In Issue #1, I introduced the three-bucket framework for AI business value: Boosting Productivity, Creating New Value, and Driving Disruption. We've explored productivity gains and disruption in previous issues. This time I focus on the middle bucket - using AI to create new value for your customers and stakeholders.
This is where AI moves from internal efficiency to direct competitive advantage. When done well, AI-enhanced offerings don't just improve satisfaction - they transform what's possible in your value proposition.
But a simple "add AI to your product" isn't a strategy. It's a recipe for wasted investment. I've seen too many organizations bolt on chatbots or recommendation engines without understanding where AI actually creates value. To avoid this trap, I use a framework with two distinct paths - each requiring different thinking and yielding different types of value.
The first path focuses on your existing customer experience. It starts not with AI capabilities, but with your customers' reality.
Begin by mapping your current journey touchpoints - every interaction a customer has with your product or service. For an online learning platform, this might include course discovery and browsing, registration and onboarding, learning content consumption, assessment and progress tracking, certification and completion, and technical support requests.
Next, identify pain points and friction moments. Where do customers struggle, wait, or settle for "good enough"? In that same learning platform, students might struggle to find relevant courses among 1000+ options. One-size-fits-all content pacing frustrates learners. Manual grading feedback takes 72 hours. Generic onboarding doesn't match individual learning styles. Support tickets take 24+ hours to respond.
Now - and only now - consider AI-powered solutions that address these specific frictions. A personalized course recommendation engine based on goals and skills. Dynamic content that responds to comprehension signals. Instant AI assignment feedback and explanation. Adaptive onboarding that adjusts to preferences. An AI chatbot for immediate support resolution.
Finally, look for delight opportunities - ways AI can surprise customers with unexpected value. AI-generated study guides tailored to weak areas. Proactive career advice based on skill development patterns. Personalized celebration messages for achievements.
The key discipline: every AI solution must trace back to a specific friction point or delight opportunity. If you can't draw that line, you're building AI theater.
The second path is more ambitious. Instead of optimizing existing experiences, you're adding capabilities that weren't previously possible.
I think of this as three ascending levels:
Level 1: Enhancing existing products. These are AI features that improve what you already offer. AI-powered search with natural language queries. Auto-generated closed captions for all video content. Smart note-taking that highlights key concepts. Automated progress reminders and study scheduling. An AI teaching assistant for basic Q&A within courses.
Level 2: Enabling new possibilities. These go beyond enhancement to create experiences that weren't feasible before AI. Virtual AI tutors that adapt teaching style to each learner. Real-time content generation based on current industry trends. AI-powered peer matching for collaborative learning. Immersive AI-generated simulations for practical skills. Predictive learning path optimization for career goals.
Level 3: AI-First Products. At this level, AI isn't a feature - it's the foundation. The product couldn't exist without it. An AI learning companion that evolves with the student over years. Dynamic skill assessment through conversational AI interviews. AI-generated immersive learning worlds. A collective intelligence engine that synthesizes learning patterns across all users to generate entirely new educational content.
Most organizations should pursue both paths simultaneously, but with different time horizons.
User journey optimization delivers value quickly. You're solving known problems for existing customers. The ROI is measurable and the risks are contained. Start here to build confidence and demonstrate results.
Product innovation requires longer investment but creates differentiation. Level 1 enhancements can often be implemented alongside journey optimization. Levels 2 and 3 require more strategic commitment - and more tolerance for experimentation.
The canvas I use with clients helps visualize both paths side by side. On the left: your customer journey, mapped to frictions and AI solutions. On the right: your product innovation ladder, from enhancements through new possibilities to AI-first offerings. This dual view prevents the common mistake of pursuing ambitious AI products while ignoring friction in your core experience.
Whether you choose Path 1, Path 2, or both - the discipline is the same. Start with customer value, not AI capability. Every AI investment should answer a clear question: What friction does this remove, or what possibility does this enable, that customers will actually value?
The organizations winning with AI value creation aren't those with the most sophisticated models. They're those with the clearest understanding of where AI solves real problems for real customers.