A critical question has emerged in the AI era: How can enterprises move beyond early experimentation to achieve sustainable, long-term value?
While over 70% of enterprises have implemented AI as of September 2025, according to the Digital Silk AI Statistics Report 2025, many organisations have yet to realise measurable returns on their investments. The Gartner Hype Cycle for Artificial Intelligence 2024 notes that under 30% of CEOs report satisfaction with those investments, despite spending an average of US$1.9 million (SG$2.4 million) in 2024.
This is less a technical failure and more a case of strategic misalignment. Initial enthusiasm for generative AI led to siloed deployments and fragmented “micro-initiatives” that lacked the scale needed for long-term impact.
Shifting from fragmentation to value
The solution lies in embedding AI within core business strategy. That means identifying high-impact use cases, deploying AI in domains with clear operational relevance, and developing cross-functional capabilities to support enterprise-wide adoption.
The challenge is simple: general-purpose AI fails at specialised, high-stakes enterprise problems. Off-the-shelf solutions may serve consumer needs, but they fall short when addressing the nuanced complexities of specialised industries.
Crucially, leaders must rethink AI as a tool for augmentation, not automation. It is imperative that AI be integrated deeply within a company’s core business strategy and talent development roadmap.
Growing evidence supports this strategic pivot. Studies show productivity gains averaging 5% to more than 25% for employees in roles such as customer support, software development, and consulting.
AI’s effectiveness depends on how well it integrates into existing human workflows. Successful deployments enhance, rather than disrupt, day-to-day operations, keeping human input central to decision-making — an approach that fosters collaboration between people and technology.
Aligning talent and culture with AI strategy
Culture remains a major hurdle to successful AI integration, particularly between the C-suite and frontline employees. A report by Writer found that nearly 70% of C-suite executives observed tension between departments over AI initiatives, with many projects operating in isolation and limited cross-functional collaboration.
This requires leaders to proactively focus on cultural readiness. Companies that have implemented formal, responsible AI frameworks report significantly higher workforce engagement and adoption.
Equally important is establishing governance structures that include ethical guidelines, data accountability, and usage transparency. The same Writer report also found that companies with a formal AI strategy achieved an 80% adoption success rate compared with 37% for those without one.
Building for long-term value
The race for technological leadership often prioritises speed and disruption. Yet long-term value is more likely to emerge from an approach that favours resilience, sustainable growth, and strategic clarity.
As AI becomes more embedded in business operations, success will depend less on the speed of adoption and more on the quality of execution. Enterprise leaders should anchor AI projects to clearly defined outcomes, establish relevant but flexible KPIs, and embed these efforts within their values and core operating models.
This approach also requires a shift from role-based to skills-based workforce planning. In this framework, competencies such as data literacy, adaptability, and systems thinking take precedence over static job descriptions.
Cross-functional AI training for non-technical staff can further demystify the technology and promote broader adoption across business units.
Governance must also keep pace. Ethical deployment, including safeguards around data privacy, algorithmic bias, and social impact, is no longer optional but foundational.
Sustained growth entails viewing technology and talent as interdependent assets that can thrive together rather than competing forces. This philosophy of co-prosperity is particularly relevant in Southeast Asia, where optimism about AI adoption tends to exceed that of Western markets.
Ultimately, organisations that place people at the centre of their AI strategy, support them with a strong culture, and implement with clarity will be best positioned to turn AI into a sustainable engine for innovation.
For enterprises seeking to advance digital business, the objective is not disruption for its own sake, but to build systems that foster shared growth, amplify human potential, and enable more intelligent, connected operations.
