If AI Compresses Consulting, the Premium Moves to Judgment

Kevin O'Leary's recent consulting warning is sharp for a reason.

He is naming something many professionals can already feel: companies are increasingly willing to ask AI for the first pass before they ask a human adviser for the first meeting.

That matters.

But "consulting is dead" is still too blunt to be useful.

The more accurate question is:

what kind of consulting work is getting cheaper, and what kind still commands trust, budget, and attention?

That is not only a consulting question.

It is a career-positioning question for PhDs, MPHs, PharmDs, scientists, operators, and senior professionals whose value has always depended on more than producing information.

Why O'Leary's warning lands

The underlying logic is easy to understand.

If AI can generate faster summaries, draft recommendations, compare options, and pressure-test assumptions, then some work that once justified junior consulting hours or slide-heavy advisory work becomes easier to do in-house.

That is a real compression risk.

It is also not limited to consulting firms.

Any role built mostly on:

gathering already-available information
producing generic analysis
summarizing obvious options
handing over recommendations without owning implementation

is more exposed now than it was even two years ago.

That is why the consulting story matters for a wider audience.

It is one of the clearest places to see where AI weakens "I can generate analysis" as a moat.

Why the "consultageddon" take still misses part of the picture

The counterpoint matters too.

The Financial Times argued on June 23, 2026 that investors may be punishing IT consultancies too indiscriminately. The logic there is straightforward: even if AI deflates some traditional service lines, consultancies still retain client access, implementation capacity, and the ability to help organizations deploy tools they do not yet know how to operationalize on their own.

That point is reinforced by Business Insider's June 21, 2026 reporting on O'Leary's criticism. Even in a bearish frame, the article notes that large firms are not standing still. McKinsey says about 40% of its work now comes from AI-related projects. BCG said 20% of its work was AI-related in 2024. Accenture reorganized around AI-led reinvention services.

In other words:

AI is pressuring consulting
consulting firms are adapting through AI work
AI providers still need distribution, deployment, and client-trust channels
clients still need humans when the stakes become organization-specific

That does not mean every consulting path is equally safe.

It means the market is repricing which work looks valuable.

What AI seems most likely to compress first

The first layer under pressure is not judgment itself.

It is the layer of work that can be described as:

polished but generic
analytically competent but interchangeable
impressive on paper but thin on implementation
recommendation-heavy and accountability-light

That includes some of the work people traditionally associated with junior consultants and some of the work many other professionals have quietly relied on for perceived credibility:

background synthesis
benchmark slides
first-draft strategy language
market scans with little original interpretation
long recommendation decks no one operationally owns

If AI can now do more of that work faster and cheaper, then being "the person who produces analysis" becomes a weaker standalone proposition.

What still carries the premium

The premium moves toward work that is harder to commoditize:

problem framing when the situation is ambiguous
implementation when systems, people, and politics all matter
translation between technical depth and business action
judgment under uncertainty
accountability when the consequences are real

That is why the Real Edge lens matters here.

The winning signal is not simply:

I know a lot.

It is:

I help people make better decisions, with clearer judgment, under real constraints.

But there is an important next step.

Most highly trained professionals understand that sentence and still do not know how to prove it.

Judgment does not become visible through claims. It becomes visible through story.

This is the part many candidates and leaders miss.

Hiring managers are not really looking for the phrase "strategic thinker."

They are looking for evidence that you can see a situation clearly, frame the real problem, notice what matters, and act in a way other people can trust.

That kind of judgment does not become legible because you say you have it.

It becomes legible because you can show:

what you walked into
what you saw that others were missing
what tension, constraint, or risk made the situation real
what you decided to do
what changed because of that decision

That is story.

Not decorative story.

Evidence-bearing story.

Be the camera, not the slogan

One of the strongest ways to show judgment is to become the camera.

Instead of summarizing yourself with abstractions, you let the reader or listener see what you saw.

You make the situation visible enough that your judgment becomes credible.

That means moving away from lines like:

strategic thinker
collaborative leader
results-driven professional

and toward grounded business narrative:

what business problem you inherited
what conflicting pressures were present
what signal you noticed early
what call you made under uncertainty
what business outcome followed

That is how problem framing becomes visible.

It is also why so many resume and interview problems are not really keyword problems first. They are translation problems. If that sounds familiar, read Why Your PhD Resume Doesn't Get Callbacks.

This is exactly what SCAR is for

This is where my proprietary method matters.

SCAR exists because strong professionals often have real judgment, but present themselves in a way that hides it.

They give background without stakes.

They describe tasks without the actual business problem.

They claim impact without letting anyone see the conditions that made that impact meaningful.

SCAR fixes that by helping you structure the story so the market can see the scene, the complexity, your decision-making, and the results in the right order.

In other words, it helps you stop sounding like a list of capabilities and start sounding like someone who has actually faced, framed, and moved a consequential problem.

That applies in resumes, interviews, networking, leadership communication, and every place where another person has to decide whether to trust your judgment.

Compensation is a useful signal, but not the whole story

Business Insider's June 15, 2026 consulting salary roundup is helpful here because it shows something subtler than "consulting is booming" or "consulting is collapsing."

Entry-level compensation at several major firms has not fallen apart.

The article cites base salaries around:

112,000 dollars at Bain
110,000 dollars at BCG
112,000 dollars at McKinsey
105,000 dollars at Accenture
up to 90,000 dollars at KPMG and PwC

That does not prove the market is easy.

It does suggest that employers still see value worth paying for, even while the mix of valued work is changing.

The same reporting also points to a demand shift toward AI implementation, digital transformation, and data-oriented roles rather than a simple continuation of the old generalist model.

That is a much more useful takeaway than raw prestige.

Prestige may still open doors.

But if the work behind the door is changing, then your positioning has to change too.

What this means for highly trained professionals outside consulting

Many Real Edge readers are not trying to become consultants.

They are trying to reposition themselves in tech, healthcare/life sciences, research, operations, strategy, or leadership tracks where AI is changing both the workflow and the hiring signal.

The consulting debate still applies because it shows where value is migrating.

Employers are becoming less impressed by evidence of effort alone.

They are looking harder for evidence of:

clearer decision-making
stronger stakeholder translation
lower execution risk
better prioritization
more useful communication

That is why an advanced degree, technical background, or long list of responsibilities is no longer enough as a first impression.

Your materials need to reveal:

what business problem you clarified
what tension you navigated
what risk you reduced
what decision improved because of your judgment

If you are still earlier in that process and need a clearer target before rewriting the whole story, start with Find Your Way: AI Career Clarity App. If the target is already clear and the issue is execution, Build Your Candidacy is the stronger next step.

This is the same broader challenge I raised in When AI Standardizes the Hiring Filter: if the first filter is becoming more repetitive, then the story the market sees has to become more decision-grade, not just more detailed.

Four better questions to ask about an AI-era role

If you are evaluating consulting, consulting-adjacent, or AI-shaped knowledge-work roles right now, ask:

Will this role move me closer to implementation, or keep me trapped in commentary?
Am I building a reputation for judgment, or only for producing analysis?
Does the compensation actually match the location, pressure, and expectations?
Can I show my judgment through what I saw, framed, and changed, or am I still hiding behind summary language?

Those questions matter whether you are choosing a job, rewriting a resume, or trying to understand why your current story is not converting.

The practical communication shift

If the premium is moving toward judgment, then your message has to prove judgment earlier.

That means fewer responsibility lists and more evidence of:

how you read a situation
what tradeoff you identified
what recommendation you made
what changed because of it

And it means telling that evidence in a structure that lets another person actually see it.

That is why story competence is not soft decoration. It is how problem framing becomes legible.

If you have already read my article on mentalization as a WIIFT tool, this is the labor-market version of the same idea: usefulness has to become legible to the other side faster.

The strategic takeaway

The consulting conversation is not mainly asking whether smart people still matter.

It is asking which forms of smartness are getting cheaper.

If AI compresses more generic analysis, then the premium moves toward:

judgment
translation
implementation
accountability
trust

That is where many highly trained professionals still have room to differentiate.

But only if their materials and conversations make that value visible.

If your current story still sounds more like "here is what I know" than "here is how I help people decide and act," the market may be reading you as easier to substitute than you actually are.

That is usually where the work starts.