Do 95% of AI Pilots Fail? Not in Customer Service.
28 Aug 2025
4.5 min read
A recent MIT report sparked headlines with a daunting claim: 95% of AI pilots are generating “zero return.” Investors got nervous, executives second-guessed projects, and skeptics felt vindicated.
But as with any bold statistic, the devil is in the details.
What the report actually shows is more nuanced – and if you're in customer service, it's even optimistic. In fact, when you dig deeper, customer service is one of the areas where AI is showing the most consistent value. Let's unpack why the “95% failure” narrative doesn’t tell the full story.
What Does "Zero Return" Really Mean?
The headline figure – that 95% of pilots are stuck – is technically correct. But what it actually refers to is that only 5% of AI pilots are generating multi-million-dollar returns with a direct impact on the P&L.
That’s a high bar for success. And it's a bar that excludes productivity gains, automation of back-office tasks, or incremental efficiencies – all of which are very real benefits for most AI deployments, especially in customer support.
Take this quote from the MIT report:
"If I buy a tool to help my team work faster, how do I quantify that impact? I could argue it helps our scientists get their tools faster, but that's several degrees removed from bottom-line impact."
So, if your AI project isn’t producing headline revenue gains, it’s counted as a failure – even if it’s quietly saving your team hundreds of hours.
Why AI in Customer Service Beats the Odds
The 2025 State of AI in Business report highlights that customer service is one of the few areas seeing consistent deployment and measurable returns. That’s because:
Customer support has clear, repeatable workflows that are ideal for automation.
AI in this space can be tied to quantifiable metrics like reduced average handle time, CSAT, and resolution rate.
Unlike ambiguous “AI-powered” efforts in strategy or sales, customer service bots can be measured and optimized in real-time.
In other words: customer service is the perfect use case for narrow, high-ROI AI.
What the Report Actually Warns Against
Digging deeper into the MIT analysis, failures stem less from technology limitations and more from organizational and strategic missteps:
1. Unrealistic ROI Expectations
Expecting multimillion-dollar savings from early pilots – especially in customer service – is often a recipe for disappointment. Cost savings from AI scale over time and may be more subtle, such as:
Delaying or avoiding additional headcount
Reducing outsourced BPO spend
Improving internal efficiency
Unless you're operating at massive scale, you're unlikely to see savings that hit the P&L in the first quarter.
2. Investment Bias
The report also highlights “investment bias” – where companies pour resources into high-visibility, top-line initiatives (like AI in sales) rather than high-ROI, back-office functions (like customer service), which are ripe for automation. Ironically, this means the projects most likely to succeed often get the least attention.
3. Build vs. Buy Failures
One of the most stark findings: internally built AI projects fail at twice the rate of vendor partnerships.
External AI vendors had a ~67% success rate. Internal builds? Closer to 33%
Why? Because building AI requires:
Deep technical expertise
Continuous iteration and retraining
Workflow mapping and integration
A strong product mindset
Experience with specific use cases
We’ve written about this before, but most companies simply aren’t set up to do this well. Buying from specialized partners accelerates time-to-value and drastically lowers risk.
Specialization > Generalization
Another key takeaway from the report: Narrow AI wins.
Organizations trying to build broad, all-purpose AI tools fared far worse than those who focused on a single, high-impact use case, nailed it, then expanded.
This is especially true in customer service. If you're in ecommerce, a vendor that deeply understands your specific workflows, customer intents, and integrations will always outperform generalist platforms.
Technology Isn’t Failing – The Approach Is
Most failures happen when organizations:
Use static, one-size-fits-all tools
Don’t integrate AI into their core systems or workflows
Expect overnight results without change management
AI isn’t plug-and-play. Success requires tight system integration, continuous learning, and workflow-specific customization.
For instance, it’s one thing for a bot to send a tracking link. It’s another to:
Check the shipping status
Identify delays or delivery failures
Provide a next-best action automatically
That’s the difference between generic automation and true AI enablement.
Our Take: The 95% Failure Stat Doesn’t Apply to You
Yes, we’re biased – we specialize in AI for customer service. But our real-world results don’t resemble a 95% failure rate. Not even close.
Most ecommerce brands don’t have the scale to “save millions” through automation – but that’s not the point. A 20-30% reduction in agent load or BPO cost can be transformative in a low-margin business.
So, here’s what does drive success in customer service AI:
Buy, don’t build – Partner with experts who have done this before
Start narrow – Nail one use case before expanding
Integrate deeply – Don’t just plug AI in – connect it to your systems
Focus on ROI, not flash – Skip the shiny demos and prioritize workflow impact
Looking to Succeed Where Others Fail?
If you’re an ecommerce brand exploring AI for customer service, let’s talk. We’ll show you how to make it work – and avoid becoming part of the 95%.
