In Issue #1, I introduced the three-bucket framework for AI value creation: Boosting Productivity, Creating New Value, and Driving Disruption. This issue focuses on the second bucket - using AI to transform how your customers experience your products and services.
This is where AI moves from internal efficiency gains to direct competitive advantage. When done well, AI-enhanced customer experiences don't just improve satisfaction - they could change what's possible in your value proposition. These may be an integral part of your products or solutions, or as enhancements to the way you communicate and interact with them.
The Customer Journey Framework
Creating AI-powered customer value requires disciplined thinking, not technology enthusiasm. I use a four-step framework that prevents the "solution looking for a problem" trap:
Step 1: Analyze a selected Customer Journey
Map the complete journey your customers take - from awareness through purchase, onboarding, usage, support, and renewal or expansion. Don't skip steps or focus only on the "interesting" parts. The friction often hides in the transitions between stages or in the mundane, repetitive interactions.
For a B2B software company, this might include: discovering your solution through search, evaluating features against requirements, requesting demos, negotiating contracts, technical implementation, user training, daily usage, troubleshooting issues, and expanding to additional teams. For a retail company, this journey will have other touch points, and for a private banking or insurance company - yet very different journeys.
Step 2: Identify Friction Points and Enhancement Opportunities
Where do customers struggle, wait, or settle for "good enough"? Where do they need expertise you can't scale? Where are they making decisions with incomplete information?
Friction isn't always obvious. Sometimes it's the prospect who abandons your complex product catalog because they can't figure out which solution fits their needs. Sometimes it's the customer who receives generic training materials when they need guidance specific to their industry context. Sometimes it's the support ticket that sits in queue because Level 1 can't handle the complexity but Level 2 is overloaded.
Look for two types of opportunities: relieving friction (removing obstacles that frustrate customers) and expanding possibility (enabling experiences that weren't feasible before).
Step 3: Match AI Capabilities to Opportunities
Now - and only now - do you think about AI. Which specific AI capabilities could address the friction or unlock new value?
Current AI excels at several categories relevant to customer experience:
Understanding and responding to natural language: Conversational interfaces, semantic search, intent recognition
Personalizing at scale: Recommendations, customized content, adaptive experiences based on context and characteristics
Processing and synthesizing information: Summarization, extraction, translation, analysis of documents or data
Generating content: Text, images, voice, code, structured outputs tailored to specific needs
Automating decision support: Triage, routing, qualification, guided workflows
The key question: Does a specific AI capability actually solve the customer problem you identified, or does it just make the problem more technologically sophisticated?
Step 4: Start Focused, Build for Scale
Here's where bias for action meets strategic thinking. Don't launch enterprise-wide. Don't build the comprehensive solution. Start with the smallest viable implementation that tests your hypothesis about customer value.
This is your two-way door decision: If you're wrong about whether AI helps, you need to be able to walk back quickly without damaging customer trust or burning significant resources.
Build the prototype. Test with 10-50 customers. Measure not just usage, but actual impact on the friction point you identified. Get qualitative feedback on whether the AI interaction feels helpful or frustrating. Read the chatbot conversation transcripts and identify patterns.
Only after validation do you invest in broader rollout, and the advanced features you're considering.
To dive deeper, watch me (well, my avatar, to be honest…) explain the Product Enhancement Matrix with Generative AI.