12th August 2026
Why AI Strategies Fail Without Purpose
Despite significant investment, many AI initiatives fail because organisations focus on solving urgent operational problems rather than creating long-term value. The article argues that successful AI strategies begin with organisational purpose, supported by patient leadership, employee development and thoughtful integration that enhances both business performance and human potential.
This article was written by David De Cremer and published in Harvard Business Review.
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?
Read the full article here: When Developing an AI Strategy, Beware the Urgency Trap
