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What Happens to the Data Team When AI Writes the Code?


    Robot with a lightbulb head surrounded by people discussing data, coding, and charts. Text: "What Happens to the Data Team When AI Writes the Code".

    If your job is building data capabilities, your job is changing.


    The data engineer.

    The analytics engineer.

    The analyst.

    Even the architect.


    Not because the times are changing (though they are).


    But because the expectations are changing.




    LLMs Are Making It Easier to Build


    A few years ago, building pipelines, structuring models, or writing performant SQL required serious technical skill.


    Today?


    You can describe what you want in plain English and get a solid starting point from an AI assistant.


    No, it’s not perfect. Yes, you still need to know what “good” looks like.


    But the hard part isn’t execution anymore, it’s judgment.



    The Bar Has Moved


    As coding gets faster, your ability to ship something technical isn’t the differentiator. The differentiator is whether what you built actually helps someone.


    And that means thinking less like a builder and more like a product manager.


    • Who is this for?

    • What problem are we solving?

    • Where and how will the solution be used?

    • Is it intuitive, actionable, and trustworthy?

    • Will it fit into their flow of work?

    • Does it drive a measurable outcome?


    These aren’t “nice-to-have” questions. They’re the difference between a solution that’s adopted and one that’s ignored.



    Orchestration Beats Execution


    You might not write every line of code anymore. But you’ll still shape:


    • What’s prioritized

    • What “done” looks like

    • How value will be measured

    • How success will be communicated


    This is the real work of data delivery. And it’s a cross-functional sport. The most impactful data teams already operate like this:


    • They co-design solutions with business partners

    • They prototype, test, and iterate

    • They work in delivery cycles

    • They align around problems, not platforms



    Final Thought


    This doesn’t mean everyone needs to become a product owner.


    But it does mean that the technical data roles of the future will need to develop product instincts:


    • Empathy for users

    • Curiosity about context

    • Focus on outcomes

    • Comfort with iteration


    In a world where code can be auto-generated, the data teams who will thrive are the ones who can shape, steer, and ship meaningful solutions not just pipelines.


    Execution is table stakes.


    Product thinking is what sets you apart.



    At Fuse, we believe a great data strategy only matters if it leads to action.


    If you’re ready to move from planning to execution — and build solutions your team will actually use — let’s talk.


    The Future of Data Is Push, Not Pull

      Man in a suit holds a smartphone, sitting at a desk with a laptop. Text: "The future of data is push, not pull." Charts show data trends.

      Most data workflows still start with a question:


      “What happened with churn?”

      “Can you pull numbers on Q2 pipeline?”

      “Why is revenue down in the west?”


      The business identifies a decision, then goes looking for data to support it.


      That’s the default motion: someone asks, and someone pulls.


      But what if it worked the other way around?



      The Shift


      We’re entering a new phase of data delivery. A phase where answers don’t need to be fetched.


      They arrive.


      Agent-style tools are making that possible.


      With embedded context, natural language capabilities, and a better understanding of business workflows, these tools are flipping the script.


      Instead of waiting to be queried, they surface what matters:


      🚨 “Churn rate just spiked for last month’s onboarded customers.”

      ✅ “Retention improved in your newest cohort — want to know why?”

      💡 “Revenue in the west is down 22% compared to last quarter.”


      These are not dashboards. They’re signals.


      They show up at the right time, in the right place, with just enough context to spark the right action.



      Why Dashboards Fall Short


      Dashboards made data more accessible, but they’ve always relied on one thing:


      Someone has to go looking.


      That worked when access was the goal. But today?

      Time and attention are the bottlenecks.

      People aren’t starved for data. They’re starved for clarity.


      And dashboards, as useful as they are, often fall short:

      • Designed around tools, not people

      • Full of content, but low on context

      • Requiring clicks, filters, interpretation


      They don’t say, “Here’s what matters right now.”


      They just show you what’s available, and hope you find it.



      Push Is Not Spam. It’s Relevance


      This isn’t about replacing dashboards with a flood of noisy alerts.

      Push only works when it’s trusted, timely, and tied to a real business moment.


      That means:

      • Knowing your audience

      • Understanding the decisions they’re facing

      • Designing signals that are specific, actionable, and grounded in context


      A well-timed signal doesn’t distract. It focuses.


      It helps the right person do the right thing without having to dig through layers of UI to find the signal themselves.



      The Role of the Data Team Is Changing


      In this new world, the data team’s role expands:

      • From building dashboards to building signal systems

      • From serving ad hoc questions to surfacing emerging patterns

      • From passively delivering insights to actively accelerating decisions


      This isn’t a replacement for exploratory analysis or curiosity-driven work.


      It’s an evolution of how we keep the business aligned, informed, and confident.


      Push makes good data visible without requiring effort from every user.



      Final Thought


      Push is not the end of pull.

      There will always be a need for digging, exploring, and playing with data.


      But the business can’t afford to run on curiosity alone.

      It needs clarity, speed, and guidance.


      And the next generation of data experiences will deliver that, not in dashboards, but in nudges.


      When your data knows what matters and tells you when it does that’s when it becomes useful.



      At Fuse, we believe a great data strategy only matters if it leads to action.


      If you’re ready to move from planning to execution — and build solutions your team will actually use — let’s talk.


      A World Without Dashboards

        For 20 years, dashboards have been the centrepiece of analytics.


        A computer screen with a graph face sits in a chair, shaking hands with a person. Banner says "Thanks for the Charts!" Cake reads "Goodbye Dashboards."

        You log in.

        You click around.

        You filter.

        You try to make sense of it.


        Some people got really good at it.

        Most didn’t.


        And now, with the rise of agent-style tools, that whole interface is (loudly) being questioned.


        Not because dashboards are broken.

        But because they were never really the the

        right solution. They were all we had and they did help a little to get people closer to an insight.


        Let’s imagine a world without them.



        No dashboards. So what’s left?


        Instead of dashboards, you get:

        • A nudge when something changes that you care about

        • A quick answer to “What’s driving this?”

        • A summary of what happened this week and what needs attention

        • A proactive heads-up when a risk is emerging

        • A decision support tool that talks like your team does


        Sound too futuristic?


        It’s already starting.


        With agents. With embedded assistants. With tools that surface what matters, instead of displaying everything and hoping you find it.



        Dashboards weren’t the goal, they were just a stop along the way


        Let’s be honest:


        Most dashboards weren’t built to help people act.

        They were built to centralize access and “let users explore.”

        But most users aren’t trying to explore.

        They’re trying to answer a question so they can move on.


        Dashboards didn’t fail.

        They served a purpose.

        They made data more visible.

        But they were built for the tool, not the task.


        That’s why people still exported to Excel.

        Still asked for screenshots.

        Still emailed the data team to “just pull the numbers.”


        Dashboards were never the destination.

        They were a necessary stop along the way.



        So what now?


        We’re not saying dashboards are obsolete.


        But if you’re planning your next analytics investment ask yourself:

        • Are we designing for discovery or for action?

        • Are we showing data or solving problems?

        • Are we asking users to log in, or bringing answers to where they already work?


        Because maybe the future isn’t just more beautiful dashboards.

        Maybe it’s less dashboards altogether and more data that just shows up when it’s needed.



        At Fuse, we believe a great data strategy only matters if it leads to action.


        If you’re ready to move from planning to execution — and build solutions your team will actually use — let’s talk.


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