How to put a clear AI strategy into focus

Only 14% of Global 2000 organizations have a documented AI strategy with clear goals, according to a 2026 HFS Research and Altimetrik survey, despite nearly 90% of companies planning to increase AI investments. Experts emphasize that establishing an AI vision statement is critical to align AI objectives with business goals, prioritize investments, and mitigate risks, yet most organizations lack even basic AI vision statements.

Most experts and top IT executives agree that establishing an AI vision statement or guide is an important first step in developing an overall strategy for AI adoption in the enterprise. Developing such a statement can help companies better align their AI objectives with business goals, prioritize investments in AI tools and internal development projects, and promote a shared understanding of why and how AI will be used within an organization. Sounds like a no-brainer in terms of logical and responsible due diligence before diving headfirst into the AI pool, right? Unfortunately, while nearly 90% of companies plan to pour more money into their existing or planned AI investments over the next three years, Gartner found in a late 2023 survey that only 9% of these organizations have even basic AI vision statements in place that could help identify any potential problem areas or unknown shoals in a widening sea of AI innovation. The situation is not much better today, with only 14% of Global 2000 organizations claiming to have a documented AI strategy with clear goals in place, according to a 2026 HFS Research and Altimetrik survey https://www.hfsresearch.com/only-14-of-enterprises-have-a-clear-ai-strategy-altimetrik-and-hfs-research-find/ . Without a vision, as outlined in a white paper https://www.ai.se/sites/default/files/2023-09/aivision eng-1.pdf by an AI Vision Working Group in Sweden of people from the business community and public sector, an organization risks putting too little focus on the most valuable projects or, in the worst case, spending resources on the wrong projects. Save the Children CTO Ron Guerrier agrees, noting that it’s critical to establish an AI vision as a foundation for a well-defined AI strategy, not only for success, but to limit liabilities down the road. “We live in hyper-competitive society, where shareholder value still drives a lot of what we think and do,” he says. “Envision a time when you’re sitting in a deposition and someone asks how confident you were in leveraging AI to make a final decision. If you feel like you can get past that audit or regulatory definition in two or three years, then that’s the barometer we can use to question ourselves because the technology has grown so fast that the legal world hasn’t caught up.” There’s no one formula or template to establish an AI vision since every company and the internal dynamics that drive it are different. Add to this the expanding number of AI tools and services, as well as the constant pressure to quickly make use of these technologies to drive revenue, reduce costs, and remain competitive. “We’re at a huge inflection point,” says Satya Jayadev, former CIO and head of AI transformation at Skyworks Solutions, and now CIO at data storage developer Sandisk. “We’re looking at AI to help us with the bottom line and the top line. So, there’s a lot of pull and push that’s happening within the business.” Developing an effective AI vision and strategy begins with analyzing the data and exploring what might be possible by applying AI tools, Jayadev says. But that approach becomes a lot more complicated for larger companies, and those involved in more challenging industries. Common action items associated with creating a basic AI vision framework include developing a structure that aligns AI goals with business priorities and establishing clear AI policies, including rules for data handling, ethical use, and risk mitigation. Jayadev went a step further in creating his AI vision and adoption strategy by taking a three-phased approach, which can be applied to most any organization. The first concerned productivity, and what can you do with the technology now to reduce time, cut costs, and improve efficiency. At Skyworks, that included using Microsoft Copilot to streamline and speed up labor-intensive tasks like creating or summarizing emails and reports, or assisting with code generation. The second phase is differentiation. How can the technology be used to do things differently from competitors, and perhaps capture more market share or develop a faster and more efficient go-to-market strategy. The third, and perhaps the one that’s still in the gestation stage since gen AI is still evolving, is disruption, and how the technology can be used to do something radically different in terms of design engineering and manufacturing. “How can we use it to create a new way of doing something, rather than a different way of doing it,” Jayadev says. Not surprisingly, the task of developing an effective AI vision and charting a course through the disruption of that last phase falls squarely on the CIO as IT leader. This phase expands and amplifies transformational activities like thought leadership and establishing collaborative partnerships, and it adds driving a focused vision and leveraging the ecosystem to the mix. “The entire organization has to be the change agent,” adds Jayadev, “and thought leadership is going to be the most important ingredient.” As AI evolves, it’s important to treat it as a force multiplier and not just a tool to reduce costs or headcount. It allows us to “start thinking about a faster go-to-market strategy, a faster operational strategy, and a more efficient, effective way of getting things done,” says Jayadev. The collateral impact of both IT and the entire organization acting as change agents might also create a shift in the hierarchical structures of the enterprise, and a restructuring in technology and business leadership. “CIOs will hate me for saying it, but what they need to do now is transform,” says Guerrier. This transformation will involve more than just a title change as some CIOs have done. It’ll require adding an adjustment in mindset to focus more on data and less on fundamental transformation activities, like IT operations and modernizing legacy systems, if they expect to remain in the upper levels of the IT org chart.