Can AI Replace Your Consultants and Insights Agencies?

The answer is both yes and no. AI can draft a strategy in an afternoon. It can also hand you a confident, wrong answer one time in five. The real question isn't internal versus external. It's how much to internalize.

Every CMO I talk to is asking the same thing right now.

Can my own team just do this with AI, and stop paying consultants and insights agencies?

Yes. And no. Both are true at the same time, and the mistake is picking one and running with it. Let me split the answer where it actually splits, because the line between what AI replaces and what it doesn't is sharper than the hype on either side suggests.

Where the answer is yes

A working session with a capable AI can help you develop a pricing and packaging strategy that is better than what a mediocre consultant would produce. Not a demo. A real, defensible strategy. Sit down with the model, feed it your situation, push on it, and in an afternoon you have something a mid-tier firm would have billed a month for.

That is not a small claim, so let me be blunt about what it means. The average expert is now replaceable. The junior analyst pulling a framework off the shelf, the generalist who repackages last year's deck, the report that could have been written about any brand in the category. AI does that work faster, cheaper, and often better.

Digital twins push it further. A well-built digital twin of your target customer can generate insights in minutes and let you iterate at a speed no fieldwork can match. Test a message. Reframe it. Test again. What used to be a six-week study becomes a morning of questions. For teams that were starved of insight because research was too slow and too expensive, this is genuinely new oxygen.

So if the question is "can AI do the work an average agency does," the honest answer is: increasingly, yes, and you should be internalizing that work.

Where the answer is no

Now the part the hype skips.

Around 20% of AI-generated recommendations may be misleading or even dangerous. Not obviously wrong. Dangerously plausible. The model produces them with the same fluent confidence it gives the good 80%, so you cannot tell which fifth is poison by reading the output. It looks equally polished either way.

This is where a great consultant earns the fee. Their value was never producing the answer. It's knowing which questions to ask to surface the blind spots, which assumption to stress-test, which single question flips the whole conclusion. AI does not know what it failed to consider. It fills the gap with a confident story instead of raising its hand. A great expert raises their hand.

The gap between the average consultant and the great one used to be quiet. AI just made it loud, because AI closes the bottom of that range and leaves the top wide open.

Garbage in, garbage out — twins included

Digital twins have the same catch, and it's easy to miss because the twin feels so responsive.

A twin is only as good as the data it was trained on. And here's the uncomfortable truth: much market research is poor to begin with. It leans on what people say rather than what actually drives them, which is the say-do gap that has corrupted survey work for decades. Build a twin on that, and you get a poor twin. A very fast, very articulate poor twin, which is worse than a slow one, because it lets you make bad decisions at scale before anyone checks the foundation.

Garbage in, garbage out didn't stop applying because the interface got conversational. If the research underneath is a guess, the twin decorates the guess and hands it back to you in seconds. Speed magnifies whatever quality you started with, in both directions.

This is exactly why the method underneath matters more now, not less. A twin built on Causal AI and implicit measurement — data that captures what moves behavior, not what respondents rationalize afterward — is a different animal from one trained on a stack of old tracker surveys. Same technology on the surface. Opposite reliability.

The question that actually matters

So the framing everyone reaches for — internal teams versus consultants and agencies — is the wrong fight.

The better questions are two.

First: how much should we internalize? The answer for most companies is "more than you do today." The routine, repeatable insight work belongs inside now. Owning it makes you faster and cheaper, and it builds a muscle your competitors who still outsource everything won't have. If you're wondering how to pressure-test that muscle, that's the same instinct behind knowing how to challenge your MBB consultant — you ask harder questions of your own AI, too.

Second, and more interesting: how do you use external experts to complement and amplify your internal AI capability, rather than replace it? A boutique specialist is no longer there to produce the deck. They're there to catch the dangerous 20%, to raise the quality of the data your twins run on, to bring the judgment AI can't fake. The best AI still needs experts pointed at the right questions, and experts are worth far more when they're multiplying an AI-equipped team instead of doing everything by hand.

That's the shift. Not experts out, AI in. Experts moved up the stack, AI doing the floor.

Brands that get this will save real time and money and move faster than the ones still choosing sides. Considering the risks and the stakes involved, AI should be complemented by human expertise — not because human expertise is sentimental, but because the 20% it catches is exactly where the expensive mistakes live.

The winners won't be the teams with the most AI, or the most consultants. They'll be the ones who knew which was which.

Can AI replace consultants and agencies: frequently asked questions

Can AI replace management consultants and insights agencies?

Partly. A working session with a capable AI can produce a pricing or packaging strategy that beats what a mediocre consultant would deliver, and digital twins of your customers can generate insights in minutes. But around 20% of AI-generated recommendations can be misleading or even dangerous, so AI replaces the average expert, not the great one. The right move is to internalize what AI does well and reserve outside experts for the judgment it lacks.

What is a digital twin of a customer, and can you trust it?

A digital twin is an AI model of a target customer that you can question the way you would a real respondent, letting teams iterate on ideas in minutes instead of weeks. Its usefulness depends entirely on the data it was trained on. Because much market research is poor to begin with, a twin built on it will be poor too. Garbage in, garbage out applies to twins as strictly as to any other model.

Why do about 20% of AI recommendations go wrong?

AI is confident even when it is wrong, and it does not know which questions it failed to ask. It fills gaps with plausible reasoning that reads as authoritative. A great consultant's real value is not producing the answer but knowing where the blind spots are, which assumptions to stress-test, and which question changes the whole conclusion. That judgment is what closes the 20% gap AI leaves open.

Should companies internalize insights work or keep using consultants?

It is not an either-or decision. The better questions are how much to internalize, and how to use external experts to complement and amplify your internal AI capability. Bring in-house the fast, repeatable work AI handles well, and use boutique specialists to catch blind spots, validate high-stakes calls, and raise the quality of the data your AI runs on.

Dr. Frank Buckler is the founder of SUPRA and a pioneer in Causal AI for marketing. He helps CEOs and CMOs make high-stakes brand growth decisions by revealing what truly drives demand, and is the author of THE TOP 5%.