How has ChatGPT changed day-to-day consulting work?
ChatGPT has compressed basic research and slide-drafting cycles from hours to minutes, raising the value threshold in consulting from information gathering to synthesis and client judgment. Junior analysts who once spent evenings building market-sizing decks now use AI to generate first drafts in under 30 minutes, then focus entirely on refining insights and preparing for client conversations.
The New Baseline for Analyst Work
McKinsey, BCG, and Bain now assume incoming associates can prompt ChatGPT for industry overviews, regulatory summaries, and framework scaffolds. What once distinguished a strong analyst, fast secondary research and clean formatting, is now table stakes.
Partners increasingly judge analysts on how well they challenge AI output, spot logical gaps, and tailor recommendations to client politics, not on raw hours logged in Excel. This is a real shift.
The bar moved up, not down.
Where Time Gets Reallocated
Consultants report spending 30-40% less time on slide assembly and data pulls. That freed capacity flows into work AI genuinely cannot do:
- Deeper client interviews to surface unspoken constraints
- Cross-functional workshops requiring real-time facilitation
- Iterative hypothesis testing with senior stakeholders who distrust AI-generated framing
Accenture and Deloitte have formalized "AI-augmented delivery" models, where teams use GPT for draft content but reserve human effort for change management and executive alignment. EY and KPMG have introduced internal GPT tools with guardrails around client data confidentiality, meaning the workflow shift is structured, not ad hoc.
How Firm Expectations Have Shifted
The change varies meaningfully by tier and function. Here is a rough picture:
| Firm tier | AI use case | Human value-add expected |
|---|---|---|
| MBB | Hypothesis framing, deck scaffolds | Insight synthesis, client storytelling |
| Big Four | Research, report drafting | Risk judgment, regulatory interpretation |
| Boutiques | Market sizing, benchmarking | Domain expertise, relationship capital |
MBB expects you to use AI and still be better than the AI output. That is a harder standard than it sounds.
What This Means If You Are Recruiting from IIM Ahmedabad
IIM Ahmedabad's case curriculum already trains you in structured problem-solving, which is the one skill ChatGPT cannot replicate on a live client call. Lean into that.
Build fluency in prompt engineering, yes, but invest far more in case storytelling and stakeholder mapping. Firms care less about whether you can research Porter's Five Forces, ChatGPT does that in 45 seconds, and more about whether you can synthesize three conflicting data points into a single recommendation that moves a CFO to act.
Summer interns who over-rely on AI-generated content without adding client context are easy to spot. Partners notice when a recommendation reads polished but lacks institutional nuance.
The interns who stood out in recent BCG and McKinsey cohorts were those who used AI to prepare faster, then spent the saved time talking to clients and stress-testing assumptions with their project leads. That combination, speed plus judgment, is what ₹35+ LPA consulting roles at the MBA level are actually paying for.
The honest truth: AI raised the floor for analyst output and raised the ceiling for what partners now expect. Neither is optional to engage with.
Pro Tip: Before your consulting interviews, practice explaining a ChatGPT-generated framework out loud and deliberately finding two flaws in it, this is exactly the critical synthesis skill partners are testing for in case rounds.