2023 was generative AI’s breakout year — its emergence swiftly captured global attention, reshaping business operations, models, products, and services. Business leaders are increasingly realising the need to prepare their data, personnel, and processes to harness generative AI’s transformative power.
Accenture’s survey found that 77% of Chief Information Officers (CIOs) in Asia-Pacific (APAC) plan to increase AI-related spending in 2024, prompting a fundamental reevaluation of work approaches. CIOs, with their comprehensive understanding of business processes and technology, are well-positioned to lead organisations toward generative AI readiness.
Yet, despite the recognition of generative AI’s significance, many leaders struggle to translate awareness into practical strategies. Globally, 67% of senior tech leaders perceive a lack of technological fluency among peers as a major obstacle to integrating technology into strategy development. In APAC, nearly half (45%) of business leaders are not fully prepared for the accelerating rate of technological change.
Successful AI integration into business operations hinges on clear objectives aligned with business goals. CIOs, already tasked with spearheading digital agendas, are increasingly prioritising generative AI at the forefront of their initiatives. Already, 98% of global executives agree that AI foundation models will play an important role in their organisations’ strategies over the next three to five years.
CIOs understand that building a solid foundational architecture is essential for their organisation’s journey to enterprise readiness — and one that will position the business to scale generative AI with maximum efficiency and effectiveness, and foster successful adoption across the enterprise.
Laying the groundwork for generative AI
To facilitate this journey, CIOs must assess six key areas to determine their enterprise readiness for generative AI.
- Choosing suitable foundational models that are accessible to your organisation: The number of generative AI models and vendors is growing, and CIOs must help ensure the chosen models fit their organisation’s requirements. It’s essential to select an architecture that ensures the outputs from the models are relevant, reliable, and usable. Upon deployment, CIOs must determine whether they need a “full control” option for accessibility on their public cloud, or a managed cloud service from an external vendor, which allows for speed and simplicity.
- Adapting models to your organisation’s data: Consider how you can adapt pre-trained models with your own data to create customised tools or use specialised services relevant to your organisation and its people. For example, a multinational pharmaceutical company is migrating its applications from on-premises to the cloud using a generative AI accelerator. This solution integrates large language models, proprietary life sciences process catalogues, and agile delivery methodologies to translate requirements into a working software platform. It can reduce the initial phases of ideation, development, and testing, thereby enhancing efficiency and quality in product delivery. This reduces the company’s application development time by 20%, enhances collaboration between business and IT functions, and improves the quality of app development.
Getting maximum value from generative AI involves leveraging proprietary data to enhance accuracy, performance, and utility within the enterprise. AI, in conjunction with data, has become a key component of a robust digital core, which is the primary source of competitive advantage for companies today.
- Overall enterprise readiness: The adoption of generative AI introduces fresh urgency to ensure every company has a robust and responsible AI compliance programme. Are your foundation models secure and safe to use? Adhering to laws, regulations, and ethical standards is critical to building a sound AI foundation. Implementing controls to assess the potential risks of generative AI use cases at the design stage is also imperative.
- Environmental sustainability and carbon footprint impact: While foundation models come pre-trained, they can still use significant energy during adaptation and fine-tuning. The extent of this energy usage and its consequences depend on whether organisations buy, boost, or build foundation models. Left unchecked, this can have a considerable environmental impact, making it crucial to prioritise sustainable considerations from the outset to make informed decisions that benefit both the business and the environment.
- Industrialising application development: After choosing and deploying a foundation model, the next step is to consider what new frameworks may be required to industrialise and accelerate application development. Prompt engineering techniques are fast becoming a differentiating capability. By industrialising the process, you can build a corpus of efficient, well-designed prompts and templates aligned to specific business functions or domains.
- Strategising for scalability and establishing a roadmap for implementation: Upending existing processes and reinventing ways of working with new tech can present challenges. However, CIOs are responsible for finding ways to monetise the value generated by AI. For instance, Unilever is exploring new applications at its global AI lab, “Horizon3 Labs,” to scale generative AI with assets like AI navigators or its proprietary “switchboard.” The integration of Horizon3 Labs’ innovative approaches with robust expertise and ecosystem partnerships facilitates rapid and responsible scaling of AI and generative AI across its business, unveiling new value pathways. AI’s capabilities offer fertile ground for fostering innovation. Given their central role, CIOs are pivotal in promoting cross-functional collaboration, which can lead to fresh insights and informed decision-making that fosters open innovation within organisations and across industries, unlocking new growth opportunities.
Navigating the future of AI in enterprise
Generative AI represents a significant inflection point in the technological landscape, promising to reshape work and life as we know it. Research indicates that a substantial portion of working hours can be transformed by large language models, underscoring the potential impact of generative AI across various industries. In this context, CIOs have a unique opportunity to lead their organisations through this transformative era, leveraging AI advancements to redefine performance benchmarks and drive value creation.
Ultimately, success in the generative AI era hinges on enterprise readiness, with CIOs playing a pivotal role in steering their organisations toward technological maturity and agility. By embracing breakthroughs in AI and adopting a holistic approach to performance optimisation, CIOs can chart a course toward sustainable growth and innovation, shaping the future of their organisations and industries.
