From Document Processing to Decision Intelligence: The Next Enterprise Shift

For years, when enterprises heard the phrase document processing, they thought about scanning PDFs, digitizing records, and extracting fields like names, dates, or totals. Useful, yes — but limited. In a world where organizations are drowning in unstructured content, simply pulling text off a page doesn’t move the needle. The reality is that value doesn’t come from extraction. It comes from the decisions that follow.

As Brad Schneider, CEO of Nomad Data, puts it:

Document processing without decisions is just busywork. The real opportunity is turning documents into intelligence that leaders can act on instantly.

Enterprises have reached a turning point. Document processing is no longer enough. The future lies in what Nomad Data calls decision intelligence — a new approach where AI doesn’t just digitize content, but produces the same decision-ready reports, summaries, and analyses that once required armies of analysts.

Document Processing: Problems with Traditional Approaches

Traditional document processing has clear limits:

• Extraction is not insight. Pulling fields into a system doesn’t tell leaders what’s important or what to do next.
• Brittle workflows. Regex, templates, and one-off rules work for narrow cases but collapse at scale.
• Human dependency. Most organizations still need analysts to read, cross-check, and summarize outputs.
• Risk exposure. “Good enough” extraction fails compliance, audit, and governance requirements.

The result? Enterprises spend enormous sums digitizing content, only to push the real work — summarization, reporting, decision-making — onto human analysts.

As Schneider explains:

Most solutions are really focused on the first piece, which is just extracting the information from the document, as opposed to doing the thing a person is manually doing next. That’s where the real value gets done.

From Document Processing to Decision Intelligence in the Enterprise

Decision intelligence is the logical next step. It bridges the gap between raw text and actionable decisions.

Nomad’s Doc Chat platform illustrates the shift. Instead of handing enterprises digitized text, it produces the exact outputs executives, compliance officers, and analysts already rely on:

• Summaries of thousands of pages distilled into executive-ready reports.
• Excel datasets that drop directly into existing pipelines.
• Detection of inconsistencies, anomalies, and risks across documents.
• Tailored compliance outputs with page-level citations.

The impact is immediate: faster cycles, fewer errors, reduced reliance on manual review, and lower compliance risk.

As Schneider describes it:

The advantage of the Nomad solution is it does the whole soup to nuts. It starts with the digitization, and it goes all the way through the value creation.

Decision Intelligence with the Power of AI

Decision intelligence is made possible by advances in artificial intelligence.

• Technology foundation. Built on OCR, NLP, and large language models, but fine-tuned for enterprise-grade tasks.
• File-type coverage. Works across PDFs, scans, images, email attachments, spreadsheets, and more.
• Reasoning capabilities. Goes beyond extraction to perform inference, context analysis, and anomaly detection.
• Governance. Every answer comes with page-level citations or structured references — ensuring outputs are audit-ready.
• Adaptability. Enterprises can embed their own rules, checks, and thresholds, ensuring outputs align with internal and regulatory standards.

This isn’t about generating generic summaries. It’s about creating decision-grade intelligence, formatted exactly as leaders expect it.

Schneider notes:

We literally sit down with decision makers to understand what output they need. What does that report look like? How are those decisions made? Nomad then produces the same outputs those armies of people would have created — only a lot faster and a lot less expensive.

Decision Intelligence Real-World Use Cases

Decision intelligence isn’t theoretical — it’s already transforming industries.

• Insurance. A 10,000-page workers’ comp claim can take weeks of analyst time or cost tens of thousands of dollars if outsourced at $3 per page. Nomad delivers the same summary in about two minutes at a fraction of the cost.
• Investment due diligence. An analyst may spend a week reviewing a deal room of thousands of documents. Nomad performs the same work — document selection, data extraction, report writing — in 20 minutes. As Schneider points out, “Instead of looking at one deal a week, you can now look at multiple deals in an hour.”
• Banking and asset management. Risk and compliance reporting at scale, complete with audit-ready citations.
• Healthcare. Summaries of patient histories and treatment regimens across massive record sets.
• Legal. Contract analysis and regulatory audits across hundreds of filings.

Ask Doc Chat This

Examples of real queries enterprises can run through decision intelligence platforms like Doc Chat:

• “Summarize all risk factors cited in this 300-page filing.”
• “List all medications prescribed with dates and dosages.”
• “Cite inconsistencies in revenue reporting across these earnings transcripts.”
• “Create an Excel-ready table of all counterparties mentioned, with contract dates and terms.”
• “Highlight all red flags in this compliance report with page references.”

Why Enterprises Move Beyond Document Processing

How Enterprises Can Get Started with Document AI

Adopting decision intelligence doesn’t require deep integration or lengthy pilots. Enterprises can see proof of value in days, not months.

The playbook is straightforward:

1. Identify a high-impact use case. Look for bottlenecks where armies of analysts are summarizing or reporting.
2. Run a low-friction pilot. Show results in real workflows with decision-ready outputs.
3. Scale across teams. Once ROI is clear, expand to compliance, risk, due diligence, and beyond.

The result: a single platform that reduces backlog, accelerates cycle times, and produces compliant, audit-ready intelligence.

As Schneider emphasizes, the speed of impact often surprises leaders:

One claim handler, instead of doing two claims in a day, can do four claims in an hour. If you double in size, you need zero additional claim handlers.

For some companies, that’s not just efficiency — it’s the difference between being able to function or falling behind under the weight of documents.

Document processing was never the end goal — it was the first step. Enterprises don’t want digitized text; they want decision-ready intelligence. Nomad Data delivers the full workflow, from OCR to compliance-ready reports. It doesn’t just automate busywork — it redefines how decisions get made. The message is clear: if your organization is struggling under the weight of documents, it’s time to move from document processing to decision intelligence.

See how quickly you can move from document processing to decision intelligence with Nomad Data.