By Jonathan Nolan

Organizations today are expected to share more information than ever before. Regulators, partners, and the public want transparency. At the same time, companies are responsible for protecting personal details, proprietary knowledge, and competitive information. The challenge is identifying what must be protected while keeping the information useful.

Manual review was the standard approach for many years. It works when the volume is small and timelines are generous. But when deadlines are tight and the information spans thousands of pages, manual review creates gaps, inconsistency, and uncertainty. Experienced reviewers make different decisions. Pressure and fatigue affect what gets flagged and what gets missed. This affects compliance, trust, and confidence.

This is the environment where I led the introduction of AI assisted identification to support data protection before disclosure. The goal was not automation. The goal was supporting expert judgment so teams could work more consistently and confidently. AI provided context and pattern recognition. Human reviewers made decisions.

AI is often discussed in terms of efficiency. It can speed up review cycles, reduce manual searching, and support consistency. These are outcomes, but not the core responsibility. When AI influences decisions, the role of product leadership changes.

Leading AI assisted products requires:

Introducing AI is not only a technical implementation. It is a change management effort. Adoption depends on understanding, confidence, and trust. Users need to see value without losing control of the decision.

AI assisted identification changes the task from searching to evaluating. A well designed system surfaces patterns and information that commonly get overlooked. It may highlight repeated phrases that appear in different sections. It may identify dates, locations, rare conditions, contextual details, or proprietary terminology that require protection before release.

The system does not make the decision. It signals that something may matter. For example, the system might point out an outlier value that falls outside typical ranges or a geographic reference repeated across documents. It provides information and context. The reviewer decides how to act.

Good identification balances protection and utility. It is not effective to redact everything that might be sensitive if the result removes meaning or value. When the AI supports judgment without taking it over, the outcome is more consistent and more useful.

AI assisted systems also introduce risks that product leaders need to manage. A system trained on historical decisions may learn assumptions that no longer reflect current expectations. If past reviewers treated information too conservatively, the system may surface too many false positives. If the training set lacked diverse examples, the system may fail to identify sensitive information in unfamiliar contexts.

The consequences of AI errors are practical:

Both outcomes reduce trust

Explainability matters. Reviewers need to understand why something was surfaced. They also need a way to defend decisions when asked. AI that cannot explain its reasoning cannot support accountability.

I led the implementation of AI assisted identification in clinical disclosure and transparency workflows where organizations were required to prepare documents for release to external audiences. Manual review created delays and inconsistencies, especially when dealing with thousands of pages across multiple document types.

The organizational challenge was significant. Oversight teams needed confidence the system aligned to expectations. Operations teams were concerned about reviewing large numbers of false positives. Quality and audit groups needed traceability for every suggestion and decision. Content preparation teams needed tools that worked with existing processes, not a replacement for them.

My role was to translate expectations into product decisions. Accuracy alone was not the measure of success. We needed to understand which types of errors mattered the most and why. We calibrated sensitivity and specificity with operational teams based on their risk tolerance.

We built a system that identified patterns using natural language processing and contextual signals. It provided suggestions with clear references and explanations. Reviewers could accept, dismiss, or annotate suggestions with equal ease. This preserved expert judgment.

The outcomes were meaningful. Teams began with a stronger starting point. Preparation cycles became more predictable. Identification became more consistent without removing context. Reviewers reported increased confidence that sensitive information was being addressed. Trust grew through accuracy, transparency, and user control.

If I were starting this initiative today, I would involve users earlier in design rather than later in testing. Their understanding of how information flows through documents should influence the data model. I would also plan the false positive experience early in the design process because managing unnecessary suggestions has a direct impact on trust.

This experience led to the Trust First Model for implementing AI assisted identification in high stakes environments.

This model applies to disclosure reviews, contract workflows, quality checks, security event processing, and any environment where identifying information accurately and consistently matters.

Organizations face increasing expectations for information sharing along with rising requirements for protection. AI can help teams manage this work more effectively, but it must be implemented with clarity and responsibility.

Product leaders who succeed with AI will use it to support human judgment, not replace it. They will communicate what the system can do and where oversight is required. They will design for transparency, control, accountability, and iteration.

AI can surface patterns and improve consistency, but decisions and responsibility remain with people. The future belongs to product leaders who scale judgment with clarity and purpose.

Written by Jonathan Nolan

Jonathan Nolan is a Director of Product Management focused on AI assisted solutions in regulated environments. He has led product strategy for systems that help organizations identify and protect sensitive information while keeping data useful for research, transparency, and communication. His work centers on supporting decision making without replacing expert judgment.