Summary: AI was supposed to replace management consultants (shock, it hasn’t), yet the opposite is happening. As AI companies partner with consulting firms to drive enterprise adoption, the real shift is in how consulting teams are structured, how firms price work, and which senior leaders they now need to hire.
Consulting didn’t die in 2026
Artificial intelligence and management consulting are often framed as competitors. For the past two years, the narrative has been simple: AI will replace consultants.
Recent developments suggest a different reality. Rather than replacing consulting firms, leading AI companies are partnering with them to drive adoption across large organisations. These partnerships highlight something many executives already recognise: the technology is powerful, but deploying it inside real businesses is far more complex than expected – as per every tech adoption/ change programme ever…
For strategy consulting firms and hiring leaders, this shift is already influencing how teams are built, how partners are hired, and how consulting firms compete.
AI companies have the technology, consulting firms have the access
Recent reporting in The Wall Street Journal ‘AI Needs Management Consultants After All‘ shows AI companies such as OpenAI and Anthropic partnering with consulting firms including McKinsey, Boston Consulting Group, Accenture, and Deloitte to accelerate enterprise adoption.
The logic behind these partnerships is straightforward. AI companies provide the technology itself, while consulting firms provide the organisational access required to deploy it.
AI providers typically bring:
• foundation models and platforms
• engineering capability
• technical support for deployment
Consulting firms contribute a different set of assets:
• long-standing enterprise client relationships
• deep familiarity with corporate workflows
• the ability to translate technology into operational change
Consultants already sit inside the organisations where AI must be implemented. They understand how decisions are made, how systems interact, and where internal resistance tends to emerge. This gives technology companies a path into large enterprises that would otherwise take years to develop.
The biggest barrier to AI adoption is organisational
Despite the pace of AI development, enterprise adoption is still uneven. Most organisations have not yet scaled AI across their operations, and many senior leaders say they have not seen meaningful financial returns from it.
Research from Harvard Business Review also highlights a growing issue of “AI Brain Fry” where employees are overwhelmed by the pace of rollout, unclear use cases, and constant tooling changes.
This gap between capability and value explains why consulting firms remain heavily involved in AI deployments.
The technology itself is rarely the primary constraint. In many cases the models work well and the infrastructure is available. The real challenge lies in adapting organisations around the technology.
Embedding AI typically requires companies to rethink:
• operating processes
• team structures and responsibilities
• governance and risk frameworks
• employee training and adoption
Consulting partners who lead AI transformation programmes often make the same observation: the majority of the effort is organisational change rather than technical development.
Technology vendors build the tools. Consultants help organisations change how they operate so those tools can create value.
AI is reshaping the consulting delivery model
At the same time that AI is creating new transformation opportunities, it is also reshaping how consulting firms deliver their services.
For decades the consulting industry relied on a classic pyramid structure. Large cohorts of analysts and associates performed much of the analytical work that supported projects. Managers coordinated delivery, while partners focused on client relationships and strategic direction.
Artificial intelligence is beginning to compress this model. Many tasks that once filled the early years of a consulting career; data preparation, structured analysis, slide development – can now be automated or accelerated by AI tools.
As a result, consulting teams are becoming leaner and more senior. The industry is gradually shifting toward smaller delivery groups supported by technology rather than large junior-heavy teams.
The emerging delivery model typically emphasises:
• smaller teams led by experienced consultants
• more technical specialists embedded in projects
• greater reliance on AI-enabled analysis
• stronger partner involvement in execution
This shift is already visible in how consulting firms design projects and allocate resources.
The apprenticeship model is under pressure
The traditional consulting career path relied heavily on repetition. Junior consultants learned by performing analytical tasks, building presentations, and supporting client engagements. Over time this experience developed the structured thinking and judgment required for leadership roles.
As AI automates much of this foundational work, that apprenticeship model becomes harder to sustain.
Firms can no longer assume that junior consultants will gain experience through volume alone. Instead, they are beginning to rethink how talent is developed, placing greater emphasis on qualities that cannot easily be automated.
Increasingly, consulting firms are screening candidates for attributes such as:
• learning speed
• resilience under pressure
• collaboration and communication skills
• the ability to work effectively with AI tools
These attributes tend to predict long-term success more reliably than traditional academic signals alone.
For consulting leaders, this raises a new strategic question: how to build the next generation of partners when the training system that once supported that pipeline is changing.
Pricing and accountability are changing as well
AI is also influencing how consulting firms price their services.
Traditional consulting engagements were typically billed based on time and team size. As AI improves efficiency and reduces the need for large project teams, that pricing model becomes harder to sustain.
In response, some consulting firms are experimenting with outcome-based pricing structures. In these arrangements, fees are linked to measurable business results rather than simply the hours spent on a project.
This approach shifts consulting closer to implementation. Clients increasingly expect firms not only to provide strategic recommendations but also to ensure those recommendations produce real operational outcomes.
Consulting firms are becoming technology platforms
Another structural change is the growing importance of proprietary systems inside consulting firms.
Many large firms are building internal AI platforms designed to capture institutional knowledge and accelerate project delivery. These platforms allow firms to automate elements of research, structure insights more quickly, and apply proven methodologies across multiple client engagements. Good legacy examples of this being McKinsey & Company’s Quatum Black, BCG X, and the newer entry of Roland Berger’s venture with Jonas Andrulis.
In this environment, the intellectual property of consulting firms increasingly lies not in the slide decks they produce but in the systems that power their delivery models.
Firms that successfully build and scale these platforms will gain significant advantages in speed, consistency, and pricing flexibility.
A strategic risk for consulting firms
The current partnership between AI companies and consulting firms may not remain balanced forever.
As consulting firms collaborate with AI vendors, they provide those vendors with valuable exposure to enterprise clients, operational workflows, and consulting talent. Over time, AI platforms may absorb enough knowledge to replicate parts of the consulting process themselves.
At the same time, software companies such as IBM, SAP, and Palantir are embedding advisory capabilities directly into their platforms. If organisations can receive automated strategic insights through these systems, the role of large consulting teams may change further.
For consulting firms, the challenge is therefore not simply adopting AI but evolving their operating models before AI platforms and technology companies move further into the advisory space.
What this means for consulting hiring and leadership
The consulting industry is not disappearing. But its talent requirements are changing quickly.
As delivery models evolve and teams become smaller, the value of experienced consultants increases. Firms are already adjusting their hiring strategies to reflect this reality.
Three trends stand out:
• Demand for senior consultants is rising. Smaller teams require leaders who can structure work and deliver results with limited support layers.
• Hybrid profiles are becoming more valuable. Consultants who combine strategic thinking with technology literacy (with former software coders being in high demand) and operational experience are increasingly sought after.
• Partner hiring is becoming more strategic. When analysis becomes easier to automate, leadership capability and a partners network become the primary differentiators.
For consulting firms across the UK, Germany, and Switzerland, these shifts are already influencing how partner search and leadership hiring are approached.
The real scarce asset in consulting
The irony of the AI era is that it may reduce the number of consultants required to deliver projects. However, it also increases the value of experienced ones.
Artificial intelligence can process data, generate analysis, and accelerate many elements of consulting delivery. What it cannot replicate (yet) is judgment; the ability to frame problems, align stakeholders, and guide organisations through complex change.
It also cannot replicate the networks of trust that experienced consultants develop with executives over years of working together as transformation partners.
Those capabilities remain central to the consulting profession. And they will increasingly determine which firms succeed as the industry adapts to the AI era.
Now lets push to the future, the year is 2036, the question is ‘did consulting die in 2026?’ – the answer, most likely ‘no, but it had to change, and did’.
That is why consulting did not die in 2026.
If we push forward to 2036 and ask, “Did consulting die in 2026?”, the likely answer is:
No. But it had to change, and it did.
The firms that survived were not the ones that defended the old model. They were the ones that stripped out low-value work, used AI hard, and doubled down on the human edge.
So the question was never whether AI would kill consulting.
The real question was which parts of consulting were worth keeping once the machine could do the rest.
p.s. Leaving this here to test in 10 years.
Ben Appleton, optimistic on 17/03/2026
Ben Appleton is the founder of Strat-Bridge, a specialist executive search partner to the strategy consulting industry. He works with global consulting firms and senior leaders across the UK, Germany, Switzerland, and beyond — helping them build capability at the Partner and Director level.
