As someone who has been deeply immersed in AI research and implementation for years, both as a practitioner and as a doctoral student, I’ve witnessed firsthand the dramatic shift in how artificial intelligence is perceived in the business world. Those who follow my work know I’m a strong advocate for AI adoption—I use it extensively in my daily operations and research. However, recent developments in both academic literature and market dynamics have revealed some fascinating, and sometimes concerning, trends that every business leader should understand.
The Academic-Practice Gap: 40 Years in the Making
The academic community has been studying artificial intelligence for over four decades, building robust theoretical frameworks and conducting rigorous research on machine learning, neural networks, and intelligent systems. Yet it wasn’t until ChatGPT-3’s breakthrough that AI truly captured mainstream business attention. This viral moment created an interesting paradox: while the technology itself has deep academic roots, the business world is treating it as if it were brand new.
This disconnect has created both opportunities and challenges. On one hand, we have decades of solid research to draw from. On the other, we’re seeing a rush to implement AI without understanding the foundational principles that make implementations successful.
The Uncomfortable Truth: Why 70% of AI Implementations Fail
Recent research has uncovered a sobering statistic that should give every business leader pause: estimates place AI project failure rates between 70-85%, with some studies citing that over 80% of AI projects fail—nearly double the rate of traditional IT project failures. In 2019, MIT cited that 70% of AI efforts saw little to no impact after deployment, and this figure has been expected to increase, with some predicting as high as 85% of AI projects missing expectations. More recent data from S&P Global Market Intelligence found that the share of companies abandoning most of their AI initiatives jumped to 42% in 2025, up from 17% the previous year.
This isn’t simply a matter of choosing the wrong technology or having insufficient budgets. The primary culprit is something I’ve written about extensively in my book “Strategic Capabilities“—the absence of AI capability as an organizational competency.
Understanding AI Capability
AI capability isn’t just about having the latest tools or hiring data scientists. It’s a complex, multifaceted organizational competency that encompasses:
Human Resources:
- Leadership that understands AI’s strategic implications
- Teams with appropriate technical skills
- Change management capabilities
- Cross-functional collaboration skills
Tangible Resources:
- Robust data infrastructure
- Computational resources
- Integration capabilities with existing systems
- Quality data governance processes
Intangible Resources:
- Organizational culture that embraces experimentation
- Clear AI strategy aligned with business objectives
- Risk tolerance for iterative development
- Knowledge management systems
This AI capability has emerged as a measurable and proven predictor of implementation success. Academic research by Mikalef and Gupta (2021) demonstrates that AI competencies significantly influence organizational performance through their impact on business capabilities. Studies examining public organizations across European countries have shown that AI capabilities indirectly affect organizational performance by inducing change in key organizational activities. Companies that score higher on AI capability assessments consistently demonstrate better outcomes in their AI initiatives. However, developing this capability is neither quick nor easy—it requires sustained investment across multiple organizational dimensions.
The Capability-Centric Future of Business Strategy
I firmly believe we’re entering an era where business strategy will increasingly center on organizational capabilities rather than traditional competitive advantages. The companies that will thrive in the AI age are those that can build, maintain, and evolve their AI capabilities faster than their competitors.
This shift represents a fundamental change in how we think about competitive advantage. It’s no longer enough to have access to the same tools as everyone else—success depends on your organization’s ability to effectively leverage those tools through superior capabilities.
Navigating the Market Noise
Unfortunately, the current AI landscape is cluttered with noise. The market has been flooded with basic AI handbooks designed to capitalize on early adopters’ enthusiasm. Social media platforms, particularly LinkedIn, are saturated with self-proclaimed experts and sales influencers pushing clickbait content that promises instant AI transformation.
This marketing frenzy creates artificial urgency—the “everyone is already using it, you’re already late” mentality that pressures businesses into hasty decisions. The reality is far more nuanced.
The Real Timeline: Patience in an Impatient World
Here’s the truth that the marketing hype won’t tell you: AI is still an emerging tool that needs several more years to mature and deliver the transformative impact many promise today. Research from the RAND Corporation, which interviewed 65 data scientists and engineers with extensive AI/ML experience, identified five leading root causes for AI project failure and confirms that while AI has vast potential, successful implementation requires careful navigation of complex challenges. While AI can certainly provide value now, the revolutionary changes that many predict are still evolving.
This doesn’t mean you should ignore AI entirely. Rather, it means you should approach it strategically, with realistic expectations and a focus on building sustainable capabilities rather than seeking quick wins.
Practical Advice for Business Leaders
Before You Buy or Sign Anything
- Assess your current capabilities honestly. Where does your organization stand in terms of data infrastructure, technical skills, and change readiness?
- Be skeptical of promises. If someone guarantees immediate transformation or claims their solution works for everyone, be cautious.
- Focus on fundamentals first. Many business development managers are still struggling to fully leverage their CRM systems. Master what you have before adding complexity.
You’re Not Late—You’re Strategic
If you’re a business owner who hasn’t yet implemented AI, you’re not behind. You’re potentially in a better position than companies that rushed into implementations without proper preparation. Use this time to build your AI capability foundation.
Start with Internal Development
Before looking externally for AI solutions, focus on internal capabilities. I explore this extensively in my book “Operation Business Development,” which addresses the internal actions managers can take to strengthen their teams and processes. Strong internal operations provide the foundation that makes AI implementations successful.
Conclusion: Building for the Long Term
The AI revolution is real, but it’s not happening overnight. The companies that will ultimately succeed are those that take a measured, capability-focused approach to AI adoption. They invest in their people, processes, and infrastructure. They resist the pressure to chase every new AI trend and instead build sustainable competitive advantages through superior organizational capabilities.
As we navigate this transformation, remember that AI is a tool—a powerful one, but still just a tool. The real competitive advantage lies in your organization’s ability to effectively leverage that tool through well-developed capabilities.
The future belongs to organizations that can build and maintain superior AI capabilities. The question isn’t whether you’re late to the AI party—it’s whether you’re building the capabilities that will matter in the long run.
For more insights on building strategic capabilities and operational excellence, explore my books “Strategic Capabilities” and “Operation Business Development.”
References
- Ryseff, J., De Bruhl, B. F., & Newberry, S. J. (2024). The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed: Avoiding the Anti-Patterns of AI. RAND Corporation, RR-A2680-1.
- Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434.
- Mikalef, P., Conboy, K., Lundström, J. E., & Popovič, A. (2022). Thinking responsibly about responsible AI and ‘the dark side’ of AI. European Journal of Information Systems, 31(3), 257-268.
- S&P Global Market Intelligence. (2025). AI project failure rates survey. March 2025.
- NTT DATA Group. (2024). Between 70-85% of GenAI deployment efforts are failing to meet their desired ROI. Retrieved from: https://www.nttdata.com/global/en/insights/focus/2024/between-70-85p-of-genai-deployment-efforts-are-failing
- MIT Technology Review. (2019). Why do 20% of AI projects fail? Retrieved from various industry reports.
- Mikalef, P., Lemmer, K., Schaefer, C., Ylinen, M., Hjort-Madsen, P., Torvatn, H. Y., Gupta, M., & Niehaves, B. (2022). Examining how AI capabilities can foster organizational performance in public organizations. Government Information Quarterly, 40(1), 101797.
- Weber, M., Engert, M., Weking, J., & Krcmar, H. (2022). Organizational Capabilities for AI Implementation—Coping with Inscrutability and Data Dependency in AI. Information Systems Frontiers, 25(4), 1473-1500.