A large energy producer gains a clear view of AI usage across the enterprise and accelerates adoption by measuring maturity, closing capability gaps, and embedding AI into everyday operational workflows.
At a glance
Success Highlights
- 3x increase in active AI adoption across business units
- 85% of employees engaged with AI tools within six months
Related Services and Solutions
- AI Adoption
- AI Readiness
Challenge
A large energy producer had invested heavily in AI tools and platforms, but had little sense of whether that investment was paying off. Licenses were purchased and pilots were launched, yet leadership could not say how many people were actually using AI, where it was creating value, or why adoption was thriving in some teams and stalling in others.
Without a way to measure adoption, the organization was effectively flying blind:
- No clear metrics meant the value of AI investment could not be quantified or defended.
- Uneven uptake left some teams highly capable while others barely engaged, with no understanding of why.
- Tool fatigue set in as employees were handed AI applications without the training or context to use them well.
Determined to turn scattered enthusiasm into sustained, measurable adoption, the client sought a way to understand where it stood and a clear path to move forward.
Solution
AIHugger partnered with the client to measure AI adoption across the enterprise and build a structured program to accelerate it, grounded in the operational realities of the energy sector rather than generic change management. Our approach centered on four pillars:
Adoption Baseline
- Defined a clear set of adoption and maturity metrics tied to business value.
- Measured usage, capability, and sentiment across every business unit.
- Established a baseline that replaced assumptions with an evidence-based view of adoption.
Gap Analysis
- Identified the skills, workflow, and cultural barriers slowing adoption in lagging teams.
- Studied high-performing teams to surface the practices worth scaling.
- Prioritized the highest-impact opportunities to close the gap between the two.
Enablement & Training
- Delivered role-based training that connected AI tools to real operational tasks.
- Embedded AI into everyday workflows rather than leaving it as a separate, optional tool.
- Established internal champions to support peers and sustain momentum.
Measurement & Iteration
- Built a dashboard to track adoption and value in real time.
- Reviewed progress on a regular cadence to refine the program where uptake lagged.
- Transferred the metrics and methods so the organization could manage adoption on its own.
By making adoption measurable, the program turned a vague sense of progress into a clear picture leadership could act on, directing effort precisely where it would move the needle.
Outcomes
The program transformed how the organization understood and grew its use of AI, turning uneven uptake into broad, sustained adoption:
3x increase in active adoption: Targeted enablement and embedded workflows tripled the number of employees actively using AI across business units.
85% employee engagement: Within six months, the large majority of employees were engaging with AI tools as part of their daily work.
Investment finally measurable: Clear metrics let leadership quantify the value of its AI spend and direct future investment with confidence.
A self-sustaining program: With the metrics, dashboards, and champions in place, the organization could continue driving adoption without ongoing outside support.
By treating adoption as something to be measured and managed rather than assumed, the organization built lasting momentum, setting a new standard for how AI takes hold across the energy sector.