95% of Enterprise AI Pilots Fail – and the Fix Isn't Better Technology

01 /The Concept

"In the age of AI, leadership can only be won and held through original thinking," declares Hupside.

The logic: anything "ordinary" in terms of reasoning and conclusions can now be produced by AI. Companies that rely solely on what AI serves up on a platter will be indistinguishable from one another – and won't be able to break ahead in the competitive race on that basis alone.

In an AI-saturated world, winning isn't about having better AI – because better AI becomes available to everyone simultaneously. It's about having people whose original thinking can "enrich" the AI: pushing it toward unusual directions and meaningfully improving on whatever it produces.

The competitive advantage of a company, then, is not artificial intelligence – it's natural intelligence. And not just any natural intelligence: the kind that thinks originally. Human originality, combined with AI's non-human logic, should produce better outcomes than either alone.

Original thinking is the capacity to move beyond the expected premise and the predictable conclusion. It's what generates new insights, breaks old patterns, and connects existing knowledge in surprising ways – and it's the only thing that can produce genuinely competitive solutions.

That doesn't mean trying to make every employee an original thinker. The more practical path is identifying people who have that capacity – assigning them work that demands it, seeding them into teams that lack it to catalyze breakthroughs, and perhaps redistributing them away from teams where original thinking is already abundant to avoid decision paralysis or unnecessary friction.

Hupside's first product, Hupchecker, gives companies a way to measure the "original intelligence" of their employees. Results feed into management and staffing decisions that, ideally, run alongside – and inform – the company's AI adoption process.

To get started, a manager selects or creates questions designed to trigger a creative spike in those who have that capacity. The best questions are playful and abstract – something like "how would you sell cauliflower to children?" – to switch respondents out of work mode and into creative mode. A full assessment covers 25 such questions.

Responses are evaluated by AI. One plausible mechanism: the AI compares each answer against what it would generate itself. The greater the divergence, the higher the respondent's originality score.

A single test isn't enough – multiple assessments across different topic areas help map originality across domains. And since people change, assessments need to be repeated periodically. Hupchecker is designed for ongoing, varied use rather than a one-time audit.

The platform generates a dashboard that organizes staff potential by archetype, scores originality across different domains, benchmarks it against various AI models, and tracks how those scores shift over time.

Granular individual recommendations are still on the roadmap – Hupside was founded this year and is in early pilots with its first clients. Despite the early stage, the startup has already raised $1.7M in initial funding.

02 /Why It Matters

Hupchecker doesn't measure originality in a vacuum – it measures it relative to and in potential symbiosis with AI. In that sense, it's a tool for making AI adoption more effective.

On that front, the numbers are grim. According to a recent MIT study, 95% of all corporate pilots of internal and external AI products are currently failing.

The leading cause isn't that the AI technology is too weak or too complex – it's that the technology adapts poorly to existing business processes and to the people expected to use it.

One of the biggest underlying reasons: organizations try to deploy AI uniformly across all employees, as if one approach works for everyone. But the actual impact of AI appears to depend heavily on who is using it and how. Hupchecker tries to identify and highlight the employees who should be at the center of any AI rollout – the ones most likely to generate breakthrough results.

The MIT research surfaced a second counterintuitive finding: most companies are pouring the bulk of their AI budgets into marketing and sales. Yet the best return on AI investment right now comes from automating back-office functions.

Look more carefully at where budgets are going and a pattern emerges: virtually all high-spend AI use cases are about cutting costs. AI is being deployed to draft outbound emails, qualify inbound leads, create ad copy, analyze competitors, summarize documents and meetings, review contracts – that kind of thing.

Notice what's absent from that list: anything about using AI to generate breakthroughs. No new product ideation. No discovery of new markets or audiences. Nothing like that.

Companies are currently using AI as a workhorse for routine tasks. The likely reason: few people know how to use AI as a breakthrough engine. And the reason for that is that it requires pairing AI not with just anyone – but with the right people, who have the right kind of thinking. Finding those people is exactly what Hupside set out to do.

03 /Opportunities

The defining characteristic of this moment is that the vast majority of people and organizations are using AI as a "dumb" assistant – replacing Google searches at one end and automating routine work at the other, purely to save time and effort.

The paradox is that AI is, in a sense, genuinely "dumb." It grew out of a tool designed to predict the next word someone would type, based on what they'd already typed. At its core, AI is an averaged imprint of what people have previously written about a given topic. New thinking is structurally hard to get out of it.

That said, truly new ideas don't appear from nowhere either. As Newton put it: "If I have seen further, it is by standing on the shoulders of giants." The trick is taking what already exists and adding some small, specific element that changes a great deal. And those small elements are, for now – and perhaps always – the exclusive domain of natural intelligence.

The most promising future belongs to systems that can combine the strengths of natural and artificial intelligence so that 1 + 1 = 3: where the outcome of their interaction exceeds the arithmetic sum of human and AI capabilities.

Getting there requires, first, identifying the humans who can make that kind of contribution, and second, configuring AI to take that contribution in the right direction. Building platforms for exactly that kind of interaction is an emerging direction – without which it won't be possible to move to the next qualitative level of AI utilization.

The emergence of other startups in this space confirms it's a real trend. The fact that there are still so few is an opportunity – this is still early, and early-entry timing still matters.

One example: Enhance Labs ([related review](/review/dva-novyh-scenarija-raboty-proryvnyh-ii-produktov)), which recently raised $1.52M in initial funding. The startup is building a voice AI companion that helps you think – not instead of you, but alongside you.

You think out loud; the AI interjects periodically with questions that push your reasoning further and sharpen your conclusions. The result is insights you might not have reached on your own – while the AI applies its logic to premises it couldn't have generated independently. Both parties arrive at conclusions neither could have reached alone.

Today's startups, then, offer a strong conceptual foundation for building platforms that take the human-AI relationship to a qualitatively new level. The challenge is figuring out what specific products best package that concept – products that are viable, scalable, and serve a reasonably broad market. What might those look like?

One more grounded way to think about it: companies are the most lucrative buyers of AI products. But if 95% of companies fail to successfully deploy what you've built, you lose 95% of your potential customers. Stripped to its essence, the problem is that the wrong people on the client side end up using your product – people who can't extract the value it's designed to deliver.

That means your economics fundamentally depend on whether you can help clients find the right people – those who can use your product most effectively. You'll need to embed something like Hupchecker's function into your own product, tailored to your specific domain. That is, if you want enterprise clients to actually deploy what you've built and keep paying for it.