Growth Vectors
Three ways organizations create enterprise value with AI
AI Enablement
“How do we equip our workforce?”
An organization-wide effort to equip every employee with the right AI copilots for their specific role — developers get coding assistants, marketers get content tools, analysts get research synthesis. This includes the tools, skills, governance, and support people need to safely and effectively use AI in their daily work. The common thread is individual productivity at the task level. Quick wins, low risk, fast time-to-value. This is where portfolio weight sits heaviest early on — but it runs alongside the other two vectors, not ahead of them.
Value Delivered
ProductivityTypical Plays
Operational Reinvention
“How do we rewire how we operate?”
Redesign entire workflows and processes from scratch, assuming AI is the default — not adding an AI step to an existing process, but rethinking the end-to-end with AI at the core. This is where you move from making individual tasks faster to fundamentally transforming how the organization operates. Efficiency gains from AI Enablement fund and de-risk these investments — but task-level productivity only becomes funding through explicit capture: capacity-release plans, redeployment, and throughput targets that convert saved hours into budget. Higher risk, bigger payoff.
Value Delivered
Efficiency, quality, speedTypical Plays
Revenue Engineering
“How do we create new value with AI?”
Use AI to create, enhance, sell, personalize, and monetize products and services — driving measurable business growth. This spans new AI products, AI features in existing products, sales and marketing productivity, customer retention, and new business models like usage-based pricing or data products. Deepens with the platform, governance, and operational muscle built in the other two vectors — but organizations with a proprietary data advantage should start these plays early rather than waiting.