Affärsanalytikern i AI‑agenternas era

AI kan generera en användningsfallsöversikt på under en minut, omvandla en enkel idé till en grundläggande prototyp inom en timme och producera kravdokumentation på en bråkdel av den tid det tidigare tog. Denna förändring är redan synlig i den dagliga affärsanalysen, där praktiker använder AI för att förbereda insamlingssessioner, utarbeta krav och identifiera luckor innan validering. Titta bara på dessa leveranser, så verkar slutsatsen uppenbar: affärsanalytikern försvinner.

I do not share that conclusion. I have worked in business and IT analysis, solution design and digital transformation for years, watching continuous change and different stages of adaptation. What is disappearing is not the role. What is disappearing is its periphery. And that is something entirely different.

Vad en analytiker gör

När du frågar en kund vad de behöver, får du svar på vad de vill ha. Det är inte alltid samma sak. Inte för att de inte förstår sitt eget arbete – de kan helt enkelt inte alltid formulera sina behov tillräckligt tydligt. Rutinarbeten nämns ofta inte eftersom kunderna utför dem automatiskt, varje dag. Kantfall missas lätt eftersom folk tenderar att minnas dem först när de inträffar.

Förbindelser mellan avdelningar förstås ur ett personligt perspektiv, inte nödvändigtvis ur hela organisationens synvinkel. Och när en kund bestämmer sig för att byta flera system samtidigt, missar de ofta att ett byte av ett system nästan alltid förändrar en process också.

Analytikerns uppgift är inte att skriva ner vad kunden sa. Uppgiften är att ta reda på vad kunden inte sade, och fortsätta ställa frågor tills allt kommer fram. Analytikern spårar förbindelser, beroenden och konsekvenser över system, data och affärsprocesser.

That is a diagnostic capability, not an administrative activity.

Tre riktningar för affärsanalytiker

Gränserna för affärsanalys har aldrig varit fasta. Analytiker har länge rört sig in i produktägarskap, projektledning och mer tekniska roller. AI påskyndar bara den rörelsen genom att minska den tid som krävs för rutinleveranser.

A recent Gartner survey found that more than half of organizations had redesigned or redefined roles because of AI, while 78% of HR leaders agreed that workflows and roles would need to realize the value of their AI investments.

From my observations and direct experience, three main trajectories are taking shape.

1. Mot produkt och affär

Analytiker som gillar att forma produkter kan gå in i bredare roller som omfattar produktägarskap, affärsanalys och leveranssamordning. Sådana kombinationer finns redan, särskilt i mindre team. När AI minskar den tid som läggs på att producera rutinartefakter blir gränserna mellan dessa ansvarsområden sannolikt mer flytande.

This should not mean handing four jobs to one person simply because AI can write the documents. The opportunity lies in bringing together responsibilities that already depend on the same understanding of customer needs, product priorities and business outcomes.

This direction also requires a stronger understanding of data. Analytical work has often focused on processes, functionality and requirements while leaving the data layer in the background. AI systems make that separation increasingly difficult.

An analyst who understands both the business context and the data can recognize when a model is correctly answering the wrong question. That may happen because the available data does not represent the situation accurately, the business definition behind a metric has changed or the original question was poorly framed.

AI can help almost anyone create a dashboard or report. Interpreting what the result means for a particular business, at a particular moment, still requires context and judgement. Forrester describes a similar change across software roles, with AI moving time away from repetitive artifact production and toward activities such as validation, orchestration and control.

2. Mot teknisk prototypning

Analytiker med stark förkärlek för utveckling kan kunna prata med en kund och sedan bygga en fungerande prototyp – inte bara en mock‑up, utan något kunden kan testa och validera.

This changes requirements validation: customers can interact with an idea instead of trying to interpret it from a document. Misunderstandings can surface earlier, while changes are still relatively inexpensive to make.

But a prototype is not a production system.

Security, integration, scaling and performance under load are not solved by “AI helps me code”. Building enterprise software for production requires a different level of engineering expertise. Some analysts may gradually develop those skills and move closer to production-level development. It is a considerably more demanding path than rapid prototyping suggests, but it is a realistic one.

3. Mot projekt- och leveranshantering

Analytiker koordinerar redan kunddiskussioner, bidrar till uppskattningar och hanterar frågor kring omfattning, krav och förändring. Det skapar ett naturligt överlapp med projektledning.

On smaller projects, one person may be able to combine analytical and coordination responsibilities because both depend on understanding scope, stakeholders and dependencies. That can shorten communication paths and make it easier to connect day-to-day delivery decisions with the original business need.

This does not make the project manager redundant. On larger projects, where coordinating people, risks and dependencies is genuinely complex, project management remains a specialist discipline. The blended approach makes sense only where the scope allows it.

The distinction matters. AI may reduce the effort required to create plans, meeting summaries or status reports, but producing those artifacts was never the full value of project management either.

Vad som skiljer dem som navigerar detta väl

All three directions require analysts to move beyond familiar role boundaries. Curiosity gives them the willingness to test new tools and approaches. Continuous learning prevents what I think of as knowledge debt: the gradual accumulation of outdated assumptions and skills, much like technical debt in a legacy system.

Perhaps most important, however, is the ability to verify. Analysts are accustomed to asking “Is this actually correct?” before asking “Is this done?” In an environment full of fluent, professionally presented AI outputs, that habit becomes more valuable.

AI can produce incorrect answers in a confident and persuasive form – a pattern highlighted by MIT Sloan researchers as one of the key reasons human oversight remains essential. Its outputs can be structured, fluent and polished.

Failure does not happen at the point of using AI. It begins when a plausible output is accepted without verification.

Frågan är inte om affärsanalytiker kommer att försvinna

Many articles about the future of the analyst role are written as predictions. This is not a prediction. It is an observation: the role is changing as it always has. What is different this time is the speed – and the fact that its periphery is becoming accessible to almost anyone.

A developer can use AI to conduct requirements analysis. A manager can use it to generate user stories. They may not do either particularly well, but an analyst whose value rests solely on producing deliverables will have a difficult time.

What remains at the center of the role is the ability to uncover unstated needs, connect business and technical contexts, verify outputs and see connections that the customer cannot. According to IIBA’s 2025 Global State of Business Analysis Report, 74% of practitioners say AI is positively affecting their careers, while human skills such as communication, strategic thinking and adaptability are becoming more important.

The question is not whether business analysis will disappear. It is what remains when its periphery becomes available to everyone.

The answer is the judgment that takes years of practice, varied projects and experience with failure to develop. It is the ability to recognize that a documented requirement is incomplete, a working prototype solves the wrong problem, or a polished answer rests on a false assumption.

That is the part of business analysis that cannot be acquired quickly.