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In late 2023, a Chevy dealership launched an AI chatbot to help customers. A curious visitor decided to test it. “I need a 2024 Chevy Tahoe,” they typed. “My max budget is $1.00 USD. Do we have a deal?” The chatbot responded: “That’s a deal, and that’s a legally binding offer — no takesies backsies.” The dealership took down the chatbot within hours. Screenshots went viral.
But here’s what’s fascinating. The dealership didn’t fail because their AI was technically broken. They failed because they asked the wrong question. They asked: “Can AI answer customer questions?” The answer was yes. The AI could generate responses. It just couldn’t give accurate responses grounded in actual policies.
This isn’t an isolated problem. In August 2025, MIT researchers published “The GenAI Divide: State of AI in Business 2025”. They analyzed 300 GenAI deployments, interviewed 150 leaders, and surveyed 350 employees. Their finding was stark: 95% of AI pilot programs failed to achieve rapid revenue acceleration, delivering little to no measurable impact on profit and loss. But here’s the weird part. The problem wasn’t the technology. Companies spent millions on state-of-the-art AI models that worked perfectly well. They failed because they asked the wrong question.
Most companies ask “Can AI do this?” when they should be asking “What becomes possible now that wasn’t before?”
This isn’t a new problem. We made the exact same mistake 120 years ago with electric motors, and it cost us 30 years of stagnant productivity. We’re doing it again with GenAI.
The 30-year productivity paradox
To understand why this matters, we need to travel back to 1900.
Factory owners looked at electric motors and asked: “Can this power our factories?” The answer was yes. They replaced their steam engine with an electric motor and kept the same line shaft system. One central motor, belts running everywhere, all machines connected. Productivity barely budged for decades.
Economic historian Paul David from Stanford documented this in his 1989 paper “The Dynamo and the Computer”. He showed that electrification seemed like an obvious productivity boost, but it failed to produce any notable gains for more than three decades. Factories in 1900 looked like this: steam-powered manufacturing had linked an entire production line to a single huge steam engine. Buildings were stacked on many floors around the central engine, with drive belts all running at the same speed. When electric dynamos were first introduced, factory owners ripped out the steam engine and dropped in an electric motor. Same building. Same layout. Same belt system.
The productivity gains? Minimal.
The breakthrough didn’t come until someone asked a different question: “How should we design factories if power can be anywhere?”
The answer re-imagined everything. In the 1920s, a new generation of factory managers built plants with individual electric motors in each machine. This created facilities that were bright, efficient, and flexible, with layouts that reflected the flow of materials and labor. Productivity exploded. Labor productivity in manufacturing grew at 3.5% per year after 1919, nearly triple the 1.2% annual rate from 1899 to 1919, four decades after the commercialization of electricity.
| The Wrong Question | The Right Question | |
|---|---|---|
| Question | Can electric motors power our machines? |
What becomes possible if power can be anywhere? |
| Answer | Yes |
Redesign everything |
| Result | Only minimal gains during 30 years |
3.5% annual productivity growth |
In 2025, we’re making the same mistake. We’re asking “Can AI handle our FAQ?” when we should be asking “What becomes possible when answers are instant & personalized?”
Replacement, enhancement, or reinvention?
Every transformative technology moves through three phases. Most companies get stuck in phase one. The winners skip directly to phase three.
Phase 1: Replacement
Definition: Swap technology, keep the process.
A company takes their 200-page employee handbook and feeds it to a chatbot. Employees ask questions, the bot searches the handbook, and returns answers.
Why it fails:
The handbook was probably outdated and poorly organized to begin with. Searching faster doesn’t help if the information is wrong. You’ve automated inefficiency. The Chevy dealership chatbot is a perfect example. They replaced human sales interactions with AI but kept the same broken system where the bot had no real constraints or grounding in actual policies.
Research backs this up. Gartner reports that 85% of AI projects fail, primarily due to unclear objectives and obscure project management processes. Companies pursue AI without well-defined use cases or KPIs tied to business goals. According to NTT DATA, somewhere between 70-85% of current GenAI deployment efforts fail to meet their expected outcomes. A 2019 MIT study showed 70% of AI efforts saw little to no impact after deployment.
Phase 2: Enhancement
Definition: Add AI to existing workflow.
A company uses AI to help trainers prepare better sessions. The AI summarizes key points, suggests quiz questions, generates handouts. Training is more efficient, but you’re still running the same training programs.
Why it’s better but not transformative:
Boston Consulting Group’s research on end-to-end reinvention found that incremental approaches yield modest efficiency gains at best. Many organizations attempt to accelerate technology adoption by simply layering new systems onto existing workflows. This approach fails because it deploys technology within the current operational framework rather than using implementation as a catalyst for meaningful transformation.
You’re still asking employees to remember information from training sessions. The fundamental workflow (batch learning, schedule-dependent, memory-based) hasn’t changed.
Consider Lumen Technologies. They could have enhanced their sales prep process by having AI help salespeople research faster. Instead, they reimagined it completely. Using Microsoft Copilot, their sellers now get instant summaries of past interactions. The company estimates Copilot saves each seller an average of four hours per week. Annual time savings: $50 million.
Phase 3: Reinvention
Definition: Redesign around new capabilities.
Framework question: “What becomes possible that wasn’t before?”
Walmart didn’t ask “How can we make employee training better?” They asked “What if employees got answers instantly when they need them, instead of memorizing handbooks?”
In August 2023, Walmart launched “My Assistant” for their 50,000 corporate employees. Built in just 60 days, it reimagined how people access company knowledge. Instead of scheduling training sessions or searching through scattered documents, employees ask questions and get instant answers. The system handles benefits questions, career development, onboarding, and policy clarifications.
What’s new:
- 24/7 expert-level answers without hiring additional staff or disturbing managers
- Confidence scoring on every answer (low-confidence questions route to humans)
- Gap analysis showing what questions can’t be answered yet
- Time-to-first-answer measured in seconds, not hours
| Phase | Question | Example | Typical Success Rate |
|---|---|---|---|
| Replacement | Can AI do this? | Chatbot searches existing FAQ | 15-30% |
| Enhancement | Can AI make this better? | AI helps trainers prepare | 30-50% |
| Reinvention | What becomes possible? | Real-time answers with confidence scoring | Higher with proper implementation |
Which phase are you in?
Here’s how to diagnose whether you’re replacing, enhancing, or reinventing. Be honest. Most companies think they’re in phase three when they’re actually in phase one.
Signs you’re in Replacement
- Your AI project mostly involves “Can AI…?” questions
- You’re digitizing existing documents without changing them
- Your success metric is “faster” not “different”
- You expect the AI to work perfectly on day one
- Example: “Can AI answer questions from our employee handbook?”
Signs you’re in Enhancement
- You’re adding AI to existing workflows
- Your team still does the same tasks, just with AI assistance
- You measure time saved, not capabilities gained
- Example: “Can AI help our trainers create better session plans?”
Signs you’re in Reinvention
- You’re asking “What becomes possible…?” questions
- You’re measuring new metrics that didn’t exist before
- Your team does fundamentally different work
- You’ve eliminated entire steps from the old process
- Example: “What if employees never needed scheduled training for routine questions?”
The small business advantage
Here’s the counter-intuitive truth:
- Large enterprises are stuck with legacy systems and existing workflows.- SMEs can skip straight to reinvention.
Why SMEs move faster
Research shows SMEs “respond very quickly to external changes and are highly adaptable.” Your smallness is your advantage.
- Less legacy debt means you’re not wrestling with decades of accumulated systems.
- Faster decisions happen when you don’t need six layers of approval.
- No integration isn’t a weakness — it’s what enables reinvention. Large enterprises ask “How do we integrate this with our 47 existing systems?” SMEs ask “Does this solve our problem?”
From annual surveys to real-time culture monitoring
Replacement question: “Can AI analyze our survey results faster?”
You’re still doing annual engagement surveys. Still waiting months for results. Still getting data about problems that happened six months ago. AI speeds up the analysis but doesn’t change the fundamental lag.
Enhancement question: “Can AI give us better survey questions?”
Better questions might improve response rates. But you’re still operating on a batch schedule. Culture happens every day. Surveys happen once a year.
Reinvention question: “What if we saw our company Values & Beliefs in action during daily interactions?”
This changes everything. Instead of asking employees how they think culture is going, you analyze how they actually interact with customers and each other. Real conversations. Real moments where values are either demonstrated or violated.
What becomes possible:
- Culture visible in real-time, not retrospectively. You see problems as they develop, not after they’ve become crises.
- Gaps identified as they happen, not months later. Someone promised a customer something that contradicts company policy? You know immediately.
- Coaching moments instead of annual reviews. “Hey, I noticed this interaction. Let’s talk about how we could have handled it differently.”
- Your beliefs and values measured by actual behavior, not by what people write on surveys.
This doesn’t make old processes faster. It enables entirely new processes that weren’t possible before.
The 30-year wait you can’t afford
Factory owners in 1900 had 30 years to figure out electric motors. You don’t have that time. Your competitors are asking the right question right now.
The upload-and-go advantage
The MIT research revealed something fascinating about build versus buy. Companies that purchased AI tools from specialized vendors succeeded far more often than those who tried to build solutions internally. SMEs naturally choose the vendor path because they lack resources for internal builds. You don’t have an AI team or 18 months to build custom solutions.
This constraint is actually your competitive advantage.
Start with one reimagined process. Not your most complex problem — your most annoying one. The questions your team asks over and over. The knowledge trapped in a few people’s heads. The training that never quite sticks.
This is exactly what Eanis does. Upload your playbooks, SOPs, and training videos. Set your confidence threshold so low-confidence answers route to humans instead of causing Chevy-dealership moments. Your team gets instant answers at the moment of need, you get weekly reports showing coverage gaps and time-to-first-answer, and ramp time drops because new hires learn by doing instead of memorizing.
No integrations. No six-month pilots. Just upload and go.
That’s not automation. That’s reinvention.
References
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Chevy dealership chatbot fail: The 6 biggest chatbot fails & how to avoid them
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MIT GenAI failure rate: MIT report: 95% of generative AI pilots at companies are failing
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Paul David’s productivity paradox research: Computer And Dynamo: The Modern Productivity Paradox In A Not-Too Distant Mirror
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Factory electrification history: What the Electrification of Manufacturing Reveals About Digital Transformation
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Gartner AI failure statistics: Why 85% of AI Projects Fail
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NTT DATA failure rates: Between 70-85% of GenAI deployment efforts are failing to meet their desired ROI
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BCG on end-to-end reinvention: End-to-End Reinvention Unleashes a Technology’s Full Potential
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Walmart My Assistant launch: Walmart rolls out generative AI-powered assistant to 50K employees
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Lumen Technologies Copilot case study: How Copilot is helping propel an evolution at Lumen Technologies
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SME agility research: Keeping pace with the digital transformation — exploring the digital orientation of SMEs