Arthur D. Little (ADL) recently released the 2025 edition of its ongoing CEO Insights study: “Proactively Embracing Change.” Based on in-depth conversations with global business leaders, the research reveals that CEOs are responding to today’s volatile geopolitical and economic landscape not with hesitation, but with confidence and decisive action. This Advisor, the third in a series exploring key themes from the research, urges organizations to embed AI into long-term strategy, governance, and business model innovation — or risk being outpaced by AI-native challengers.

AI is transforming industries at an unprecedented pace, yet many organizations continue to focus on delivering incremental improvements rather than fully unlocking AI’s disruptive potential. Many companies are using AI to do more of the same, only better, running a large number of pilot projects. This is not a replacement for strategy. Start-ups are rapidly embracing AI-driven business models, while leading incumbents struggle to scale AI across the enterprise. A recent ADL report, “Moving Innovation Forward,” identifies more than 900 AI use cases solely in the domains of development and engineering, and many more will materialize.

To future-proof their organizations, CEOs and their leadership teams must take a structured approach to AI adoption — one that moves beyond experimentation and embeds AI into long-term strategy and operations. They must create a vision that goes beyond Waves 1 and 2 to reach Wave 3, creating genuine disruption.

Based on extensive project experience and global studies, ADL has identified five focus areas to maximize AI’s impact; however, there is a significant risk that AI use is plateauing. When asked to compare their current and future use of AI, CEOs don’t expect programs and projects to change dramatically moving forward. These use cases overwhelmingly focus on the first three dimensions of AI (driving greater efficiency in the core business, increasing the effectiveness of the core, and improving noncore activities). There is much less usage in the more disruptive fourth dimension of creating and deploying new business models. AI is being used to improve what is already there rather than to innovate at scale. Adding to this, the lack of a unified strategy in 71% of organizations may cause them to lose value and leaves them open to disruption by start-ups/competitors.

To maximize AI’s impact, organizations need to:

1. Define long-term industry development and scenario planning. Organizations must develop a clear vision of AI’s role within the business environment over the next five to 10 years. Scenario planning can deepen the understanding of how AI can and will change the business, predicting future competitive dynamics and providing support to help define a strategic response. A holistic AI strategy can then be more clearly defined. Continuously tracking technology, business, and ecosystem trends ensures companies stay ahead of disruptions. One method for better monitoring and evaluating relevant industry driving forces is through technology and business trend radars, which categorize technology and business developments based on their maturity and relevance. These can be used to identify where to watch, explore, accelerate, and invest — ensuring CEOs are alert to key future technologies and business opportunities. 

2. Develop a competitive AI strategy. A strong AI strategy should include no-regret moves that hold value across scenarios. Companies must determine how AI drives differentiation and enhances business models while increasing efficiency and effectiveness in the core business. Leveraging data, ecosystems, and partnerships is key to sustaining a competitive edge.

3. Overcome the proof-of-concept trap. Moving beyond isolated pilots requires a shift to enterprise-wide AI adoption that leads to measurable financial impact in the core business. Impact should be clearly tied to overall strategy and business objectives, with AI use cases and clusters of use cases further mapped to overall business objectives to avoid efforts being deployed too widely. Clear KPIs and value metrics should be established, supported by structured processes and ongoing outcome tracking to ensure AI delivers lasting business value.

4. Implement AI governance and organizational readiness. Scaling AI demands a governance framework that balances central oversight with decentralized execution. Leadership alignment, new ways of working, and structured upskilling and reskilling are essential on a broader scale in the organization to integrate AI into core business functions and prevent fragmented efforts. This cultural shift is often overlooked, but getting employees to embrace, experiment with, and integrate AI is crucial.

5. Build scalable and accessible AI infrastructure. Democratizing AI requires making tools more broadly accessible across the organization. Furthermore, investing in scalable technology, data, and policies ensures seamless AI integration. Solutions must be flexible and adaptable to support evolving business needs.

The Path Forward

To fully unlock AI’s potential, organizations must shift from short-term experimentation to strategic execution. AI must be embedded into core business models, supported by robust governance, and enabled by scalable technology stacks. CEOs who take a proactive approach — investing in structured AI strategies, empowering their teams, and fostering a culture of continuous AI-driven innovation — will not only enhance their competitive advantage, but they will also redefine their industries for the future. The time to act is now.