Adoption
AI-driven change management that made 2,000 users understand why, not just how
Case Studies
Real problems solved with AI. No hype, no demos — just practical delivery.
Every engagement below started with a business problem, not a technology agenda. AI was the approach that made the difference — here’s how.
Discuss a Similar ChallengeReal Problems, Real AI Solutions
Each of these started with a practical business problem. AI was the approach, not the objective.
CRM Adoption — Global Consultancy
Challenge
A PE-backed consultancy with 2,000 users worldwide was rolling out a unified CRM to replace fragmented systems that were killing cross-sell and upsell visibility. Adoption was the make-or-break factor for growth.
Approach
I trained an AI agent on the project’s objectives, change management materials, CRM training content, and the commercial rationale for consolidation. Staff got a conversational guide that explained not just how to use the CRM, but why it mattered to the business.
Impact
Adoption rates climbed because users finally understood the purpose behind the change. Support ticket volumes dropped as the agent handled the questions training sessions couldn’t.
Pricing Intelligence — Membership Organisation
Challenge
A membership body running conferences needed to know exactly when to reduce pricing to maximise attendance without leaving revenue on the table. Pricing decisions were made on gut feel and internal politics.
Approach
Using historical attendance and pricing data, I built an AI model that identified the inflection points: when prices should change, by how much, and what the expected effect would be — grounded in standard deviation analysis.
Impact
The commercial team moved from instinct to evidence. Pricing decisions became faster, defensible, and tied to data the board could trust.
Tech Debt Reduction — Engineering Team
Challenge
A tech team was drowning in legacy code and undocumented config from previous projects. Every change was slow, risky, and heavily dependent on tribal knowledge from a shrinking pool of original developers.
Approach
I introduced AI coding tools that helped engineers understand unfamiliar codebases, navigate complex configurations, and pick up new technologies faster — turning months of onboarding into days.
Impact
Tech debt was reduced faster than the team thought possible. New engineers became productive in weeks instead of months, and the team’s confidence in tackling legacy systems transformed.
Legacy CMS Migration — Enterprise
Challenge
A tech team tasked with migrating a legacy CMS to a new version was dealing with uncertainty, unfamiliar patterns, and a system no one fully understood any more.
Approach
AI coding tools were embedded into the team’s workflow — providing real-time context on the old system, explaining patterns, suggesting migration approaches, and validating changes against the existing configuration.
Impact
Productivity and confidence increased across the team. Developers who’d been cautious about touching legacy code started shipping migration work at pace.
Change Management — Corporate Strategy
Challenge
A large organisation was rolling out a new corporate strategy, but employees were reluctant to ask questions about how it would affect their department and role — for fear of looking uninformed or resistant.
Approach
I created an AI agent trained on the corporate strategy, departmental impact analysis, and role-level implications. A chatbot interface let people ask honest, private questions without judgment.
Impact
Engagement with the strategy jumped. People who’d stayed silent in town halls started asking the questions that mattered — and leadership got visibility into the real concerns across the business.
Finance Automation — AI Vision
Challenge
A finance team was spending hours manually extracting data from multiple form formats to complete compliance and reporting paperwork. The process was slow, error-prone, and soul-destroying.
Approach
I deployed AI Vision to scrape financial information from a variety of form layouts automatically — extracting, validating, and pre-populating the data that previously required manual entry.
Impact
Form completion time was drastically reduced. The team reclaimed hours per week and error rates dropped, freeing finance staff to focus on analysis rather than data entry.
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Seen enough to know the pattern? Let us map your next practical move.
Patterns Across Engagements
The common thread: AI applied to the problem that actually matters, not the one that sounds impressive.
Revenue intelligence
Data-backed pricing thresholds replacing gut-feel decisions
Engineering velocity
Legacy codebases understood in days instead of months
Operational efficiency
Hours of manual finance processing eliminated with AI Vision
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