The Death of Organisational Memory

AI may automate finance work faster than companies can reproduce expertise.

• AI may be removing the friction through which financial judgment traditionally formed.

• Finance teams can become more productive while understanding less underneath.

• The real risk is not bad outputs, but fewer people knowing when outputs are wrong.

Jamie Dimon, CEO of JPMorgan Chase. AI may automate finance work faster than companies can reproduce financial judgment.

The Productivity Boom Nobody Is Questioning

A junior finance analyst can now generate a polished board-level variance summary in under five minutes using AI. Five years ago, that same analyst may have spent half a day tracing transactions, checking assumptions, investigating anomalies, and understanding what actually drove the numbers. The new process is dramatically faster. It may also produce weaker financial judgment over time.

That tension sits underneath much of the current excitement around AI inside finance functions. Most executive conversations understandably focus on productivity gains. Faster close cycles, leaner reporting teams, automated reconciliations, AI-generated commentary, and improved forecasting all present compelling operational and economic advantages. If AI can safely compress hours of analytical work into minutes, every finance organisation will eventually face pressure to deploy it.

The more difficult question is what happens once large portions of the underlying work disappear.

Productivity and capability are not always the same thing.

Finance Functions Run On Judgment

Finance functions do not ultimately operate on software. They operate on judgment. Experienced finance leaders develop an instinct for anomalies, inconsistencies, fragile assumptions, and operational risks that rarely appears explicitly inside systems or dashboards. Much of that judgment forms through repetition and exposure.

Analysts learn by tracing discrepancies across systems. Accountants learn by manually resolving broken reconciliations. Teams build commercial intuition by repeatedly working through situations where the numbers do not initially make sense.

Historically, much of this work looked inefficient. In reality, it was also training.

That distinction matters because AI is arriving at the exact layer where many organisations unknowingly developed future financial leaders.

Finance Has Seen Versions Of This Before

Most finance teams have already experienced smaller versions of this dynamic through spreadsheet dependency.

A model becomes operationally critical over time until only one or two people fully understand the underlying logic. Everyone else learns the workflow but not necessarily the assumptions, calculations, or structural weaknesses embedded underneath it. The spreadsheet continues producing outputs while organisational understanding gradually narrows.

The problem only becomes visible when something changes. A key employee leaves. A formula breaks. A reporting anomaly appears. A business assumption shifts. At that point, the organisation discovers it can still operate the process, but fewer people can confidently explain how the model actually works.

That is organisational memory loss in practice. Not dramatic collapse, but a gradual separation between producing outputs and understanding how those outputs are produced.

The process survives. The understanding becomes concentrated.

AI Changes The Development Path

Generative AI may scale this dynamic significantly because it reaches much further upstream than previous forms of enterprise automation.

Earlier generations of finance technology primarily automated workflows, processing, and administration. AI increasingly automates interpretation, synthesis, explanation, and analysis. Those activities have historically been where junior finance professionals developed judgment.

This creates a potentially uncomfortable paradox.

The first generation of AI-enabled finance teams may become highly productive before they become deeply experienced.

At first, the indicators look entirely positive. Reports improve. Commentary becomes more polished. Teams move faster. Junior staff contribute earlier. Executives receive cleaner summaries and faster responses.

Operationally, the transformation appears successful because, in many respects, it is successful.

The challenge is that highly polished outputs can obscure weakening capability underneath.

The first generation of AI-enabled finance teams may become highly productive before they become deeply experienced.

The first wave of AI adoption has been dominated by productivity gains. The second wave will be defined by organisational consequences that most leadership teams still underestimate.

That is the focus of For Every Scale.

I write for CEOs, CFOs, COOs, boards, and executive teams navigating how generative AI is changing operational capability, management structures, and decision-making inside large organisations.

If this article resonated with you, subscribe for analysis on the strategic and organisational implications of AI beyond the hype cycle.

The Friction Was Doing More Than People Realised

A surprising amount of financial judgment historically came from doing slow, difficult work manually before automating it.

Analysts built instinct by repeatedly encountering inconsistencies, exceptions, and unexplained variances. Over time, they learned not just how to produce numbers, but how to interrogate them.

Generative AI may remove portions of that developmental layer. Not intentionally, but structurally.

There are already signs of this pattern emerging inside highly automated accounting environments. Researchers studying automated accounting workflows documented situations where staff struggled to manually reconstruct processes once automation systems were removed.

The systems operated effectively while the automation layer functioned normally. The vulnerability only became visible once people needed to work without it.

The problem with removing all friction from knowledge work is that friction is often where understanding comes from.

The Leadership Question Is Changing

For CFOs and COOs, this changes the leadership question entirely.

The challenge is no longer simply determining how much work AI can automate. The harder question is how the organisation continues developing financial judgment once many of the hard parts disappear.

That is not purely a technology problem. It is an organisational design problem.

Most executive dashboards are designed to measure productivity, throughput, cost reduction, reporting speed, and close-cycle compression. Those metrics matter and should matter. However, they do not necessarily indicate whether the organisation is still producing future experts.

They do not measure whether junior finance professionals are developing the judgment required to operate effectively under ambiguity, stress, or abnormal conditions.

Boards do not simply want accurate reporting. They want confidence the organisation understands why the numbers are accurate.

A Framework For Leadership Teams

The practical test for leadership teams is not whether AI saves time. It is whether the organisation has a plan to replace the learning that the saved time used to create.

A useful starting framework is what I think of as the Capability Preservation Test.

1. What work are we automating?

Not the process name. The actual judgment being removed from human practice.

A reconciliation process may also be teaching anomaly detection. A forecasting workflow may also be building commercial intuition. A reporting task may also be developing pattern recognition.

2. What capability did that work used to build?

Many repetitive finance activities were unintentionally serving as apprenticeship systems.

Before removing the work entirely, leadership teams should identify what forms of judgment were historically being developed through repetition and exposure.

3. Where will that capability now be developed?

This is where many organisations currently have a blind spot.

If AI removes large portions of manual analytical work, where does the next generation of financial instinct come from? What replaces the developmental pathway that previously existed?

4. How will we know people still have the capability?

Most organisations test process compliance. Far fewer test operational judgment.

That may need to change.

Manual reconstruction exercises, anomaly reviews, edge-case simulations, and model challenge sessions may become increasingly important in highly automated environments.

5. Who owns the capability, not just the system?

System ownership is not the same thing as capability ownership.

Someone inside the organisation needs to be accountable for ensuring critical financial judgment continues developing over time, even as automation increases.

The Risk That Arrives Slowly

The real long-term risk may not be hallucinations or bad outputs. It may be a gradual erosion in the organisation’s ability to independently understand its own operations.

Not because the systems stop working, but because fewer people are repeatedly exposed to the friction through which deep financial judgment historically formed.

The irony is that organisations moving fastest with AI may not notice this dynamic until years later because the early indicators look exactly like success. Productivity improves. Reporting accelerates. Teams become leaner. Outputs become more sophisticated.

Until the organisation encounters something the system was never designed for and discovers too few people still know how to think through the problem from first principles.