Most current enterprise AI systems are designed for rapid response, applying AI to content to generate immediate answers to user queries. They excel in quick information retrieval and basic task completion.
But what if you need to go beyond quick summaries to deeper analysis of mountains of unstructured data? That’s called deep research.
Deep research automates the sort of complex report-making that would typically require extensive human effort (or even an outside consulting team). As Box CTO Ben Kus describes it, "Deep research is when AI goes beyond simple question-answering, conducting extensive analysis across various sources to produce a sophisticated, comprehensive report.”
In a recent discussion on the Box AI Explainer Series podcast episode Agentic deep research: When AI thinks before it answers, Ben Kus and Box Senior Product Marketing Manager, Meena Ganesh, explore deep research in depth. In particular, they discuss how deep research leverages agentic reasoning to empower businesses to synthesize information and deliver more impactful results.
Key takeaways:
- Deep research emulates the comprehensive research process of a human consultant, synthesizing multiple inputs and employing various revision cycles
- Deep research can help employees get up to speed quickly when handling cases, projects, or problems requiring deep knowledge synthesis
- Agentic AI is key to deep research because it can critique itself and revise outputs, ensuring the final deliverable is comprehensive and accurate
The distinction between conventional AI and deep research
As enterprises grapple with increasingly complex challenges, there's more demand for advanced research capabilities powered by AI. Deep research can synthesize multiple inputs, revision cycles, and clarifying feedback loops. This capability emulates the comprehensive research process of a human expert, incorporating myriad information sources, iterative refinement, and the ability to seek clarification.
The ultimate AI output resembles the work of an experienced professional. As Kus says, “If you've ever wanted to use AI to get really comprehensive answers — but weren't sure if you could trust the answers or couldn’t get the complexity you were looking for — now, deep research will be able to give you that kind of very detailed holistic answer that’s useful in many different situations.”
Why deep research matters in the enterprise
Consider a lawyer joining a new, convoluted case. Armed with deep research, AI can compile a summarized, customized report tailored to the lawyer's specific requirements. The lawyer can gain deep knowledge quickly without wading through countless documents or systems.
By enabling faster, smarter decision-making, deep research becomes a game-changer for enterprises handling cases, projects, or problems requiring comprehensive knowledge synthesis. From legal cases to market analysis, the implications are profound: Businesses gain the capability to synthesize scattered information into unified, targeted reports in a fraction of the time. This creates efficiency but also empowers innovation, accuracy, and adaptability at scale.
Agentic AI puts the deep in deep research
Deep research is made possible by advancements in agentic AI. Instead of simply producing basic responses based on predefined training data or straightforward reference checks, agentic AI actively engages in a structured, iterative information-finding process and then presents information in a customized way.
AI agents are driven by an advanced agentic reasoning framework that looks like this:
- Plan: First, the agent sets objectives by understanding the goal, identifying required tools or data, and outlining a task sequence
- Discover and comprehend: The agent retrieves relevant content, parsing the instructions (and constraints)
- Analyze and iterate: Agent thinks through solutions, evaluates options, handles ambiguity, and refines with feedback
- Synthesize and execute: Agent delivers the outcome and takes an action — notifying the human owner or updating a system based on the results
Or as Kus succinctly puts it: “Deep research makes a plan, finds the right information, is permissions-aware, iterates, revises, and critiques itself.”
This self-checking capability is what makes the research deep. Driven by agentic AI, deep research mirrors the methodology of professional consultants, who meticulously delve into layers of information, revise findings, and ensure high-quality, actionable results.
In critical workflows that require a high degree of accuracy and thoroughness, deep research can find data points across different sources, refine its insights, and synthesize comprehensive conclusions into a highly specific format.
Here’s another example: A marketing strategist is asked to pull together an FY25 marketing performance report and use it to create strategies for FY27. This might require digging through mountains of marketing assets, reports, trends, and other content types to pull together a comprehensive report with just the right elements. The report might need information broken down into key highlights, data snapshots, and future recommendations. This might take the human strategist anywhere from days to weeks (searching for content, asking others for help finding data, and pulling it all together). Deep research cuts the time down to minutes.
Catch the full episode
Deep research is not just a buzzword; it’s a tangible tool to propel your enterprise into the future. Whether your team is navigating a labyrinth of legal documents, aggregating market research, or analyzing scattered policy data, deep research, propelled by agentic AI, delivers both clarity and depth.
Are you ready to leverage this transformative technology? Don’t miss episode 9 of the Box AI Explainer Series.Subscribe now to stay informed and get inspired about the AI-first era. Start listening today to learn practical, actionable strategies for integrating AI into your organization or industry.
