THE SITUATION
Context
Enterprise leaders were exploring analytics, machine learning and AI across media, technology and other complex operating environments.
SELECTED EXECUTIVE EXPERIENCE · Large-enterprise environments
Commercializing Enterprise AI
AI creates enterprise value when use cases are connected to funded business problems, adoption and a credible path to scale.
THE STRATEGIC CHALLENGE
What had to change
The work required separating attractive demonstrations from opportunities with executive sponsorship, economic value and a realistic path from proof of concept to adoption.
MY ROLE
Senior involvement
Led enterprise AI and analytics opportunity development, use-case design, executive engagement and commercialization.
THE APPROACH
From decision to execution
- Started with the business problem and executive decision rather than the technology.
- Used workshops, demonstrations and paid proofs of concept to validate value.
- Prioritized opportunities based on strategic relevance, feasibility and adoption requirements.
- Connected technical teams, sellers and client stakeholders around a path to funded work.
EVIDENCE & RESULTS
Business impact
- $29M in documented AI / analytics pipeline
- 30+ enterprise AI engagements
- Paid proofs of concept ranging from $50K to $250K
- Additional work included a $400K gaming data strategy spanning 5,000 titles and 110M users
WHAT LEADERS CAN TAKE FROM THIS
Strategic lessons
- The AI business case should come before the AI use case.
- A proof of concept is useful only when it resolves a decision required for scale.
- Commercialization requires executive sponsorship, adoption planning and measurable value.