AI is a catalyst for strategy and business transformation across all organizations, calling for a new era of leadership and governance. Treat it as simply another technology disruption, and we can easily become distracted by training objectives, tool licenses and token use while missing the bigger strategic opportunity. At the same time, it’s apparent that unforeseen risks abound, with even the leaders of the most advanced models expressing the need for caution as the technology frontier advances at a speed that could surpass human’s capacity to keep up.
These risks on both sides—both playing too small and also moving too fast—reinforce the need for rigorous governance and an agile strategic partnership between the board and management.
For leaders, part of what makes AI’s impact so elusive is that it emerged from the same arc of digitalization that moved processes and data to the cloud, enabling everything from early machine learning to generative AI and now agentic systems. That continuity can make AI feel evolutionary rather than revolutionary, creating a familiarity that can almost mesmerize leaders—whose instincts are built around experience-based pattern recognition—into approaching AI adoption like the tech deployments that came before.
That’s a mistake. While AI requires a solid digital foundation, it is fundamentally different from prior transformations that leveraged new technologies.
Those of us who have been involved in ERP implementations, a new CRM rollout or a migration to the cloud recognize the shape of that work. There was a champion. There was defined functional scope—finance, sales or service. There were data migration and training plans, user acceptance testing, a go-live date and eventually a defined end point. These initiatives were strategic in the sense that they could improve efficiency and effectiveness, but they only rarely enabled a fundamental reimagining of how business could be done. And even in the case of major shifts, such as a transition to e-commerce, the timelines were far slower and the roadmaps better defined than they are today.
In many organizations, adoption and innovation are already happening from the bottom up—crowdsourcing on steroids, with experimentation moving up, across and sideways, often in disconnected ways and without vision. Co-creation and ideation can happen almost anywhere; the barrier to entry may be little more than the ability to write a sentence. And the process does not stop. Most transformations we have led had a beginning and an end. This one will continually unfold cycle after cycle after cycle.
Some are looking down from the top and trying to organize all of it, like a traffic cop at an intersection with a broken stop signal. Because enterprise AI depends on coherent, secure, accurate data and the infrastructure to serve it, organizations can stay very busy battening down the data hatches. AI is hard to govern, hard to organize and hard to lead. It is also easy to over-constrain.
Far fewer leaders are looking down from the top and articulating a new vision for how AI can help the organization fulfill its purpose. Some of what looks like an AI slowdown right now may simply be companies discovering that this larger vision is missing.
The hesitation among experienced leaders is understandable. A multi-decade career may span a dozen major technology shifts, and in almost every one someone else owned and drove the technology. That instinct is now a liability. AI fluency is not just technical. It includes strategy and culture—workflows and the workforce at large—and the judgment to connect it all to the organization’s larger purpose.
Management needs to step up, and boards need to support them. Board oversight of AI must extend beyond risk, compliance and capital allocation to the strategy and culture questions that will help guide how the technology is ultimately used, and what kind of organization it creates.
Start With Value, Not Return
When management brings a strategic AI initiative before their board, they’re often asked about the expected return on investment and how that return advances the company’s strategic growth plan.
But there is a question that belongs before that one: What value is this initiative intended to deliver, and have we identified a way to effectively measure it?
That sounds elementary. Yet there often is no ready answer. There is a great deal of AI activity inside most companies right now, but activity and productivity are not the same thing. Token costs climb. Governance complexity grows. Pilots accumulate. This moment demands discipline in determining how, specifically, AI can create new value, whether incremental or transformational.
Once the aspiration is clear, the question of return comes next. Value and ROI are fully interdependent. Too often, the sequence is reversed—and that is a governance problem for management and board alike.
AI can create value in three primary ways. They are worth separating, because each one asks something different from the executive team, from management, and from the board.
Cost, Automation and Productivity
This is where most companies started, and where they’ve experienced early wins. Alas, the early losses have captured more attention.
Thirty-two percent of U.S. hiring managers say they eliminated a role primarily because of AI and later rehired for the same or a similar position, according to Robert Half data reported by CNBC. Orgvue found that, among the 39 percent of business leaders who made AI-driven cuts, 55 percent now believe the decision was wrong. Gartner projects that at least half of the organizations that cut customer service jobs to AI automation will be rehiring for the same or very similar responsibilities by 2027.
We’ve seen the headlines. One automaker spent three years bringing back 350 veteran engineers after automated systems failed to catch quality problems experienced team members would have flagged. A major technology firm found that its AI was able to resolve 94 percent of internal HR requests, then discovered the remaining 6 percent—the cases turning on ethical judgment—were the ones that mattered. The firm has since tripled entry-level hiring on its HR team.
And rehiring laid-off staff is not as easy it sounds. There are hidden costs, including the reputational damage associated with reversing a highly public decision. And it accrues a trust deficit that’s far more difficult to reverse.
Boards should press hard on the productivity case. On the surface, it’s easy to argue in favor of automation-driven efficiencies. But the cultural implications are equally easy to gloss over. Where the gains are real, a second question matters just as much: Where will the value derived from those gains go? Back into the team? Investment in innovation? Into M&A? Into the dividend? A frontline supervisor who no longer takes scheduling reports home can reallocate that time to analyzing trends and anomalies, making judgment calls that draw on their years of experience. Alternatively, that supervisor can absorb more of the same work and end up feeling utterly burned out.
The board doesn’t manage teams or processes, of course. But we can provide a strategic backstop to management in ensuring there is a thorough understanding of AI’s risks and rewards.
Revenue, Process and Reinvention
The second area AI’s value can show up is in revenue, and it is the one most often underplayed in current board materials. How far can you compress the innovation cycle, or time to market? Which partners do you choose, and how does their use of AI compound yours? Where does personalization and workflow transformation improve the customer experience? Agents are changing the nature of work itself, which makes the business growth opportunity considerably larger than the automation opportunity.
A company’s purpose should not change because of AI but how it delivers on that purpose can change entirely. Yet, according to forthcoming survey data from FranklinCovey, only 31 percent of employees say AI has enabled something their organization couldn’t do before.
This is where imagination comes into play, and where I see the widest disparities in performance. I have seen some genuinely impressive use cases. (Here’s one: AI applied to manufacturing-floor safety, running against around-the-clock sensor and camera data, flagging risks that no human review could have caught.) What I have seen less of is reinvention with true innovation at the enterprise level: a leader standing back and concluding that the business could deliver its products and services in a way that would have been unimaginable five years ago. It may even mean discovering that the existing portfolio is no longer enough—and using that realization to catalyze bigger thinking about product development, partnerships and the future shape of the business.
Culture, Talent and the Workforce
The third domain for AI value creation receives the least attention in governance conversations, including some of my own. Culture is an integral part of strategy rather than an adjacent concern, and it is a material risk when it is not equally prioritized for investment. That makes it a governance question.
It’s also where funding decisions become visible to employees. A $2 billion services company suspended its 401(k) match for 16,000 U.S. employees and told them the money was going to AI tools and training. A Big Four firm eliminated a $50,000 fertility planning benefit, halved paid parental leave and ended pension accruals for one tier of its workforce.
Any one of those decisions might be defensible. As a package, they send a message: We are investing in the business, but not really in you. Employees hear it clearly, and so do the people watching from outside. Many leaders forget that employees remain stakeholders after they leave. They become future customers, future industry partners, future shareholders. A company that hollows out goodwill to finance AI deployment is borrowing against an asset it cannot easily rebuild. If the technology delivers, the company may still have overpaid. If it underdelivers, as it already has for a nontrivial share of companies, the goodwill has been spent with little to show for it.
This tension is brewing right now and will likely continue to do so—overtly and under the surface—for the foreseeable future. In FranklinCovey’s new research, workers were asked to select the one word that best describes how they feel about AI’s impact on their careers. The good news is that about 54 percent of respondents chose “curious,” “hopeful” or “excited.” But nearly 40 percent cast votes for “uncertain,” “anxious,” “angry” or “resigned.”
What I know with confidence is that culture does not wait while leaders decide what role they wish to play in it. The board’s job here is oversight, the same as it is with all elements of strategy. Directors should not be driving culture. They should be insisting that leadership is while providing the necessary oversight across the enterprise.
Technology has a long-established governance taxonomy; it spans subdisciplines like cybersecurity and data privacy, energy consumption and grid capacity, and more. Growth has well-developed benchmarks by company stage and industry. As of yet, cultural transformation, including workforce transformation, in the AI era has no such standards.
Someone Has to Own This
If everyone owns AI (and right now, everyone does), then no one is wholly accountable. So accountability must be proactively assigned, and whoever ultimately owns it will find that their purview extends well beyond their functional responsibility. Who that responsibility sits with—the CIO? The chief strategy officer? a new chief AI officer?—matters less than that it sits with an executive who has enterprise reach—who brings the rest of the C-Suite into the work as co-owners rather than as consultees.
Further, anyone in a strategic role should be fluent in AI. And so should their team. If AI is the mechanism of enterprise transformation, fluency becomes a requirement for enterprise leaders. That requires leadership, and the organization as a whole, to treat AI fluency as a measurable capability, with KPIs that go far beyond training compliance and tool adoption to assess observable behaviors in human-AI interactions.
Finally, ownership of the AI transformation demands the discipline of prioritization. No company can run a thousand AI projects at once with equal funding and equal expectation. Gates and thresholds are critical, allowing enough flexibility for some longshot bets while imposing enough rigor to cut initiatives that cannot show a path to broad adoption. The test is whether an initiative can scale to create value that materially affects the P&L. A tool that helps one person is interesting; a tool that helps six hundred can be a breakthrough.
The AI Questions Boards Must Keep in Mind
On cost: Can this deployment deliver on management’s promises, and where does the savings go when it does?
On revenue: What does this technology allow us to accomplish in service of our mission, purpose and growth strategy that we could not accomplish before? What does it mean for our customers?
On culture: What are the trade-offs, overt and hidden, of adopting AI at scale, and will the organization that emerges be capable of turning the technology into sustained and perhaps even breakthrough performance? What does this mean for our desired workforce of today—and of tomorrow?
Organizations who deploy AI to gut their headcount may also get a workforce that understands precisely where it sits in the company’s hierarchy of value—and a culture too depleted to absorb, sustain and capitalize on the changes AI will continue to demand. At a time when AI risks abound, humans need to lead with discernment and comprehension of the larger considerations.
Boards have a huge role to play in supporting management’s navigation through these risks: Risks of playing too small and focusing only on cuts. Risks of moving too fast and creating vulnerabilities. Risks of damaging culture.
The ideal outcome is a workforce that trusts its organization, is trusted in return and thus keeps learning and adapting. Amid transformation, boards must actively partner with management to ensure they remain focused on innovation, continuously developing new ways to create and capture value.
