GEFIE 3 AI Hackathon - Glenveagh

Client: Glenveagh 

Event: GEFIE 3 - AI Hackathon 

Industry: Construction 

Date: 26 February 2026 

Consultant: Niels Footman, Samiul Hoque

Driving applied AI innovation through collaborative experimentation in construction

Glenveagh partnered with Dixon AI to deliver a GEFIE 3 AI Hackathon, bringing together participants to move from early experimentation into structured, team-based AI development. The session focused on turning existing ideas into working prototypes, building on prior literacy and application stages within the AI Transformation Playbook. By the end of the day, teams had developed and presented practical AI solutions grounded in their own workflows, creating a clear bridge between learning and applied value.

The event took place at a point where Glenveagh had already begun building organisational AI literacy and initial application capability. Within the AI Transformation Playbook, this corresponds to Stage 5: Collaborative AI Experimentation. At this stage, the focus shifts from individual exploration to coordinated innovation, where teams work together to test, refine and scale ideas that have emerged through earlier phases of learning and application.

Objectives of the Event

The Hackathon was designed to:

• Convert early-stage AI ideas into structured, testable solutions

• Enable cross-functional collaboration on shared challenges

• Build confidence in applying AI within real operational contexts

• Establish a pipeline of projects for further development

• Identify individuals and teams capable of leading ongoing AI initiatives
  

What Happened During the Event

Participants arrived with initial concepts developed during earlier stages of the programme. These ideas were refined into clear project briefs, outlining purpose, intended execution and how success would be assessed.

Working in small teams, participants focused on building and testing solutions rather than discussing theory. The emphasis was on iteration. Teams developed prototypes, tested outputs and adjusted their approach throughout the day, supported by Dixon AI facilitators. Projects reflected practical use cases relevant to the construction environment, with teams focusing on improving workflows, enhancing access to information or supporting decision-making processes. The session concluded with presentations, where each team demonstrated what they had built and explained its relevance to their day-to-day work.

Several patterns emerged from the Hackathon. First, once individuals have a working understanding of AI tools, value creation depends less on the technology itself and more on how clearly problems are defined. Teams that anchored their work in specific operational challenges progressed more quickly and produced more usable outputs.

Second, collaboration across functions proved critical. Bringing together different perspectives exposed opportunities that would not have surfaced within single teams. This was particularly relevant in a construction context, where workflows often span multiple roles and stages.

Third, iteration remains central to effective use of AI. Teams that tested, adjusted and refined their work throughout the session developed stronger solutions than those attempting to define a perfect approach upfront. This reinforces the role of experimentation as a practical discipline rather than a one-off activity.

Impact

The Hackathon created a shift from isolated experimentation to coordinated innovation. Participants left with working prototypes or clearly defined solutions, along with a better understanding of how to apply AI within their roles.

It also surfaced a group of individuals who demonstrated the ability to move from idea to execution within a short timeframe. This provides Glenveagh with a clear view of where practical AI capability is emerging inside the organisation.

The portfolio of projects generated during the session offers a tangible starting point for further development, reducing reliance on abstract planning and grounding next steps in tested ideas.

What Happens Next

Following the Hackathon, the next step aligns with Stage 6 of the AI Transformation Playbook: Strategic Integration. This involves reviewing the projects created, identifying those with the strongest potential and defining how they can be developed, governed and embedded into existing workflows.

This phase also includes establishing clearer structures around ownership, prioritisation and ongoing development, ensuring that the momentum created during the Hackathon translates into sustained progress.

The Hackathon demonstrates how quickly organisations can move from learning about AI to building with it when the right structure is in place. In sectors such as construction, where operational complexity is high, this ability to test and refine ideas collaboratively becomes a practical advantage rather than a theoretical one.

Organisations that create space for this kind of structured experimentation are better positioned to translate emerging tools into meaningful improvements in how work gets done.

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