Here’s why many AI projects are doomed to fail

The real path to AI value is incremental, focused, and relentlessly pragmatic, writes Dr Gleb Tsipursky.

A dangerous narrative is taking hold among leadership teams across the country, a story of effortless, overnight transformation powered by artificial intelligence. It is a seductive mirage shimmering in the desert of corporate ambition, promising untold riches and seamless automation from a technology that is still in its turbulent adolescence. This story sells a future that is as intoxicating as it is illusory, and it threatens to poison the well for everyone investing in this powerful new capability.

This mirage is sold with breathless enthusiasm by reports such as McKinsey’s recent playbook, Seizing the Agentic AI Advantage. The article paints a dazzling picture of autonomous AI “agents” that will seamlessly orchestrate your entire business, delivering returns in under a year and creating exponential value. Yet this vision, while directionally fascinating for the distant future, is perilously disconnected from the messy reality of 2025. This level of hype is not just optimistic; it is actively harmful, setting the stage for a brutal crash into the trough of disillusionment that could undermine the real, tangible benefits of AI for years to come.

The seductive, flawed promise of the agentic enterprise

The McKinsey article tempts us with a future where “no-code agent builders” allow any business user to create AI workers, which then form an “agentic AI mesh”, an interconnected ecosystem of programs autonomously negotiating, planning, and executing complex workflows. It is a powerful fantasy. Imagine an AI agent in procurement autonomously identifying a supply need, negotiating terms with a vendor’s AI agent, and executing the purchase order without any human touching a keyboard. Now, imagine that agent misinterpreting a regional sales forecast and ordering $10 million of the wrong component, or the vendor’s agent exploiting a loophole in your agent’s programming to lock you into unfavourable terms.

This is the core of the problem. The vision of full autonomy dramatically underestimates the monumental challenges of reliability, security, and integration. As documented in Stanford University’s comprehensive AI Index Report, even state-of-the-art models exhibit surprising fragility and can fail in unpredictable ways. These agents must operate within a company’s tangled web of legacy systems – the decades-old enterprise resource planning (ERP) software, the proprietary databases, the custom-built applications – that were never designed for this kind of interaction.

Granting an AI agent the keys to the kingdom in this environment is not a strategic advantage; it is a security nightmare waiting to happen. The governance frameworks required to prevent catastrophic errors, malicious exploits, or simple but costly “hallucinations” are monumental undertakings that the hype conveniently glosses over. The promise of an easy, no-code revolution is a fallacy when the underlying foundation is so complex and the cost of failure is so high.

The ground-level reality: Where AI shines today

So, should we abandon AI? Absolutely not. We must simply look past the science fiction and focus on the incredible tools we have now. The true revolution is not in full autonomy, but in powerful augmentation. In my own work advising over two dozen organisations on AI integration, the most profound successes have come from grounded, pragmatic projects that solve today’s problems. By targeting specific, repetitive tasks, generative AI delivers spectacular and measurable returns without the existential risks of the fully agentic vision.

Consider a mid-size manufacturing firm here in Ohio. Its accounts payable department was drowning in a sea of paper invoices, each requiring manual data entry and a tedious three-way matching process against purchase orders and delivery receipts. We implemented a generative AI solution that ingests PDF invoices via email. The AI intelligently extracts key data – vendor name, invoice number, line items, and totals – and automatically matches it against the purchase order in the company’s ERP system. Over 80 per cent of invoices now process automatically. The AP team’s role has transformed; they no longer perform mind-numbing data entry but act as supervisors, managing only the 20 per cent of invoices the AI flags for exceptions, like a price mismatch or a missing PO. The result was a clear-cut 43 per cent improvement in accounting efficiency and faster payments to suppliers.

Or take the case of a regional insurance carrier. Its claims adjusters spent a significant portion of their day writing repetitive claim settlement letters. While each letter needed to be accurate and personalised, the underlying structure was largely the same. By implementing a generative AI tool, they automated the first draft. The system pulls structured data from the claim file – policyholder name, claim number, dates, settlement amounts – and generates a complete, contextually accurate letter based on a pre-approved template. The adjuster’s job shifts from author to editor. They review the draft, add a layer of human nuance, and approve it. This simple augmentation saved an average of 28 per cent of the time spent per letter, freeing adjusters to handle more complex claims and spend more time speaking with customers.

Building a pragmatic path to value

These case studies reveal the real path to AI value. It is incremental, focused, and relentlessly pragmatic. It is about augmentation, not abdication. While one company chases the dream of a fully autonomous AI manager, another is saving thousands of man-hours by automating invoice processing. While one executive team puzzles over the governance of an “agentic mesh”, another is improving customer satisfaction by helping their claims team respond faster. The hype pushes us towards a dramatic, all-or-nothing transformation that is still a couple of years away from being practical or safe for most enterprises.

As Gartner’s Hype Cycle methodology consistently shows, after the “peak of inflated expectations” comes the “trough of disillusionment”. The current frenzy is accelerating our descent into that trough. The companies that thrive will be those that ignored the siren song of total automation and instead got to work. They chose to build a solid foundation, brick by pragmatic brick, solving real problems and delivering measurable value. They are creating lasting advantages while their competitors remain lost in the mirage.

Dr Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps leaders transform AI hype into real-world results. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts and wrote seven best-selling books, including “The Psychology of Generative AI Adoption”.