A great shapeshifting idea is emerging in consulting.
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What happens to management consultants in a world where the old costs of insight have collapsed, where analysis that once demanded teams of twenty and budgets with seven zeroes can now be run by clients themselves, silently, quickly, and often competently?
The industry has been telling itself two competing stories about this moment.
One imagines a future where external expertise is steadily replaced by internal AI fluency. The other sees a resurgence, where consultants are finally freed to do the work organisations have always struggled to staff for themselves, and AI becomes the multiplier for judgement, not the substitute for it.
The more accurate view is less cinematic.
Consulting is splitting into new shapes because the work itself has changed. AI makes the repetitive tasks cheaper, faster, and more automatable, which shifts commercial leverage upward. Clients can self-serve analysis, but they cannot self-serve the consequences of strategic decisions if those decisions break internal cadence, compliance, or integration logic.
That leaves firms thinning their entry-level cohorts, concentrating responsibility among seniors, and leaning harder on tooling layers that orchestrate decisions through internal systems.
It is neither doom, nor is it utopia. It is reconfiguration under pricing pressure, capability pressure, and workforce exposure.
AI and the pyramid shape that no longer delivers
For decades, consulting was known for its two-by-twos, the frameworks, and the steep pyramid that churned them out.
Juniors staffed the base, partners staffed the apex, and the commercial engine sat in the middle charging organizations for pattern recognition that consultants could scale through labor, training, and repeatability. The pyramid was sharp because the work was bounded and templated equally sharply, all priced for throughput.
Nigel Vaz, CEO of technology company Publicis Sapient, remembers those early years with the clarity of someone who began at the base of that pyramid, watching the model scale globally, then hit its limits.
“When I started out, consulting was very much a labor-leverage business,” Vaz said. “You hired a lot of people, trained them on a set of frameworks, and scaled the work through repetition. That model worked at the time because the work itself was quite predictable, and clients were essentially paying for speed—how quickly you could generate an answer. But a lot of that dynamic has changed now.”
The labor gradient for juniors was famously demanding, but it was also famously stable. You learned a method, repeated it across cases, and climbed a predictable ladder toward influence, client access, and commercial judgement.
AI has now turned that gradient into a fault line. Not because juniors lack capability, but because the tasks that once made them billable have become automated commodities inside client systems.
“AI can generate answers far faster than any analyst ever could, so the consultant is no longer the fastest path to an answer,” Vaz said. “Where the value sits now is in understanding whether that answer will actually work inside the organization—whether it fits the operating rhythm, the systems, the constraints. That’s not something you get from a model alone; it comes from experience and judgment.”
This shift also reweights the value of relationships and recasts them as strategic infrastructure and the core asset of the firm.
Organizations are pulling consultants into their systems earlier, embedding them in shared data environments, decision logs, and orchestration platforms where insight is tested alongside internal stakeholders.
“What clients increasingly want is for us to be working inside their environments,” Vaz explained. “We use AI to move much faster on analysis, but judgment still comes from understanding how decisions land inside the organization. That’s where relationships matter. They’re what allow insight to turn into action. That naturally leads to a more diamond-shaped model where fewer people are doing low-value repetition, and more people are focused on making higher-quality decisions, supported by platforms that orchestrate work from signal through to outcome.”
The pyramid was sharp where the diamond is dense and capable of resisting pressure. And density rewards seniors who can implement decisions without breaking internal cadence.
The consulting’s AI moment, and the competence clients now require
Consultants have always justified their value by extracting commercial clarity from organizations that struggle to produce it internally.
There was nothing mystical about it, it was all an act of diagnostics. You entered a company, mapped the problem, modelled the opportunity, and priced the gap between the organization’s internal capacity and the expertise required to solve it.
Zander Ross, who leads change and M&A integration work at BTS, frames the compression logic that once defined consulting economics.
“M&A diligence used to mean importing outside expertise, crystallizing it, and exporting it into a set of decisions,” Ross said. “Today, that expertise still comes from outside, but the crystallization happens earlier, deeper, and inside the client’s operating and beyond traditional M&A business diligence. AI makes traditional diligence democratized and commoditized and it makes the work auditable, but it also makes the work exposed. The client expects the consultant to know whether the decision chain will play out as anticipated before the machine starts, and the integration train has left the station.”
In specialist areas like M&A, AI is not lowering the stakes as much as it . It is raising the expectations on the consultants that deliver the work. Modelling a multi-million transaction is the price of entry. Certifying its implementation risk is the price of survival, which now asks consultants to go beyond financial and operational analysis and deeper into the culture side of the equation as well.
“Clients are pushing us earlier into the decision chain than ever before,” Ross continued. “They expect 95%+ accuracy out of diligence work, because the cost of getting it wrong is a failed acquisition. This requires going beyond making sure the integration makes sense on paper. That expectation changes staffing. Juniors are not obsolete. They are unpriced. The diamond is the consequence of expertise compression moving upward and earlier, not downward and later..”
Even the largest consulting firms are reacting to similar client-driven pressures.
Nigel Vaz points to the same pressure reshaping incentives inside firms and inside client systems. Our clients don’t want more paper or more decks,” Vaz said. “They want decisions that can be implemented inside their organizations—decisions that are connected to their data, their workflows, and their technology from the start, not handed over at the end. AI can optimize parts of the work, but it only creates value if those decisions fit the organization’s operating model, which is why more of the work sits with senior judgment, supported by platforms that drive execution.”
That shift toward system-level integration is the natural handoff point, where the story moves from the macro pressure to the companies building the machinery to meet it.
Joseph Kim, CEO of Druid AI, argues that consulting’s new competence requirements are pulling the technology stack itself into the commercial spotlight.
Kim, a former operating partner in private equity and a 25-year veteran of enterprise software and security, has spent his career building inside large systems and acquiring companies that could not staff specialized expertise in house.
That background is precisely why his voice matters now, as he describes the problem Druid AI set out to solve. “Enterprises are exhausted by the cost of stitching intelligence into execution,” Kim said.
“We built orchestration because organizations were trying to self-serve analysis without the decision chain,” Kim said. “AI gives you analysis. It does not give you compliance. What enterprises are buying now is fewer, sharper decision-makers supported by orchestration that can prove the path from signal to outcome without breaking cadence or compliance. That is the work that stays billable.”
Kim emphasizes that firms are reacting not by adding bodies, but by configuring geometry around tooling that certifies decision paths, rather than generating answers.
“The boardroom concern is not hiring more people,” Kim explained. “It is hiring the right people who can prove their judgement through auditable orchestration. The question CEOs ask is which geometry of talent extracts durable value under AI exposure without breaking operating current. That geometry is more often now a diamond, not a pyramid.”
At the same time, the rise of AI orchestration also changes what the consultants are willing to pay for themselves.
One driver of the diamond shape is the quiet outsourcing of coordination work that once consumed internal consulting hours. Lauri Eurén, CEO of Operating.app, has become one of the clearest voices describing why tooling, not junior headcount, is now absorbing the work that seniors should never have been doing manually.
Eurén came into the enterprise technology world by building coordination systems for professional services firms that were burning out their most expensive talent on billing hygiene and operational bookkeeping.
Eurén ties this directly to contract theory and incentive design. “In contract theory, the rational firm does what it can certify best,” Eurén told me. “Everything else is either partnered, procured, or outsourced. Companies do the work they can prove. They outsource the work that steals senior judgement from commercial outcomes.”
This trend is now playing out across the largest consulting firms and their clients simultaneously. Instead of juniors and backoffice staff absorbing coordination work as part of their apprenticeship, the task is being priced into orchestration platforms that specialise in workflow certainty and compliance.
“Contract theory always rewarded the rational firm,” Eurén explains. “Today, AI makes the pyramid shape less appealing than ever, because the value of the work isn’t at the bottom anymore, it sits much closer to the relationship with the client”
The diamond shape of consulting emerges when the most expensive human judgement is liberated from the least valuable human coordination work.
It emerges when the firms themselves are forced to price their value not by the amount of hours or answers, but by the decisions that survive the test of implementation and the client’s growing expectations.
Whether this new geometry will hold with the strength of diamonds in nature remains to be seen.
