Case Studies

The bank had three separate initiatives exploring AI. No one could explain how they connected. The board was confused. Risk officers were skeptical. The CTO wanted to move fast. The bank was spending money without direction, and leadership couldn't agree on whether AI was worth the investment or just another vendor pitch.

We spent two weeks interviewing everyone from the boardroom to the operations floor. We found that the real problem wasn't technology. It was clarity. No one agreed on what success looked like. We mapped the three initiatives. We looked at what was working and what was wasting money. Then we built a strategy framework first. Only then did we look at tools.

Within 90 days, the bank had a documented AI adoption roadmap with three prioritised pilots. The board approved a $500,000 commitment with clear success metrics. Six months into execution, the first pilot delivered a 12% efficiency gain in manual compliance review. The team now owns the system instead of being dependent on a vendor. The second pilot is in progress, and the third is queued.

A government agency wanted to modernise operations using AI. But they had no internal expertise. Their teams were skeptical of technology. Procurement rules made vendor relationships complex. They needed to move fast, but the traditional approach would take 18 months and $2 million.

We worked with the agency's operations and IT teams to identify one high-impact workflow: document classification in their regulatory review process. The existing process took 40 hours per week and was bottlenecking approvals. We designed a lightweight AI solution, trained the team, and ran a 90-day pilot in parallel with their existing process.

The pilot reduced processing time from 40 hours to 12 hours per week while improving accuracy. The agency's own team built and maintains the system. Total investment was $35,000. The system is now in production and delivering daily value. The team is confident enough to tackle the next workflow. Training and capability transfer meant they own it—not a vendor.

A university wanted to modernise student administration and academic delivery using AI. But the rector and senior leadership team had no framework for evaluating AI investments. Faculty was skeptical. The IT director was ready to move fast. Leadership was divided and couldn't make a clear decision.

We ran a three-session leadership programme tailored to academic priorities. We taught the team what AI can and cannot do in education. We gave them frameworks to evaluate vendors and proposals. We showed them how universities in similar markets were using AI. We walked them through governance and risk management specific to academic institutions.

After three sessions, the leadership team spoke a common language about AI. They approved a $200,000 pilot focused on student administration efficiency. Faculty concerns were addressed because leadership understood them. The IT director had clear constraints. The project moved forward with alignment instead of disagreement. The university now has a documented AI governance framework and clear decision criteria for future investments.