Next stop, Munich. In preparation for the latest in our series of roundtable discussions exploring the challenges and opportunities facing senior IT professionals, Tech Monitor and AMD headed to the Bavarian capital in early July.
Seeking to understand how organisations marry the potential of technological opportunity – exemplified by artificial intelligence – with strategies to mitigate the impact of geopolitical, economic and regulatory uncertainties, we sought out the views of senior IT decision makers operating in southern Germany and the surrounding area. From automotive to manufacturing, and from financial services to technology service providers, the senior executives around the table represented large swathes of the economy.
Topics discussed included the obstacles standing in the way of AI implementation, practical use cases in operation today (or planned for tomorrow), how to manage cultural fears of new technology, and the merits (or otherwise) of public cloud as part of the infrastructure supporting AI experimentation and deployment.
Here are the key takeaways from a night of lively and insightful conversation.
From change management to data quality, AI barriers explored
The evening started with senior IT leaders in attendance sharing the obstacles that are complicating successful AI adoption. A reluctance to embrace change management, coupled with cultural inertia, emerged as a key area where improvement is required. This didn’t just apply to a sector such as manufacturing, which has been typically slow to embrace digital transformation. It is prevalent in some service providers and technology firms, too.
The change management conundrum reflects three related issues – organisational complexity, cultural inertia, and a failure of integration. For large, multifaceted organisations – particularly those that have evolved through merger and acquisition – bringing together data and processes from different departments and disciplines remains testing. Similarly, one attendee identified a tendency to create small, ad hoc projects – resulting in “clouds everywhere” – rather than a concerted effort to pool knowledge, effort, and experience.
This piecemeal approach to AI deployment reflected a “lack of strategy”, he said, and that is likely to slow progress in the long run. Equally testing, the group observed, are the challenges of regulation, the threat of vendor lock-in, and the impact of poor data quality. “Garbage in, garbage out” remains a live concern.
AI in action: use cases
Asked to share how they are adopting artificial intelligence today, our executives shared a range of use cases. Some predate generative AI (GenAI) and rely instead on machine learning. In the automotive industry, for example, one ML-led application identifies long-term, systemic faults by aligning those with less serious, short-term problems such as an exhaust fan failure. Capturing issues earlier in the lifecycle acts as a form of predictive maintenance. Another AI application, meanwhile, handles post-sales customer care correspondence through multi-language translation.
Elsewhere, organisations are using GenAI to support software engineering teams. For example, one is training a new generation of engineers in the intricacies of legacy software and hardware, future-proofing old IT that today is only understood by the long-serving technologist who is heading towards retirement.
Finally, most organisations are making good use of co-pilot applications. One, for example, is accessing meeting summaries when his diary is overloaded. Faced with three clashing appointments, he chooses the meeting that is typically most interesting and attends that in person. The other two? He’ll happily rely on the summary.
AI: existential threat or unmissable opportunity?
A number of attendees acknowledged that some colleagues fear AI is a direct threat to existing jobs and detrimental to future career prospects. While AI is undeniably a disruptive force, the consensus around the table was largely positive. The overwhelming view? There is an optimistic case for A,I and that message needs to be made again and again.
As one attendee put it, “How many jobs did the wheel create?” He was alluding to the fact that new technologies may make existing roles redundant, but they tend to create new ones in their place. Another attendee drew a parallel with the music industry and the popularisation of sampling in the 1970s and 1980s. Much like large language models, sampling relies on the appropriation of existing work. While many musicians feared that this experimental technique would threaten the emergence of new music makers, it has arguably had the opposite effect, creating new genres and an extended period of creativity.
Public cloud and its ongoing role
Despite ongoing geopolitical uncertainties, this group of IT leaders remain keen proponents of the big hyperscalers. If organisations based in Germany are disengaging from US public cloud providers, there was little evidence of it among this particular group of organisations. Of the dozen or so represented around the table, only one was actively moving to a local cloud provider. The rest declared themselves happy to continue using the big US players. These providers, the senior IT leaders present insisted, can deliver sovereign cloud through regional data centres. And they can provide other advantages besides, including levels of physical and cyber security which are difficult to provide independently. As for fears that sensitive data could find its way to unauthorised parts of the world, organisations are mitigating this risk by anonymising (or synonymising) customer information before it enters the cloud.
A new kind of talent
Reflecting the GenAI use cases beginning to emerge, different skill sets will become essential. That view was expressed by one attendee who said: “When I hire people, I want to know whether they are able to be precise in articulating a ‘problem statement’.” Those able to apply logic, reasoning, and understanding will thrive, he said. Applying those skills will allow this newly-skilled workforce to instruct AI to query existing ERP systems, for example, in order to extract valuable information otherwise likely to be locked away.
In a similar vein, others have noted that the expert ‘prompt engineer’ will thrive in this new era by possessing the same critical reasoning skills. A generalised fear that GenAI might lead to deskilling was thus dismissed by the group of voices assembled. Borrowing a saying often attributed to an American software engineer, one observed: “A fool with a tool is still a fool.”
‘AI, Cloud, and the Data Centre: the Future of IT in an Age of Uncertainty’ – a Tech Monitor and AMD executive roundtable discussion – took place on Wednesday, 2 July 2025 at the Charles Hotel, Munich.
