When AI Does McKinsey, What Happens to Consultants?
Key Takeaway: An Indian startup called Rocket is delivering strategy reports, competitive intelligence, and product guidance at costs dramatically lower than traditional consulting firms. The commoditization of strategic advice has started.
McKinsey charges $500,000 for a strategy report. Most mid-market companies can't access that. Most of the time, they don't need to either. They need 80% of the strategic insight at 20% of the cost.
Rocket, an Indian startup that launched quietly in early 2026, is betting on exactly that gap.
The platform offers what it describes as McKinsey-style consulting at a fraction of the price: strategy development, competitive intelligence, product building guidance. The full range of services that have traditionally been locked behind big consulting contracts.
A recent TechCrunch piece covered Rocket's positioning. I want to push past the startup story and look at what this actually signals.
The Commoditization of Strategic Advice
Strategy has always had two components: the framework (what to ask) and the judgment (what to do). Consulting firms charged for both, and packaged them together.
AI is separating them.
The framework component, gathering data, structuring analysis, running scenarios, synthesizing findings, is increasingly automatable. Large language models are genuinely good at this. Ask a well-prompted AI system to analyze a competitive market and produce strategic options, and you'll get something that looks like a McKinsey slide deck.
The judgment component is different. Knowing which of those options is right for a specific organization, with its specific constraints, culture, and competitive position, requires context that goes beyond what any model currently holds. That judgment lives with experienced humans.
What's happening now is that platforms like Rocket are compressing the framework cost to near zero and selling the output at a price point that opens the market to customers who were previously priced out.
This is the same compression that happened to market research in the 2010s. Tools like SurveyMonkey and eventually SparkToro democratized data that previously required expensive research firms. The firms didn't disappear. They moved up-market to more complex, judgment-intensive work.
What This Means for Agencies and Professional Services
At difrnt., we've watched the commoditization of execution services accelerate for three years. Creative production, basic copywriting, social media management, these services are under continuous pricing pressure from AI-assisted alternatives.
Strategic consulting was supposed to be immune. It required deep expertise, relationship trust, and contextual judgment. Those attributes still have value. But the entry-level strategy work, the competitive audits, the market maps, the growth option analyses, is being automated.
The operational implication is clear: if your agency or consulting practice earns revenue on deliverables that can now be produced in hours by an AI system, that part of your revenue is structurally at risk.
The response isn't to ignore it. It's to identify which parts of your service are framework (automatable) and which are judgment (not automatable), and to ruthlessly deprioritize the former while doubling down on the latter.
I've seen this play out in our own practice. Clients who used to pay for channel-level strategy reports now expect those frameworks as basic table stakes, delivered fast. The AI adoption and trust dynamics I wrote about recently are part of the same shift: people use AI tools even when they have reservations, because the utility is undeniable. They pay for the judgment layer: what specifically to do given their constraints, and how to execute it well.
The Democratization Effect
There's a real upside here that's easy to miss in the disruption framing. Mid-market companies with $5M to $50M in revenue have historically operated with limited access to strategic insight. Too small for McKinsey. Not able to retain a strategic advisor. Relying on whatever knowledge exists internally.
Platforms like Rocket change that. A company with a $10M revenue run rate can now access competitive intelligence and strategic frameworks that were previously only available to Fortune 500 companies.
This is a commercial outcomes story. Companies that previously made strategic decisions based on incomplete information will make better decisions with better data. That's good for the market overall, even if it's uncomfortable for the existing consulting ecosystem.
The question for every professional service firm is the same: what does the judgment layer of your work look like, and can you articulate it clearly enough to charge for it at a premium?
Based on TechCrunch's coverage of Rocket AI startup, published April 7, 2026.
FAQ
What does AI consulting mean for traditional consulting firms?
AI is automating the framework component of consulting (data gathering, analysis structuring, scenario modeling) while the judgment component (what to do in a specific context) remains human. Traditional firms face pricing pressure on deliverables that can be produced cheaply by AI, forcing a move toward judgment-intensive, high-context work.
Can an AI generate the same quality strategy as McKinsey?
For framework-level analysis, increasingly yes. For contextual judgment specific to an organization's situation, culture, and constraints, no. The gap is narrowing for the former and remains significant for the latter.
How should an agency adapt to AI-driven consulting commoditization?
Map your service portfolio to distinguish framework work (automatable) from judgment work (not automatable). Deprioritize the former and premium-price the latter. Invest in tools that let you deliver framework-level outputs faster, so you can spend more time on judgment-level advice.