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Inside Bain: How OpenAI's Deep Research Is Rewiring Consulting

OpenAI Blog · Jul 15, 2026 · 2 min read · Read original article →

Curated by the Inblix editorial team


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If you want to see where elite knowledge work is heading, look at what Bain & Company is doing with OpenAI’s deep research tools. Researcher Reem Anchassi is using them to chew through complex industry trends, and her assessment is refreshingly pragmatic — not the breathless hype you usually hear. “These kind of tools increase my personal capacity so that I can use my time doing other research tasks,” she says. That sentence gets at something real. The tool isn’t replacing the researcher’s judgment; it’s absorbing the grinding, time-intensive synthesis that consultants have always done manually.

This shift matters because top-tier strategy firms bill on brainpower. Moving a portion of the analytical heavy lifting to an AI fundamentally changes the economics of a consulting engagement. Anchassi’s comment points to a future where the value of a Bain researcher isn’t measured by hours spent in a data room, but by the quality of the questions they can now afford to ask. The grunt work gets automated; the strategic thinking gets more room to breathe.

For an industry built on proprietary methodologies and bespoke analysis, this is a delicate dance. If every firm has access to the same powerful research synthesis, the competitive edge shifts to something less replicable. It moves toward the interpretation, the client relationship, and the specific, high-stakes judgment call that no large language model can make. The tool standardizes the input; the consultant’s craft becomes entirely about the output.

The quiet signal here is about skill inflation. A first-year analyst armed with deep research can potentially operate at the level of a more senior colleague from five years ago, at least for the initial phases of a project. That doesn’t make experience obsolete — it makes it more essential for the parts of the job that are messy, political, and deeply human. The question Bain and others will have to answer is how they train that judgment when the apprenticeship model of doing the grunt work yourself starts to erode.

💡 Key Takeaways

  1. Bain researcher Reem Anchassi frames AI deep research as a capacity multiplier that frees up time for higher-value tasks, not as a replacement for human analysis.
  2. The economic model of consulting engagements shifts when AI absorbs the synthesis work, forcing firms to differentiate on interpretation and client judgment rather than raw data analysis.
  3. Junior consultants with AI tools may reach analytical parity with more experienced predecessors, which challenges the traditional apprenticeship model of professional development.

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