We were delighted to co-host an evening in Copenhagen with Profisee, EDM Association and a room filled with senior data leaders, where the conversation around data management moved quickly from theory to reality.
Attendees from across industries came together to share their challenges and insights, and three themes consistently rose to the surface: governance, AI, and sustainability.
“Although this was badged as an AI event the main pains raised in the room were with quality and governance of data: data to both support traditional business operations and to provide trustworthy input into models.”
Stuart SquiresSVP Strategy and Consulting
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.
The persistent challenge of governance
If one challenge united every participant, it was governance. Whether the issue was unstructured data, sustainability requirements or AI oversight, the same questions kept returning.
How do we adapt traditional governance models to support modern data realities?
How do we balance control with innovation?
Existing frameworks were not built for today’s pace of change, and many attendees admitted they were still searching for practical answers.
What set this event apart was the openness of the discussion. Those in attendance said they valued being able to speak freely and hear from others facing the same problems. It felt less like a presentation and more like a genuine exchange between peers.
4. “We need to do AI”
“Saying ‘we need to do AI’ is following the hype, but is it meaningful or just another passing trend?”
Many organisations are feeling the pressure to tack on AI without asking if they should first. AI should be a means to an end, not the goal itself. The pressure to follow the AI hype is mirroring past tech cycles. The group pointed out the pattern: it was “We need to do Digital” in the 2000s and “We need to do Big Data” in the 2010s. Now, it’s “We need to do AI!”
Rather than jumping in for the sake of it, businesses should define the problem first. Is this something that can be solved with AI or are there better methods or solutions available? Chasing technology without strategy leads to wasted investment, and the focus should be on practical applications.
5. “How do traditional long-established organisations achieve AI success?”
“Established businesses have data goldmines, but are they making the most of them?”
Unlike newer organisations, many traditional businesses weren’t designed with digital technologies, including AI, in mind which makes it harder to integrate. Their biggest challenge in keeping up and modernising is their fragmented data. Customer and operational information are spread across systems, making AI harder to apply. Before thinking about AI, businesses need to clean and unify what they already have.
The senior data officers agreed that there is opportunity in large historical datasets, but data alone isn’t enough. Many organisations already have vast reserves of valuable information, but without the right architecture, AI can’t deliver the expected results. The real opportunity is in unlocking and structuring this data properly, rather than just chasing new models. The focus should be on fixing the foundations first, those who get it right will be in the best position to succeed.
6. “AI on top of AI – is it levelling the playing field?”
“Are we entering a dynamic where AI is now competing with AI?”
A side discussion explored AI-generated CVs being screened by AI hiring tools. Leaders questioned whether this creates an AI loop, where automation is countering automation rather than adding value.
For qualification checks, AI screening can be useful. But when a CV is meant to showcase skills like writing, AI evaluating AI raises questions about whether the process still serves its purpose. If automation is handling both sides, is anything real being assessed?
The conversation pointed to a wider challenge. As AI becomes more embedded, businesses will need to consider where it genuinely improves outcomes and where it risks making systems more detached from the people they serve.
7. “Be in the game, you don’t have to be the best”
“AI doesn’t have to be perfect, progress beats inaction.”
The final key point is where leaders compared AI adoption to GDPR compliance. When GDPR was introduced, organisations didn’t aim for perfection. They focused on minimum compliance and then a bit more to stay competitive. Should AI be approached the same way?
Businesses don’t need to be AI champions but ignoring it completely risks falling behind. A small, well-targeted use case can still push an organisation forward.
Start somewhere. AI doesn’t have to transform everything at once, but incremental improvements make a difference. Having something is better than having nothing.
Even senior data leaders are uncertain and nervous around AI. There needs to be a shift moving past trends and focusing on applications that will make a genuine difference and impact. Success of AI is dependent on the integration, strong governance and clear integration with business goals and values.
Amplifi has a three-pronged approach to Artificial Intelligence to ensure we’re empowering organisations to deploy successful AI solutions that deliver real value.
One of the hosts of the evening, commented:
“There is concern around governance in relation to AI. Employees are interacting with LLMs and it’s hard to manage, hard to provide oversight and people just seem to be willing to accept the risk associated AI usage in exchange for individual productivity gains.”
Malcolm HawkerChief Data Officer
The conversations in Copenhagen made one thing clear: while every organisation is at a different stage of its data journey, the challenges are shared and solvable.
Turning ideas into impact requires not just technology, but the right data foundations, governance, and collaboration.
Turn insights into action
If these challenges sound familiar, our experts are here to help. Whether you’re navigating the realities of data governance, building responsible AI frameworks, or embedding sustainability into your strategy, a short conversation can make a big difference.
At Amplifi, we help organisations turn ambition into measurable progress. Our specialists work with you to understand your goals and create practical, achievable steps that move your data strategy forward.
Speak to one of our team
If you’re looking to tackle the challenge of governance, put sustainability into practice or increase your AI maturity, we’re here to help. Just fill out the form to schedule a consultation with one of our specialists. Let us know what you’d like to explore and share your availability. We’ll discuss your goals, current data landscape, and the practical steps to move your organisation forward.
