AI Has a Big Problem In The Enterprise
Critical Headwinds Are Slowing Adoption
👋 Hello Technocrats!
Did you know that AI is failing in the Enterprise? A surprising number of CEOs & CTOs reveal that their AI adoption efforts are yielding little to no return. Today we talk through the reasons why.
Cheers & let’s dive in! 🦈
Bobby
I’ve been talking to a lot of CEOs & CTOs in the last 6 months who don’t think AI will pay off for their business anytime soon.
This was a little bit shocking to hear at first but when I dug deeper the reasons behind it became pretty obvious.
The headwinds against AI are real in the Enterprise. Leaders can ignore the challenges but they do so at their own peril! 😱
In fact, despite what the AI hype crowd says, it could take years for many businesses to see any real payoff from their AI investments.
AI Is Fast, Companies Are Slow
AI innovation is traveling at lightning speed (just look at any news headline these days). But companies are still operating as slow as they always have.
Sure, there are some organizations that go fast, but that’s maybe 5%. Most organizations are slow to change and new technology generally scares them.
But What’s the Real Reason?
The slow moving behavior of companies isn’t the entire reason for slow AI adoption though. The list of problem is a lot more substantial actually (I noted them in the list below).
Some of these headwinds are common to any new technology adoption process, while others are unique / specific to AI.
ELTs & senior leadership don’t understand AI beyond ChatGPT.
The AI hype is so overwhelming that making sense of what’s real is incredibly difficult.
Customer demand is so high that it’s forcing companies to build fake AI.
Marketing is pressuring Engineering to come up with AI & this isn’t helping.
The % of roadmap companies invest in AI can fluctuate massively.
Showing a before & after with AI often is impossible.
Showing cost savings from AI efficiency projects can take 10x longer than expected.
Some businesses build AI for the sake of AI & waste money doing so.
Businesses think AI costs will come down but that’s not guaranteed.
When AI is the answer to every problem in a company it can be rendered useless.
Are These Challenges Solvable?
A lot depends on your particular business and its current state, of course.
But I’ve come to believe the big claims of AI delivering exponential returns are rare (regardless of what the slide decks say in all-hands meetings). 😃
You can remediate against these headwinds, however only if you first fully understand them & how they manifest in your organization.
Senior Leadership Thinks AI is Just ChatGPT
First and foremost there’s a knowledge problem where a lot of senior leadership don’t know what AI means in the context of the Enterprise.
Sure, they understand how to use ChatGPT. But they don’t know what kind of AI to apply to enterprise-worthy use-cases, or even how to evaluate the use-cases themselves.
Ask companies what they want AI to solve, and many will give you a general answer like "efficiency" or "productivity."
But ask them which business function, tied to which KPI, would benefit most from machine learning and you’re likely to get blank stares.
This lack of clarity leads to vague directives like "let’s make the product more AI-driven" or "can we add some AI to this feature?".
It’s like asking for electricity without knowing if you’re building a toaster or a Tesla.
Worse still, without a firm grasp of what a valuable use-case looks like, senior leaders can’t prioritize AI investments correctly.
They either over-invest in shiny demos with no ROI, or under-invest in boring but high-leverage back-office improvements.
The Hype Overwhelms Everything
There’s just too much hype around AI right now and it’s overwhelming for most organizations.
Sales, Marketing, Engineering… every department is being slammed with AI vendors promising 10x productivity, total automation, or magical cost reductions.
It’s not just noise but noise with a megaphone 📣 and the result is organizational paralysis.
Most leaders don’t know who or what to believe. They hear one vendor say they’ll replace the entire onboarding team with a bot.
Another claims their platform writes perfect marketing copy in seconds. A third says they can optimize your supply chain in real time.
But very few of these promises are accompanied by results.
Teams get stuck trying to validate what’s real. There’s no clear standard or maturity model for AI tools. Due diligence processes start to drag, AI pilots get delayed, and internal debates spiral.
Instead of doubling down on one or two real AI bets, companies end up testing dozens of mediocre tools and never committing to any of them.
The irony? In all the noise about innovation, progress stalls. Hype turns into confusion, which turns into indecision.
And when everyone is chasing the loudest headline, no one is building for the quiet but valuable use cases that actually moves the business forward.
Customer Demand Drives “Fake” AI
Companies actually struggle with their customers’ demand for AI.
All their clients want it but few can articulate what exactly they expect it to do.
This leaves companies stuck in a difficult position: they feel pressured to deliver AI-driven features, even when there's no clear need or strategic benefit.
Worse still, failing to deliver AI makes companies look outdated. But delivering it poorly risks customer frustration or churn.
It’s a lose-lose situation unless there’s a clear path to value.
Companies bolt on basic LLM integrations, slap the word "AI" on a release, and hope that satisfies the market.
But customers can smell the difference between real innovation and buzzword theater.
The irony is that many customers may not even use these features once launched. They were excited about the label but not really the function.
And that leads to wasted engineering cycles, diluted product focus, and, ultimately, damaged credibility.
Marketing Adds Unnecessary Pressure
Typically, new technology starts in Engineering.
A novel capability emerges, gets prototyped, tested, and—when it shows promise—Marketing wraps a narrative around it and Sales brings it to market.
With AI, that playbook is flipped.
Marketing and Sales are now leading the charge, demanding AI-driven features to satisfy customers, impress investors, or just keep up with industry noise.
Engineering teams, meanwhile, are being asked to conjure up something—*anything*—that looks like AI. It doesn’t matter if it solves a real problem. It just has to be demoable.
This pressure leads to an inversion of priorities.
Instead of solving meaningful, long-term challenges, engineers end up building thin wrappers around off-the-shelf models or slapping AI onto existing interfaces with minimal value.
Everyone wants to be able to say "we shipped AI.”
The result is an erosion of product quality.
Real AI innovation takes deep thinking, thoughtful design, and technical rigor. When that’s replaced by PowerPoint-driven development cycles, everyone loses especially your customers.
Deciding the Amount of AI Investment is Challenging
It’s tough for businesses to decide what percentage of their product roadmap should be AI.
Go all-in, and you risk overextending on dollars, infrastructure, talent, and hype-driven features. Do nothing, and you look like a dinosaur.
Striking the right balance is hard, especially when the benchmarks don’t yet exist.
Some teams try to hedge with exploratory investments: “let's do a few pilots and see what sticks." Others dedicate specific roadmap slots to AI features just to show forward momentum.
But these strategies often lack a clear framework.
Without strong criteria tied to impact, customer need, and feasibility, AI prioritization turns into an internal struggle.
Every product owner wants a piece of the AI pie, but not every problem needs machine learning to be solved.
This leads to poorly justified initiatives and wasted cycles. Worse, it creates noise that crowds out the genuinely promising use cases.
Showing Before & After Is Sometimes Impossible
For a CTO, showing "before and after" results on an AI initiative is harder than it looks.
AI often introduces capabilities that never existed before—so there’s no baseline.
If you’re replacing a manual process with automation, the benefit can be quantified. But when AI adds something entirely new, it becomes much harder to prove its worth in simple terms.
This is especially true for features that are quietly powerful.
For example, AI might be improving search relevance, enhancing personalization, or streamlining internal workflows, but unless you explicitly surface the change with clear metrics or user feedback loops, stakeholders won’t feel the impact.
Even when the ROI is real, it may be hidden in subtle time savings, happier customers, or long-term platform scalability.
And without something dramatic to show on a dashboard, the story of success often falls flat. This might make AI seem underwhelming even when it’s working.
CTOs must work twice as hard to frame these wins.
Cost Savings Can Take Years
CFOs love the possibilities of AI as an efficiency-driving, cost-lowering mechanism.
The narrative is compelling: fewer repetitive tasks, leaner teams, more automation. But actually realizing those savings is more complex and drawn out than most expect.
The initial investment is often steep with AI. Even once everything is running, the benefits are often indirect.
AI may improve the accuracy of customer support, reduce the time it takes to analyze data in Sales, or increase the velocity of engineering but translating those outcomes into concrete dollars on a spreadsheet isn’t easy.
That disconnect makes Finance teams nervous.
To make matters more complicated, some efficiency gains are only visible in aggregate over time.
It may take 12, 24, even 36 months to see measurable shifts in headcount, margins, or throughput.
Companies that promise AI will immediately slash operating costs often find themselves backpedaling a year later when the financials tell a different story.
Some Companies Build AI for the Sake of AI
Of course, some organizations are building AI for the sake of AI.
They have no real need for it, but they’re caught up in the excitement, pressure, and market noise.
Executives hear competitors talking about AI in investor calls, they see press releases touting "cutting-edge features," and suddenly it feels like doing *nothing* is a bigger risk than building *something*, even if it’s meaningless.
The result? AI becomes a checkbox exercise.
Instead of asking, "What problem are we solving?" teams ask, "What AI feature can we announce this quarter?" It’s innovation theater.
What’s worse is that these companies often overlook better, cheaper, and simpler solutions in pursuit of an AI label.
They build complex pipelines where basic automation would do. They layer on models when a simple business rule would solve the problem faster and more reliably. And they burn valuable engineering time chasing results that never materialize.
This happens when tech strategy is guided by FOMO, not first principles.
AI Prices May Not Come Down
Businesses are trusting to the idea that AI prices will come down.
It’s an appealing narrative: just like any new tech, costs drop over time, right? But history suggests otherwise.
Did Cloud prices come down? Not exactly. If anything, cloud costs became more complicated and, for many, more expensive as usage scaled.
AI could follow the same trajectory.
While the cost of some foundational models may fall, the total cost of ownership for businesses implementing “application layer” AI could climb.
Especially for businesses that bake AI into customer-facing products, where uptime and latency become non-negotiable.
Some companies are already making long-term bets based on cost assumptions that may not hold.
They’re building expensive AI into their products under the belief that prices will drop or credits will continue.
But what if they don’t? What if AI follows the cloud’s path: cheaper to enter, but more expensive to scale?
Beware If AI Is an Answer to Every Problem
Lastly, for some businesses AI has become the catch-all answer to every problem.
Sales pipeline weak? Build an AI sales agent. Customer support lagging? Add a chatbot. Product not differentiated? Slap on some predictive intelligence.
This kind of thinking turns AI into a magic wand rather than a strategic tool.
When companies fall into the habit of overprescribing AI, they blur the lines between innovation and wishful thinking.
Not every issue needs AI or real-time automation. Often, the real root of the problem is something far more human: poor process design, unclear goals, or lack of accountability.
AI can be powerful but only when deployed with purpose. If it’s treated as a one-size-fits-all fix, it risks becoming an expensive distraction.
Teams start building tech that nobody asked for, or worse, tech that makes the problem harder to understand. Over time, trust in AI erodes because it never seems to "solve" anything clearly.
Treating AI as a universal solution dilutes its value.
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By the way, reach out for help on this topic if you need it: bobby@technocratic.io.
And in the meantime, keep the shark swimming! 🦈