The rise of edge computing will define 2025, offering more efficient and cost-effective solutions by decentralising computing power, reducing dependence on centralised data centres, and enhancing performance. As businesses recognise that the cloud alone cannot meet real-time performance and cost demands, edge computing will gain traction.

– Amitabh Sarkar, Vice President & Head of Asia Pacific and Japan – Enterprise at Tata Communications

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Quantum computing

Investment in Quantum Technologies

As traditional computing faces limitations, quantum computing is emerging as a critical investment area. Companies are encouraged to develop quantum-ready solutions for this next wave of technological advancement. Companies are exploring quantum communication technologies, particularly QKD, which allows secure communication channels immune to eavesdropping. This application is crucial for industries requiring high data security, such as finance and healthcare. As quantum computers threaten current encryption methods, organisations are focused on developing quantum-resilient algorithms to protect data against potential quantum attacks.

– Amitabh Sarkar, Vice President & Head of Asia Pacific and Japan – Enterprise at Tata Communications

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Smart cities

Smart Cities Will Evolve into Cognitive Urban Ecosystems

By 2025, urban areas will transform into cognitive ecosystems, utilising AI, IoT, and big data analytics to optimise city functions. Real-time data exchange and decision-making will enable more efficient urban management. For instance, AI-powered digital twins — virtual replicas of physical assets — are being adopted by cities like Amsterdam and Singapore to enhance resilience and improve planning. Over 500 cities are expected to implement digital twin technology by 2025, potentially saving US$280 billion by 2030. This progression highlights the role of cognitive technologies in fostering sustainable and responsive urban environments.

– Theo Scherman, Chief Strategy Officer, Hitachi Asia

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Software testing

As we look forward to 2025, there are three major trends that we can expect to reshape software testing and quality assurance.

Firstly, as organisations seek larger market shares, the pace of digital innovation is set to accelerate to maintain a competitive edge. The focus on doing more with fewer resources will continue to increase, driving strategic initiatives to streamline technology stacks. These efforts will require faster software development, as well as more efficient software testing processes, putting additional pressure on software engineers and DevOps teams to deliver high-quality applications or software updates quickly. At the same time, ongoing geopolitical uncertainties are prompting businesses to adopt more versatile approaches to maintain business continuity and resilience in a challenging landscape.

Secondly, generative AI will continue to have an immense impact in driving the speed of innovation. By harnessing the power of generative AI, the amount of code developed is set to grow at unprecedented rates. To cope with this surge, the demand for test automation will increase to ensure quality. Software engineers and DevOps teams will look to embed generative AI in various phases, such as test case generation, release management, deployment, platform engineering and planning. We can also expect more non-technical users will also be able to participate in testing through generative AI-powered low-code and scriptless tools. There will also be improvement in the quality of code and software outputs, since generative AI tools are able to identify weaknesses and vulnerabilities in test scripts.

Lastly, change intelligence will become critical as organisations cannot test everything due to limited resources and time. It enables swift visualisation and evaluation of how code changes could alter their systems and software. This enables teams to quickly identify potential impacts, assess risks, and make informed decisions about which tests to prioritise, ensuring quality is maintained even under tight constraints.

Another thing that really woke us up was the CrowdStrike incident that brought several industries to a halt. We were taught an important lesson: effective testing must move beyond system silos to encompass all the business processes that a system supports.

Companies need to be cognisant that business processes increasingly span across multiple interconnected applications and systems, and the real risk lies in failing to account for the ripple effects of changes across these integrations. Hence, testing must be conducted in an end-to-end, model-based continuous testing approach.

Additionally, any outdated practices should be replaced with modern methods, such as generative AI testing tools, automated test case generation and AI-augmented DevOps. We found that 60% of DevOps practitioners see testing as the area where AI offers the greatest benefit. Real-time analytics offered by these AI-driven solutions provide predictive and actionable insights that enable developers to focus on more high-value tasks.

Effective testing is not just a spot check. It should be a continuous, evolving process. Adopting these steps can enable a company to innovate at speed without compromising on system reliability or customer trust.

– Damien Wong, Senior Vice President for Asia Pacific and Japan (APAC), Tricentis