Why Do Large Companies Fail at AI Implementation?
- ecmadore2
- 6 days ago
- 3 min read
An MIT Study Shows Why 95% of AI Projects Fail and How Startups Will Win the Race
And why this is the biggest opportunity for startups in a generation. Tejas Sharma
Note to Readers: Suggest reading the full article. As always remember to do your due diligence.
Excerpts
An MIT study last month that looked at over 150 CEOs found that 95 percent of AI pilot programs at companies fail. But the problem isn’t the AI. Instead, it’s about how these companies are trying (and failing) to fit it into the old way they already work.
It’s an implementation problem rather than the tool’s flaws………..
But here’s something more interesting that I found in the report: companies that build their own internal AI fail more than those who buy the tools from outside vendors. Their data says that external purchases succeed 67% of the time, while internal builds succeed only a third as often.
In other words, startups — not the big corps — are the ones who hold the keys to the future of AI.
Why Do Large Companies Fail at AI Implementation?
You’re probably thinking that a 95% failure rate sounds like an exaggeration. But it’s not just the MIT study saying this. A McKinsey report from earlier this year basically backed this up, finding that over 80 percent of executives say they haven’t gotten any tangible ROI.
So, what is behind their failures? Wharton professor Ethan Mollick points out that the real benefits of AI come when you let AI reshape your workflow, not when you try to force it to fit your workflow. And, since big companies are famously bureaucratic and often prefer doing things the way they’ve “always been done,” it actually makes sense.
Another factor is the ‘learning gap.’ A lot of companies try to build their own AI, but they end up using open-source LLMs that just aren’t as good as the proprietary ones. ………..
These models also don’t remember and learn from feedback. So you’re basically starting from zero every single time, which is incredibly slow and inefficient.
The truth is, building an AI that actually learns and adapts takes serious expertise and a ton of experimentation. Most big companies just don’t have that kind of talent in-house, and they can’t afford to hire for it.
The 5 Rules for Winning as an AI Startup
1. Build an AI That Actually Learns
One of the employees they interviewed is a corporate lawyer from a mid-sized firm that invested $50,000 on a new AI analysis tool. What surprises me is that she said she still preferred using her personal $20/month ChatGPT subscription for drafting her work because their AI tool ‘doesn’t retain knowledge of client preferences or learn from previous edits, repeats the same mistakes, and requires extensive context input for each session.’
ChatGPT learns and remembers. Their $50,000 AI system doesn’t.
“The next wave of adoption will be won not by the flashiest models, but by the systems that learn and remember and/or by systems that are custom built for a specific process.”
— MIT NANDA Study
2. Start with Small, High-Value Wins
The winning strategy is to forget about a massive, company-wide implementation. Instead, focus on a narrow, specific workflow where you can show immediate value and break skepticism. Once you have the company’s attention and trust, you can expand into bigger processes. Things like voice AI for summarizing calls, automating contract drafting, or generating code for boring, repetitive tasks are perfect starting points, according to the research.
3. Sell Outcomes, Not Just Software
The report shows that most companies are judging AI startups by the same high standards they use for top-tier consulting firms. They don’t care about your performance benchmarks; they care about the business outcome. Are you saving them money? Are you making their team faster? They want a partner who is deeply invested in their success, not just another vendor pushing a product.
4. Give Companies What They Want
The report sums up that there are the six things executives are looking for in an AI partner:
A vendor they trust.
A deep understanding of workflows.
Minimal disruption to their current tools.
Clear boundaries for their data.
A system that improves over time
Flexibility when things change.
5. The Time to Act is Now
When you build a system that continuously learns and adapts, you’re compounding ‘switching costs.’ As your tool evolves with a company’s unique data and workflows, the harder it becomes to replace your product.
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