The bottom line: consulting is not going away. It is changing—fast. AI removes a lot of manual work and shifts the focus to speed, reusable tools, and results that can be measured. This has consequences for how firms are organised and how clients buy and use consulting.
What HBR says
The main message: AI is reshaping the structure of consulting firms. Tasks that used to occupy many junior people—research, analysis, and first-pass modelling—are now largely automated. Teams get smaller and more focused. Think of a move from a wide pyramid to a slimmer column.
New human roles matter more: people who frame the problem, translate AI insights into decisions, and work with executives to make change happen. HBR also points to a new wave of AI-native boutiques. These firms start lean, build reusable assets, and aim for outcomes rather than volume of slides.
What The Economist says
The emphasis here is on client expectations and firm economics. Clients want proof of impact, not page counts. If AI can automate a lot of the production work, large firms must show where they still create unique value. That means clearer strategies, simpler delivery models, and pricing that links fees to outcomes.
The coverage also suggests this is a structural shift, not a short-term cycle. Big brands will need to combine their access and experience with technology, reusable assets, and strong governance to stay ahead.
What AI can do in consulting — now vs. next (practical view)
Now
- Discovery & synthesis. AI can sweep through filings, research, transcripts, and internal knowledge bases to cluster themes, extract evidence with citations, and surface red flags. This compresses the preparation phase of understanding so teams spend time on framing the problem and implications.
- First-pass quantification & modelling. It produces draft market models and sensitivity analyses that consultants then stress-test. The benefit isn’t perfect numbers; it’s cycle-time—from question to a defendable starting point—in hours, not days.
- Deliverables at speed. From storylines to slide drafts and exhibits, AI enforces structure and house style, handles versioning, and catches inconsistencies. Human effort shifts to message clarity, executive alignment, and implications for decision makers.
- Program operations & governance. Agents can maintain risk and issue logs, summarize meetings, chase actions, and prepare steering packs. Leaders can use meeting time for choices, not status updates.
- Knowledge retrieval & reuse. Firm copilots bring up relevant cases, benchmarks, and experts. Reuse becomes normal, improving speed and consistency across engagements.
Next (12–24 months)
- Agentic due diligence. Multi-agent pipelines will triage vast data sets (news, filings, call transcripts), propose claims with evidence trails, and flag anomalies for partner review—compressing weeks to days while keeping human judgment in the loop.
- Scenario studios and digital twins. Reusable models (pricing, supply, workforce) will let executives explore “what-ifs” choices live, improving decision speed and buy-in.
- Operate / managed AI. Advisory will bundle with run-time AI services (build-run-transfer), priced on SLAs or outcome measuress, linking fees to performance after go-live.
- Scaled change support. Chat-based enablement and role-tailored nudges will help people adopt new behaviors at scale; consultants curate and calibrate content and finetune interventions instead of running endless classroom sessions.
Reality check: enterprise data quality, integration, and model-risk constraints keep humans firmly in the loop. The best designs make this explicit with approvals, audit trails, and guardrails.
Five industry scenarios (2025–2030)
- AI-Accelerated Classic. The big firms keep CXO access but run leaner teams; economics rely on IP based assets and pricing shifts from hours to outcomes.
- Hourglass Market Strong positions at the top (large integrators) and at the bottom (specialist boutiques). The middle gets squeezed as clients self-serve standard analysis.
- Productised & Operate. Advice comes with data, models, and managed services. Contracts include service levels and shared-savings, tying value to real-world results.
- Client-First Platforms. Companies build internal AI studios and bring in targeted experts. Firms must plug into client platforms and compete on speed, trust, and distinctive assets.
- AI-Native Agencies Rise. New entrants born with automation-first workflows and thin layers scale quickly—resetting expectations of speed, price-performance, and what a “team” looks like.
What clients should ask for (and firms should offer)
- Ask for assets, not documents. Ask for reusable data, models, and playbooks that you keep using after the engagement. —and specify this in the SOW.
- Insist on transparency. Demand visibility into data sources, prompt chains, evaluation methods, and guardrails so you can trust, govern, and scale what’s built.
- Design for capability transfer. Make enablement, documentation, and handover part of the scope with clear acceptance criteria.
- Outcome-linked pricing where possible. Start with a pilot and clear success metrics; scale with contracts tied to results or service levels.
Close
AI is changing both the shape of consulting firms and the way organisations use them. Smaller teams, reusable assets, and outcome focus will define the winners.
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