Applied AI
AI is now the talk of the town. Teams are experimenting, startups are launching add-ons, and boards are asking: "Where is our AI strategy?"
While information and enthusiasm are in huge supply, there is a scarcity of strategic clarity. Organizations need to move from disconnected experiments to real business impact.
The Current Landscape
Most efforts to apply AI are currently channeled through one of these tracks:
Generic Keynotes
High on inspiration, low on implementation. Often leaves meaningful action undefined.
Technical Bootcamps
Code-heavy training too deep for non-tech teams. Focuses on libraries over business problems.
DIY Experimentation
Fragmented, personality-driven efforts without governance or shared learning.
Add-ons & Plugins
Features tailored to existing systems, often becoming a problem in search of a solution.
The Surplus of Disconnected Experiments
Without strategic clarity, these isolated efforts often result in:
- Massive disconnect between technological capability and tangible business value
- Pilots that appear successful but fail as enterprise-wide solutions
- Proliferation of unmanaged 'Shadow AI' and governance risks
- Fragmentation of data infrastructure and growing technical debt
What is crucial for success
Leaders must have a clear non-technical understanding
Initiatives must start from business problems unique to the organization
Clear success metrics and value hypotheses must exist
A defined governance structure is essential
Comprehensive understanding of ethics, compliance, and failures
Make the Shift
Organizations need to move from "We need to do something with AI" to "Here are our top, validated AI bets."
This shift is possible with Applied AI—a strategy and implementation workshop for non-technical managers and a structured framework to track initiatives.