🎯“AI” is Not a Strategy — Neither a Business nor a Product one
The Critical Mistake Destroying Customer and Business Value
In the age of AI, CEOs are demanding “AI strategies,” CTOs are scrambling to implement the latest AI tools, and product teams are desperately trying to figure out how to integrate AI into their roadmaps.
But here’s the uncomfortable truth: most of these efforts may actively be destroying customer and business value.
❌The Wrong Approach: Backwards Thinking
🔹 The “Shiny Object” Syndrome
If something like this happens at your company, then it’s a mistake, and you will most likely destroy value:
- CEO sees AI headlines → “We need an AI strategy!”
- Teams scramble to find AI use cases → “How can we use ChatGPT/AI agents/machine learning?”
- Force AI into existing processes
- Measure success by “AI adoption” metrics
This is like buying a hammer and then walking around looking for nails to hit, even when you might need a screwdriver, glue, or a completely different solution.
🔹 Examples of this in real life include:
- Retail company implements AI chatbots because competitors have them, but customers actually prefer email support
- Manufacturing firm deploys predictive AI for equipment maintenance, but its real problem is poor employee training
- Financial services company builds AI fraud detection, but most fraud comes from internal processes that need better controls
💥Why This Usually Fails
- Resource Waste: i) Companies spend millions on AI infrastructure that doesn’t solve real problems. ii) You build complex AI systems that customers don’t value. iii) Time and talent diverted from actual customer needs.
- Strategy Confusion: i) AI becomes the goal instead of the means to an end. ii) Teams lose sight of business outcomes. iii) Success gets measured by technology adoption rather than customer value.
- Customer Disconnect: i) Solutions that look impressive in demos but don’t improve real experiences. ii) Over-engineered products that add complexity rather than simplicity. iii) Features customers never asked for and don’t use.
✨ The Correct Mental Framework: Customer-First Problem-Solving
✅ Start with Customer Problems, Not Technology
- Customer research → Identify real pain points
- Define success metrics that matter to customers
- Explore ALL possible solutions (not just AI)
- Choose the best tool for each specific problem
- Measure success by customer outcomes
This is like being a skilled craftsman who first understands what they’re building, then selects the right tool for each task.
🤔The Questions to Ask
Instead of “How do we use AI?”, ask:
- For Customer Support: i) What frustrates customers most about getting help? ii) How long do customers wait for answers? iii) What problems do they solve themselves vs. need help with? iv) What would make them feel heard and valued?
- For Developer Productivity: i) What slows down our engineering team the most? ii) Where do developers spend time on repetitive tasks? iii) What causes the most bugs and rework? iv) How can we help them focus on creative problem-solving?
- For Marketing: i) Why do customers ignore our communications? ii) What information do they actually want from us? iii) When and how do they prefer to hear from us? iv) What would make them more likely to engage?
🏢 How This Plays Out in Real Organizations
😵💫The Common Organizational Dysfunction
What might happen:
- CEO: “What’s our AI strategy?”
- CTO: “Let’s evaluate AI tools and platforms.”
- Engineering: “Here are 10 AI technologies we could implement.”
- Product Team: “How do we fit these into our roadmap?”
- Marketing: “How do we message our AI capabilities?”
The problems with this flow:
- No customer input in the entire process
- Technology drives decisions instead of customer needs
- Success gets defined by implementation, not outcomes
- Teams optimize for demos and marketing, not real value
🌟What High-Performing Product-Led Organizations Do Instead
- User Research: “What are our biggest customer pain points?”
- Product Strategy: “Which problems, if solved, create the most value?”
- Solution Exploration: “What’s the best way to solve each problem?”
- Technology Selection: “Which tools (AI or otherwise) fit our solutions?”
- Implementation: “How do we build and measure what customers need?”
📊 Measuring AI Roll-Out Success the Right Way
❌Wrong Metrics (Technology-Focused)
- Number of AI models deployed
- Percentage of processes “AI-enabled”
- AI infrastructure utilization rates
- ML model accuracy scores in isolation
- “AI adoption” across the organization
These metrics tell you nothing about customer value or business impact.
✅Right Metrics (Outcome-Focused)
For Customer Experience:
- 😊 Customer Satisfaction (CSAT) scores
- ⏰ Time to resolution for customer issues
- 🔄 Customer retention and loyalty rates
- 💬 Net Promoter Score (NPS) improvements
For Business Operations:
- 💰 Revenue per customer increases
- 📉 Cost per transaction reductions
- ⚡ Time to market for new features
- 🎯 Conversion rates at key funnel steps
For Employee Productivity:
- 🚀 Feature delivery velocity improvements
- 📈 Quality metrics (fewer bugs, better performance)
- 😊 Employee satisfaction and retention
- ⏱️ Time spent on high-value vs. repetitive tasks
The A/B Testing Framework ⚖️
- Establish the baseline measurement of current customer experience
- Formulated hypotheses about what improvement looks like
- Test solutions (AI-powered vs. traditional approaches)
- Statistically validate customer outcome improvements
- Assess business impact before full rollout
I’d love to hear your thoughts!
Share your insights and feedback in the comments below and let’s continue this discussion.
Lets connect on LinkedIn and give me your feedback. Would love to stay in touch and connect for the future.
🎭 Common Misconceptions and Myths
Myth 1: “AI First” Companies Are More Innovative
✅Reality: The most successful companies are “Customer First” companies that just happen to use AI effectively.
- Success usually comes from simplifying complex customer engagement, not from AI adoption.
- Their AI tools succeed because they solve real customer problems.
- The technology is invisible to customers who just experience better outcomes.
Myth 2: “We Need an AI Strategy to Stay Competitive”
✅Reality: You need a customer value strategy that “may” include AI tools.
- Competitors using AI doesn’t mean you need AI.
- Customer needs should drive technology choices.
- Better execution of customer-focused solutions beats flashy AI implementations.
Myth 3: “AI Will Transform Everything”
✅Reality: AI will enhance solutions to specific problems, not magically solve everything.
- Some problems are better solved with process improvements.
- Some customer needs require human touch, not automation.
- AI works best when combined with other tools and approaches.
🌟 Why This Matters
🏆The Strategic Advantage
Companies that follow the “customer problems first, then tools” approach:
- Build products customers actually want instead of impressive demos
- Achieve better ROI on technology investments
- Create sustainable competitive advantages through customer loyalty
- Scale more effectively because they understand what drives value
- Make better technology decisions because they have clear success criteria
⚠️The Risk of Getting It Wrong
Organizations that lead with an “AI minded strategy”:
- Waste resources on solutions that don’t create customer value
- Lose focus on actual business strategy and customer needs
- Fall behind competitors who execute better on customer problems
- Create technical debt with AI systems that don’t integrate well
- Struggle to prove ROI because they’re measuring the wrong things
When using AI, you need to use a model and train it, having the data, a feedback loop, and all of this requires investment and you need to be serious about it.
🌟The key Insight
Be serious about solving customer problems first. Then be serious about choosing the right tools — which may or may not include AI — to solve those problems effectively.
This isn’t being anti-AI; it’s pro-customer value.
And when AI genuinely creates customer value, it becomes a powerful competitive advantage rather than just expensive technology.
Thanks for reading!
To stay connected and get more insights like this, be sure to follow me on Medium.
As a fellow product enthusiast, be sure to connect with me on LinkedIn to continue this discussion, network, and access my professional network.
Remember to share this article with colleagues and professionals that might find this useful. Thanks!
