The rise of the AI knock-off McKinsey consultant
In recent years, artificial intelligence (AI) has begun to permeate various industries, including the consulting realm. A new trend has emerged where AI agents are designed to mimic the work of traditional consulting firms, notably McKinsey & Company. This article explores the rise of these AI knock-off consultants, their capabilities, and the implications for the consulting industry.
Understanding AI Skills in Consulting
Developers are now creating open-source “skills” that can be integrated into AI agents, enabling them to perform tasks typically associated with human consultants. These skills are designed to replicate the consulting process, which has traditionally involved providing advice packaged in slide decks and billed by the hour.
One notable example is Vercel’s “skills” library, which boasts nearly 90,000 reusable skills for AI agents. These range from copywriting and code review to consultant-style problem-solving. Vercel, an AI startup valued at over $9 billion, has created a cloud-based AI platform for developers to leverage these skills effectively.
The Emergence of Consulting-Related AI Skills
The concept of AI skills gained traction after Anthropic introduced “skills” for its chatbot Claude in October. Since then, developers have been actively building and sharing thousands of skills that can be plugged into various AI systems. A review of Vercel’s skills library reveals at least four skills labeled with the term “McKinsey” and 26 labeled as “consultant.”
The most popular consulting-related skill in Vercel’s library is labeled “mckinsey-consultant.” First uploaded on January 25, it averages 445 installs per week. While this number is respectable, it still pales in comparison to the most popular agents in Vercel’s library, which can have hundreds of thousands of installs. The skill has garnered 200 stars on GitHub, indicating its popularity and viability among developers.
How AI Mimics McKinsey’s Consulting Process
Vercel’s library describes the “mckinsey-consultant” skill as a prompt framework originally designed for Claude. It guides AI through defining problems, generating hypotheses, conducting structured analysis, and creating presentations, thereby replicating the classic workflow of a typical McKinsey consultant.
Expert Opinion: Limitations of AI Consultants
To evaluate the effectiveness of the AI consultant skill, Business Insider consulted Arvind Vasudevan, a former McKinsey consultant. He noted that while the AI agent can perform basic analyses, it lacks a crucial element that defines the value of a McKinsey consultant: the ability to engage in meaningful conversations.
Vasudevan emphasized that a significant part of a consultant’s value lies in the questions they ask and the dialogues they facilitate. These interactions help clarify thinking, uncover unstated assumptions, and ensure deep analysis. He remarked, “None of that is happening in this agent, which is doing a set of boilerplate analysis without that Socratic questioning and thinking.”
The Financial Impact of AI Consultants
Despite their limitations, AI agents mimicking consulting work have already begun generating revenue for companies such as PromptQL, an AI enterprise platform launched by the open-source unicorn Hasura. PromptQL assists clients in building custom AI analysts by integrating their internal data with existing foundation models.
Tanmai Gopal, co-founder and CEO of PromptQL, highlighted that the primary challenge in providing valuable analysis is understanding the intricate relationships between people, data, and revenue. He stated, “McKinsey’s teams spend weeks embedded inside a company absorbing how it actually operates: the exceptions, the tribal knowledge, the definitions that differ between departments. That company-specific context is what makes their advice worth millions.”
Challenges Facing AI in Consulting
Gopal pointed out that many enterprise AI tools fail because they lack the necessary grounding, often resorting to guessing rather than asking critical questions or learning from feedback. He explained that PromptQL aims to address these challenges through a shared layer of understanding that evolves with each new input.
He elaborated, “When a team member corrects the AI, teaches it a definition, or resolves an ambiguity, that knowledge becomes permanent and available to everyone. It’s not a semantic layer that data engineers maintain. It emerges from conversations.” This approach seeks to overcome the limitations of AI models, which often struggle with internal nuances such as pricing changes, team-specific terminology, or conflicting definitions of revenue.
The Future of AI in Consulting
The rise of AI knock-off McKinsey consultants raises important questions about the future of consulting. While these AI agents can perform certain tasks traditionally handled by human consultants, they still lack the nuanced understanding and judgment that human consultants bring to the table.
As AI technology continues to evolve, it is likely that these systems will improve in their ability to engage in meaningful dialogue and provide contextually relevant advice. However, the core value of consulting—understanding complex human relationships and organizational dynamics—remains a challenging frontier for AI.
Conclusion
In conclusion, the emergence of AI agents attempting to replicate the work of McKinsey consultants signifies a notable shift in the consulting landscape. While these AI knock-offs offer potential benefits, they also highlight the limitations of current AI technology in understanding the complexities of human interaction and organizational behavior.
Note: The insights presented in this article are based on current developments in AI and consulting as of October 2023. As the field continues to evolve, further advancements may reshape the dynamics of consulting in the future.
