The Shift from Advice to Outcomes
Why 65% of enterprises stopped believing in consulting, and what's replacing it
In November 2025, HFS Research surveyed enterprise buyers about their relationships with consulting firms. The verdict was clear: 65% said traditional consulting models no longer deliver value.
That same month, Deloitte was caught submitting an AI-generated report to the Canadian government containing fabricated citations. Real researchers paired with papers they never wrote. A month earlier, they’d done the same thing in Australia, forced to refund $290,000 for a welfare compliance report that included made-up quotes from a federal judge.
An Australian senator put it simply: “Perhaps procurers would be better off signing up for a ChatGPT subscription.”
The pyramid problem
McKinsey’s headcount is down to 36,000, roughly 25% below its peak. They’re laying off up to 10% of non-client-facing staff over the next two years. BCG, Bain, the Big Four, all restructuring. Job postings at top consulting firms are down 50% year over year.
The structural issue isn’t that AI is making consultants more efficient. It’s that AI is automating exactly what junior consultants do: research, synthesis, slide creation, benchmarking. The pyramid model that made consulting profitable, partners at the top leveraging large teams of associates at the bottom, is collapsing from the base up.
Harvard Business Review proposes replacing the pyramid with an “obelisk.” Fewer layers, smaller teams, more leverage at each level. New roles like “AI facilitators” and “engagement architects” rather than armies of analysts.
But this misses the deeper problem.
Advice versus outcomes
The consulting business model was always based on selling time. Tom Rodenhauser from Kennedy Intelligence puts it plainly: “You charge for time, and when time goes away, you have to change the commercial model.”
But time isn’t the only thing that’s going away. The entire value proposition is shifting.
What enterprises actually want now is problems solved. Not analysis. Not a 200-slide deck explaining what they could potentially do.
The HFS study found that headcount-based contracts are collapsing. 49% of consulting contracts today are tied to staff numbers. Within two years, only 16% expect to use this model.
What’s replacing it? Outcome-based pricing. McKinsey says a quarter of their engagements are now outcome-based. But outcome-based pricing only works if you can deliver outcomes. And delivering outcomes requires building things, not advising on them.
This is where the traditional consulting model breaks. There is no outcome. There’s advice.
The verification problem
The Deloitte scandals are instructive. Not because Deloitte is uniquely bad, but because they reveal what happens when you try to use AI to cut costs without understanding the domain deeply enough to verify the output.
Their AI tools saved time. They also fabricated citations that a first-year university student would have caught. The consultants reviewing the work didn’t catch it because they were doing exactly what the pyramid model trained them to do: moving information between formats, not deeply understanding it.
This is the trap. AI makes synthesis faster. But synthesis without verification is worse than useless. And verification requires domain expertise that can’t be compressed into a 10-week engagement.
The new model is already here
While consulting firms struggle to adapt, a different model is emerging. Last week, Goldman Sachs revealed they’ve spent six months embedding Anthropic engineers directly into their operations to build autonomous AI agents for trade accounting and client onboarding.
Notice what’s happening here. Goldman embedded engineers from an AI company into their teams to build systems that do the work. The output isn’t a strategy deck or a report on the future of automation in financial services. It’s running software.
Marco Argenti, Goldman’s CIO, described the agents as “digital co-workers for professions within the firm that are scaled, complex, and process intensive.” Internal tests showed 30% faster client onboarding and over 20% developer productivity gains. The agents manage operations for $2.5 trillion in assets.
There’s another detail worth noting. Goldman also reported that over 10,000 employees, roughly a quarter of the firm, now use an internal AI assistant built on large language models. The tool started in one division and expanded to others based on internal demand, not a top-down mandate. I see the same pattern in European enterprises like Allianz. The companies with the broadest AI adoption are usually the ones where a team gets real value from a pilot, tells their colleagues, and momentum builds organically, all while being sponsored and guided by strong executive commitment. At a certain scale, internal word-of-mouth becomes your best adoption engine. The challenge shifts from “how do we convince people to try this” to “how do we support the teams that are already using it.”
This is what outcome-based work looks like in practice. Not a deck about what could be automated. Running systems that actually automate it.
The same week, ElevenLabs announced a partnership with BCG to deploy voice agents across industries. BCG isn’t being hired to advise on conversational AI strategy. They’re partnering with a technology company to actually deploy production systems for clients.
The consulting firms see the shift. They’re adapting. McKinsey has Lilli. BCG has Deckster. They’re building internal AI tools, launching partnerships, restructuring around implementation. But they’re doing it from within a business model that was optimised for something else entirely.
Where the opportunity is
Here’s my hypothesis. Many problems that took years and millions to solve with traditional consulting and systems integration can now be solved for 20% of the cost in a tenth of the time. ERP replacements. Process automation. Data integration. The categories of work that consultants scoped at 18 months are becoming 8-week projects.
This creates a gap. Not a small one. The AI consulting market is projected to grow from $11 billion this year to $91 billion by 2035. That growth has to go somewhere.
Some of it will go to the Big Four adding AI practices. They already are. They’re not standing still.
But they’re also not structurally built to capture this shift. Their economics depend on billing hours. Their pyramid depends on leverage. Their credibility depends on the “no one gets fired for hiring McKinsey” dynamic.
That dynamic isn’t going away, by the way. Enterprises that need a stamp of approval from a trusted name, whether for board presentations, regulatory cover, or simply career protection, will keep hiring the big names. Some buyers will always prioritise safety over speed.
But they’ll be the last ones to shift. And the buyers who actually want problems solved, the ones who care more about outcomes than process, are already looking elsewhere.
The shift away from advice isn’t really about consulting firms. It’s about the end of a model where describing problems was valuable enough to build a $300 billion industry around it.
What comes next is a model where solving problems is the only thing that counts.