AI that changed how the work gets done.

The challenge

Strategy was cascading as a mix of measurable outcomes and vague intentions, and goals at one level didn't feed the next. The result was "watermelon KPIs": green on the dashboard, red underneath.

Our approach

1. 1

   Aligned leadership on what a strategic KPI is and how goals should cascade through the organisation.

2. 2

   Equipped managers to use AI to turn their own real goals into measurable, connected KPIs.

3. 3

   Embedded a simple system for validating and tracking KPIs, so the method outlived the programme.

The impact

• One common KPI language, from CXOs to line managers, across three regions
• Strategy linked to team-level measures that every function can own
• A repeatable way to set, validate and track KPIs, now part of how managers work

The challenge

Copilot was available, but habits hadn't changed. The organisation needed AI to become the first step in everyday work, used safely, across very different markets and roles.

Our approach

1. 1

   Two journeys designed separately for people leaders and individual contributors.

2. 2

   Two guiding principles anchored every session before any tool was introduced.

3. 3

   Mindset into daily practice with Copilot, plus one-on-one coaching so executives led the change.

The impact

• AI adoption reframed as a business-value decision, not a software rollout
• A shared AI-first language and framework across five-plus markets
• Leaders equipped to build AI-ready teams, with executives visibly leading the change

The challenge

Classroom process training was slow to scale and the same for everyone. An AI Trainer could change that, but only if it learned from the right content and could be trusted to teach.

Our approach

1. 1

   Rebuilt classroom content into bite-sized, AI-ready modules, structured so the AI finds the right piece for each learner.

2. 2

   Personalised learning through self-assessment and hands-on simulations.

3. 3

   Built trust in with validation at every step and human maker-checker certification before go-live.

The impact

• A faster, more scalable path from training to the operations floor
• Learning that adapts to each new hire instead of one pace for all
• A governance model the client can rely on for AI-led training

The challenge

Knowledge sat across teams and folders. SOPs lacked version control, documents fell behind whenever products or processes changed, and building training from them was slow and one-off.

Our approach

1. 1

   Vetted before trusted. Every source document was checked for currency, ownership and conflicts, then centralised in one AI-native repository.

2. 2

   Knowledge into learning, by design. The repository feeds learning built on instructional-design principles, matched to the topic, the learner's level and how it's delivered, with a mastery check before anything is marked complete.

3. 3

   Governance built in. Design and safety checks plus a named human reviewer before anything publishes, running on the AI models the client already approves.

The impact

• One trusted, current source for SOPs and process knowledge
• Learning built directly from approved knowledge, reused across programmes and updated everywhere when a source changes
• An AI layer that fits the client's existing technology and governance, with no rebuild