Here’s Why Many Gen AI Projects Are Doomed to Fail
A dangerous narrative is taking hold in boardrooms 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 ten million dollars of the wrong component, or the vendor’s agent exploiting a loophole in your agent’s programming to lock you into unfavorable 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 organizations 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.
The practical wins come when employees learn to wield today’s generative platforms themselves. Capability transfer—not solution delivery—puts workers in charge of design decisions, refinements, and governance, so expertise stays inside the firm rather than walking out with a vendor. In every engagement I have led, the fastest ROI arrived once staff saw, step by step, how a no-code prototype is built, then copied those moves on a second use case. Showing the build process demystifies AI, shrinks resistance, and sparks the curiosity that fuels continual iteration; those steps map neatly to an introductory build, a behind-the-curtain demonstration, guided co-creation, independent innovation, and long-term self-sufficiency—the same five-stage arc we recommend for any rollout. When insiders become makers, the technology ceases to look like a threat and starts to feel like a power tool.
One midsize automotive-parts manufacturer in Indiana staffed three HR generalists who spent most mornings assembling forty-page onboarding packets for every hire—printing, alphabetizing, checking I-9 IDs, and chasing missing signatures. During a two-week workshop the most junior generalist, who had never opened Visual Studio, built a conversational intake assistant with a drag-and-drop workflow in Power Automate. New employees now upload identification through a secure chat link, complete direct-deposit details, and e-sign policies in minutes; the bot verifies completeness, writes a personalized first-day agenda, and drops the finished PDF into SharePoint. The generalists monitor exceptions—typically an expired driver’s license or a mismatched address—instead of burning hours on packet collation. Onboarding lead time collapsed by sixty-eight percent, and the generalist who built the flow now coaches supervisors on adding contractor and intern variants.
Recruiting saw similar dividends at a 1,200-bed regional hospital that faced chronic nurse shortages. Two nurse educators, frustrated by résumé triage that stretched to nearly two weeks, worked to build a ranking copilot after a workshop. They fine-tuned an off-the-shelf language model with anonymized hiring records and a clinical-skills rubric, then linked it to the applicant-tracking system. The copilot highlights licenses, shift preferences, and bedside experience, pushing the best twenty résumés to the top. Educators skim the short list, add human nuance, and schedule calls the same week applications arrive. Time to phone-screen fell from thirteen days to four, first-year nurse turnover dropped eight percentage points, and the educators—now proud tool builders—update their rubric every quarter to reflect culture fit insights gleaned from exit interviews.
Consider a mid-size manufacturing firm right 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. I worked with them to help them build 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% 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% of invoices the AI flags for exceptions, like a price mismatch or a missing PO. The result was a clear-cut 43% improvement in accounting efficiency and faster payments to suppliers.
A metro-area government managing seventeen labor contracts wrestled with email floods for FMLA, paid-family-leave, and short-term-disability requests. A payroll analyst and a union liaison joined a guided co-creation sprint, sketched a decision tree in a whiteboard session, and within six weeks launched a bot that extracts leave dates from email text, validates tenure in the ERP, and drafts approval letters with the correct contract language. Clerks who once re-keyed dates across three systems now coach employees on policy nuances and flag edge cases for legal review. Processing time per request shrank from twenty-five minutes to six; grievances over miscalculated leave fell by nearly a third, and the city earmarked stipend pools for frontline staff who replicate small automations elsewhere.
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 personalized, the underlying structure was largely the same. By building a generative AI tool after a workshop, 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% 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 toward 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.
Key Take-Away
Gen AI projects succeed when grounded in practical, incremental use cases—not in overhyped visions of full autonomy. Avoid the mirage; real value comes from augmentation, not automation. Share on XImage credit: Alena Darmel/pexels
Dr. Gleb Tsipursky was named “Office Whisperer” by The New York Times for helping leaders overcome frustrations with hybrid work and Generative AI. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his two most recent ones are Returning to the Office and Leading Hybrid and Remote Teams and ChatGPT for Thought Leaders and Content Creators: Unlocking the Potential of Generative AI for Innovative and Effective Content Creation. His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.