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A conversation with Philipp Diesinger, Partner at Rewire, on what European companies often get wrong when hiring senior data talent, AI readiness, and the cost of moving too slowly.
In this conversation, Matthew Miles, Managing Director at MAM Gruppe speaks with Philipp Diesinger, Partner at Rewire, about the realities of hiring and building data and AI capabilities in European enterprises. Drawing on extensive experience advising leading European companies and transformation programmes, Philipp shares his insights on where organisations misread the talent market, why technical brilliance alone is not enough, and what it takes to build a data function that retains exceptional people.
Matthew Miles: When you scope a data & AI transformation, what is the gap between what a client thinks they need in a hire versus what you see as the capability gap?
Philipp Diesinger: Clients typically ask for technical depth: a strong engineer, a seasoned architect, someone who knows the stack. What they actually need is someone who can translate data capability into business value. The real gap is rarely technical. It is the ability to navigate organizational complexity, build trust with the business, and drive adoption. A brilliant data engineer who cannot get a CFO to act on an insight is not solving the actual problem.
What is the most common misalignment you see between a job spec and the real problem a data leader is being hired to solve?
The job spec describes a builder. The real problem requires a change agent. Companies write specs that read like wish lists for someone who can modernize the stack, implement a platform, and upskill the team simultaneously. But what they actually face is a political and cultural challenge: siloed ownership of data, lack of executive sponsorship, and a business that does not yet see data as an asset. The hire needs to navigate that reality first, before building anything.
| “The job spec describes a builder. The real problem requires a change agent.”
Q: How do you define “data ready”, and how often are their internal hiring briefs aligned to that?
Data ready means three things: governed data that people trust, a business that asks data questions before making decisions, and a leadership team that funds and protects the data function. In my experience, fewer than one in four European company meet all three criteria when they start a transformation. Yet their hiring briefs routinely ask for someone to lead advanced analytics or scale AI. One client was hiring a Head of AI while their core CRM data had no single owner and three conflicting definitions of “active customer.” In that sense the brief was not making explicit the reality of things. And so I go back to the observation made above - about the real problem requiring a change agent rather than a builder.
Q: What is the number one reason you reject candidates for senior Data & AI roles?
First: inability to make the abstract concrete for a non-technical audience. I have interviewed candidates who can design elegant architectures but cannot explain in plain language what value their last project delivered to the business. One senior candidate described a complex ML pipeline in detail but could not tell me what decision it improved or by how much. In a leadership role, that is disqualifying. You are not just building systems. You are building confidence in data across the organisation.
Second: lack of analytical or AI skills. This happens very often with junior candidates fresh out of university.
| “You are not just building systems. You are building confidence in data across the organisation.”
Q: If you had to pick two traits that separate a truly indispensable data professional from one who is replaceable by AI or outsourcing, what are they?
First: contextual judgment. AI can generate analysis; it cannot judge which insight matters most in this company, at this moment, given this leadership team. That requires deep situational awareness that only comes from being present, curious, and politically attuned.
Second: trust building. The professionals who survive every wave of automation are the ones that people turn to when they need to make a hard call. That is a human relationship, not a skill set. It is earned over time and it cannot be outsourced.
Q: You work with clients on AI transformation at scale. What does “business acumen” look like in a data leader who has it versus one who does not?
A data leader with genuine business acumen walks into a conversation about a use case and asks: what decision does this change, who owns that decision, and what would it cost us to get it wrong? They think in terms of risk, margin, and accountability.
A data leader without it asks what data is available and what model could be trained. Both are smart people. But the first one gets budget and the second one gets deprioritised. Business acumen in this context means understanding that every data initiative is ultimately a bet on changing behaviour, not just improving infrastructure.
| “Every data initiative is ultimately a bet on changing behaviour, not just improving infrastructure.”
Q: The “outgoing introvert” — someone who is deep in numbers but can also hold a room with a senior stakeholder. How rare is that profile, in your view?
Genuinely rare. Not impossible, but rare. In a pipeline of 100 strong candidates for a senior data role, I would identify perhaps eight to ten who have both the analytical rigour and the presence to be credible in front of a board or an executive committee. What I find more often are candidates who have learned to fake one side. They present well but lack depth, or they have real depth but are exhausting for non-technical people to follow. The authentic combination, where depth and communication reinforce each other rather than compete, is what makes certain candidates exceptional.
Q: What does a data function look like in a company that retains great people versus one that consistently loses them?
Companies that retain great data talent share a few characteristics. Decisions actually get made using data, so people feel their work has impact. The CDO or data lead has real access to the C-suite, not just a dotted line to the CIO. And there is a visible career path that goes beyond becoming a better technician. Data talent is enabled by leadership.
Companies that lose people tend to treat the data team as a service function: reactive, under-resourced, and perpetually blocked by governance or IT. The best people leave not because of salary but because they stop believing the organisation wants what they are capable of delivering. They stay because of purpose.
| “The best people leave not because of salary but because they stop believing the organisation wants what they are capable of delivering.”
Q: How important is visible AI investment to attracting senior data talent right now?
It is a qualifying criterion, not a differentiator. Senior candidates now expect to see meaningful AI investment before they will engage seriously. What they want to see is not a press release about AI strategy. They want evidence of real use cases in production, a clear data foundation, and leadership that understands what AI actually requires. Companies that are still debating their cloud migration while advertising for a Head of AI will find it very hard to attract credible candidates. The market has moved and expectations have risen sharply in the past 18 months.
Q: If you were advising a Chief Data Officer today, what are the main things you would tell them about the talent market?
First, the pool of senior candidates with both technical credibility and executive presence is small and they are mostly not actively looking. You have to go and find them, which means investing in your employer brand and your network, not just posting a job.
Second, competing on salary alone will not work. The candidates you want have options. They are choosing based on mandate, culture, and whether they believe the organisation is serious about data.
Third, do not underestimate the international dimension. Some of the best candidates may not be based in your country and may not want to relocate full time. If you insist on five days on site, you are filtering out a significant portion of the available talent pool before the first conversation has taken place.
Q: What is the most dangerous assumption European companies are making right now when it comes to data & AI hiring?
That they have time. There is a widespread belief in Europe that AI is something to get right before deploying, and that careful, methodical planning will eventually produce a competitive position. Meanwhile, companies in the US, UK, and parts of Asia are iterating at speed, making mistakes in production, and learning faster. The talent that knows how to move at that pace is being absorbed into organisations that operate that way. By the time a European company has finished its internal approval process for a new data role, the candidate they wanted has accepted an offer elsewhere. Caution is a cultural strength in many contexts. In the current AI talent market, it is becoming a liability.
| “Caution is a cultural strength in many contexts. In the current AI talent market, it is becoming a liability.”
About the participants
Dr. Philipp M. Diesinger is a Partner at Rewire DACH, where he advises enterprise clients on data and AI strategy, transformation, and capability building. Before Rewire, Philipp held roles at BCG and has a PhD in natural sciences.
Matthew Miles leads Technology, Data & AI at MAM Gruppe, a specialist recruitment agency placing -senior professional & leadership roles across Germany’s technology, legal, and finance markets since 2009. Matthew has worked exclusively in the German market for over 15 years.
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