Why Your AI Initiatives Are Failing

AI Adoption Isn't Driving Business Value For Most Organizations

$12M Wasted on AI

Let’s be clear: AI adoption in most organizations is going much worse than anyone wants it to in terms of delivering business value to the Enterprise and its customers.

Keep in mind the MIT study that said 90% of AI projects are failing. 😱

And this situation is hurting the credibility of leaders like CTOs, CPOs and VPs, who championed AI in the last couple of years and are building their careers on the promise of AI delivering big value.

One of my good friends runs Product for a large organization that spent roughly $12M in the last 24 months on AI adoption & delivered very minimal business results.

$12M is a lot for almost any company.

I asked this friend why he believed things weren’t going well, and although he’s a super sharp leader it was hard for him to really pinpoint the exact root causes.

He did complain about the lack of AI product & technical talent and although that’s a valid reason, I wasn’t so sure it was the complete picture.

However, after a couple hours we unpacked all the root causes and I want to share them with you here — my bet is that many of these will apply to your organization.

Reason #1: Skipping Change Management

Any new, major piece of technology needs a lot of organizational change management to be impactful and most companies completely skip over it.

They either don’t think about change management or just assume that since everyone in the org is using ChatGPT that change management is not needed.

The problem is that AI isn’t plug-and-play at all. It’s complex and takes a lot of behavior changes in your organization.

Yet most companies treat it like installing a new analytics dashboard: flip the switch, run a few workshops, and boom, transformation.

Except it doesn’t work that way.

Just like with Cloud or Open Source, the real challenge isn’t the technology, it’s the people.

Your engineers, analysts, and product teams have deeply ingrained ways of working and those don’t change because a new AI tool was added to the stack.

If you don’t deliberately manage that transition then teams will experiment for a few weeks or months and then go back to old habits.

As a result your AI initiatives become a collection of disconnected pilots with no measurable ROI.

Change management truly is the connective tissue between “we bought the tool” and “it’s creating business value.”

Reason #2: Poor Cost Forecasting

What’s the death knell of all projects? Not having enough budget. 💰

AI initiatives are already notorious for missed cost projections.

Teams underestimate costs, oversell outcomes, and burn through funding long before any measurable value is actually realized.

Everyone assumes the biggest expenses are ChatGPT or 3rd party tools. Those can be expensive, but the hidden costs are people and integration: data engineers to prep pipelines, MLOps to maintain models (if you’re building in-house), product teams to re-platform workflows, compliance work, cloud sprawl, etc.

Most enterprises don’t budget for the downstream work. They think “pilot with customers” and forget “maintaining production.”

So when the CFO asks for ROI six months in, there is nothing positive to show. Misunderstood or poorly forecasted AI costs are killing the “I” part of ROI.

The result: missed milestones, credibility hits, and another round of budget triage.

Reason #3: Insufficient Customer Discovery

Lots of companies are simply choosing the wrong use-cases because they rush into AI and don’t talk to their customers to do discovery.

They assume that if the tech is impressive enough, value will magically appear.

Just like any other project, AI projects fail when they don’t solve actual customer pain.

You see it everywhere: teams automating what’s easy, not what’s impactful. Chatbots that answer questions nobody asks. Recommendation engines that nobody uses. Predictive models that never influence a single customer decision.

In every failed AI program I’ve looked at, the missing ingredient was the same: customer discovery. No deep interviews, no problem validation, no willingness to question whether the use case actually mattered to the end user.

You can’t build meaningful AI without understanding customer behavior (whether internal of external).

The companies that win at AI start with real user pain, then work backward to AI features, not the other way around.

If your AI roadmap doesn’t include actual customer conversations and discovery work, you’re not doing it right.

Reason #4: Tech Talent Not Matching Use-Cases

Companies are hiring the wrong engineers and product people. Their use case might be voice but they won’t have anyone who’s ever done voice before.

This happens constantly.

Enterprises chase the AI label instead of the AI skill.

They’ll bring in data scientists who’ve only worked on tabular models, then ask them to build generative systems.

Or they’ll hire a “Head of AI” with a great résumé but zero experience operationalizing models inside complex product ecosystems.

The result? A talent stack that looks impressive on paper but can’t actually deliver the use cases in the roadmap.

AI work is highly domain-specific. Building a recommendation engine, a fraud detector, and a conversational agent all require completely different skill sets — different data architectures, model types, testing frameworks, and UX patterns.

Private Equity backed firms are especially vulnerable here because they’re under pressure to move fast. They over-index on speed of hiring rather than precision of fit.

But the mismatch compounds and you end up with smart people doing the wrong kind of work, and nobody owning the outcome.

Reason #5: Not Accounting for Governance

AI needs governance much more than other technologies. Mess this component up and it can stop your AI projects cold in their tracks.

Most companies underestimate how quickly governance can become the bottleneck.

It’s not just about data privacy anymore, it’s about explainability, bias mitigation, auditability, and the entire trail of how your AI reached its conclusions.

In traditional software, a bug is an inconvenience. But in AI, a governance failure can easily get your company in the papers & upset a whole lot of customers. 👻

Technology and product leaderes will often skip Governance because it feels “legal” or “compliance-oriented,” not technical or innovation related.

But ignoring it means your AI outputs can’t be trusted, your board can get nervous, and your legal team locks everything down just when you start gaining momentum.

PE-backed firms feel this pain the hardest: investors want velocity, but regulatory realities mean slowing down and handling compliance carefully. That tension can kill timelines if you don’t plan for it from the start.

Skipping governance doesn’t make you faster btw, it just ensures you’ll have to redo the work later under much worse scrutiny! 🔬

Reason #6: Poor Selection of AI Tools & Vendors

Companies frequently get taken by the powerful marketing of various AI vendors and often never properly vet their tools and platforms.

Companies fall for the hype every day. 😵‍💫

A flashy demo, a few buzzwords, and suddenly an enterprise is writing a six-figure check for a tool that doesn’t actually solve their problem.

The AI vendor landscape is exploding and that’s exactly the problem.

There are thousands of startups pitching identical capabilities with different wrappers. Most can’t handle enterprise-grade data, compliance, or security needs. But the pitch decks look great, and everyone’s afraid to miss the “next big thing.”

What usually follows is a graveyard of half-adopted tools: an LLM platform that no one uses, a data labeling service that doesn’t fit the workflow, or a voice AI that doesn’t deliver accuracy.

The pressure to show “AI progress” leads to vendor bloat, overlapping spend, and fragmented 3rd party tools that don’t deliver ROI.

Reason #7: Data Is Not Ready

Companies often don’t fix their data before beginning big AI initiatives. They leap headlong into an AI project and figure they’ll “clean it up later.”

That’s a disaster waiting to happen b/c AI is only as strong as the data feeding it.

If your data is fragmented, inconsistent, or riddled with duplicates, your model will amplify that chaos even faster and at scale.

Garbage in, garbage (at light speed) out. 🗑️

Most enterprises have decades of technical debt sitting in their data layer: conflicting schemas, siloed systems, and manual workarounds that no one wants to touch.

Yet they expect AI to “magically” make sense of it. It won’t. It’ll just expose every flaw in your data that you’ve ignored for years.

If your data isn’t clean, connected, and governed, AI won’t save you, it will end up ruining your project and potentially your credibility to be careful about this one.

Reason #8: Internal Systems Aren’t Well Configured

One of the biggest silent killers of enterprise AI adoption is misjudging the internal system dependencies.

Most organizations underestimate how intertwined their internal systems really are to their AI projects.

The data exchange from internal systems to AI ecosystems are critical to AI initiatives.

When you introduce AI into a messy ecosystem of ERP, HRIS and other platforms — the AI amplifies those systems’ data problems.

Every fragile internal system dependency becomes a bottleneck for your AI project. Every bad data source poisons your AI output. And every bad integration blocks AI-based automation.

In other words, AI exposes the architectural truth of your companies internal systems whether good or bad (and it’s usually bad!)

And for PE-backed companies after years of acquisitions and bolt-ons, most organizations’ internal ecosystems are Frankenstein monsters of SaaS tools, homegrown platforms, and point-to-point integrations.

The dependency map looks like a spiderweb drawn by someone having a panic attack. 😀

So even if your AI team is brilliant and your AI tools are cutting-edge, your AI projects still can stall if the underlying systems can’t support it properly.

Concluding Thoughts

The good news is every one of these problems is fixable.

You don’t need a moonshot strategy or a dozen new hires. You just need the right thinking, discipline, and execution.

AI failures aren’t destiny, they’re the byproduct of skipping fundamentals.

The same fundamentals great leaders already know how to master: driving change, managing budgets, aligning people, and building systems that actually work.

The organizations that will win with AI are careful and methodical in their approach, they are not the gunslinger teams.

They’re the ones that apply operational excellence to AI innovation, that approach AI the same way they’d approach any major / critical transformation.

In other words, fix the basics and the momentum for driving business outcomes via AI will build quickly.