Rocket AI Product Strategy: The Real Disruption in Consulting

Every few years, someone declares the death of management consulting. First it was the internet. Then it was big data. Then it was no-code tools. Now it is AI. The target is always the same: McKinsey, Bain, BCG. The trillion-dollar question is always the same too. Can software replace the people who charge $500 an hour to make PowerPoint slides?

Indian startup Rocket is the latest entrant with this thesis, launching an AI platform designed to automate the core deliverables of strategy consulting. The pitch is compelling on paper: feed in your business context, get back frameworks, competitive analyses, and strategic recommendations that would normally take a team of MBAs six weeks and cost half a million dollars.

But the framing of this as a McKinsey-killer misunderstands what McKinsey actually sells. And it obscures the far more interesting disruption happening underneath.

What McKinsey Actually Sells

The persistent myth about top-tier consulting is that clients pay for insights. They do not. Clients pay for three things that AI cannot currently replicate.

First, they pay for political cover. When a CEO needs to lay off 15,000 people or exit a market, they need a blue-chip name on the recommendation. "McKinsey said so" is organizational armor. No board member ever got fired for hiring McKinsey. No AI platform provides that insurance.

Second, they pay for the network. McKinsey alumni sit on boards and in C-suites across the Fortune 500. Hiring McKinsey means accessing that web of relationships, getting warm introductions, and tapping institutional knowledge that exists in human brains, not databases. The firm's real product is its alumni network operating as a distributed intelligence layer across global business.

Third, they pay for implementation muscle. The dirty secret of strategy consulting is that strategy is maybe 20% of the engagement. The rest is change management, stakeholder alignment, organizational redesign, and the grinding human work of getting a 50,000-person company to actually do something different. AI can generate a beautiful transformation roadmap. It cannot sit in a room with a hostile divisional VP and negotiate their cooperation.

This is why McKinsey, Bain, and BCG have survived every previous disruption wave. Their moat is not analytical capability. It is institutional trust, human networks, and organizational influence. Rocket's AI platform does not touch any of these.

The Real Victims: Everyone Below the Top Three

Where AI consulting platforms will be genuinely devastating is one tier down. The strategy consulting market is not a monolith. It is a pyramid with very different dynamics at each level.

At the top, MBB (McKinsey, Bain, BCG) serves Fortune 500 CEOs and charges $500K to $5M per engagement. Below them sit firms like Oliver Wyman, Roland Berger, Kearney, and L.E.K., charging $200K to $1M for similar but less prestigious work. Below them are thousands of boutique firms and independent consultants charging $50K to $200K. And at the base are the internal strategy teams at large corporations.

AI platforms like Rocket are a direct threat to the second and third tiers. These firms cannot lean on brand prestige or network effects. They compete primarily on analytical quality and cost. When an AI platform can produce a credible market entry analysis in hours instead of weeks at a fraction of the cost, the value proposition of a mid-tier strategy firm collapses.

The historical parallel is what Bloomberg Terminal did to sell-side equity research in the 2000s and 2010s. Goldman Sachs research remained prestigious and relationship-driven. But hundreds of smaller research shops that competed purely on analytical output were decimated. The same pattern is about to play out in consulting.

Consider the numbers. Gartner estimated the global management consulting market at roughly $330 billion in 2024. MBB captures maybe $50 billion of that. The technology consulting giants like Accenture and Deloitte take another large chunk but operate differently. That leaves well over $100 billion in what we might call the "analytical middle": firms that sell thinking, frameworks, and recommendations without the brand premium or implementation capacity of the top players. This is the segment AI will compress violently.

The Democratization Angle Nobody Is Talking About

Here is what makes this moment genuinely interesting. The most important effect of AI consulting is not cost reduction for existing buyers. It is access expansion for people who could never afford consultants in the first place.

A Series A startup with $5 million in funding has never hired McKinsey. They have never hired anyone from the second tier either. Their "strategy consulting" has been the CEO reading a few books, talking to advisors over coffee, and making gut calls. AI platforms could give these companies access to structured strategic thinking for the first time.

The same applies to mid-size businesses in emerging markets, nonprofit organizations, government agencies with tight budgets, and internal teams at large companies that cannot justify a consulting engagement for every strategic question. The addressable market for strategic analysis is orders of magnitude larger than the current consulting market. It has just been gated by price.

This is the classic Clayton Christensen disruption pattern. The new technology does not initially compete with the best incumbents on their terms. Instead, it serves a vast underserved market that the incumbents have no interest in addressing. A $500-per-month AI strategy tool is not competing with a $2 million McKinsey engagement. It is competing with "we just guessed."

Rocket and similar platforms should be optimizing for this market, not positioning against McKinsey. The McKinsey-killer framing makes for good headlines but terrible business strategy. The real opportunity is the millions of organizations that have strategic questions and zero budget for human consultants.

The Technical Ceiling and When It Breaks

Current AI consulting tools have a fundamental limitation: they operate on public and semi-public information. They can synthesize market reports, analyze financial filings, process news, and apply frameworks. What they cannot do is access proprietary data, conduct primary research, or interview stakeholders. This is a significant constraint because the most valuable consulting insights come from primary research, not from repackaging secondary sources.

But this ceiling is not permanent. Three technical developments over the next two to three years will push AI consulting capabilities significantly higher.

First, enterprise data integration. As companies connect AI platforms to their internal data lakes, CRM systems, financial systems, and operational databases, AI consulting tools will be able to generate analyses grounded in proprietary data. This is already happening in adjacent categories. Tools like Glean and Hebbia are connecting LLMs to enterprise knowledge bases. It is a matter of when, not if, this capability reaches strategy tools.

Second, agentic research workflows. Current AI tools generate responses from their training data and retrieval context. The next generation will autonomously conduct research: scraping competitor websites, analyzing patent filings, monitoring social media sentiment, running financial models, and synthesizing findings across dozens of sources. This moves AI from "framework application" to something closer to actual analytical work.

Third, multimodal reasoning over complex documents. Strategy consulting involves digesting enormous volumes of messy information: board presentations, regulatory filings, internal memos, market research reports with charts and tables. As multimodal AI improves at extracting insights from these document types, the quality gap between AI-generated and human-generated analysis will narrow considerably.

The firms that should be most worried are not watching AI capability benchmarks. They should be watching enterprise data integration timelines. The moment an AI platform can access a company's Salesforce data, financial actuals, and competitive intelligence database simultaneously, the analytical advantage of a human consulting team shrinks dramatically.

A Prediction and a Warning

Here is what I think happens over the next five years. MBB firms will acquire or build AI tools and use them to make their own consultants more productive, widening their margin advantage. They will serve fewer, larger clients at even higher prices, leaning further into the relationship and implementation aspects of their value proposition. McKinsey with AI-augmented consultants is a more formidable competitor, not a weaker one.

The mid-tier consulting firms that survive will be those that specialize deeply in specific industries or functional areas where proprietary expertise and relationships still matter. Generalist mid-tier strategy firms will struggle badly. Some will pivot to implementation. Others will simply disappear.

AI consulting platforms will build a large and growing market serving the previously unserved. The best of them will look less like "McKinsey in a box" and more like "a smart chief of staff who never sleeps." They will be embedded in workflows, continuously monitoring and analyzing, rather than producing one-off deliverables.

The warning is for Rocket and every other startup in this space: do not chase the incumbents. The consulting firms you want to replace have survived for a century because their product is trust, not analysis. Build for the millions of organizations that have never had access to strategic thinking. That market is bigger, growing faster, and completely undefended.

The companies that figure this out will not kill McKinsey. They will build something McKinsey never bothered to build. And that is far more valuable.