Why 95% of In-House AI Projects Fail (and Why Companies Keep Doing It Anyway)

We live in a "blame" culture and in-house AI projects are all about that!

This is a sobering reality in enterprise AI: despite the hype, 95% of in-house AI projects are failing. MIT’s State of AI in Business 2025 report makes it clear that while generative AI has transformative potential, most corporate efforts stall out well before delivering meaningful impact.

Executives may point to regulation, data quality, or immature technology, but the research suggests otherwise. The problem isn’t the models themselves—it’s implementation. And too many companies are treating AI like a shiny toy, not a serious business transformation.

Why In-House AI Projects Fail

So, why are so many companies falling into the 95%?

1. The Learning Gap – Generic tools like ChatGPT are good for individuals because of their flexibility. But they stall in the enterprise because they don’t integrate with workflows, don’t adapt to organizational processes, and lack the “stickiness” to drive change.

2. Resource Misallocation – The MIT report highlights that more than half of enterprise AI budgets are being spent on sales and marketing tools, even though the greatest ROI lies in back-office automation (eliminating outsourcing, cutting agency costs, streamlining operations) – more on this later.

3. DIY Mentality – Internal builds succeed only one-third as often as purchased solutions. Yet many firms still insist on going solo. It’s the corporate equivalent of assembling Ikea furniture without instructions—you might eventually get something resembling a table, but it’ll wobble every time you touch it.

4. Lack of Ownership – Successful AI adoption isn’t driven by central AI labs alone. It’s driven by empowering managers and frontline teams to embed tools into their daily work. Without that, the tech sits in a corner gathering dust.

   
   

And beyond these organizational barriers, there’s some very human factors:

·         People assume AI is easier than it is.

·         The novelty fades and teams get distracted or re-assigned.

·         AI is too often seen as “cheap” or even “free,” it receives less serious attention than it should. The result is a graveyard of half-finished pilots and underwhelming outcomes.

·         Structured failure – people don’t want to hear it, but failure is sometime deliberate. People fear AI and see an internal project as an easy way to prove it wrong!

The Psychology of the 95%

Here’s the kicker: while only 5% of in-house projects appear succeed (according to the MIT report), almost no company believes they’re in the failing majority. Statistically, of course, most of them are. But human nature kicks in, and no one wants to admit they’re part of the herd, lest they get “cut” from it!

This collective denial means organizations keep making the same mistakes: underestimating complexity, overestimating their capabilities, and clinging to the idea that “this time will be different.” It’s like watching 95% of drivers skid off the same icy corner and insisting, “We’ll be fine.”

In-House vs. Packaged: Why the Odds Are Stacked Against DIY

The second layer to this conversation is the difference between building AI in-house versus deploying purpose-built solutions. The MIT data already shows purchased solutions succeed twice as often as in-house builds, but the reasons go even deeper.

• AI Is Not Just About the LLM – Tools like Shadow (blatant sales pitch!) for example, don’t just sit on top of GPT. They blend an LLM with procedural code, custom workflows, session memory, and domain-specific expertise. In Shadow’s case, that means sales strategy, behavioral psychology, objection handling, and real-world B2B sales scenarios. You can’t replicate that from scratch with a general-purpose model.

• Security and Governance Are Already Solved – Enterprise AI requires airtight security: SOC2, GDPR, HIPAA compliance, SSO, access controls, and audit trails. Shadow, for example, is deployed in Microsoft Azure environments and uses enterprise-grade APIs where no customer data is retained. Building that level of governance from scratch is harder than people think, it’s expensive and risky.

• Integration Matters – A packaged solution integrates with Outlook, Teams, Salesforce, or whatever stack your team uses. More importantly, the UX is designed for how sellers actually work—so adoption is far smoother. A homegrown chatbot might “exist,” but if no one uses it, you haven’t solved anything.

• Time, Cost, and Risk – A proper in-house build isn’t weeks or months—it’s 6–12+ months of hard development time, requiring cross-functional teams of engineers, AI specialists, UX designers, and subject matter experts. Even then, the risk of missed edge cases and lack of adoption is high. In contrast, packaged solutions are live today, tested in real-world use, and refined through hundreds of iterations.

• Depth of Functionality – Beyond text generation, packaged solutions often include scraping engines, enrichment, retrieval-augmented generation (RAG), structured usage tracking, and clean export options. Replicating that stack from scratch is like building a skyscraper because you think the office rent is too high.

  
  

The Harsh Truth

The dream of in-house AI is seductive: control, customization, bragging rights. But the reality is that most companies don’t have the talent, time, or resources to pull it off. And the numbers don’t lie: 95% of in-house AI projects are failing.

Yet because no one wants to admit they’re part of the 95%, companies keep wasting money, attention, and momentum. The paradox is brutal: the very confidence that drives firms to build in-house is the same overconfidence that ensures their failure.

In contrast, packaged solutions—especially those built with domain expertise and hardened through real-world use—offer a path out of the cycle. They’re not just tools; they’re accelerators, designed to integrate into workflows, safeguard data, and deliver measurable business outcomes quickly.

Unless your core business is developing AI SaaS platforms, building in-house is at best a distraction and at worst a slow, expensive failure. Or put more bluntly: why try to reinvent the wheel when someone’s already built a Formula 1 car?

The Tap-and-Done Illusion: Why Mobile Convenience Is Costing Sellers Real Readiness

Micro-completion syndrome

There’s a quiet epidemic going on…yes another! It’s the illusion of completion. The illusion of understanding. The illusion that, because you skimmed something on your phone while juggling a coffee and dodging Slack pings, you're now “done” with it and can say convincingly “I got it.”

Let’s call it what it is: convenience masquerading as comprehension.

1. The Convenience Trade-Off

Humans will trade almost anything for convenience (as we’ve demonstrated with security time and time again) — and increasingly, what we’re sacrificing is understanding. When we engage with complex ideas or strategic content on mobile devices, we often feel like we’ve dealt with it. But we haven’t. We've just swiped over it.

There’s plenty of research to back this up:

• Reading comprehension is significantly lower on phones compared to desktops or printed formats, especially for complex material (Nielsen Norman Group).

• In a meta-analysis of 450,000 readers, those reading on paper had up to 8x better retention and comprehension than digital readers (Axios).

• Multitasking and distraction are far more common on mobile, further eroding the ability to process and retain information (MDPI Study).

  
  

We mistake exposure for comprehension — and mobile devices make that mistake easier to fall into than ever.

2. The Age of “Everyone’s an Expert”

Add to this our modern tendency to want to be the smartest person in the room, and it’s no wonder we’ve created a culture of superficial expertise.

People show up to meetings having “seen the doc” — which is code for “I opened it on my phone, scrolled a bit, then forgot everything except the title.” The mobile-first mindset makes it look like we’re engaging. But it often replaces actual diligence with a false sense of productivity.

That’s not preparation. That’s just proximity to content.

3. Why Some Companies Are Re-Thinking “Mobile First”

This is why some of the more insightful technology and sales enablement companies have deliberately not prioritized a mobile-first experience — as counterintuitive as that may sound today.

Because real preparation — especially in sales, strategy, or decision-making — doesn’t lend itself to two-thumb input and screen real estate the size of a credit card.

Instead, they optimize for comprehension over convenience. They design tools that support actual understanding, not just surface-level interaction.

To borrow a football metaphor (and we often do):

A bog-standard LLM might get you to the 50-yard line faster. A more specialized tool can get you into the red zone. But scoring the touchdown? That still takes you. It takes comprehension. It takes judgment. It takes knowing the play — not just glancing at the playbook on your phone.

TL;DR (for those of you reading this on your phone 😉):

Convenience ≠ comprehension. Mobile makes it easy to engage with content. But it also makes it dangerously easy to believe you’ve understood it — when you haven’t. Some companies are deliberately rethinking mobile-first design for this reason.

Because in high-stakes environments like B2B sales, real readiness beats the illusion of it.

A Little Knowledge Is a Dangerous Thing—Until You Add AI

“A little knowledge is a dangerous thing.” That line—first penned by Alexander Pope in the early 1700s—was never meant as a compliment. It was a warning. The idea was that a shallow understanding of something is actually worse than knowing nothing at all. It gives people misplaced confidence, prompting them to leap into situations they’re not qualified to handle. Think Dunning-Kruger with a powdered wig.

In business, this has historically played out in all kinds of ways—overconfident leaders making reckless decisions, consultants bluffing their way through client meetings, or employees misinterpreting data and taking the company off a cliff. And until recently, the antidote was clear: more depth, more expertise, more time spent drinking deeply from the fountain of knowledge.

But then… AI happened.

The Rise of “Just Enough” Knowledge

We’re now living in a time where a little knowledge + a powerful AI assistant can sometimes get the job done. In fact, some are calling this shift the democratization of expertise. You no longer need a PhD in supply chain optimization to ask ChatGPT for a workflow improvement model—or a marketing degree to create a full-funnel campaign plan.

And that’s… unsettling. Especially if you’re in the business of being the expert—consultants, advisors, analysts, strategists. AI can draft your proposal, outline your go-to-market strategy, recommend KPIs, and even roleplay your next sales call. It can do in seconds what used to take hours of billable work.

But here’s the catch...

Knowing What to Do Isn’t the Same as Knowing How to Do It

This is where the distinction matters. AI can give you the “what” and sometimes even the “how.” But it can’t give you the judgment, experience, or execution muscle to make it all work in the real world.

Case in point: Mike Burry.

Before he became famous for betting against the housing market in The Big Short, Burry wasn’t a traditional finance guy. He was a medical doctor—a neurosurgeon-in-training who taught himself how to invest. He started reading 10-Ks for fun (who hasn’t) after his hospital shifts. He didn’t know Wall Street, but he had a capability to process massive amounts of data, spot patterns, and follow the logic where it led—even when no one else could see it, and no one else believed him.

In many ways, Burry had a “little knowledge”—but it was paired with obsessive learning, something of an unusual personality, giving him the ability to connect dots. Now we all have access to AI we can ALL process bigger amounts of data and use the AI to surface patterns and connect those dots. It’s a force multiplier.

So Where Does That Leave Us?

AI isn’t replacing experts. But it is changing the rules of what it means to be one. Here’s the new reality:

• AI helps you do more with less expertise—but not without consequences. Misapplied, it can enable confident amateurs to make very professional mistakes.

• AI raises the bar for experts. It forces real professionals to move beyond surface-level knowledge and focus on the judgment, nuance, and action AI can’t replicate. Someone said we’ve moved from the information age into the interpretation age.

• AI empowers a new kind of operator. Like Burry, those who can combine AI’s raw processing power with human insight, skepticism, and strategic action will reshape industries.

  
  

Final Thought: We’re All Burry Now—Or We’d Better Be

A little knowledge is still dangerous. But in 2025, the bigger danger may be assuming AI knows enough for you.

The opportunity? Learning how to think like Mike Burry in a world where AI can give you answers, but not necessarily the whole picture – that remains up to us.