By Dr. Israel Mariaca, Innovation Manager at SRC
Innovation is not just about chasing the newest tool. It is about seeing our everyday work more clearly, where decisions slow down, where information is missing, and where small frictions add up to cost and delay and then improving those moments with data and intelligent automation. That is the spirit behind our recent series of AI Engineering Innovation Workshops with SRC’s engineering team and project leads. Our aim was not to “do AI”; it was to strengthen our engineering delivery processes, enabling teams to make data-driven decisions that increase confidence and reduce the need for rework.
How We Structure Innovation
Our workshops follow a deliberate structure of exploration and focus. We begin by opening up the problem space, then narrow to what truly matters, before widening again to explore solutions and finally converging on the most valuable ideas. Working in a collaborative, face-to-face setting creates space for open debate and brings clarity to complex challenges. This process ensures that every idea is grounded in a real delivery challenge, not technology for its own sake.
Building a Common Understanding
Before exploring new ideas, we refreshed our understanding of AI in plain language. We looked at how it already appears in everyday tools, from smarter search and document summaries to image recognition and coding assistants. The discussion focused on what these techniques are good at, where they struggle, and how to use them responsibly. This created a shared vocabulary and reduced the sense of hype or uncertainty that can surround AI, giving everyone a clear and practical starting point.
Discovering What Really Gets in the Way
We brought together people from various teams across the company to identify where work slows down or clarity is lost. By working openly and visually, people could explain the context behind their experiences and challenge assumptions. Through this process, we uncovered not one big barrier but a pattern of small frictions that, taken together, have the potential to impact on time and quality. By clustering and prioritising these issues, we created a clear baseline of shared problem statements. This baseline now anchors our innovation work and keeps future pilots focused on what will genuinely improve delivery.
Turning Ideas into Action
With priorities clear, we moved into solution mode. Every idea was welcome and no early stage suggestion was dismissed. Once ideas were shared, we tested them against consistent criteria, including delivery value, feasibility, data readiness, governance needs and measurability. This helped separate what was interesting from what was actionable. The result was a short list of opportunities with named owners, clear hypotheses and defined measures of success.
Testing and Refining the Plan
To make sure the plan was robust, we ran a short simulation inspired by lean construction principles. Participants rotated through different perspectives such as data, benefits, risk and user impact. This ensured every voice in the room was heard and helped identify dependencies or challenges early. The exercise produced a practical AI roadmap, focused on achievable pilots, sensible guardrails and measurable outcomes.
From Tools to Transformation
This programme was never about technology alone. It is about disciplined, thoughtful change. By starting with real problems, working collaboratively and treating AI as a companion rather than a replacement for engineering judgment, we have built a clear, grounded roadmap for improvement. As pilots progress, we will share the results, including time saved, rework avoided and stronger assurance across projects.
That is how insight becomes impact.
