When Developing an AI Strategy, Beware the Urgency Trap

Despite huge investments on AI deployments, companies have seen returns remain stubbornly modest. Last summer, an MIT report found that 95% of gen AI projects fail. A recent comprehensive large-scale survey conducted by the National Bureau of Economic Research among more than 6,000 senior executives in the United States, United Kingdom, Germany, and Australia found that roughly 90% reported no measurable improvement in productivity attributable to AI cross the last three years. The problem, however, is not that AI does not work. The problem is how leaders think about it.

As an academic who also consults with organizations across industries, I have observed a consistent pattern in failed or underperforming AI initiatives. Specifically, I’ve found that business leaders tend to frame AI through the lens of what they see as the most urgent problems—bottlenecks in productivity, rising costs, slow decision-making, or inefficiencies in workflows. AI is then positioned as the solution to these immediate and present challenges. This default framing feels logical, urgent, and defensible. But I believe that it is also precisely what undermines long-term value creation when adopting AI.

Because of a focus on urgent and immediate challenges, many AI efforts start fast and generate excitement, but ultimately fail to transform organizations in meaningful or lasting ways. Once leaders understand this trap, there are three actions they can take to avoid employing AI as a quick fix and instead use it as a tool for long-term value creation.  

The Urgency Trap and the Obsession with Speed

In my experience, the urgency trap emerges when leaders over-prioritize problems that are easy to see and measure while neglecting deeper, longer-term issues that are harder to diagnose. In most organizations, these problems are operational: inefficiencies, delays, duplicated work, or underutilized talent. They’re tangible, show up in dashboards, and demand immediate action. AI appears attractive because it promises quick gains—automation, optimization, and acceleration.

This dynamic is reinforced by a broader sense of urgency surrounding AI adoption. Executives fear falling behind competitors, missing opportunities, or being perceived as technologically outdated. The prevailing belief is that speed is the true competitive advantage: The faster an organization deploys AI, the more likely it is to win. But if the goal is to implement AI quickly to address immediate and urgent challenges, then AI will only improve what already exists. For example, in some professional services firms, consultants are encouraged to use AI to streamline research and produce more client deliverables in less time.

There are a few problems with this approach. First and foremost, it does little to innovate, grow the company, or achieve sustainable value. Indeed, this approach is supported by the idea that AI needs to be primarily used to work on the short-term and as such reduce costs and immediate risks. Such a mindset does not focus on growing and transforming the organization but rather on sustaining the status-quo. But there’s also evidence that it has negative consequences on employees. For example, a July 2025 survey from Upwork and Workplace Intelligence reveals that while output increases when employees start working regularly with AI (up to 77%), burnout among those users also rises significantly (88%). Employees feel pressure to do more, faster, with fewer opportunities to exercise judgment or creativity. Consequently, and as the survey shows, employees using AI to do more of the same work in less time, see little connection with the organization’s strategy (and purpose) and are therefore twice as likely to consider quitting their job.

To be clear, what these findings suggest is not that AI fails technically, but that it fails strategically. It amplifies the desire to accelerate productivity without addressing the deeper question of the organization’s primary goals and how AI could help in achieving that vision.

The data support this observation. A 2024 survey by EY revealed that across organizations 88% of employees use AI at work, but only 28% of those organizations position and empower their employees in ways that they can create real organizational value and, hence, transformative business impact, by their use of AI. Ironically, the obsession with speed slows organizations down in the long run. Instead, leaders should focus on how AI can help improve work and stimulate growth.

Pivot to Purpose

I believe that the real competitive advantage of AI does not lie in speed. It lies in purpose-driven integration. Organizations that succeed with AI do not start with the technology; they start with a clear understanding of why they exist and what kind of value they want to create for customers, employees, and society.

Organizational purpose provides a stable reference point in an environment of rapid technological change. It helps leaders distinguish between what is urgent and what is important. When AI adoption is guided by purpose, leaders can ask better questions and consequently create real sustainable value. Which decisions matter most to us? What vision do we have when it to comes to transforming our organization and the workplace? Where do humans add unique value and where can AI do so? What kind of new value do we want to create for our stakeholders?

In my work with healthcare organizations, the notion of what is important to the vision of the company matters greatly to their use of AI. AI is used not to increase output alone but to improve patient outcomes and clinician well-being. With it comes a focus on reducing administrative burdens that distracted clinicians from patient care. AI is, for example, used to summarize medical records and support diagnostic reasoning, rather than dictating decisions. AI can as such improve productivity, but more importantly, helps clinicians be more satisfied about their job as patients feel more cared about (see also this Kaiser Permanente case and a study of the AI platform Abridge for evidence). The AI succeeded because it was aligned with the organization’s purpose: delivering high-quality, humane care.

What does this mean for the kind of leadership that is needed when adopting AI?

Three Leadership Shifts for Purpose-Driven AI

Most organizations are not struggling with access to AI—they are struggling with direction. To turn AI into a true engine of value, leaders must rethink how to put purpose center-stage in their decision-making to re-imagine their organization. I recommend that leaders take three steps.

Prioritize clarity of purpose.

Many leaders right now are motivated by a fear of missing out on the newest AI advancements and are therefore convinced that the company’s future can only be secured through the latest and most advanced AI model. In practice, AI adoption projects are often led by technology managers like CIOs, who prioritize a narrower focus on tech deployment over systemic work culture changes and organizational transformation efforts that are needed to facilitate leveraging AI for the organization. In other words, they focus on the technology itself, not the people who use it.

Unfortunately, they’ve got it backwards. Companies need to align their AI strategy with organizational purpose before even touching AI tools. For their part, leaders charged with guiding this kind of transformation need to ask themselves the following questions:

• What core problem are you trying to solve—and why does it matter to your mission?
• Where can AI meaningfully elevate how you create value, rather than simply automate what already exists?
• What would success look like in human terms—not just technical performance metrics?

This will help your organization to decide the kind of AI model needs to be solicited, what value you want to offer your stakeholders, and what long-term AI solutions look like in your vision. When AI is governed as a core business capability grounded in the company’s purpose, it becomes sustainable and able to leverage real and new value.

Resist the urgency bias.

Based on my experience, it will be patience rather than speed that will turn AI adoption into new and holistic value creation. When speed is prioritized above all else, organizations cut corners. They underinvest in change management, neglect skill development, and treat AI as a plug-and-play solution. The result is fragile systems that struggle to scale and deliver diminishing returns.

Contrary to a focus on speed, purpose-driven AI adoption is inherently patient. It recognizes that meaningful transformation requires reflection, dialogue, and learning—and that not every problem should be solved with AI. Sometimes, process redesign, leadership development, or cultural change is more appropriate. Purpose helps organizations move deliberately, invest in people, and anchor technology decisions in the interests of all stakeholders. These AI initiatives may start slower, but they’ll likely scale more effectively and endure longer.

To resist the pull of urgency, leaders need to ask themselves the following questions:  

• Are you prioritizing speed because it addresses urgent problems—or because it creates value over time?
• What critical capabilities (skills, processes, culture) must be built before this AI initiative can scale?
• Who in the organization needs time to learn, adapt, and trust this change—and are you giving them that time?

Advance the company’s vision.

AI is not a simple add-on, but rather a catalyst that can change how you pursue operational and strategic goals. Leaders therefore need to possess the complimentary abilities of envisioning the future and, at the same time, executing that vision—they need to be visionary integrators. This means being able to inspire others, but also guide them on how to implement AI in consistent, purpose-driven ways across the organization. For example, rather than deploying AI simply to automate existing tasks, visionary integrators identify where AI can help employees make better decisions, collaborate more effectively, and create greater value for customers. They ensure that AI initiatives across functions—from operations and marketing to human resources and innovation—are aligned with the organization’s strategic priorities and long-term purpose.

To become visionary integrators, leaders need to ask themselves the following questions:

• Are you integrating AI consistently across the organization, or allowing fragmented, isolated use cases to emerge?
• What narrative will inspire your people to see AI not as a tool, but as part of a shared ambition?
• Do you have the leadership alignment and execution discipline to turn vision into sustained action?

. . .

Leading AI adoption in this way ensures that it is no longer a peripheral tool or a collection of disconnected initiatives but something that becomes woven into the fabric of how value is created and, importantly, sustained. It shifts the focus from isolated gains to systemic impact—where technology, people, and purpose reinforce one another. Leaders who embrace this mindset don’t just implement AI; they reshape their organizations so that AI elevates human potential, sharpens strategic intent, and drives performance in ways that endure.