A consulting firm can improve current project margins by automating junior work while weakening the supply of judgement it will need a decade from now. The immediate saving appears in delivery economics; the capability cost may emerge much later, when the firm needs experienced managers and partners whose developmental work no longer exists.
Junior work has always served two purposes: it produces the client deliverable and develops the practitioner who will eventually be expected to exercise judgement independently. People learn by framing problems, finding weak evidence, making mistakes, receiving detailed edits and seeing how client context changes a technically sound answer. AI can reduce the effort required to produce the deliverable, but unless the firm deliberately replaces those experiences, it can also weaken the capability it expects to sell later.
HBR has examined the changes in professional-services structure and entry-level hiring, while recent working papers describe a judgement vacuum, pipeline floor and hollow pyramid. The underlying issue is whether a firm can continue producing the expertise on which its future delivery model depends after removing part of the work through which that expertise was formed.
The billable apprenticeship
The traditional leverage model did more than move work from expensive people to cheaper ones. It embedded professional development inside client delivery. Junior practitioners researched, modelled, drafted, checked and reconciled. The work advanced the engagement. The repetition and feedback developed the practitioner. The client paid for hours that produced both outputs.
Not every hour was valuable training. Repetitive work without challenge or feedback can teach very little. Research on expertise places more weight on the quality and variety of practice than on time served alone. A 2025 Annual Review of organisational expertise emphasises active practice, varied conditions and informative feedback.
But the old model contained a subsidy: some of the cost of creating future senior judgement sat inside billable production. AI makes it possible to produce the artifact with fewer junior hours. The firm then needs to decide where the learning will occur, how it will be funded and who will be accountable for developing the practitioner.
The margin arrives first
The immediate economic case is attractive: reduce junior production time, maintain quality and complete the engagement faster or with fewer people. The resulting margin improvement can be measured at project level, while the capability cost falls on a different owner and a much longer schedule.
It appears later, when a manager cannot identify the weak source behind a fluent answer; when a senior reviewer has to reconstruct reasoning that the team never formed; or when the firm needs a new partner with judgement built through situations that no longer reach junior staff.
The project may be closed years before that cost becomes visible. I describe the gap as capability depreciation: future capability consumed when developmental experience is removed without a funded replacement. The term matters because "training risk" sounds like an HR issue. Capability depreciation sits inside the economics of automation. The project captures a current saving by drawing down an asset the project P&L does not record.
The wrong question
Most automation programmes ask:
How much junior work can the model remove?
The better question is:
Which experiences are required to create the judgement we expect to sell later, and where will those experiences come from now?
That question changes the automation decision. A task that is low-value as production can still contain a useful developmental exposure. A task that consumes hundreds of junior hours can still be poor training. The work should not be protected because it is old or billable. Its developmental function should be identified before it disappears.
The capability-depreciation ledger
Every material automation of junior work should add five lines to the business case.
1. Production removed
Name the work: initial research, model construction, document review, interview synthesis, drafting, reconciliation or scenario analysis. Estimate the hours removed. Measure the checking and rework added. Use accepted output rather than first-draft speed.
2. Experience removed
Name what the practitioner used to encounter while doing the work:
- Forming an independent problem frame
- Finding and assessing primary sources
- Discovering how data fail
- Building and testing assumptions
- Reconciling conflicting evidence
- Explaining a position under challenge
- Seeing how client context changes the answer
- Receiving detailed feedback on an attempted solution
If none of those are present, the task may be a strong automation candidate with little capability cost.
3. Replacement funded
Decide how the experience will be recreated. Protected first-pass analysis can require a junior to form a view before seeing the model's answer. Structured source audits can preserve evidence judgement. Historical cases and seeded errors can create repeated practice. Progressive client responsibility can expose practitioners to the organisational context no simulation fully captures.
There is useful learning science underneath some of these choices. Research on prediction as a learning strategy finds benefits when learners generate an answer before seeing the solution. But a generic curriculum is unlikely to replace the apprenticeship of every professional-services firm.
The replacement has to match the experience removed.
4. Senior time
The replacement experience needs senior practitioners with explicit time and accountabilities to observe, challenge and teach. AI may reduce the number of junior production hours while increasing the concentration of senior attention required to develop each person. That time is becoming more valuable at exactly the moment the firm needs more of it.
"Partners should mentor more" is not an operating model. The business case needs the hours, staffing ratios, incentives and budget. Accenture reported US$1 billion of learning and development investment and approximately 47 million training hours in fiscal 2025. The figures do not tell us whether a particular method produced better judgement. They show the scale of explicit investment that capability building can require.
5. Evidence of capability
Training completion is not the outcome. The firm needs to know whether people can:
- Identify unsupported claims and weak sources
- Build sound assumptions before senior correction
- Detect errors in historical or seeded cases
- Escalate appropriately under uncertainty
- Perform across varied case types
- Explain and defend reasoning with a client
- Reduce rework as responsibility increases
Confidence scores and course attendance are easy to collect. The ability to catch a consequential mistake is the thing being built.
Illustrative ledger: market-scan automation in a 200-consultant practice. Assume each consultant team completes about 90 recurring market or competitor scans per year. The agent handles first-pass search, clustering and source extraction; juniors still own question framing, source challenge and final inclusion decisions.
| Ledger line | Illustrative entry |
|---|---|
| Production removed | About 1,400 junior hours per year: 90 scans x 20 old first-pass hours x 78 percent automated. At a blended junior cost of $95 per hour, the gross production saving is about $133,000 before review and rework. |
| Experience removed | Repeated source-quality judgment across about 90 scans: deciding which filings, earnings calls, trade sources and expert interviews matter; resolving contradictory market definitions; finding unsupported claims. |
| Replacement funded | Protected first-pass design: each junior frames the question, proposes a source hierarchy and writes a pre-model hypothesis before seeing the agent output. Budget 4 protected junior hours per scan, or 360 hours per year. |
| Senior time | Reviewer challenge sessions: 1.5 senior hours per scan to test exclusions, evidence quality and final synthesis. At $275 per senior hour, the explicit teaching cost is about $37,000 per year. |
| Evidence of capability | Quarterly source-audit score, unsupported-claim detection in seeded cases, reduction in reviewer rewrites, and observed ability to defend inclusion/exclusion decisions in client-ready discussion. |
The example is not a pricing recommendation. It shows the accounting move: the automation case should book the production saving net of the replacement experience required to keep future judgement forming.
One task, redesigned
Take a market and competitor scan. An agent can assemble the first pass quickly. The production case measures time saved, source coverage, error rates and revision effort. The capability question is what the junior used to learn while doing it.
If the task required decisions about source quality, contradictory evidence and ambiguous market definitions, removing the first pass removes useful practice. The redesigned workflow could require the junior to:
- Define the question and source hierarchy before invoking the agent
- Form an initial hypothesis
- Audit a sample of citations against primary sources
- Find evidence that contradicts the synthesis
- Defend the final inclusion and exclusion decisions to a reviewer
The agent still removes production work. The junior still performs the acts most likely to build evidence judgement. Whether that design works should be visible in subsequent performance, not assumed from the elegance of the exercise.
The governance dependency
Lawyers using AI retain duties of competence and supervision under ABA guidance. Auditors retain duties of due professional care and scepticism under PCAOB standards. Management consulting does not carry the same formal professional regime. The operating problem is still relevant.
If a firm expects people to challenge, supervise and stand behind machine-assisted work, those people need enough expertise to know when the machine is wrong. A firm that automates the pathway to that expertise creates a dependency on the stock of judgement it already has.
Existing senior expertise can sustain the firm for years, which makes the missing development pathway easy to overlook until the next generation is required.
The practical test
Before removing a junior task, ask:
If this work disappears, which experience disappears with it?
If the answer is "none", automate it. If the answer includes evidence judgement, ambiguity, client context or progressive responsibility, put the replacement inside the investment case before taking the saving. The decision should depend on the developmental function of the work. Where that function is negligible, automation can proceed with little capability cost. Where it is material, the sponsor should fund a replacement experience, assign the teaching accountability and measure whether judgement continues to develop. The business case then reflects both the current production saving and the cost of maintaining future capability.