AI training across six offices for 300+ team members
Frontier Economics trained 300+ people across six offices, recovered £478k in annual productivity, and achieved a 13x return on their training investment. The programme included Mastering AI workshops, an AI Activation Programme, AI Strategy development, and 52 one-to-one coaching sessions.
13x
Return on investment
£478k
Productivity recovered annually
300+
Team members trained across six offices
47 min
Average time saved per person per day
The challenge
A leading microeconomics consultancy with 400 employees across six European offices had fragmented AI adoption. Some staff used ChatGPT, others hadn't started. Leadership wanted systematic AI capability across the entire organisation to shift culture toward AI-augmented work, not just improve individual productivity.
What we did
We delivered bespoke Mastering AI workshops for 300+ attendees across all six offices, an AI Activation Programme for sustained adoption, an AI Strategy workshop for senior management, an 18-month AI Strategy & Opportunities roadmap, and 52 one-to-one coaching sessions for power users. Every session focused on real work: drafting expert reports, synthesising research, building models. We tracked adoption and productivity metrics throughout.
Key AI use cases
Across six offices, teams built AI tools tailored to the specific demands of microeconomics consulting. Economists created custom assistants for drafting and comparing expert reports, generating cross-examination questions for arbitration cases, and converting statistical specifications directly into Stata code. Teams working on competition litigation built precedent trackers, while energy specialists automated weekly monitoring of German policy updates and emissions analysis for LNG imports.
Beyond core consulting work, the programme uncovered productivity gains in areas teams hadn't anticipated: converting PowerPoint decks into structured prose for clients, building internal wikis for energy model documentation, and creating formatting checkers that enforce house style across expert reports. One team even built a persona-based GPT that simulates stakeholders at different seniority levels to pressure-test recommendations before client meetings.
I learned about lots of different functions I had never used before. For example, deep research, the thinking mode and creating my own GPT.
Training participant
Frontier Economics
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