I have been asked this question at every conference I have attended in the last two years. Not always directly. Sometimes it arrives as “what do you think about Copilot” or “is there still a point learning DAX properly.” But the underlying question is always the same: is my job going to exist in five years?
It is a fair question. When you watch GitHub Copilot complete a CALCULATE function before you have finished typing the first argument, or paste a business requirement into Claude and get back a working Power Query transformation, it is easy to understand why people are asking it.
Here is my honest take, as someone who has been building BI solutions since 2006 and who has spent the last year testing these tools in real work, not in demos.
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What These Tools Can Actually Do Today
I have been using ChatGPT, Microsoft Copilot and Claude regularly over the last eighteen months, in actual client work and personal projects. Not in theory. In my editor, on real data models, with real business requirements.
ChatGPT is strong at generating DAX and SQL when you give it enough context. If you describe the table schema, the business logic and the expected output format, the first attempt is often close enough to use with minor adjustments. I have used it to draft measures I would have spent an hour on, in five minutes. It is not always right on the first pass, but it moves fast enough that iteration costs less than starting from scratch.
Microsoft Copilot inside Fabric and Power BI has improved noticeably over the last year. The report creation assistant went from generating generic placeholder visuals in early 2024 to producing layouts I would actually take and refine for production use in 2025. It will not replace design judgment, but it removes the blank canvas problem. For report creation at scale, that matters.
Claude has been the most useful for reasoning about model design. I used it to reverse-engineer a semantic model from an annotated schema diagram and it handled the relationships and measure dependencies better than I expected. I also hit real limits: it had no knowledge of my actual data, made assumptions that were wrong for our domain, and I needed five or six iterations before the output was usable. The hiccups were real. I am not smoothing those over.
The pattern that holds across all three: if the task is well-defined, the context is fully provided, and the output can be verified quickly, these tools are fast and genuinely useful. That describes a significant portion of the day-to-day coding work in a typical BI role.
Where They Still Fall Short
None of these tools know your business.
That sentence sounds simple, but it is where the real gap sits in practice. Generating a CALCULATE function is not difficult once you know the filter context. The difficult part is knowing that in your organization, “active customers” means accounts with at least one transaction in the last 90 days, but only in the consumer segment, and that this definition changed in Q2 2024 and needs to be handled differently across historical and current period comparisons.
That context lives in your head, in Confluence pages nobody reads, in a meeting that happened eighteen months ago, and in an email from a finance analyst who has since left the company. No AI tool picks that up from a prompt. You bring it, or it does not exist in the output.
The same gap shows up in data quality judgment. AI tools will generate a transformation pipeline from a spec without questioning whether the spec is correct. They will not notice that the order date column has 8% null values, or that this matters for the revenue calculation, or that the exceptions exist because of a legacy system migration that your company did in 2019. You notice, because you have seen the data before and know what those patterns mean.
Report creation has the same ceiling. The AI can build a layout, suggest a chart type and write a title. It cannot make the call that this dashboard will be shown on a 40-inch screen in a warehouse and that the font size from the default template will be unreadable from three meters away. That judgment comes from having sat with the people who use the reports.
What This Actually Means, by Role
The impact is not uniform. It depends on how much of your current working week involves mechanical repetition.
For BI developers, the most immediate change is in code generation. Boilerplate DAX, repetitive ETL transformations, standard report templates: these are exactly the tasks AI accelerates most. If this work is a large part of your week, your output volume will increase and expectations around it will rise with it. That is not a threat if you understand it early enough to stay ahead of it.
For data analysts, the change is most visible in exploration speed. Asking Claude or Copilot to produce a first-pass analysis of a dataset, flag anomalies or suggest groupings gets you to a hypothesis faster. The interpretation of that hypothesis, and the judgment about whether it is the right hypothesis for the actual decision being made, remains yours.
For data engineers, pipeline boilerplate generation and schema documentation are straightforward wins. Debugging complex transformation failures where the error is opaque is an area where these tools also provide real value, particularly if you can paste the full stack trace and table definitions into the prompt.
For data architects, the tools work well as thinking partners for structured design questions. Talking through a proposed model, generating documentation drafts, checking naming conventions across a schema. The decisions about what to govern, where domain boundaries sit, and how to design for the organizational reality rather than the theoretical ideal still require judgment that comes from knowing the context.
For data governance specialists, the upside is in documentation and lineage drafting. The real governance work, defining what quality means for a specific data product and making that stick across teams, is still a people and process problem that no AI tool solves for you.
My Perspective as a Practitioner
I am not going to tell you AI will not change BI work. It already has. But the question of whether it replaces BI developers wholesale is the wrong framing.
The more useful question is: which parts of your current work are mechanical enough that AI tools can do them faster and cheaper? Be honest about that list. If it is long, that is information worth having now rather than in two years when the market has already adjusted.
Here is what I have observed across the work I do and the people I talk to at conferences: the work that clients are most anxious, most uncertain, and most willing to pay for is not the part AI is good at. It is the part that requires knowing their business, understanding who trusts what report and why, having the conversation with the finance director who distrusts the new model, and standing next to the operations manager while they explain what the dashboard actually needs to show.
That is still practitioner work. AI does not do it. It accelerates the technical work that happens around it, which gives you more time for the part that matters most.
There is one shift I have started to notice though, and it is worth naming. The finance director on the other side of the table is also using these tools. They arrive at the meeting having already asked Claude to explain the variance, or having had Copilot summarize the dashboard. They come in with a better baseline understanding than they had two years ago, and that makes for a more productive conversation. You spend less time on mechanics and more time on the actual decision. That is a good thing, not a threat.
The hallucination problem is real and it is the most reasonable objection anyone raises to trusting AI output in production work. But it is also improving faster than most people expected a year ago. The gap between what these tools got wrong in early 2024 and what they get wrong now is measurable. I expect that to continue. The sensible approach is to verify outputs where the stakes are high and to track how often you have to correct them over time. That number has been going down in my experience.
The BI developers I have watched get uncomfortable recently are the ones who built careers around syntax knowledge, template maintenance and format conversion. Those jobs are genuinely changing. The developers who are doing well are the ones who understood that the syntax was never the point: the business problem was the point, and tools that help with the syntax give them more time for the problem.
My advice is straightforward: learn the tools, use them on real work rather than tutorial datasets, and find out where they fail on your actual data in your actual domain. That is the only way to know where your judgment matters more than their suggestion.
For me, the answer to whether AI will replace BI developers is: not the ones who are clear about what they are actually being hired to do.
What has your experience been with AI tools in your daily BI work? I would like to know what people are actually finding useful versus what sounds better in a conference session than it works in practice.