AI in Consulting: How It's Changing the Cases You'll Face

Through 2024 and the first half of 2025, AI-themed case prompts were rare and usually superficial — "should our retail client invest in a chatbot." That has shifted. In the last two recruiting cycles, candidates have reported AI-related cases at every MBB firm and several tier-2 firms, and the bar for what counts as a good answer has moved sharply.

The reason is that the firms themselves are rebuilding around these tools. The cases reflect what consultants are actually being asked to evaluate by clients in 2026.

Three case archetypes you should expect

1. The "should we adopt" case

Familiar in shape, harder than it looks. A client wants to deploy an LLM-based capability across a function — customer support, contract review, code generation. The case asks whether to do it, how, and what value to expect.

The trap is treating this like a generic technology adoption case. The differentiated answer engages with the specifics: which workflows are actually automatable, what the accuracy/oversight tradeoff looks like, where the cost savings come from (headcount? throughput? consistency?), and what breaks at scale (data privacy, audit trails, regulatory review).

2. The "build vs buy vs partner" case for an AI capability

Variant of the M&A or partnership case, sharpened by the fact that the build-vs-buy economics for AI capabilities are unstable. Foundation models are a commodity for most use cases, but vertical-specific data, domain workflow integration, and human-in-the-loop systems are not.

Strong candidates reason about which layer of the AI stack creates moat: model layer (rarely worth building), application layer (sometimes), data and workflow integration (almost always).

3. The "AI is disrupting our market" case

A client whose business is being threatened by AI-native competitors. Could be a legal services firm facing AI contract review, a market research firm facing AI synthesis, a traditional software company facing AI-assisted code editors.

These cases test whether you can think about disruption beyond surface signals. The right structure asks: what part of our value chain is most exposed (data? distribution? trust? regulatory access?), what is the rate of substitution, and what defensible position can the client move to in time.

What you should know cold

You do not need to be an AI expert. You should be able to articulate, in plain language:

• What an LLM can and cannot reliably do. Pattern recognition, summarization, and structured generation on familiar tasks. Not novel reasoning, not deterministic arithmetic, not anything that requires citations of facts without retrieval.
• Why "automate the workflow" is harder than it sounds. Most enterprise workflows have implicit tribal knowledge, judgment calls, and review steps that do not appear in any documentation. Lifting these into an AI system is the actual project.
• The cost structure. Inference costs are dropping quickly but are still meaningful at scale. Training and fine-tuning are usually overkill for non-research use cases. RAG (retrieval-augmented generation) is the standard enterprise architecture for now.
• The risks. Hallucinations, privacy/data leakage, regulatory ambiguity (EU AI Act, sector-specific rules), reputational risk, vendor concentration on a small number of model providers.

What "good" sounds like in an AI case

A candidate who recommends "we should pilot an LLM in customer support to deflect 30% of tier-1 tickets, with human review on any case involving a refund or account change, expected payback in 14 months at $4M annual cost saving" is showing the kind of specificity that gets noticed.

Compare that to "we should embrace AI to drive efficiencies across the organization." The first answer is the work consultants are actually being paid for in 2026. The second answer is what AI itself can produce in 30 seconds.

How to prepare

• Read 1–2 management-consultant-published reports on enterprise AI deployment from the last 6 months. These are easy to find on firm websites.
• Practice 2–3 mock cases on AI topics. Healthcare diagnostics, legal document review, and code generation are all fertile prompts.
• Have a point of view on the long arc — not "AI will change everything", which is true and useless, but a specific thesis about where the value pools shift in your sector of interest.

AI cases are not going away. The candidates who treat them as an annoying new genre will keep underperforming. The ones who treat them as the most important thing happening in the industries they want to consult to will pull ahead.