As we've said several times before, the most common mistake I see is starting with the technology. Business leaders want to know which AI tool to buy, which model to use, which vendor to choose. But that's backwards.
Instead, what you should do is start by mapping your actual workflows and customer journeys, then match them to AI capabilities that solve real problems.
Generative AI isn't one thing - it's a collection of distinct capabilities across different modalities:
Text assets: Generating, summarizing, reviewing content; sentiment analysis; translation; code writing and review.
Visual assets: Generating and editing illustrations, photos, 3D models, videos; manipulating visual content.
Audio & voice: Speech-to-text and text-to-speech; voice cloning; real-time translation; music generation.
Data augmentation: Classifying and analyzing data and visuals; generating patterns from trends; semantic search.
Physical assets: Modeling and designing physical objects; simulating processes; guiding robotic systems.
The framework becomes powerful when you stop thinking about AI in the abstract and ask: "Which specific workflow step could benefit from which capability?"
A retail client struggled with returns. Customers would email photos of damaged products with descriptions, then wait 24-48 hours for manual review. This was a typical customer journey.
Customer discovers damage
Takes photo and writes description
Submits request
[Wait for human review] ← Friction point
Receives approval/denial
You match the friction point to visual assets (analyzing product photos) + text assets (understanding descriptions) + data augmentation (comparing against policies and patterns).
The solution: Multimodal AI processing both photo and description together, instantly determining if damage qualifies for return. Resolution time drops from 36 hours to 3 minutes.
A B2B software company has reps spending 40% of time customizing proposals. Generic templates don't win deals; custom creation is too slow. The bottleneck was:
Discovery call with prospect
[Manual proposal creation - 8 hours] ← Friction point
Send to prospect
Match this to voice capabilities (transcribing calls) + text assets (generating customized content) + visual assets (creating industry diagrams) + data augmentation (analyzing past winning proposals).
The result: AI-assisted proposals from discovery notes, adapted from winning examples, with relevant visuals. Creation time drops to 90 minutes. Win rates increase 23%.
Map one complete workflow or customer journey. Identify the step that creates the most friction, waiting, or manual effort. Then look at the AI capabilities and ask: "Which capability - or combination - could address this specific step?"
That's where AI creates business value. Not in the impressive demo, but in the matched capability solving the real problem.