Document Mining & Analytics in the Age of Agentic AI

Market Trends, Use Cases & Buyer Guidance

Agentic AI has changed what’s possible in document processing — but it’s also created a new and consequential question: should your organization buy a proven platform, or attempt to build one?

Document mining and analytics platforms have emerged as a critical layer in the modern AI and automation stack — but the market is highly fragmented, and the cost of choosing the wrong approach, whether the wrong vendor or the wrong build-vs-buy decision, is significant.

In this webinar, guest speaker Boris Evelson of Forrester shares independent research and analysis on the state of the document mining and analytics market, key use cases driving adoption, and practical guidance for enterprise buyers evaluating platforms in 2026.

What You’ll Learn

• Why document automation is accelerating — and what the data says about where enterprises are investing
• How Forrester defines the document mining and analytics platforms market, and why vendor selection needs to start with use case alignment
• The key capability areas enterprise buyers should evaluate — from agentic AI architecture to human-in-the-loop design and deployment flexibility
• Why agentic AI augments document processing platforms but doesn’t replace them — and what that means for your build-vs-buy decision
• Practical benchmarks for time to value, pricing, and accuracy to inform your business case

Read the full analyst report

Interested in reading the report for yourself? Access the Q2 2026 Forrester Wave report to discover why Hyperscience was named a Leader and Customer Favorite in Document Mining and Analytics Platforms.

Transcript

Brian Weiss: Hi everyone. Thank you for joining us today. I’m Brian Weiss, the CTO at Hyperscience, and it is my pleasure to welcome you to this webinar. Today, we’re going to be discussing document mining and analytics in the age of agentic AI. Over the past year, it seems like every business conversation begins and ends with either AI or agentic AI. I live in the Bay Area, and every single billboard from the base of the Bay Bridge all the way down to Palo Alto mentions agentic AI. We could practically play bingo on how many times we see the term just driving back and forth.

Joking aside, this is a truly fascinating market shift driven by rapid technological advances. Companies that rely on documents for critical workflows—whether it’s their core business or an ambition to extract data from legacy files—are asking highly practical questions: What can agentic AI do for my business? Can it create meaningful business value through cost savings or by accelerating operational insights? How do I build a modern infrastructure framework that takes advantage of this technology today and adapts to future innovations?

Those are the core topics we will explore today. We’ll be looking directly at Forrester’s latest market research, including evolving trends, market direction, and a deep evaluation of competing technologies. I am incredibly pleased to be presenting alongside Boris Evelson. Boris is a long-time Forrester analyst who is exceptionally deep in this market. It is always a pleasure to read his research and listen to his perspectives. Boris, thank you so much for joining us today. I will primarily act as the narrator for this session, so I’ll hand it over to you to dive into your latest findings.

Boris Evelson: Excellent. Thank you so much, Brian. I’m really looking forward to this session. Hyperscience and Forrester have collaborated for many years; we constantly learn from each other, so I anticipate a highly engaging, bi-directional discussion today. From the outset, I want to make a strong statement because many clients incorrectly view agentic AI as a panacea for all operational challenges. We frequently hear enterprises ask, “Why do we need to buy or build a specialized solution? Why can’t agentic AI just handle this for us?”

While agentic AI can indeed automate certain standalone processes, applying it to intelligent document automation introduces unique complexities and hidden costs. Agentic AI by no means eliminates the need for robust, customizable solutions equipped with comprehensive human-in-the-loop capabilities. This is an important distinction to clarify so that organizations approach the technology with the right mindset.

Furthermore, enterprises are appropriately questioning why they should purchase a third-party vendor solution when they are already paying for an enterprise AI platform. Over the next 30 to 40 minutes, our research will demonstrate why you must think critically before attempting to build an in-house document mining solution from scratch. It is not simple. At Forrester, we are strong advocates of buying specialized solutions for document mining rather than building them independently. Let’s look closer at the operational data.

I wouldn’t be a Forrester analyst if I didn’t start with market data. Looking at large enterprises, three-quarters of organizations with more than 1,000 employees store and process over 100 terabytes of data. Crucially, up to 64% of that data is unstructured or semi-structured. Unstructured data refers to completely open text, such as an email, while semi-structured data includes documents containing distinct sections, headers, and footers. Processing 64% of a 100-terabyte footprint represents a massive operational hurdle.

Brian Weiss: I’ve been analyzing that metric for the better part of fifteen years. The reality that 70% to 90% of enterprise data remains unstructured and difficult to access has been a constant challenge. You can almost map the evolution of modern technology—from enterprise search to big data, and now to AI—against our ongoing ambition to make sense of this information. It ultimately comes down to a few fundamental questions: Is it cost-effective to retrieve this data? Can we afford to process it? Will the available technology deliver tangible business value? We are finally at an inflection point with AI where these questions are being answered affirmatively. We can now achieve outcomes that go far beyond simply tossing up another static business intelligence dashboard. Would you agree?

Boris Evelson: I totally agree. We track multiple market segments where the core use cases rely heavily on unstructured and semi-structured data, and the return on investment is undeniable. However, you cannot simply build an unorganized data lake and assume users will find value—to use a baseball analogy, it isn’t a case of “build it and they will come.” We have successfully executed that model with structured data for decades by centralizing transactions into an enterprise data warehouse for financial or marketing analytics.

The market is simply not ready to apply that exact same approach to unstructured data. You cannot dump it into a single repository and expect a general AI model to handle everything out of the box. You still require purpose-built solutions tailored to specific use cases—such as enterprise content management, customer experience text mining, or Voice of the Customer initiatives—to capture real value. We have to chip away at the problem case by case.

Confirming this reality, more than three-quarters of large organizations have already adopted or plan to adopt intelligent document extraction and processing (IDEP) technologies, with over a quarter planning to accelerate their investments. At Forrester, we refresh our research every 18 months, and we have just updated our 2025 landscape and 2026 Wave evaluation on this topic.

We begin by assessing a broad landscape of over 100 vendors that serve the large enterprise market. We narrow this down to 33 core vendors for our non-evaluative landscape report, and from that group, we select a highly qualified subset for deep technical evaluation in the Forrester Wave. For this specific cycle, we evaluated eight vendors. To be included, a vendor must demonstrate comprehensive enterprise-grade support, a substantial market presence, and a mature standalone product that our clients actively inquire about.

Our evaluation scales across more than 20 distinct criteria, utilizing a fine-tooth comb to analyze over 120 detailed questions verified through live demos and customer references. We do not view AI as a single, monolithic concept. Our framework evaluates generative AI alongside specialized machine learning models and traditional knowledge-based AI, such as linguistic rules and ontologies. We evaluate how a platform manages model lifecycles, tracks accuracy drift, and orchestrates distinct models across a multi-step document workflow.

Given the massive market shift toward agentic AI, we also deeply analyze architectural flexibility. We examine whether a platform allows you to bring your own machine learning models, swap out foundational LLMs, and orchestrate multiple autonomous agents. Furthermore, the platform must seamlessly integrate with downstream enterprise systems, implement strict guardrails to handle the probabilistic nature of LLMs, and manage context engineering for both human and AI consumption.

Image: Forrester’s high-level conceptual framework

Brian Weiss: This piece of research is incredibly timely. The concept of strategically inserting autonomous agents directly into human workflows is top of mind for Hyperscience. The way we orchestrate a document automation pipeline is by stacking multiple models. We utilize narrow, specialized models trained on customer data alongside broader, probabilistic models. At every stage of that pipeline, you must harness accuracy while ensuring the overall system remains improvable.

We have always been deeply committed to keeping humans in the loop. This ensures that a person is not only validating low-confidence outputs but also capturing edge-case errors to automatically retrain and improve candidate models in the background. Managing this loop allows enterprises to optimize both accuracy and cost. You must avoid using a financial helicopter to cross the street; there is no reason to incur the high token costs of a massive foundational model if a simple, deterministic query can extract the required data.

We are applying this exact framework by deploying background agents that monitor primary model performance, identify errors, and automatically construct the next iteration of the model pipeline. This has moved our customers toward a “human-on-the-loop” operational model, where autonomous agents manage the manual data processing and human supervisors manage the agents. This architectural framework allows you to establish strict data boundaries and clear token budgets, which is why our enterprise customers are seeing immense value in this approach.

Conclusion

Ultimately, my closing advice to enterprise leaders is to think very hard before attempting to build an in-house document mining solution. It is a sobering reality that this cannot be solved by simply pasting text into a basic LLM prompt and wrapping it in a simple batch script. That approach may work for ten documents during a trial, but it will inevitably break at scale. I highly encourage organizations to download the detailed Forrester Wave evaluation spreadsheet; its 100-plus verified technical questions provide an exceptional foundation for seeding your company’s RFI and RFP documents.