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. Read our guide 6 expert tips for driving business value with AI below, or get in touch with one of our experts.
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