Projects & Case Studies

Real leadership challenges, not technical ones. How we help organisations navigate AI strategy, delivery, and governance.

Financial ServicesStrategy & Governance

Global Bank: AI Strategy & Governance Framework

Context

A major financial institution needed to establish AI strategy and governance before scaling AI initiatives.

Human Problem

The bank had multiple AI pilots running without clear strategy, governance, or risk frameworks. Board members lacked confidence in AI decision-making.

Decision Tension

Pressure to move fast on AI opportunities conflicted with regulatory requirements and risk management standards.

Our Role

We conducted a comprehensive AI strategy review, designed a governance framework aligned with regulatory expectations, and established a board-level AI committee.

Outcome

Clear AI strategy with prioritized use-cases, robust governance structure, and board confidence in AI oversight. Successfully scaled 3 high-value pilots to production.

HealthcareDelivery & Governance

Healthcare Provider: Clinical Decision Support System

Context

A large healthcare network wanted to implement AI-assisted clinical decision-making while ensuring patient safety and regulatory compliance.

Human Problem

Clinicians needed better decision support, but the organisation lacked frameworks for AI explainability, bias detection, and clinical validation.

Decision Tension

Improving patient outcomes through AI assistance required balancing innovation with strict safety and regulatory requirements.

Our Role

We designed an explainability framework, established clinical validation processes, and created governance structures for AI-assisted decisions.

Outcome

Deployed AI decision support system with full explainability, reduced diagnostic errors by 15%, and maintained regulatory compliance. Clinicians report high trust in the system.

ManufacturingDelivery & Scale

Manufacturing Company: Predictive Maintenance at Scale

Context

A global manufacturer wanted to scale predictive maintenance AI across multiple facilities.

Human Problem

Initial pilots showed promise but failed to scale due to lack of change management, unclear ROI, and technical integration challenges.

Decision Tension

Proven technology value conflicted with operational resistance and unclear business case for scale.

Our Role

We redesigned the change management approach, established clear ROI measurement, and created a scalable implementation framework.

Outcome

Successfully scaled predictive maintenance to 12 facilities, reducing unplanned downtime by 25% and establishing a repeatable model for future AI initiatives.

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