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 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 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 — decades-old software, proprietary databases, 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. 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 more than 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. 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 system. More than 80 percent of invoices now process automatically. The department’s role has transformed as well; they no longer perform mind-numbing data entry but act as supervisors, managing only the 20 percent of invoices the AI flags for exceptions, like a price mismatch or a missing order. The result was a clear-cut 43 percent 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 personalized, 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 percent of the time spent per letter, freeing adjusters to handle more complex claims and spend more time speaking with customers. 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 number 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.

Gleb Tsipursky, Ph.D., serves as the CEO of the hybrid work consultancy Disaster Avoidance Experts and authored the best-seller “Returning to the Office and Leading Hybrid and Remote Teams.”

Source: Don’t believe the hype: AI is the future, but we’re not there yet (The Hill, August 5, 2025)

Frequently Asked Questions (FAQ)

On AI Hype and Realism

Q: Is the idea of fully autonomous AI agents a realistic goal for businesses in the near future? A: The article suggests that the vision of fully autonomous AI agents, capable of orchestrating entire businesses or autonomously negotiating complex workflows, is a distant future prospect and is perilously disconnected from the current messy reality of 2025. Current state-of-the-art AI models still exhibit fragility and unpredictability. Q: What are the primary risks associated with the hype around "agentic AI"? A: The main risks highlighted are setting unrealistic expectations, leading to a "trough of disillusionment" after the "peak of inflated expectations." This hype can also cause businesses to overlook the monumental challenges in reliability, security, and integration with legacy systems, potentially leading to catastrophic errors or security breaches. Q: Instead of full autonomy, what does the article propose as the current practical application of AI in business? A: The article advocates for "powerful augmentation," where AI tools are used to enhance human capabilities and solve specific, repetitive tasks. This approach delivers measurable returns without the existential risks of full autonomy. Q: Can you give examples of practical AI augmentation in business? A: The article provides two examples: automating invoice processing for an accounts payable department, which led to an 80% automation rate and a 43% improvement in efficiency, and automating the first draft of claim settlement letters for insurance adjusters, saving them 28% of their time per letter. Q: What is the "agentic AI mesh" described in the article? A: The "agentic AI mesh" is a concept where AI "agents" (programs) autonomously orchestrate business operations, negotiate, plan, and execute complex workflows. The article uses this as an example of the more futuristic and potentially risky vision of AI.

Understanding AI Implementation

Q: What are the biggest challenges in integrating AI agents into existing business systems? A: The primary challenges lie in the complexity and tangles of legacy systems (old software, proprietary databases, custom applications) that were not designed for AI interaction. Reliability, security, and the development of robust governance frameworks to prevent errors or exploits are also major hurdles. Q: Is the "no-code agent builder" concept presented as a current reality? A: The article mentions "no-code agent builders" as part of the enticing fantasy presented by some reports, but implies that the underlying complexity and cost of failure make this a fallacy in practice for achieving full autonomy. Q: What role does human oversight play in successful AI implementation, according to the article? A: Successful implementation, as demonstrated in the case studies, involves AI augmenting human tasks, with humans acting as supervisors or editors who manage exceptions or add nuance. This highlights the continued importance of human oversight. Q: How does the article suggest businesses should approach AI adoption to avoid the "trough of disillusionment"? A: Businesses should adopt a pragmatic, incremental approach, focusing on solving specific, current problems with AI augmentation. This involves building a solid foundation by tackling real-world issues and delivering measurable value, rather than chasing the dream of complete automation.

Crypto Market AI's Take

The sentiment presented in this article strongly resonates with our approach at Crypto Market AI. While the allure of fully autonomous AI agents managing complex financial operations is exciting, our focus remains on providing practical, AI-driven tools that augment user capabilities. We believe in the power of AI to analyze market data, identify trends, and execute sophisticated trading strategies, but always with the user in control and informed. Our platform offers advanced analytics and AI-powered trading bots designed to enhance decision-making and operational efficiency, mirroring the article's emphasis on augmentation over abdication. We see AI not as a replacement for human intelligence, but as a powerful collaborator, especially in the volatile and complex world of cryptocurrency trading. For those interested in exploring how AI can pragmatically enhance their investment strategies, our resources on AI-powered crypto trading and our insights into market analysis provide a grounded perspective.

Don’t believe the hype: AI is the future, but we’re not there yet

Frequently Asked Questions (FAQ)

On AI Hype and Realism

Understanding AI Implementation

Crypto Market AI's Take

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