The AI Hamster Wheel

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Aug 21

It typically starts with a buzz. A CEO hears that “AI is the new electricity” and fears missing out on the next tech revolution. In boardrooms across industries, especially manufacturing, artificial intelligence has become the hot topic. Most surveys show AI remains a top priority for business leaders in 2025, with a strong focus on “not getting left behind”. In practical terms, this means budgets get approved and teams are told, “Find an AI project… fast!” In fact, 92% of companies plan to boost AI investments in the next three years, according to McKinsey. This exuberance is fueled by success stories and flashy demos; think predictive maintenance saving millions, or generative AI designing parts in seconds. With nearly 78% of organizations using some form of AI by 2024 (up from just 55% a year earlier) according to 2025 AI Index Report by HAI Stanford, the pressure is on.

From Big Promises to Pilot Purgatory

Eager to harness AI’s potential, companies rush to launch pilot projects. Initial proof-of-concepts (POCs) are often greenlit at record speed, sometimes simply because the board insists on “doing something” with AI . It’s now common to see a dozen experiments spring up: a chatbot here, a machine-learning model there, each aimed at showcasing quick wins. Unfortunately, launching a flashy pilot is one thing; scaling it across multiple factories is another. Many firms quickly find themselves mired in what insiders jokingly call “pilot purgatory.” The statistics are sobering: According to Manufacturing Leadership Council in their 2025 AI Survey, 66% of manufacturing companies remain stuck in pilot mode, unable to scale their AI proofs-of-concept into production. Some studies paint an even bleaker picture: According to Lenovo in their 2025 CIO Playbook, they found 88% of AI pilots never reach wide deployment. For every 33 AI POCs launched, only 4 become real, working solutions. In other words, there’s a massive gap between AI ambition and AI reality.

Why do so many pilots stall out? Often, pilots aren’t designed to scale in the first place. They might work great in one test cell or on one production line, but they live in isolation. Data may be manually patched together for the demo, code might be brittle, and the pilot might bypass the IT systems that run actual production. Unfortunately it is very easy for pilots to show promise but never achieve enterprise-wide impact because they lack integration, require too much manual intervention, or simply weren’t built with broader deployment in mind. The result: lots of exciting demos that never graduate to the factory floor, like science projects forever stuck in the lab. Little wonder executives experience ‘pilot fatigue’ where enthusiasm fades when the nth pilot yields pretty dashboards but no tangible ROI.

The Underwhelming Reality Check

After the initial hype, reality sets in. Early results of AI pilots are often underwhelming, especially when compared to the sky-high expectations. There are several reasons the AI magic might fizzle on first attempt:

Data Quirks and Quality Problems: AI runs on data, but in factories that data can be messy. Sensors drop out, machines use different naming conventions, and critical context is missing. It’s said that up to 95% of operational data in manufacturing goes unused according to Frost & Sullivan. All that dark data means the AI is drawing conclusions from an incomplete picture. No surprise the insights can be disappointing (garbage in, garbage out). Often times introducing AI into a business often uncovers process inefficiencies and misalignments; According to an MIT Study companies actually saw a short-term 1.3% drop in productivity after adopting AI before things got better. It turns out AI isn’t a plug-and-play miracle but a catalyst for deeper change, and initially, change is hard.
Lack of Skills and Clear Strategy: Many firms jump into AI without a game plan. They might have brilliant data scientists, but those folks might not know the nitty-gritty of shop-floor operations. Conversely, veteran engineers may not trust a black-box algorithm. This talent and culture gap leads to miscommunications and half-baked projects. It’s telling that even though nearly all companies are investing in AI, only about 1% of leaders say their company’s AI is fully integrated and “mature” in deployment according to McKinsey. In practice, that means most organizations don’t yet have the right mix of people, processes, and technology to get beyond piecemeal experiments. Unclear objectives from the start can doom a project. if no one defined what success looks like, it’s hard for a pilot to ever “win” the confidence needed for scaling.
Integration and Infrastructure Issues: The dirty little secret of AI pilots is that many are held together with tape and glue (figuratively speaking). They rely on manual data exports, work on a separate cloud instance, or aren’t compliant with company security standards. Scaling such a pilot means essentially rebuilding it. Many companies also face siloed systems, the AI might not easily connect factory Operational Technology (OT) with corporate IT. One common scenario: the pilot AI can predict a machine failure, but there’s no automatic link to the maintenance scheduling system, so the insight doesn’t translate into action. Organizational silos and legacy systems often bog down progress. Without serious investment in data architecture and MLOps (Machine Learning Ops), the pilot remains a clever demo rather than a daily tool.
Skepticism and Change Resistance: Let’s not forget the human element. A pilot might demonstrate a 5% quality improvement in theory, but if line workers or managers don’t buy into it, implementation stalls. Frontline employees may feel threatened or simply annoyed by a new AI tool that complicates their routine. Change management is critical yet frequently overlooked, leading to what one expert called ‘organizational friction.’ A humorous truth: if your AI requires everyone to update three extra forms and learn Python, it’s not going to catch on. The cultural resistance and lack of trust can quietly smother an AI project that looked good on paper.

As pilots struggle, momentum fizzles. Perhaps the champion who launched the project moves to another role, or the next budget review asks, “What have you done for me lately?” and the answer is awkward. Gradually, the AI pilot slides to the back burner. It might not be officially canceled, it just… stalls. This pattern is so common that ‘pilot purgatory’ has become a buzzword in its own right.

Given these challenges, it’s no surprise many AI initiatives deliver a collective shrug rather than a bang. The ‘trough of disillusionment’ (to borrow Gartner’s hype cycle term) is real; that phase where enthusiasm dives as trials hit complications. Only after this painful adjustment can they climb toward real gains. But many projects never make it past the dip, instead, they stall out when ROI isn’t immediately obvious.

Here We Go Again: The Next Hype Cycle

If you wait long enough (in this case it may be only months), a new AI breakthrough will emerge – and with it, renewed hype to reboot the cycle. Recently, that boost came from generative AI (the tech behind ChatGPT and its cousins). In 2023 and 2024, gen AI ignited a fresh wave of excitement in even the most jaded organizations. Suddenly that stalled predictive maintenance pilot from last year gets a generative AI twist (“Let’s have ChatGPT analyze sensor readings!”). Management, prodded by breathless news of AI’s latest feats, is once again loosening the purse strings. According to that same Lenovo study, they observed that generative AI prototypes “are getting approved much more easily” than previous tech, mostly due to CEO and board pressure to experiment rapidly with gen AI. Essentially, the cycle resets: new buzz, new pilots, rinse and repeat. Indeed, 38% of manufacturers are now piloting generative AI use cases, even though only 24% have deployed any at scale so far according to a 2025 Deloitte Study. It’s a classic case of “try, try again” – or less charitably, short corporate memory.

This time around, companies hope it will be different. But many of the underlying challenges haven’t vanished. As one report quipped, the glut of gen AI experiments is partly “panic-driven thinking” from the top, lots of projects with no strong business case or adequate funding, launched simply because “we need to be doing AI”. If that sounds familiar, it’s because it is. Without learning from past stall-outs, the risk is the next cycle ends just like the last: another pilot graveyard, now with a pile of chatbot prototypes on top. The names of the AI trends change (machine vision, big data, deep learning, gen AI…), but the pattern often repeats. It’s enough to give a CIO déjà vu.

Breaking Out of the Loop

Is the hype-and-stall cycle inevitable? Industry veterans will tell you it is possible to break free, but it requires a sober approach during the hype. The companies that finally succeed with AI tend to do a few things differently: they pick use-cases with clear business value (not just cool tech demos), invest in the unglamorous groundwork (clean data pipelines, integration, user training), and set realistic goals and timelines. Rather than expecting a pilot to revolutionize the business in one quarter, they plan for incremental rollouts and improvements. A 12-18 month timeline from pilot to real ROI is a reasonable expectation for successful adopters, giving time to iron out kinks and scale up methodically. Leading manufacturers are also forming cross-functional AI teams, pairing engineers, data scientists, and operations managers, to ensure the solution fits the workflow, not the other way around.

The payoff for getting it right is no joke. Those few firms that climb out of pilot purgatory and fully deploy AI at scale see substantial benefits. Manufacturers have achieved double-digit percentage gains in productivity and significant cost reductions by using AI for things like predictive maintenance, quality control, and supply chain optimization. In other words, the promise can become reality, but only after the hard work of scaling beyond the flashy pilot. As a lighthearted illustration: you must slog through the “trough of disillusionment” before reaching the “plateau of productivity,” where AI is just a normal part of operations delivering consistent value.

So, the next time your CEO comes back from a tech conference brimming with AI dreams, you can smile (knowing what likely lies ahead) and say: “Let’s temper the enthusiasm with a solid plan.” The AI hype-and-stall cycle doesn’t have to keep spinning in circles. With a bit of humor about our past foibles and a lot of determination to address the root causes of pilot failure, we might finally turn that cycle into a straight line, pointing toward sustained, scalable success. And if not? Well, there’s always another buzzword on the horizon… get ready to strap in for the next ride!