The right AI strategy for businesses means doing these three things

Business today is anything but usual. Economic volatility, a turbulent political landscape, rising stakeholder demands and the rapid rise of generative AI are shifting the ground under our feet faster than traditional strategy models can adapt.

For years, businesses adapted to change by creating flexible strategies focused on resilience and agility. That mindset helped them navigate past crises, but leaders are increasingly concerned that it is no longer enough. In a recent global CEO survey from PwC, four in 10 CEOs said their companies will not be viable in ten years if they continue on their current path.

This concern is justified. Strategy has become a dynamic system that needs to continuously learn, react and evolve alongside changing conditions. The greatest threat to most organizations isn't their rivals – it's their own failure to adapt.

Under this pressure, many leaders have embraced AI as an apparent solution to bridge the widening divide between the pace of external change and their organization's response time. They greenlight new projects, roll out pilot programmes and highlight transformation efforts in shareholder communications. Yet, when outcomes are evaluated, meaningful impact of AI projects remains elusive.

Research shows that 95% of generative AI pilots fail to produce measurable returns. Further, while most companies are using AI, only 39% see any impact on earnings, and even then that impact is largely marginal. Just 4% of companies have built AI capabilities that consistently create value. And according to Gartner, fewer than half of digital initiatives meet their business objectives. The list goes on.

In my role as Chief Strategy Officer at Workiva, delivering impactful AI capabilities to our customers and optimizing our internal AI usage and governance is something I’m thinking about daily, in partnership with our executive team and board. What I’ve learned can be applied in other organizations seeking to apply AI the right way.

A closer look at the data reveals that most organizations approach AI as a technical upgrade rather than a fundamental rethinking of how strategy is developed, implemented and assessed. There are countless obstacles and traps companies run into when determining the best way to deploy AI.

Common missteps include: grafting AI tools onto existing processes rather than redesigning workflows and decision-making from the ground up; rolling out generic AI solutions without providing employees with adequate training or role-specific resources; and launching ambitious transformation initiatives while critical data remains scattered across legacy systems, on-premise platforms, and spreadsheets.

The root problem is not a lack of AI. It's the lack of a connected, trusted and strategically guided AI across the enterprise.

Successful strategy depends on three elements working continuously together, shaping, informing and balancing one another: the right tools, the right internal partnerships and the right data.

AI must deliver measurable impact on core business processes, such as closing the books faster, streamlining regulatory filings, strengthening risk management or improving sustainability reporting.

This requires purpose-built tools that understand your data and workflows, integrate seamlessly into daily work and deliver value quickly. Companies that realize the most value from AI when they move away from one-off experiments in favour of integrated platforms — tools that unify data and enable specialized applications built on that shared foundation.

Organizations that turn AI into real ROI build a coalition at the top where the CEO, CIO and CFO share one transformation agenda with shared investment targets. While the entire executive team is paramount to this collaborative effort, these three roles typically anchor execution.

Chief Strategy Officers should understand: when strategy, technology, and capital allocation move in sync, AI transforms from experiment to infrastructure. It becomes embedded in planning, resource allocation and performance measurement. That shared ownership converts AI from scattered pilots into enterprise-wide impact with clear accountability.

Trusted data is the prerequisite. It trains models, reveals insights and drives automation. More fundamentally, it's what ensures integrity — the proof that what a company claims matches what it reports and does.

For most organizations, too much critical information remains trapped in on-premise systems, siloed tools and offline documents that AI can't access. Scaling AI requires connecting financial, risk and sustainability data and then establishing governance controls that satisfy boards, regulators and investors. When leaders have connected data they can trust, they can leverage AI to validate higher-stakes decisions with confidence.

With the right tools, partnerships and data infrastructure, strategy becomes dynamic; leaders still govern decisions, but AI generates options, detects risks early, spots emerging trends and recommends new directions based on real-time insights.

In an era of compounding uncertainty, continuous adaptation has become strategy itself, with AI as its engine. Companies that grasp this will define the next decade. Those that resist will be defined by it.