The most profound transformation happening in financial services isn’t technological – it’s cultural. While organisations rush to implement generative AI tools and advanced analytics platforms, the real competitive advantage lies in fundamentally reshaping how people think about, interact with and trust data. More than 57% of companies struggle to build a data-driven culture, despite massive investments in AI and analytics infrastructure (Wavestone, 2024).
The challenge isn’t technical, it’s all too human.
The real competitive advantage lies in fundamentally reshaping how people think about, interact with and trust data… The challenge isn’t technical, it’s all too human.
The Cultural Foundation Crisis
The share of businesses with partially or purely data-driven decisions has risen from 14 percent to 34 percent over the past seven years (Barc, 2024). Let me say that another way: in a world where chatbots are used to weigh in on legal proceedings, are trusted to route callers with tech emergencies, and are already making inroads toward becoming financial advisers, two-thirds of organisations continue to make critical decisions without leveraging data & insight in part or all of their business.
The problem isn’t access to data or even sophisticated AI tools. The real hurdle is subtle yet much more important: fostering an environment within an organisation where individuals instinctively turn to data anytime, they must make a decision. This is Data Culture.
The “if you build it, they will come” strategy toward fostering good cultural customs around trust and use of data has been disproven. Organisations built data warehouses, hired data scientists and implemented business intelligence platforms, yet still find employees defaulting to intuition over insights, excel over real-time analytics solutions, siloes over networks.
Building a data-driven culture is incredibly hard because it calls for a behavioural change across the organisation. Something that no amount of technology can force. Recent research reveals that 72% of organisational transformations fail, with employee resistance accounting for 39% of these failures (McKinsey, 2023) demonstrating that behavioural change, not technological sophistication, remains the primary barrier to successful data culture adoption.
Generative AI changes this dynamic by making the cultural transformation more accessible and natural. Instead of requiring employees to learn complex query languages or navigate complicated dashboards, AI enables conversational interactions with data that feel as natural as asking a colleague for advice. This accessibility is breaking down the final barriers to widespread data adoption, but only for organisations that approach it with culture-first thinking.
The Coming Reckoning: Why Data Culture Determines Success
The gap between AI-ready and AI-struggling organisations is widening fast. Microsoft research shows that 96% of organisations in the “realising” stage of AI readiness see significant returns, compared to only 3% still “exploring”. This isn’t about having better technology, it’s about having cultures where AI adoption feels natural.
Companies in the “realising” category share one characteristic: they’ve solved the human side of AI adoption. Their employees trust data quality, feel confident experimenting with AI tools, and see clear connections between AI capabilities and their own professional growth. These organisations didn’t just deploy AI. They prepared their people to succeed with AI and given them the drive to do so.
The competitive advantage isn’t temporary. Organisations that crack the culture code become better at adopting new AI capabilities as they emerge. They’ve built the muscle memory for technological change.
Generative AI changes this dynamic by making the cultural transformation more accessible and natural… AI enables conversational interactions with data that feel as natural as asking a colleague for advice.”
Building AI-Ready Data Culture: Start Now with These Five Steps
Creating an AI-ready culture isn’t about motivational speeches or change management theatre. It requires systematic attention to the practical barriers that prevent people from embracing data-driven decision-making.
Here are five concrete actions that work:
1. Build Unshakeable Trust in Data Quality
Nothing kills AI adoption faster than employees discovering that the data feeding AI systems is incomplete, outdated or just plain wrong. When people doubt data quality, they’ll reject AI insights regardless of how sophisticated the algorithms are. Believe it or not, that’s a great thing! What would be worse is if they acted on false or misleading information.
The solution isn’t just better data governance. It’s making data quality visible and collaborative. Leading organisations develop real-time data quality dashboards that everyone can see, implement simple processes for employees to flag data issues and celebrate teams that improve data quality. They make data accuracy everyone’s responsibility, not just the IT department’s problem.
Most importantly, they’re transparent about data limitations. Instead of pretending data is perfect, they teach people how to interpret AI outputs knowing the data constraints. This builds genuine confidence rather than blind trust.
2. Create Safe Spaces for AI Experimentation
Research shows that 50% of employees feel embarrassed using AI at work, worried they’ll appear lazy or incompetent (SHRM, 2024). This psychological barrier is often bigger than any technical challenge.
Organisations are overcoming this by creating explicit “AI experimentation zones”, dedicated time and space where trying AI tools is not just allowed but encouraged. This might be weekly “AI office hours” where people can get help with tools, monthly “show and tell” sessions where teams share their AI experiments or simple recognition programs that celebrate innovative AI use cases.
The key is making it clear that AI experimentation is valued work, not something people should feel guilty about. Leaders need to model this behaviour by openly sharing their own AI experiments, including what didn’t work.
3. Democratise AI Skills Across Your Organisation
Instead of training a few AI specialists, winning organisations are building AI literacy throughout their workforce. They create what experts call an “AI skills pyramid”: everyone becomes “AI Aware” (understanding what AI can do and how to use it effectively), some become “AI Builders” (able to implement AI solutions) and a few become “AI Masters” (solving complex challenges with AI).
The training focuses on practical skills people can use immediately in their current roles. This means hands-on experience with specific AI tools they’ll actually use, not abstract AI theory. Equally important, training addresses the “why”: helping people understand how AI skills enhance their professional value rather than threatening it.
The most effective programs pair AI training with career development conversations, showing people explicit pathways for how AI skills lead to promotions, new opportunities and higher compensation.
4. Practice Radical Candor About Job Transformation
The biggest mistake organisations make is avoiding honest conversations about how AI will change work. This creates anxiety and rumours that are often worse than reality. Instead, successful organisations practice “Radical Candor”: caring personally about employees while being directly honest about changes ahead (Scott & Rosoff, 2024).
This means having transparent discussions about which aspects of roles will likely be automated, which skills will become more important and what new opportunities will emerge. But caring personally means pairing honesty with concrete support: skill development programs, career coaching, and sometimes help transitioning to new roles either within the organisation or elsewhere.
Radical Candor builds trust because people appreciate honesty paired with genuine care. Employees would rather know what’s coming and have help preparing for it than be kept in the dark while their jobs slowly change around them.
5. Position AI as Amplifying Human Value
The organisations succeeding with AI don’t position it as a replacement for human capabilities – they showcase how AI makes human skills more valuable. Customer service representatives become relationship specialists when AI handles routine inquiries. Financial analysts become strategic advisors when AI processes data. Risk managers become expert interpreters when AI identifies patterns.
This requires actively redesigning roles to emphasise uniquely human capabilities: creative problem-solving, relationship building, complex judgment, and contextual interpretation. It means creating advancement opportunities specifically tied to effective human-AI collaboration and measuring success based on outcomes that require both human insight and AI capabilities.
When people see AI as making their human skills more valuable rather than obsolete, adoption accelerates dramatically. The key is showing, not just telling—giving people concrete examples of how AI enhances rather than threatens their professional worth.
The organisations that succeed will be those that remember AI transformation is fundamentally about people.
Making It Work in Your Organisation
Building an AI-ready data culture isn’t about grand transformation programs. It’s about consistent, practical steps that help your people succeed. Start with one area where you can demonstrate quick wins: maybe it’s improving data quality in one critical system or helping one team experiment safely with AI tools.
The goal isn’t to solve AI adoption for the entire industry. It’s to help your specific people, in your specific organisation, navigate this specific change successfully. Focus on removing barriers, building confidence and creating clear pathways for growth.
The organisations that succeed will be those that remember AI transformation is fundamentally about people. They’ll invest as much in human development as they do in technology, create environments where AI adoption feels empowering rather than threatening and build genuine trust through transparency and support.
Your people want to succeed in a changing world. Your job is giving them the tools, skills, and confidence to do it. Get that right, and the technology adoption becomes the easy part.
If you’d like to talk to an expert about how you can transform your organisation’s data culture to succeed in an AI world, get in touch.
Araminta Huitson
Data Culture practice lead, Ortecha
contentS
TALK TO AN EXPERT
Get in touch to talk to one of our data experts and
About the Data Culture Club
A cross-industry community for an evolving discipline
Data Culture is relatively new as a concept, and still developing:
- everyone seems to have a slightly different definition of what Data Culture actually means
- it doesn’t feature much (yet) in traditional Data Management best practice frameworks
- many companies don’t yet have dedicated teams to drive it forward
So we created the Data Culture Club, a worldwide cross-industry community to bring together the people who are leading the way in changing their organisation’s attitude to data (whether they have ‘culture’ in their job title or not), to share their successes (and failures), and to provide inspiration and support to others in the same situation.
Our definition of Data Culture
A culture can be described as “the way things are done around here” – the shared values, attitudes and behaviours that are expressed in everyday activity.
So, a good Data Culture is one where data is a natural part of the way people work:
- everyone understands the meaning and business value of the data
- they have the tools, skills and confidence to use it
- they feel collective responsibility for looking after it
This is a broader term than Data Literacy, which is commonly used to refer to the training and education of individuals.
Join the club
We hold quarterly roundtables to allow members to meet in person, and have a thriving virtual forum for online discussion. If you work in this space, you’d be most welcome to join! Please get in touch with Araminta to learn more.
