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In partnership with Count.co

In this webinar, leaders from Intruder — including the Head of Data, VP of Product, and Head of Marketing — share how they made OKRs actually work across teams.
Learn how they used Count’s collaborative, canvas-based BI tool to align stakeholders, tie team output to company growth and turn performance data into strategic decisions.
This session is ideal for anyone looking to make OKRs less theoretical and more actionable.

Check it out here

🤖 Do you guys even care about AI?

According to a recent data leaders survey by Hex, 77% of data teams are excited about AI, but only 3% said it was a main focus for their team (aka wtf?).

Right now, AI is the hot topic in every boardroom. The problem? Most data teams are either ignoring it completely or tinkering with random use cases that don’t amount to much. And while they’re stuck in experimentation mode, other teams in the business are running off to play with AI tools themselves.

Why should you care?

If we don’t lead the charge, someone else in the business will. And when non-data people start making AI decisions without your involvement, the results are rarely pretty.

Get it right, though, and AI can help your team shift from low-level reporting to delivering genuine competitive advantage.

Unfortunately… ignoring AI or treating it as a side hobby means your team risks becoming irrelevant. Leaders who fail to provide direction will watch AI strategy get handed to marketing, operations, or IT teams instead.

🎰 The cost of sitting back

• Loss of Influence

  When other functions take the lead on AI, the data team quickly loses its seat at the table (see image above). Suddenly, marketing is deciding on AI-driven customer segmentation, or operations is choosing a vendor for predictive maintenance.

  Without your team guiding the conversation, decisions are made with little thought for data quality, integration, or long-term scalability, and you’re left trying to clean up the mess rather than shaping the strategy. No bueno.

• Shadow AI sprawl

  If you don’t create some structure, people will experiment with whatever tools they find online. That means sensitive company data getting dropped into ChatGPT, unvetted vendors providing “quick-fix” models, and dozens of half-baked pilots nobody can track.

  Shadow AI might feel innovative at first, but it introduces huge security, compliance, and reputational risks that eventually land back on your doorstep.

• Missed quick wins

  AI doesn’t need to be a five-year moonshot. Many companies are already using it for document summarisation, customer support triage, or automating repetitive reporting tasks. These are easy opportunities to save hundreds of hours or unlock new insights, but if your team isn’t scanning the horizon for them, they’ll slip by unnoticed.

  Your competitors will take advantage while you’re still debating what model to use.

• Career Stagnation

  For you as a leader, there’s also a personal cost. Executives increasingly expect data leaders to have an AI point of view. If you’re not the one bringing opportunities to the table, you risk being seen as someone stuck in the old world of dashboards and pipelines.

  That reputation follows you into performance reviews, pay conversations, and future job interviews.

🐴 How to take the reigns

• Map AI to real business pain points

  Start by asking senior stakeholders: “What’s keeping you up at night?” Listen out for answers that feel repetitive, inefficient, or unscalable. Then explore whether AI can play a role.

  For example, customer support directors might be drowning in ticket backlogs, while HR could be spending hours on manual CV screening. Those pain points are fertile ground for practical AI pilots that solve real business problems, not just tech curiosities.

• Run contained experiments

  Don’t announce a grand AI strategy no one asked for. Instead, pick one of those pain points and run a time-boxed experiment.

  Example: deploy a simple LLM-based chatbot to handle FAQs for internal IT requests and measure how many tickets it resolves. Keep the scope small, track clear metrics (time saved, satisfaction scores), and then present results back to the business. Momentum builds from small wins, not massive promises.

• Set the rules of the game

  Rather than trying to ban AI outright (which doesn’t work), take ownership of building guidelines that help people use it responsibly. Work with legal, risk, and IT to create clear do’s and don’ts. I.e. no sensitive data in public tools, and mandatory review of any externally generated outputs.

  By framing yourself as the enabler, not the blocker, you position the data team as the natural home for AI governance.

• Upskill your team

  Get your analysts and engineers comfortable with AI tools now. Encourage them to use these in their workflows, experiment with prompt engineering, or build simple prototypes.

  This isn’t about turning everyone into AI researchers, it’s about building literacy so your team can speak the same language as vendors, stakeholders, and execs.

  A technically fluent but AI-illiterate data team will be yesterday’s news.

• Build in public

  Let those around you know what you’re trying and don’t be afraid to highlight both what’s worked as well as what’s failed. Take the organisation on a journey of AI discovery with you, not alone in the dark.

  Great ways to approach this include hosting lunch and learns or kicking off a newsletter and asking others to contribute. Speak at internal events and tell as many stories as you can.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call