AI Enablement
Enablement answers “how do we help people succeed?”
It's the set of activities that turn policy into practice and frameworks into adoption. The skills required for training design and change management are fundamentally different from the skills required for policy writing and risk assessment. Trying to do both simultaneously usually means doing neither well.
Four components of effective enablement
1. Literacy programs that address real work
Abstract “What is AI” training doesn't work, and frankly we're past that now. Your finance team doesn't need to understand how models get trained - they need to understand what AI can do for month-end close, variance analysis, and forecast accuracy. Role-specific training that connects AI capabilities to actual job functions works, and generic overviews just don't.
The progression runs from consumer (using basic AI chat tools ) to power user (getting better outputs through better prompts) to builder (identifying opportunities and specifying requirements). Focus on the problems people actually face rather than comprehensive capability surveys.
2. Adoption support beyond documentation
Thinking that people crack open documentation when they have a question is a glorious mythology that continues to be perpetuated. Office hours where people bring actual work problems and get help solving them with AI tools produce better results. This is a shift to coaching, not just information delivery or basic skill development.
Learning from failures without blame completes the picture. Whether it's the wrong tool for the problem, insufficient data, or the wrong problem entirely, each failed experiment teaches something if you create space to discuss it along with the wins.
3. Internal champions who aren't IT people
The most effective AI advocates in your organization aren't the people building the systems. They're the business users who figured out how to apply AI to their actual work and want to help others do the same. McKinsey research shows that companies investing in trust-enabling activities through change management are nearly two times more likely to see revenue growth rates of 10% or higher.
These people need three things: time (it's not free labor to be an advocate), tools (access to what they need to demonstrate capabilities), and recognition (acknowledgment that this contribution matters). Skip any of these and your champion network collapses.
Peer learning networks built around these champions work because people trust colleagues doing similar work more than they trust IT experts explaining possibilities. When a fellow marketer shows how they use AI for campaign analysis, other marketers pay attention.
4. Use case discovery through observation
Suggestion boxes for AI ideas generate noise. Process observation generates signal. Instead, watch how people actually work. The repetitive tasks, the places where they're struggling, the workflows they've built workarounds for. Those reveal opportunities where AI might help.
Watching someone spend days on explanatory text for standard variance patterns, product explainers, or brute-force tasks can make the opportunity obvious. Connecting similar problems across departments builds a portfolio of proven patterns from which other teams can learn.
Enablement without governance
Enablement without governance produces risk accumulation. Teams adopt tools enthusiastically with zero oversight. Boston Consulting Group's 2024 survey of 1,000 executives found that roughly 70% of AI implementation challenges stem from people and process issues, not technical problems.
There's a feedback loop that runs both ways: enablement surfaces what governance makes too hard, governance identifies what enablement needs to emphasize. Enablement brings opportunities forward and governance evaluates and clears them, but the more useful framing is that each function improves the other through constant contact.
When governance only says “no” or “slow down,” enablement can't succeed. Effective governance creates fast paths for low-risk use cases while maintaining appropriate scrutiny for high-risk ones. Enablement then channels activity through those paths, making compliance the easier route.
Shadow AI is self-enablement
One particularly effective argument for enablement investment: it reduces shadow AI risk. When you have governance but no enablement, people still need to get work done. They find AI tools that solve their problems, subscribe with corporate cards or personal accounts, and start using them without any review process.
Enablement channels this activity. Instead of teams finding their own solutions and creating shadow AI sprawl, you provide approved alternatives with faster access than unauthorized procurement. The path of least resistance leads through governance rather than around it.
This reframes enablement as risk management investment, not just adoption acceleration. It helps people use AI more effectively, but it also reduces the governance challenge by making compliance the easier path.
Build enablement that drives real adoption
Documentation doesn't drive behavior change. Ordovera builds enablement programs with literacy pathways, champion networks, and adoption support that turn AI policy into daily practice.