IM BLOG: What We Can Learn from Atlassian

When Atlassian announced it would cut around 1,600 jobs while simultaneously accelerating its investment in artificial intelligence, the headlines focused on one thing: AI taking jobs.

The framing is understandable. But it misses the more important story.

For records and information managers, the Atlassian announcement is not simply technology news. It is a signal about how organisations are restructuring themselves around AI and what that means for the people responsible for managing information.

AI is reshaping knowledge work and our profession is in the middle of it

Consider what is already happening in organisations worldwide.  Legal teams are using AI to review contracts and flag risk clauses. Finance departments are running automated reconciliations across thousands of records. HR functions are summarising performance data and generating reports without manual input.

In each of these scenarios, someone still needs to ensure the information feeding those systems is accurate, consistently described and properly governed. That someone is - or should be - a records and information management practitioner.

Atlassian's restructuring reflects a broader shift: as AI becomes capable of handling routine cognitive tasks, organisations are reducing headcount in those areas alongside increasing investment in the platforms that automate them. The balance between human labour and automated capability is changing.

This dynamic is not confined to technology companies. Any organisation that works primarily with information is affected. And records management sits directly in that territory.

AI is only as reliable as the information behind it

Here is a scenario you’ve all seen. An organisation deploys an AI tool to help staff locate and summarise policy documents. Within weeks, the tool is surfacing outdated policies that were never formally disposed of, returning results without meaningful context and in some cases presenting superseded guidance as current.

The problem is not the AI. The problem is the information it was pointed at.

This is not a hypothetical. Organisations everywhere are encountering exactly these issues as they rush to implement AI without first addressing the quality of their underlying information. Fragmented file storage, inconsistent metadata and poor lifecycle management - problems that seemed manageable when humans were doing the searching - become acute when automated systems are doing it at scale.

Records and information practitioners understand the concepts that now sit at the heart of responsible AI deployment: context, classification, retention. These are not abstract principles. They are the mechanisms that determine whether an AI-generated output can be trusted and whether a decision based on that output can be explained to a regulator, a court or a board.

Information governance is now AI infrastructure

Think about how organisations treated network infrastructure in the early days of the internet. Initially it was seen as a technical concern, something for IT to manage. Over time, leaders came to understand that reliable connectivity underpinned everything the organisation did. It was not a support function. It was strategic infrastructure.

Information governance is at a similar crossroads

AI systems require structured, accessible and trustworthy information to function effectively. Without it organisations face unpredictable outputs and the very real possibility that automated decisions cannot be adequately explained or defended.

Yet many organisations still treat records management as a back-office administrative function. That view is becoming untenable.

As AI becomes embedded in everyday business operations, information quality directly determines automated output quality.  Executives may not frame it in those terms but the connection is impossible to ignore.

AI compounds risks that already existed

The rapid adoption of AI has created the impression that entirely new categories of risk are emerging. In reality, many of the risks associated with AI already existed in organisational information. The technology has simply made them larger and more visible.

Consider retention

An organisation that has accumulated years of unmanaged legacy records may not have considered it a significant risk while that information sat dormant. But when an AI system is trained on, or given access to, that volume of sensitive, superseded or irrelevant information, it can surface in unexpected ways. Information that should have been disposed of years ago suddenly has reach.

Or consider metadata

Records that lack adequate contextual description have always been harder to find and interpret. When an automated system is tasked with analysing those records at scale, the absence of metadata compounds into systematic misinterpretation.

These are not new problems. Any experienced records manager will recognise them immediately. What is new is the scale at which they now present and the speed at which they can cause organisational harm.

The profession needs to reframe its contribution

Atlassian's restructuring ultimately reflects a question that organisations everywhere are beginning to ask: as technology changes, where does human expertise add value?

Professions that define themselves narrowly around routine tasks tend to find themselves displaced as automation increases. Professions that articulate their contribution in strategic terms tend to grow in relevance.

The information management profession has an opportunity to make that case clearly and confidently.

The conversation should not be limited to storage systems, retention schedules or classification schemes. It should be about organisational accountability, decision transparency and what it means to operate responsibly in an AI-enabled environment. When an automated decision is challenged someone needs to be able to explain what information it was based on, where that information came from and why it can be trusted.

That is as much a governance question as it is an evidence question. And it sits squarely within the expertise this profession has been developing for decades.

A moment of transition and opportunity

The Atlassian restructuring is a useful reminder that technology transitions rarely change organisations in isolation.  What matters most is how quickly people and organisations recognise where value is moving and adjust their capabilities accordingly.

Automation will reduce demand for certain types of routine information processing work. There is no denying that. But it will increase the importance of information governance, assurance and accountability.

Those capabilities are at the core of records and information management.

Organisations that deploy AI most effectively in the years ahead will be those that build trustworthy information environments first. The practitioners who help organisations build those environments will not be sidelined by this transition. They will be central to it.

 Meet your blog author:

 

Peta Sweeney CXRIM FRIM (Life), Information and Content Specialist, RIMPA Global