case study
Building the Foundations for AI at Scale: A Governance-First Approach to Sustainable Innovation
Sven Van Hoorebeeck
In this client case, discover how BrightWolves helped a leading services company accelerate AI adoption, improving both daily operations and strategic initiatives. Our mission? To break down AI’s complexities, guide management in identifying and prioritising high-impact use cases, equip employees with the right tools and training, and establish agile, compliant processes for the safe and scalable development of AI-powered solutions.
Challenge
Our client’s teams were experimenting with multiple AI use cases, aiming to create value for their users. However, these efforts were scattered, without collaboration, making best practices sharing difficult. Without a common approach or strategy, teams worked in silos, each following their own: methods, infrastructure requirements, AI-models selection, security measures. As a result, top management had little visibility on ongoing initiatives, and there was no impact measurement.
Approach
Our consultants supported the teams at four core stages:
1. Establishing Governance
We started by mapping all ongoing AI use cases. This helped us propose a common business and technical approach, ensuring teams worked in alignment. From there, we identified key requirements for AI-driven projects, such as team composition and collaboration with other departments. To put this into action, we created a dedicated AI team and facilitated cross-functional collaboration with other teams. We also strengthened AI adoption by increasing top management involvement and introducing a common approach to business and functional analysis. This includes the security, legal and ethics considerations that are critical to successful AI projects.
2. Technical Coaching
Once the AI team was in place, our experts coached technical teams on MLOps and LLMOps best practices. We worked closely with them to integrate these practices into their workflows. At the same time, we helped finalise ongoing PoCs, ensuring they were ready for the go-live.
3. Measuring PoC ROI
With a well-completed business and functional analysis, teams could now track the impact of their AI-driven PoCs. We introduced regular reporting - weekly updates for the steering committee and monthly updates for the executive committee - to maintain visibility and ensure informed decision-making.
4. Co-creating a Roadmap
With clear reporting in place, teams were able to make better, data-driven decisions. Through workshops, we helped team leaders define concrete, ambitious, yet realistic AI objectives. The result was a structured yearly roadmap considering AI’s rapidly evolving capabilities.
Impact
Through this project we wanted to deliver impact on four levels:
1. Boosting AI adoption: we raised awareness by establishing go-to persons (AI team) and AI champions across the organisation. We also introduced a clear, transparent process for AI-driven use cases and organised sessions for both technical and non-technical teams.
2. Accelerating AI solution development: by streamlining workflows and improving collaboration, we reduced the development time for AI solution by three months.
3. Tracking business value: we established structured reporting to measure and communicate the impact of AI solutions, ensuring management had visibility on the value they created.
4. Scaling successful use cases: we put in place strong technical foundations to support the scaling of successful AI use cases, setting the company up for long-term growth.