The consulting industry has long stood at the intersection of expertise, strategy, and influence. In the United Kingdom and beyond, consulting firms advise governments, corporations, NGOs, and institutions on everything from business model innovation to regulatory compliance and organisational change. But the arrival of generative AI — notably systems like ChatGPT — threatens to reshape the fundamentals of how consulting is delivered, who delivers it, and what value clients expect to pay for.
In this commentary, I aim to offer a balanced, forward-looking perspective tailored to a general UK readership: what ChatGPT and its successors might bring to the consulting sector; which strengths and weaknesses they will expose; how consulting firms and clients might adapt; and whether, in the long run, AI will complement or cannibalise the role of human consultants.
Before imagining the future, it helps to understand the vulnerabilities that make consulting especially exposed to AI disruption.
Knowledge asymmetry and content production
A key part of consulting is diagnosing client issues, producing analysis, writing reports, and developing strategic narratives. In many cases, these are content-driven tasks. Generative AI systems like ChatGPT are already competent at drafting memos, executive summaries, preliminary analyses, and even first-cut slide decks. For many clients, this may reduce the marginal value of a consultant’s writing and content output.
Repetitive tasks and frameworks
Consultancy often uses standard frameworks (SWOT, Porter’s Five Forces, PESTLE, etc.), benchmarking, data tabulation, and comparative industry research. These tasks can often be systematised and automated. ChatGPT and associated tools may increasingly absorb the lower-complexity work (drafting, data structuring, summarising), leaving human consultants to handle the highest-value judgment, synthesis, and client relationship work.
Cost pressures and democratisation of insight
Clients are under constant cost pressure and are demanding more accessible, faster, leaner consulting models. If AI can deliver useful insight at a fraction of the cost, many organisations (especially SMEs, public sector bodies, or non-profit organisations) might skip legacy firms or boutique providers in favor of AI-augmented platforms.
Scaling and commoditisation
One of the risks for consulting firms is that what was once a bespoke premium offering becomes commoditised. If AI can scale the production of strategic insight, then parts of consulting may shift from bespoke, high-margin models to subscription, platform, or tool-based delivery.
Because of these pressures, consulting firms must think carefully and act early if they do not wish to be disintermediated.
To understand the future, we must look not only at present capabilities but plausible near-future developments in AI.
Rapid drafting and iteration
ChatGPT and its kin can draft memos, reports, slide outlines, and executive summaries in minutes — a process that would otherwise take days. This gives consultants a “first draft assistant” that can accelerate output.
Synthesis of diverse sources
The system can integrate multiple documents, research reports, and online sources to produce coherent narratives or diagnostic memos.
Question answering and ideation
Users can prompt AI to brainstorm strategic options, propose alternative structures, or offer hypotheses — useful in early scoping or divergent thinking phases.
Language translation, rewriting, editing
For multinational consulting or cross-border engagements, AI can assist in translation, paraphrasing, rewriting for tone, and polishing writing.
Domain fine-tuning and expert models
Over time, consulting firms or AI vendors may train fine-tuned models on internal firm knowledge, case libraries, specialized industry datasets (e.g., finance, energy, biotech). This will improve accuracy, relevance, and “consultant-quality” output.
Plug-in connectivity and live data integration
Systems might connect with data feeds (financial markets, macroeconomic indicators, regulatory databases) or clients’ own data systems to produce real-time insight, dashboards, scenario simulations.
Conversational and interactive consulting agents
Clients could engage AI in a conversational format: “Tell me the five biggest risks of my proposal,” “How would that reshape our cost structure by 2030?” The AI could respond, refine, ask clarifying questions — more like a junior consultant in chat form.
Multi-modal reasoning
Next generations may handle charts, images, diagrams, even proprietary modelling. They might also generate visual outputs (network diagrams, strategy maps) automatically.
Integration with tools & platforms
AI may be embedded within client tools (ERP systems, BI platforms) or consulting suites to provide advice in situ. This turns consulting from external reports to built-in intelligent assistance.
Let us now map out possible trajectories over the next decade, outlining risk and opportunity zones.
Consulting firms increasingly adopt AI as a force multiplier — enhancing speed, lowering internal costs, and enabling lower-cost offers. Human consultants focus on high-value judgment, client relationships, scenario interpretation, and change management. The tiered structure of consulting intensifies: junior layers become more AI-assisted or outsourced; senior layers remain hands-on.
In this scenario, consulting firms that adopt early and build proprietary AI assets will gain a competitive edge. Firms that cling to legacy models will lose their edge in cost, speed, and perceived value.
Some parts of consulting — especially diagnostics, benchmarking, preliminary analyses, sector scanning — get packaged into software / AI platforms. These platforms compete directly with boutique or entry-level consultants. Clients subscribe to “smart consulting as a service” with instant insight generation.
In time, this platformization reduces the demand for mid-tier consulting firms. Only projects with deep complexity, trust, or high stakes continue to require bespoke human consulting.
Consultants pivot to human-AI coach roles: they guide clients in using AI systems, interpreting AI output, setting strategy, and steering organizational transformation. Their role becomes less “source of insight” and more “interpreter of insight.” Trust, ethics, oversight, and holistic judgment become their value add.
In some industries, the “consultant as steward of AI” becomes central. Firms that combine domain expertise, ability to manage AI output risks, and change leadership will thrive.
If firms fail to adapt, clients may increasingly bypass traditional consultants entirely and turn purely to AI tools or in-house analytics teams. Consulting as a sector may shrink.
This scenario is less likely for high-end or highly networked consultancies with established brands and relationships, but it is a viable risk for mid-tier or niche firms.
In any scenario, several challenges and risks will define winners and losers.
One of the biggest obstacles is the risk of AI producing erroneous or “hallucinated” content — confident but incorrect statements. In strategic or high-stakes engagements, such mistakes may be costly. Consulting firms must invest in verification, guardrails, human oversight, and quality assurance.
Consultants often handle sensitive client data or proprietary secrets. Training AI models, feeding them client documents, or using third-party models carries risks of data leakage. Consulting firms will need robust governance, privacy protection, and possibly on-premise or custom models rather than public ones.
Consulting is not only about content — it’s about trust, persuasion, facilitation, facilitation of organizational change, and stakeholder negotiation. AI lacks emotional intelligence, political sensitivity, and deep tacit judgment. Human consultants will still be needed for relational and adaptive work.
As AI becomes more powerful, regulatory scrutiny may grow. Who is liable when AI gives flawed advice? What about bias or fairness? Consulting firms must navigate a new ethical frontier, including transparency, explainability, and responsible AI policies.
Large consultancies may have the capital to build or license advanced AI systems, but smaller firms may struggle. Internally, consultants accustomed to traditional methods may resist adoption. The reputational risk of errors or misuse can produce reluctance.
As more consulting work becomes partially automated, differentiation becomes harder. Firms may compete more on brand, specialisation, network access, or reputation than on raw technical capability.
Given these opportunities and challenges, what should consulting firms (large, medium, boutique) and practitioners in the UK do to stay relevant — and ideally lead?
Relying solely on generic public AI (e.g. base ChatGPT) is risky. Firms that develop fine-tuned domain, sector, or client-specific models will differentiate themselves. Over time, these proprietary datasets and models become competitive moats.
Consulting processes must incorporate AI in a structured way: initial drafts, triage, rapid prototyping, and human review cycles. Humans should focus on tasks that still demand human judgment: pattern recognition, synthesis, conflict resolution, client interaction.
Consultants must be comfortable working with AI, designing prompts, interrogating outputs, correcting errors, and driving innovation. Upskilling and change management are essential — those unwilling to adopt may be left behind.
Traditional consulting sells reports, PowerPoints, and deliverables. The future is outcomes, performance, and transformation. Firms should shift pricing models from time-based billing to value-based, subscription, or performance-linked models.
Offering “explainable AI” and auditing of AI outputs will become a differentiator. Firms that can justify their methods, guard against bias, and offer accountability will build trust — essential in advisory work.
In many cases, the best approach is human and AI working in tandem — consultants bundled with AI as a service. Clients may pay for “human-AI teams” rather than pure AI or pure human work.
Consultancies should set up internal labs or skunkworks to explore new AI-first products, platform services, subscription consulting tools, embedded advisory engines, or internal client apps. Some of tomorrow’s revenue may come not from traditional projects but from productising AI insights.
While the focus so far has been on consulting firms, the arrival of ChatGPT and generative AI will also reshape client behaviour, industries, and public expectations.
Clients will expect faster deliverables, lower costs, continuous insight, real-time dashboards, and transparent AI-driven reasoning. The consulting waterfall (months of research and then deliverable) may give way to agile, iterative engagement.
Smaller and mid-sized clients may bring more advisory capabilities in-house, using AI as a force multiplier in their own strategy or data teams. Their reliance on external consultancies will shrink.
If AI lowers the barrier to high-quality analysis, more organisations will have access to strategic thinking. This may intensify competition, since insights are no longer the preserve of elite consultancies.
Some industries (e.g. finance, tech, energy, pharmaceuticals) may adopt AI consulting more rapidly due to data intensity and regulatory monitoring; others (e.g. public sector, social organisations) may lag. Consulting in regulated sectors will have to wrestle with compliance constraints and trust deficits.
As AI gains prominence in strategy and public policy, there is a public interest in ensuring accountability, avoiding bias, and maintaining transparency. Consultants and AI vendors may face pressure for open audits, public scrutiny, and regulatory oversight.
To bring the future into sharper relief, let us imagine a typical UK strategic consulting engagement in 2030 for a mid-sized manufacturing client — and how ChatGPT (or its successor) participates.
Scoping & diagnosis (Week 0–1)
The client uploads internal data, revenue trends, cost structure, market reports. An AI assistant runs preliminary diagnostics: trend breakouts, ratio anomalies, competitor scans, scenario modelling. The human partner reviews, clarifies, and refines the problem statement.
Hypothesis generation & scenario ideation (Week 2)
The AI generates ten strategic hypotheses (diversification, vertical integration, technology partnership, sustainability repositioning). Human consultants prune, deepen, and challenge these, constructing “rapid prototypes” in outline form.
Deep dive & validation (Weeks 3–5)
For selected hypotheses, the AI assists in gathering specialist research, modeling cost trajectories, regulatory impact simulations, and scenario stress tests. Consultants guide the modelling, refine assumptions, debate sensitivities, and interpret results.
Synthesis & storytelling (Week 6)
The AI creates draft slide decks, memos, narratives, charts, summarising findings. Human consultants revise, polish, emphasise client context, adapt messaging, and craft recommendations.
Client co-creation & feedback (Week 7)
Using an interactive platform, client leadership can query the AI (“what if we invest £10m more in R&D?”) and get real-time adjustments. Consultants moderate this dialogue, flag risks, and coach client interpretation.
Implementation and monitoring (Months 2+)
The AI generates dashboards, monitor indicators, scenario shift alerts, “what if” modelling in live mode. Consultants act as change architects: guiding leadership, handling stakeholder dynamics, and recalibrating strategy as needed.
In this future, the consulting engagement is far more integrated with AI, faster in pace, continuous in feedback, and hybrid in nature.
It is tempting to fear that consultants will become obsolete, replaced by “consulting bots.” But I argue that full obsolescence is highly unlikely — though the nature of the work will transform.
Why humans will still matter:
Trust, persuasion, and social capital
Especially in complex, high-stakes contexts (government policy, turnaround strategy, regulatory negotiation), clients value human judgment, reputation, empathy, gravitas, and assurance.
Ambiguity, novelty, and radical uncertainty
When clients face unprecedented challenges (e.g. pandemics, climate shocks, geopolitical disruption), structured AI reasoning may fall short. Human creativity, intuition, lateral thinking, and domain gestalt remain essential.
Ethics, accountability, and risk oversight
Humans will be needed to audit, validate, argue, intervene, contest AI outputs, and take responsibility when stakes are high.
Political dynamics, stakeholder management, and implementation
Strategy is only useful if executed. That often needs relationship building, conflict resolution, persuasion, internal alignment, and human leadership.
Interpretation and meaning-making
Clients often don’t just want “what” to do, but “why,” and how to adapt to human, cultural, emotional, or situational constraints. That demands human narrative and judgment.
In short, human consultants are unlikely to vanish, but their role will shift from content creators to orchestrators, interpreters, coaches, risk mitigators, and change agents. The firms that recognize and embrace that shift will endure; those that treat AI as a threat rather than an enhancer risk obsolescence.
If I were advising British businesses, public bodies, universities, or NGOs today about how to harness ChatGPT in consulting, I would suggest:
Pilot internal experiments and small use cases
Don’t overhaul everything at once. Begin with supporting tasks (draft memos, sector scanning, competitor profiles) to build confidence and internal capability.
Treat AI as an assistant, not a substitute
Use AI output as a starting point — always critically review, correct, and question its assumptions.
Build internal AI strategy and ethics governance
Establish clear policies on data use, privacy, model oversight, error mitigation, and attribution. Train staff to understand AI limitations and mechanisms.
Negotiate hybrid arrangements with consultancies
Ask consultants how they are using AI, request lower-cost “AI-augmented” tiers, and ensure transparency over what is machine vs human work.
Invest in internal analytics capability
Use AI tools to build in-house capacity for scenario modelling, dashboards, and strategic experimentation — thereby reducing reliance on external advisors.
Use AI for continuous insight, not just episodic projects
Embed AI models or tools in your ongoing operations so you get real-time alerts, scenario tracking, predictive analytics rather than only at discrete projects.
While opportunities are exciting, missteps could be costly.
Overtrust in AI
Blind faith in AI outputs, especially in novel domains, may lead to flawed decisions.
Complacency and deskilling
Relying too heavily on AI may degrade internal human analytical skill, making the organisation vulnerable if AI fails or misleads.
Model drift and obsolescence
AI models need constant updating. A model trained on outdated data or shifts in market structure can lead to misleading output.
Vendor lock-in and monopoly risk
Firms relying on external AI vendors may lose control or face dependency. Proprietary models, portability, and exit strategies matter.
Erosion of brand premium
If consulting becomes commoditised, firms will struggle to sustain margins or differentiate. The rush to compete on cost may erode professional standing.
Regulatory backlash or public distrust
Scandals or misuse of AI in advisory work could provoke regulation, stricter oversight, or public backlash.
Uneven adoption and stratification
Large firms with capital may leap ahead, leaving smaller consultancies behind. An innovation gap may emerge, reshaping the consulting ecosystem in the UK and globally.
Gainers:
Consulting firms that invest early in AI, build proprietary models, and offer hybrid human-AI services.
Clients with sufficient scale or innovation capability who can partner with or develop internal AI advisory.
Startups or platform providers that embed consulting AI into subscription models.
Professionals who upskill in AI prompt engineering, model oversight, interpretability, and human judgement.
Losers (or challenged):
Mid-tier consultancies reliant on legacy margin models and unwilling to change.
Consultants who resist AI adoption or cling to old models of hourly billing and project pyramids.
Clients that insist on traditional engagement models without regard for cost efficiency or speed.
Consultancies that fail to safeguard confidentiality, data governance, or manage AI risk, losing client trust.
ChatGPT and its successors do not herald the immediate death of consulting — but they do challenge the status quo. The real disruption lies not in replacing consultants outright, but in reshaping how consulting is delivered, priced, differentiated, and consumed.
For the UK consulting sector to thrive, firms must pivot: become fluent in AI, embed it deeply within workflows, shift to outcome-oriented offerings, and double down on the human strengths of trust, judgment, persuasion, and change leadership.
Clients too must evolve: savvy organisations will embrace hybrid models, nurture internal AI capability, demand transparency from advisors, and shift toward continuous insight rather than episodic projects.
In the decades ahead, the most successful consulting organisations will be those that view AI not as a competitor but as a collaborator — using it to amplify human judgment, speed, and scale. Those that resist risk being outflanked.
The future of consulting is not human vs machine — it is human plus machine. The question for the UK consulting industry is simple: will you lead the fusion, or be left behind?
