Have you noticed how suddenly every team, conference and job title now seems to have morphed into something to do with AI?
And the terms ‘Data’ and ‘AI’ have almost become interchangeable?
During our recent virtual event, I posed a question that’s been bothering me for a while: What do we do with our Data teams in the age of AI?
AI Everything, Everywhere, All at Once…
The acceleration of AI over the last couple of years has clearly shaken things up for everyone, and in particular for data teams in our enterprises. It presents much more of a people and organisation challenge than a tech one.
Let’s be clear about something which is hopefully obvious: data is not AI, and AI is not data. They’re related, adjacent disciplines with some overlap, but they’re not the same thing.
A strange thing has happened both within data teams and in the wider enterprise—the sheer number of AI “experts” that have popped up out of the woodwork! As Bjørn Broum aptly noted not so long ago in “The Data Delusion“: “The most remarkable transformation isn’t in technology but in people – the speed at which professionals with limited statistical backgrounds have rebranded themselves as AI experts is truly the most impressive algorithm of all.”
Sound familiar? We’ve all seen the LinkedIn profile updates, the online certifications and the sudden expertise in technologies that barely existed a year ago. Many people in data teams have been tweaking their job titles to include AI, doing a course online and hoping for the best. But in reality, we haven’t figured out how to organise ourselves or how best to train our people in light of this emergent technology.
Why Data Still Matters (Maybe More Than Ever)
In our rush to embrace AI, we risk overlooking the enduring importance of solid data foundations. The AIM Council puts it well: “data silos can derail even the most ambitious AI initiatives, leaving businesses grappling with fragmented insights and missed opportunities.”
And further, we may have lost sight of what we ultimately need data and AI for—making better decisions, optimising operations and improving experiences for customers and employees.
Most AI applications need the right data, and many enterprises are sitting on a treasure trove of proprietary data. Without quality data foundations, even the most sophisticated AI will struggle. And us humans, not least our organisational data teams, will remain a pivotal part of making this happen.
The fundamental questions remain:
- How do organisations get a handle on this in how they operate?
- How can they use AI to maximise value internally and for customers—while keeping it secure?
- How do we help people build the right skills in this new era?
Three Key Connections That Drive Value
When I work with enterprises trying to navigate this landscape, I see three critical connections that need strengthening.
1. Connecting AI to Strategy and Use Cases
I continue to be surprised at how many organisations don’t do this. I see AI initiatives with no connection to what the business actually needs. They come from a desire to ‘keep up’ rather than create value.
Some specific pain points I’ve noticed:
- “Go do some AI stuff” mandates (AI as a shiny toy)
- Lots of tactical experiments and “Proof-of-Concept hell”
- Limited data involved in actual decision-making
I worked with a consumer goods firm last year caught in this exact situation. They had technical PoCs mostly in the hands of technical staff, all costing money, none with clear business rationale. We revamped their approach to focus on clear business objectives where AI could play a role. We chose sales optimisation as our top use case (it stemmed from known inefficiencies and opportunities for better customer experience) and made a clear link between data capabilities and concrete, measurable business goals. They’re now seeing promising early results.
What you can do:
- Push back on vanity projects and demand to see ROI. Ask: “What’s the decision this will help make? What will this make more efficient? What impact will this have on customer experience?”
- Better yet, start with the business decision or action first. Articulate the priority use cases in your organisation that could be data or AI-enabled.
- Ensure business strategy has data and AI elements that connect to key decisions and objectives.
2. Connecting Teams and Functions
Organisational silos are killing effective data and AI use. Many enterprise organisations deal with their data in isolation—sales with sales data, supply chain with supply chain data, finance with finance data. Or worse, the data team has all the data but limited business context.
This creates some predictable problems:
- Siloed data that’s fundamentally difficult to draw together for analytics or AI needs (e.g. to understand the end-to-end customer experience)
- Lack of cross-functional collaboration (where data or reports are passed over the fence)
- Limited innovation capability that spans departmental boundaries
- Uncertain accountability for AI initiatives (often assumed to sit with IT)
A client example: We worked with a revenue growth management (RGM) team and data group on a joint project focused on data maturity to support RGM use cases for AI. They created what we call a “fusion team” with clear principles around experimentation and test-and-learn approaches. One of their principles was “fail fast,” and they embraced it. They identified specific data assets to build and improve that would enable their AI models to perform more effectively. They couldn’t have reached this level of clarity without deliberate cross-team collaboration.
What you can do:
- Get seats at each other’s tables. Do you have data people sitting in your business decision forums? Do you have business decision makers involved in data product sessions?
- Explore your operating model. Not just roles and responsibilities, but what forums exist for collaboration
- Build a culture of experimentation. As MIT’s Sloan School puts it: “encouraging experimentation and not punishing failure is critical”
- Create cross-department “fusion teams” with clear, value-driven purposes
3. Connecting Skills to Reality
I’m seeing a lot of confusion about what skills are actually needed in this new landscape:
- Given my role, which bits of the vast world of AI training do I need to bolster my skillset?
- Are there elements beyond just things like prompt engineering that will help me?
- How do I build on my existing expertise rather than completely reinventing myself?
For organisations and leaders specifically, please provide training opportunities! But leaders also need to role model this learning journey and show they’re upskilling themselves in accordance with their role and current skillset.
Adapt and build on what you already have—don’t try to become something completely different overnight. It’s probably more important for a typical data professional to build knowledge in a business domain than to go all-in on deep AI technical skills. Those business-data connections are what will create real value.
Figure out whether people in your organisation are pursuing AI qualifications because they’re fearful for their jobs. Recent surveys have found that people in enterprise organisations are anxious or sometimes actively resistant when it comes to AI.4 Careful communication about how the organisation is thinking about this subject is crucial.
What you can do:
- Provide targeted training that builds on existing skills and aligns with roles
- Foster business literacy for data teams as well as data literacy for business teams
- As an individual, lean into AI but don’t feel you need to become an overnight technical AI guru (most people claiming expertise don’t have it either!)
- If you’re in a data team, get to know your organisation’s key objectives and articulate how data and AI could play a role
It’s Still About Business Value
Remember why we started building data capabilities into our organisations in the first place. It wasn’t to form a cottage industry of bloated data management teams (although perhaps that’s where some organisations have strayed). We did it because organisations have a competitive advantage if they can:
- Make better, faster decisions that lead to beneficial actions
- Enhance customer experiences
- Make operations more efficient
This has not changed.
What has evolved are the tools available, the required skills and the literacy and culture needed to succeed. We should be laser-focused on what business outcomes we’re seeking, and build our teams, skills and—yes, technology—around that.
Moving forward requires a return to fundamentals. The most successful data organisations focus relentlessly on business outcomes; speak in terms their colleagues understand and implement solutions that match the actual complexity of the problem—not the perceived complexity that justifies their existence.
How S&S Can Help
The tools and technology will continue to evolve. What won’t change is the need for humans who can connect dots, ask the right questions, understand context and drive real business value.
How might you build a stronger bridge between business outcomes and those who can deliver the decision support? What are some steps you could take today?
At Sullivan & Stanley, we help organisations navigate these challenges by:
- Assessing your current data capabilities and identifying practical paths to value
- Designing cross-functional operating models that connect data teams with business teams
- Creating fusion teams that combine business domain expertise with data capabilities
- Developing tailored learning journeys that build on existing strengths
We believe in bringing people together across organisational boundaries to solve complex challenges. If your organisation is struggling to realise value from your data in the age of AI, we’d love to help you bridge those critical gaps.
Want to discuss how to strengthen the people side of data in your organisation? Get in touch with our Data & AI team or connect with me directly on LinkedIn.
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