By Luc Simpson-Kent, Managing Consultant – Harnham US

Candidates want growth. Employers want retention.

And in today’s market, both goals rely on the same thing: continuous learning.

The pace of change in data and AI means even the most experienced professionals are going back to school, literally and figuratively.

Across the US, data scientists, engineers and analysts are reskilling faster than ever, not just to keep up with new technologies, but to stay relevant in an industry where roles evolve as quickly as the models they build.

The rise of the always-learning data professional

In Harnham’s latest US Data & AI Salary Guide, a lack of career progression ranked among the top three reasons professionals consider leaving their roles. It’s a clear signal: growth is non-negotiable.

At the same time, 67 % of respondents said they view AI positively, and nearly two-thirds already use it in their daily work. That shift isn’t just changing what people do, it’s redefining how they learn.

Many professionals are now stacking certifications across cloud architecture, data engineering, and AI/ML tools to strengthen career mobility.

Common routes include:

• Databricks, AWS and Azure certifications for cloud data infrastructure. 
• Coursera and LinkedIn Learning bootcamps for applied machine learning and Python. 
• Specialist courses in GenAI, data governance and AI ethics 

Download the Data & AI Salary Guide here.

Learning isn’t just about tools, it’s about trust

AI has pushed many companies to rethink how they train and manage their people.

Accenture research found that nearly half of all working hours in the US are now in scope for automation or AI support. In that context, professionals who can demonstrate adaptability, not just technical ability, are becoming indispensable.

It’s why communication, stakeholder management, and data storytelling are increasingly appearing alongside hard skills in job specs. Upskilling now means learning to translate insights into decisions, not just code into outputs.

AI is reshaping learning itself

What’s interesting is how AI isn’t just the subject of learning; it’s also the teacher.
Modern platforms use AI to map existing skills, spot adjacent strengths, and recommend courses that fill real gaps.

That’s turning training into something personalised and proactive, not reactive.
Instead of waiting for a promotion or performance review, data professionals can see exactly where their next opportunity lies and move toward it before the business even asks.

“Skills-based organizations are 107% more likely to place talent effectively, 52% more likely to innovate, and 57% more likely to anticipate change and respond effectively and efficiently.” (Source: Deloitte)

For employers, this precision matters. Skills-based learning data helps them redeploy talent, plan for automation, and retain people who want to grow without leaving.

It’s no coincidence that companies investing in development see stronger loyalty.

How employers can build a learning-led culture

When learning is relevant, practical, and supported, it becomes part of how teams operate

Start with business goals.
Connect learning to real projects and outcomes so employees see how new skills drive value day to day.

Adopt a skills-first mindset.
Focus on what people can do, not just their job title. Map learning around tasks, capabilities, and future needs.

Make learning hands-on.
Combine expert-led sessions with real projects so people learn by doing.

Personalise the path.
Use AI tools and internal data to tailor learning to each person’s strengths, gaps, and career goals.

Reward and recognise growth.
Celebrate progress through certifications, applied learning, and visible impact on team performance.

Support continuous learning.
Offer post-training resources and peer sessions to keep knowledge fresh and encourage collaboration.

Moving Forward

The best data professionals are already building the skills the market hasn’t even asked for yet.
And the employers that retain them are making sure those skills are built inside their own teams.

Connect with Luc Simpson-Kent for insights on how US businesses can attract, develop, and retain world-class data and AI talent.

Contact Harnham to explore talent development and workforce strategy solutions.

The post Reskilling & Upskilling: What US Data Professionals Are Doing appeared first on Harnham.

Every company wants to build world-class AI capabilities. The challenge is that technical brilliance alone is not enough. The strongest AI teams are those that combine elite skills with diverse perspectives. Without diversity, the risk of bias multiplies and the value created by AI is capped.

Why Diversity Matters in AI

AI systems are only as strong as the data and the people behind them. Large Language Models (LLMs) are trained on datasets that reflect human bias, and those biases are amplified in production. When you add Agentic AI, multi-agent systems that make autonomous decisions, the consequences of bias become even more significant.

Without diversity in the teams designing, testing, and governing these systems, blind spots emerge. And blind spots in AI are not just ethical issues. They are commercial risks. A single oversight can mean reputational damage, compliance failures, or missed opportunities in key markets.

Moving Beyond Culture Fit

Traditional hiring models often over-emphasise culture fit by looking for people who mirror existing teams and values. In AI, this approach is not just outdated, it is a hindrance. Teams built on sameness will inevitably replicate the same biases in their models.

It is the responsibility of Data and AI leaders to create a culture that does not reward conformity but instead maximises diversity of thought, background, and approach. The best AI cultures are ones where differences are not only accepted but actively harnessed to challenge assumptions, stress-test models, and surface new ideas.

Why Automated Screening is Not Enough

Many organisations use HR tech or AI-driven screening tools to streamline hiring. While these tools have a role to play, they often work by pattern-matching against historical data or keywords. That means they are optimised for similarity rather than difference.

In the context of AI teams, this is risky. If your hiring pipeline filters for candidates who look like those already in the organisation, you reinforce sameness and overlook the very diversity that makes AI models stronger.

It is important to remember that LLMs and AI systems themselves are shaped by the diversity of their training data. The same principle applies to the teams building them. Screening out diverse backgrounds and perspectives may feel efficient in the short term, but in the long term it limits innovation and amplifies bias.

This is why leaders in Data and AI must ensure their hiring process goes beyond automated filtering. Diversity is not a hurdle to overcome. It is the foundation for building AI that is both commercially valuable and socially responsible.

What Diverse AI Teams Bring

Diversity in AI teams is not limited to demographics. It spans experience, discipline, and perspective. Strong AI teams bring together:
Researchers advancing the frontier of LLMs and generative AI
Engineers building and scaling infrastructure
Governance and compliance specialists ensuring responsible innovation
Voices from diverse cultural and professional backgrounds who challenge assumptions and spot hidden risks

This mix produces systems that are not only more accurate and reliable, but also more innovative and commercially valuable.

The Bottom Line

Diversity is not a tick-box exercise. It is the single most important factor in building AI teams that are resilient, innovative, and commercially impactful. Homogenous teams replicate bias and limit potential. Diverse teams spot blind spots, challenge assumptions, and create models that perform better in the real world.

In AI, the strongest competitive advantage is not just better technology. It is better teams, built with diversity at their core.

Need Support Building Your AI Team?

Download our AI Hiring Guide — with practical advice on where to start, which roles to prioritise, and how to structure contract and permanent hiring around real business goals.

Or book a consultation with one of our hiring specialists.

The post The Key to a Strong AI Team: Diversity appeared first on Harnham.

While nearly every business is investing in AI, McKinsey reports only 1% believe they’ve reached maturity. This suggests that leadership hesitation, not talent availability, is now the most common barrier to scaling AI.

To bridge the gap between ambition and action, companies are increasingly turning to AI contractors. These specialists allow businesses to scale fast, launch projects, and stay lean without compromising delivery or governance.

Here’s what recent reporting, investor sentiment, and Harnham’s hiring data tell us about why contractor models are accelerating in 2025.

1. The Rise of Specialist Contractor Models

Bloomberg reports that US companies are keeping their internal AI teams small and supplementing with contractors to drive execution. Rather than building full departments, they’re embedding niche experts, such as LLM engineers or RAG developers, to support high-impact delivery.

This model gives businesses flexibility to move fast, plug skill gaps, and reduce permanent headcount risk.

2. Companies Are Under Pressure to Deliver AI ROI

73% of investors expect companies to scale AI efforts this year, according to the PwC’s 2024 Global Investor Survey. That pressure is driving demand for immediate capability. Contractors offer a way to meet expectations now, without waiting for long internal hiring cycles.

The companies that are moving fastest aren’t necessarily the ones with the biggest budgets. They’re the ones with clear goals and flexible delivery models.

3. Contractors Fill the Gap Across Sectors

Over the last 24 months, Harnham has supported 120 companies across sectors like fintech, healthtech, private equity, and energy with AI contractor hiring. Roles span:

• Prompt engineers
• GenAI product owners
• ML engineers specialising in LLMs
• MLOps contractors
• AI governance analysts

These roles are critical to scaling pilot projects, delivering secure production systems, and building credibility with boards and investors.

Why Contractors Win in 2025

• Speed to deploy: Onboard in 12–15 days on average
• Access to niche skills: Fill gaps fast without over-hiring
• Flexible budgets: Pay only when work is happening
• Reduced risk: Scale teams up or down based on delivery needs

Looking Ahead

In the second half of 2025, investors are watching for measurable outcomes, and companies are under pressure to show that AI budgets are translating into business value.

We expect to see:

• More businesses formalising their AI governance policies
• Higher demand for short-term contractors with GenAI, LLM, and MLOps expertise
• Tighter timelines from project brief to delivery, particularly in financial services, healthtech, and retail

For hiring managers: define where you need flexibility, and build partnerships that can support quick mobilisation.

For AI specialists: demand remains high but the most in-demand contractors will be those who can work independently, communicate clearly, and deliver impact from day one.

Need Support Building Your AI Team?

Download our AI Hiring Guide — with practical advice on where to start, which roles to prioritise, and how to structure contract and permanent hiring around real business goals.

Or book a consultation with one of our US-based AI contract hiring specialists.

The post Why US Companies Are Scaling AI with Contractors in 2025 appeared first on Harnham.

Sacher.AI, a UK-based innovation lab focused on behavioural health, needed to scale its GenAI capability to meet growing client demand.

With deadlines approaching and projects in motion, they needed contractors who could deliver from day one, combining deep technical expertise with the confidence to work directly with clients.

They partnered with us to deliver high-trust, high-performance talent who could operate independently and align with the company’s purpose-led culture.

The Brief

• Objective: Place three UK-based GenAI contractors
• Timeline: Immediate delivery, without compromising on quality
• Context: Roles were essential to winning and delivering new client projects
• Requirement: Hands-on experience in GenAI, RAG, LLMs, and the ability to work directly with stakeholders in client-facing environments

The Result

We submitted seven carefully matched CVs. All seven were interviewed. All three placements were made within an average of 12 working days.

The roles:

• Senior Machine Learning Engineer – LLM
  Delivered RAG development, prompting, and fine-tuning for client-facing projects
• GenAI Engineer
  Built and scaled Sacher’s GenAI infrastructure across multiple engagements
• AI Product Owner
  Led agile delivery, internal coordination, and stakeholder management 

What Changed

Each contractor contributed immediately; strengthening internal delivery, supporting client pitches, and improving how the team engaged with external partners. Their work has helped Sacher scale delivery across sectors, speed up product development, and increase client confidence at a critical stage of growth.

Sacher’s growth isn’t slowing and neither is ours. Post-placement, we’ve remained actively involved in team engagement and retention through onboarding lunches, quarterly reviews, and forward planning.

3 Lessons for GenAI Hiring Leaders

1. Define the non-negotiables
   Sacher knew they needed more than technical skill. Culture fit, autonomy, and client engagement were just as important. That clarity meant we could focus on what mattered.
2. Prioritise trust and delivery equally
   The roles required production-grade GenAI experience and the ability to represent the business externally. Each candidate had to bring both.
3. Move quickly, but selectively
   Seven CVs. Seven interviews. No wasted time. The shortlist was tailored for fit: technical, commercial, and cultural.

The Bigger Picture

According to Harnham’s internal research, 61% of professionals are already using AI at work, but only 37% of companies have a formal AI policy in place.

This gap creates risk, not just in compliance, but in delivery, hiring, and retention.  

Whether you’re scaling, launching a new capability, or supporting client demand, finding the right contractors can shape the next phase of your growth.

Hiring for GenAI? Get Practical Support

Download our AI Hiring Guide — a clear, actionable resource on how to structure and scale your AI hiring plans.

Or book a consultation with a contract hiring specialist who understands the UK GenAI market.

The post How Sacher.AI Strengthened Its GenAI Delivery in Just 12 Days appeared first on Harnham.

No team. No roadmap. Just a mandate to lead the charge into intelligent automation, better risk modelling, and scalable innovation.

They came to Harnham with a clear brief: find senior AI leaders who could build the plan and deliver it.

Here’s what happened and what your business can take from it.

The Brief

• Objective: Make two senior AI hires from scratch 
• Timeline: Urgent. 
• Context: No existing AI infrastructure or internal team 
• Requirement: Deep AI/ML expertise and the credibility to engage at C-suite level 

The Result

Within just 18 working days, we took the brief, identified top talent, and made two high-impact placements, delivering exactly what the client needed without wasting time or overwhelming their internal team.

The roles:

• Director of AI Strategy – Defined the AI roadmap, budgeted initiatives, aligned internal stakeholders, and evaluated vendors. 
• Director of AI Engineering – Built the architecture and models to bring that roadmap to life, from pipelines to deployment.

These were $400k+ hires based in New York, shaping the client’s internal capabilities and the digital transformation of their portfolio companies.

What Changed

These hires laid the groundwork for real change. With leadership in place, the client could start applying AI where it mattered: automating manual processes, improving how risk is modelled, and identifying new ways to support their portfolio companies.

Midway through the process, the brief evolved. One role became two. We adapted quickly, and the result is a structure where strategy and delivery are clearly defined; with one leader now managing the other.

It’s a setup designed not just to scale, but to last.

3 Lessons for AI Hiring Leaders

1. Start with leadership
   Engineers alone can’t set vision, structure governance, or align the board.
2. Match your sector
   This client operates in private equity, which means they couldn’t rely on generic AI talent. They needed leaders who understood financial services, regulatory requirements, and how to drive long-term value across a portfolio.
3. Hire for impact
   We didn’t flood them with profiles. We delivered 14 targeted CVs and filled both roles in under 4 weeks.

The Bigger Picture

You’re not alone if you haven’t got it all figured out.

According to Harnham’s internal research, 61% of professionals already use AI in their roles but just 37% of companies have a policy in place. (Source: Harnham AI Report – April 2025)

That’s a big risk especially in regulated industries like finance. The good news? The right hire can change that.

Want the full story?

Download the case study and discover how Harnham delivered senior AI leadership in under three weeks and what your business can learn from it.

Hiring for AI? Let’s Make It Simple

Download our AI Hiring Guide – a practical resource to help you identify which roles to hire and when.

Or book a consultation with an AI recruitment expert who understands your industry.

The post From Zero to AI: How One Investment Firm Built a World-Class Team in 18 Days appeared first on Harnham.

By Roshni Baillie, Recruitment Consultant – Marketing & Insight, Harnham

One thing I’m hearing again and again lately is the question: “Is AI going to replace analysts?”

At Harnham, I have a front row seat to the AI revolution, and to how companies and candidates are responding. While AI is having a clear impact on everyday life, from ChatGPT recipe recommendations to iPhone message summaries, some companies are still behind the curve when it comes to applying these advancements to their data. Once a buzzword, AI has become the engine driving how businesses think, plan, and grow. From predictive forecasting to real-time decision-making, AI is transforming analytics from dashboards and SQL queries into something far more powerful.

Working day-to-day with Marketing & Insight professionals, I’m seeing an ever-growing trend: quality candidates favouring innovation over benefits. The days of “just reporting numbers” are behind us – top talent want the opportunity to experiment, innovate, and drive change. Time and time again, I hear frustration from those stuck with outdated tools and mindsets, knowing they could deliver far more if given the space and support to do it.

From retrospective reporting to predictive and prescriptive insights

While traditional analytics focused on what happened, utilising AI allows teams to ask what should happen next and what should be done about it. Businesses can now forecast customer churn, demand fluctuations, and supply chain risks with unprecedented accuracy.

More importantly, prescriptive analytics can recommend specific actions and data-led solutions rather than just surfacing problems. I’ve found that building a team of analysts with a strong understanding of algorithms, not just dashboards, is game-changing. Talent with a handle on predictive modelling, time series forecasting, and tools such as Python, R, and AutoML platforms are in high demand – and they’re raring to go.

From static dashboards to real-time decision-making

In today’s landscape, BAU and end-of-week reports aren’t enough. Businesses want insights delivered as they happen, acting as the data lands to keep one step ahead.

In Marketing & Insight, that might look like campaign performance being adjusted mid-flight, or personalisation engines updating in real-time based on customer behaviour. AI is making this possible, whether it’s fraud detection models flagging anomalies in milliseconds, or ops teams rerouting deliveries based on live traffic data.

Static BI tools are no longer enough. Data professionals comfortable with stream processing, APIs, and real-time tools such as Kafka, Snowflake, and Spark Streaming can deliver insights that prevent issues before they even surface.

From gut feel to AI-enhanced strategies

AI is far more than a backend tool, it’s a major player in frontline strategy. Natural language processing and generative AI can surface trends from millions of customer reviews, summarise complex data into digestible insights for technical and non-technical stakeholders, and help marketing teams test thousands of ad variations in minutes.

For me, this is where soft skills matter now more than ever. The ability to translate AI insights into business action to tell the story behind the data is what separates good analysts from great ones.

The hiring takeaway

AI is changing how businesses maximise the value of their data, but it’s also changing what the best data professionals expect from a role.

If you’re looking to build a high-performing analytics function in 2025 and beyond, your hiring strategy needs to reflect that. Innovative, forward-thinking talent will help you get the most out of your data, giving it more value than ever before.

The future of analytics is intelligent and AI-driven and it takes a strong, well-equipped team to get you there. If you’re hiring in Marketing & Insight and want to explore how to bring this talent into your business, I’m always happy to share what I’m seeing in the market.

Hiring for AI? Let’s Make It Simple

Download our AI Hiring Guide – a practical resource to help you identify which roles to hire and when.

The post How to Maximise Your Data’s Value: The Use of AI in Advanced Analytics appeared first on Harnham.

By Kiran Ramasamy, Managing Consultant, Harnham 

When it comes to AI it is no longer a question of adoption for businesses, the challenge now is how to use it responsibly and at scale.

That’s where governance comes in. And it’s not just about tech; it’s about talent.

A recent ISACA report reveals that while 83% of IT and business professionals say AI is already in use within their organisation, only 31% have a formal, comprehensive AI policy in place.

That gap isn’t just in numbers. Without the right people who can interpret regulation, assess risk, challenge bias, and build actionable frameworks, your AI strategy lacks a stable foundation.

Which is why responsible AI starts with people, those who can manage the tech and human factors that make or break it.

Why AI Governance Roles Are No Longer Optional

Governance-first roles are now essential to deploying AI responsibly. 

When there’s no one in place to manage oversight and accountability, things break down:

• Biased models go unchecked
• Decisions can’t be explained or justified
• Teams move faster than the safeguards in place

To prevent that, organisations are hiring people who can translate regulation into real-world practices, spot risks before they scale, and build frameworks that hold up under scrutiny.

Legal pressure is growing too. In Europe, the EU AI Act sets strict rules for high-risk systems. In the US, the AI Bill of Rights is reshaping how organisations approach fairness, transparency, and oversight.

Compliance might be the starting point, but trust is what makes AI sustainable. And trust is built by people who can guide the system, challenge assumptions, and ensure AI delivers on its promise without compromising on values.

Governance Roles in Action

UK Case Study: Building a Responsible AI Function

To support its global push into AI and machine learning, a leading entertainment company partnered with Harnham to build out its machine learning function.

Harnham supported the organisation to hire two specialists with deep expertise in GenAI, cloud-first engineering, and computer vision, enabling the company to launch its UK AI capability in just 48 days.

These hires brought immediate impact, supporting cross-functional collaboration, leading strategic delivery, and helping shape a responsible AI roadmap from the ground up.

Read the full case study here. 

US Case Study:  Building Strategic Leadership for Scalable AI

When a global investment advisory firm set out to build its AI capability from scratch, there was no roadmap; just a clear need for senior leadership who could deliver innovation and embed governance from the start. 

Harnham was able to deliver two strategic hires in under four weeks: Director of AI Engineering and Director of AI Strategy. Both brought deep experience in AI/ML, LLM integration, and C-suite stakeholder engagement, helping the firm align innovation with compliance, trust, and long-term value.

Read the full case study here. 

Key Takeaway

AI governance is a capability, and it doesn’t come from tools alone. It comes from people with the skills to ask the right questions, build the right frameworks, and help AI deliver on its promise responsibly.

If you’re thinking about how to strengthen your organisation’s approach to AI governance, we’re here to help. 

Explore how Harnham builds responsible AI teams at harnham.com. 

Hiring for AI? Let’s Make It Simple

Download our AI Hiring Guide – a practical resource to help you identify which roles to hire and when.

The post AI Governance Starts with People appeared first on Harnham.

  By David Farmer, CEO, Harnham Global

We haven’t just hit 15,000 placements, we’ve launched 15,000 careers, changed 15,000 lives and it’s a marker. A marker of how far the Data & AI industry has come and what it’s taken to get here. From our very first Infrastructure Analyst in 2006 to placing CDOs, Directors of Data, AI Directors and Heads of AI, we’ve been on this journey with the industry since the beginning.

We’ve seen it all: the early days of SAS and SQL, the shift to Python and the cloud, the growth of ethics and governance roles, and the ongoing challenge of finding people who cannot just crunch numbers but tell stories with data. And recently, we have seen another shift – the move from the generic ‘Data person’ role, required to wear multiple hats (analysts, developers, engineers), to more specialised roles with defined purpose such as ‘Data Engineers’ ‘BI Analysts’ and ‘AI engineers’. 

Now, we’re sharing intel about what we’ve learned and where we think it’s all going next.

What’s Changed?

Of course, the obvious is the size and scale of the industry. According to ChatGPT, the number of roles in data has grown from 1-2 million in 2006 to somewhere between 15 million and 25 million today. That change has had a huge impact on the roles within the industry, moving the data science role from predominantly academia or statistics-based work to a much broader role within a huge spectrum of industries. The Data scientist title became synonymous with one of the zeitgeist moments of the data boom, with the “need” for a data scientist or the desire to be one coming in advance of a true definition of the role. This was borne out in the data analysis for our salary guides through that period, with candidates with the title Data scientist performing roles which would traditionally have been called Marketing Analyst, CRM Analyst or even Data Governance manager.

The shift in Governance is another ongoing shift. Historically viewed as a compliance / audit function, with the growth of Data and more recently a boom in AI deployment, this role has shifted in scope and placement, moving from an audit position to a centralised role of data stewardship, MDM and AI ethics. The latter will continue to grow, with Cambridge University’s MPhil in Ethics of AI, Data and Algorithms showing what we believe to be a necessary direction of travel. 

Day to day, Harnham started out by placing analysts who could build dashboards and run reports. Now, we’re placing AI Engineers, MLOps specialists, and those designing LLMs. The pace has accelerated. The roles are deeper. The stakes are higher. The tools have changed, too. From SAS, Excel and VBA to Docker and Kafka. From structured datasets to messy, real-time, unstructured data. There’s a shift from siloed analytics to fully embedded teams, and from back-office reporting to real-time, business-critical decisions.

In further articles we will focus in on individual roles, responsibilities and titles to highlight the shift seen within each part of the industry.  

The graph shows the percentage increase or decrease in the number of roles Harnham has filled with ‘AI’ in the job title over the years.

What Hasn’t Changed?

One thing that’s remained consistent: the ability to take what the data is saying and tell someone else about it in a way that matters. That’s always been the difference-maker. It’s never just been about the code or the model – it’s about translating complex insights into business action. Finding people who can bridge that gap, who can make sense of numbers and influence decision-making, remains one of the toughest challenges, just like it was when we started. 

We will be covering this in a whitepaper, which will be available soon.

Yes, there’s growing demand for Chief AI Officers and Directors of AI. But our 15,000th placement? A Rockborne-trained Business Data Analyst. Proof that the next generation coming through still underpins it all. Looking at that role and candidate in more detail tells us a lot about how the industry has moved.

Meeting Abby, candidate 15,000, we learned that no one day is the same – some days the focus is all about engaging and presenting to stakeholders, other days focus on managing projects, diving into code to help teams out, building power BI dashboards for clients, and other days the focus is to study data lineage and architecture diagrams.

That breadth of skill is something companies seek at the junior talent levels, so the breadth of training Abby went through prior to her starting in role has enabled her to be more successful. 

Her ability to communicate complex outcomes in a clear business language and easy to understand way highlights the importance of the story telling point from earlier. 

Overall though, these 15,000 candidates don’t just highlight a tech journey. They show a story of capability. A move from reporting to real-time insight. From projects to products. From centralised teams to embedded ones. Some things change. Some things stay the same.

For Candidates: The Shifting Landscape

• It’s not about having the perfect CV anymore. It’s about showing you can learn, adapt, and apply.
• People who started as SAS programmers are now AI Architects. That shift didn’t happen overnight, and it didn’t happen by accident.
• Lifelong learning isn’t a buzzword. It’s the reality. Be prepared to talk about your engagement in learning.  
• The average CDO will want to see actual demonstrations of work outside of your CV. If you’re a tableau analyst it is important not just to show dashboards you have built, but just as important to talk about what the outcome from those dashboards was.

For Businesses: Solutions Over Tools

• The best companies start with the problem, not the job title. They figure out what they need to solve and then hire someone to do that.
• Hiring in layers works. We’ve seen it — data engineers, AI leads, and governance heads.
• Governance isn’t optional anymore. Neither is training. Whether it’s upskilling your own teams or bringing in early-career consultants, you have to be thinking long-term.

What’s Next?

To celebrate 15,000 placements, we’ll be sharing a range of insights and interviews with clients, candidates and others from our Harnham network over the years, including a live panel discussion on what’s next for the industry. Watch this space.

Check out our 15,000 placements exclusive podcast:

The Data & AI Podcast: 15,000th Placement Exclusive with Charlie Nicholls from Dexata: LISTEN HERE

• What it takes to get personalisation right at scale
• How AI is reshaping the workplace and decision-making
• The role of experimentation in creating human-centric experiences
• Why customer experience optimisation is more critical now

Are you ready for AI?

In a landscape dominated by discussion of AI, companies can find it hard to navigate the marketplace.

Download our “AI Hiring How-to Guide” for insights on key roles, salaries and more:

The post 15,000 Placements Later: What We’ve Learned About the Global Data & AI Talent Market appeared first on Harnham.

Sign up below to download our “AI Hiring How-to Guide”:

The post Download ‘How to Hire in AI’: Our AI Hiring How-to Guide appeared first on Harnham.

The transformative power of technology has entered an exciting phase with artificial intelligence (AI) and machine learning (ML) actively reshaping how organisations operate and make decisions.

As we embrace these innovations, a crucial factor often dictates whether they deliver real value. Without high-quality, well-managed data, even the most sophisticated AI or ML model will struggle to produce reliable insights. It appears increasingly clear that the long-term success of AI initiatives is closely tied to one foundational element: data governance.

Often overlooked in the race to innovate, data governance is now emerging as a foundational requirement not just for compliance, but for building scalable, ethical, and trusted AI systems 

What is Data Governance?

Data governance refers to the structured approach to managing data quality, accessibility, security, and usability within an organisation. It sets the policies, processes, and responsibilities that define how data is used and protected, ensuring that it’s accurate, consistent, and compliant with evolving regulations.

In essence, good data governance means you can trust your data. And if you can’t trust your data, you can’t trust your AI.

Why It Matters for AI

AI models are only as good as the data they’re built on. Without trustworthy data, algorithms can produce biased, inaccurate, or misleading outcomes. In fields like finance, healthcare, and public services, the risks of poorly governed AI are significant, ranging from reputational damage to legal liability.

As Robin Buitendijk, Data Governance Specialist at Harnham, puts it:

“Data management and data governance are the pillars of a successful AI. Building an AI without them is like building a house without pillars. As the Dutch market increasingly prioritises data-driven innovation, ensuring strong data governance is no longer optional—it’s essential for sustainable growth.”

Where the Netherlands Stands Today

The Dutch market is steadily keeping pace with AI adoption, mirroring broader European trends while setting its own ambitious benchmarks. According to recent industry insights, 88% of organisations in the Netherlands report using AI technologies, well above the European average. However, only 35% have applied a robust measurement strategy to assess impact, suggesting a maturity gap when it comes to governance.

Mischa Luyf, IT, Data & Transformation Executive, sees a clear shift:

“In the Dutch market, Data Governance is shifting clearly from a compliance necessity towards becoming the critical foundation for AI initiatives, especially when scaling trustworthy AI. Companies increasingly recognise that solid Data Governance isn’t just supportive—it’s essential.”

Julien Hoornweg, Enterprise Data Architect, adds:

“I notice that organisations across Europe increasingly prioritise the ethical use of AI, which is a positive step. The next challenge is integrating AI governance, data governance, and data management operations into a unified framework, rather than isolated efforts, with data architecture serving as the glue that solidifies this alignment.”

Steven Veldstra, AI Data Strategy and Management Specialist, offers a cautionary note:

“AI should be an integral part of a company’s Data Strategy, placing it squarely within the Data Management domain. Without the adoption of adequate AI-focused data management guidelines, regulations, and policies, AI usage poses significant risks to organisations without them knowing it.”

Insights from Data Leaders

Mark Lemmen, a seasoned data governance expert, highlights the evolving perception of governance:

“More and more organisations are realising that strong data governance is a critical prerequisite for reliable and ethical AI applications. AI is accelerating data governance adoption as companies take data quality, data ownership and security more seriously and increasingly recognise that companies in control of data and processes achieve higher efficiency and profitability. In every sector, organisations that handle their data best will eventually become the leaders in the specific industries.” 

Lex den Doop, Trainer & Consultant in Data & AI Governance, sets the stage with a thought-provoking observation:

“Organisations typically lack a holistic and integrated approach to governance of data, processes, and AI.”

He continues by introducing the LEXIM framework:

“If governance is the foundation for data, process, and AI, what is the foundation for governance? A Digital Twin of your organisation according to LEXIM is the foundational environment and structure for applying governance to business-critical data, thereby delivering value from data by design and default.” 

Meanwhile, Norbert Hofmeester, Digital Transformation Expert, focuses on the rise of agentic AI:

“Agentic AI thrives on the interplay of rich context and clear processes, yet it’s data governance that ensures data quality remains the heartbeat of it all. Context and process info give agentic AI the wings to act smartly, while data quality, guided by solid data governance, keeps it grounded and trustworthy.” 

What Comes Next?

From my perspective, the future looks bright for the Netherlands. 

The country is well-positioned to lead in ethical and scalable AI, but to realise this potential, data governance must be embedded in organisational culture, strategy, and operations. AI’s evolving nature and its potential to amplify bias presents new risks that demand targeted governance.

Research suggests that effective governance of AI includes priorities like transparency, fairness, and explainability, grounded in trusted pillars such as data quality, stewardship and compliance.

Data and analytics leaders should begin by asking:

• Do we have clear oversight of our data and AI models?
• Are we equipping our teams with the knowledge to make responsible decisions?
• Are we balancing innovation with risk management?

Organisations that can confidently answer ‘yes’ are better prepared to deliver AI that is trustworthy, responsible and aligned with their goals.

Interested in exploring how Harnham can help your organisation strengthen its data governance for AI success? Get in touch with our team.

Sources:

Harvard Business Review
Forbes Council: Before You Can Trust AI and ML
Forbes Tech Council: Beyond the Algorithm
Data Governance & Quality
Gartner Research: AI 

The post The Role of Data Governance in AI: Landscape in the Netherlands appeared first on Harnham.