Key takeaways
The discussions on the evening were full of value, so we thought we’d bottle the best bits and share the key highlights. There was commonality of three main pain points, being:
- Increasing AI maturity: organisations are using AI, but are wanting to drive more value
- Sustainability: ambitions are strong, but implementation remains difficult
- AI governance: existing programmes need to evolve to meet a new data landscape
Sustainability in practice
Attendees were eager to dive into the pressing challenges they faced in governance, architecture, and platform-related issues, and sustainability was the thread that tied many conversations together. Everyone in the room shared the same goal of using data to support environmental responsibility, but most agreed that the practical side is far more difficult than expected.
As new regulations such as the EU’s Digital Product Passport come into view, the question is not just how to collect the right data, but how to trace and report it with the level of detail these rules demand. For manufacturers in particular, it is a challenging requirement. When a single product contains several materials and components, with complex supply chains, tracking every piece to that degree of accuracy becomes a monumental task.
Several participants shared early attempts to tackle the issue, yet even the most developed strategies were still described as works in progress. The desire to act is there, but the tools and frameworks to make it achievable are still catching up. The opportunities for AI assistance are obvious, but achieving results is complex.
Increasing AI maturity
Most organisations are already using AI in some form, mainly GPTs or off-the-shelf agents, but few have progressed to true customisation or full-spectrum AI.
As Malcolm Hawker noted, only a small number of companies are experimenting with tailoring AI behaviour or building proprietary models. While enthusiasm for AI is universal, genuine advancement still belongs to the few who are pushing beyond plug-and-play solutions
The discussion also revealed a governance gap. Most participants admitted that there are very few controls in place, and that AI usage across their organisations often operates on an “all or nothing” basis.
For now, many are accepting this lack of oversight in exchange for short-term productivity gains. It is a compromise that highlights how far there is to go before AI is both effective and responsibly managed.
