Bernard Aceituno is Cofounder of StackAI and holds a Ph.D. and SM in Computer Science.gettyMany in the enterprise AI world are trying to answer one question: Which use cases are really working inside regulated organizations right now?After three years embedded in enterprise AI deployments across financial services, legal, healthcare and more, I've seen a few clear patterns emerge. The agents producing the most value tend to share three traits: They're event-triggered; they're often direct translations of a real-world process; and they're tightly integrated with the systems where the work already lives. What follows is a guide to five of the most common use cases I've seen succeed, organized by industry, with a sketch of how each one works.1. Private CapitalAccording to 2025 KPMG research, 80% of surveyed PE leaders viewed GenAI as a critical component for gaining competitive advantage and market share. I expect to see increasingly more use cases centered around diligence, Know Your Business (KYB) and Know Your Customer (KYC) processes.For example, consider a triggered workflow for the top of the funnel. An inbound email lands in a shared deal-team inbox. Then, several agents work to classify the opportunity by sector and stage, parse the attachments, extract a structured financial summary, screen it against the firm's stated criteria, draft a one-page preliminary memo and write the record into the customer relationship management (CRM) or deal-tracking system. The associate opens their morning queue and finds the initial work already done, letting them jump straight into higher-level thinking.2. Law Firms And Legal TeamsLaw firms and in-house legal teams share a common pain point: The volume of contracts and inbound documents far exceeds the volume of senior attorney time. A 2026 American Bar Association survey found that "38% of respondents report saving one to five hours per week" with AI, while "14% report saving six to 10 hours weekly.”With an AI agent architecture I helped implement successfully, a contract would land in a shared folder or document management system. One agent would extract the key clauses, a second would compare them against the firm's playbook and prior precedent and a third would draft a redline summary with proposed alternative language. The output would then be sent to the reviewing attorney's queue, ensuring nothing is sent without approval.3. HealthcareAccording to Eliciting Insights, over 40% of surveyed health systems in February 2026 were considering implementing AI for prior authorization, denial prediction and technical appeals.For prior authorization, the right architecture I've helped develop is a human-in-the-loop AI system that assembles the relevant clinical evidence from the Electronic Health Record (EHR), retrieves the applicable payer criteria from a maintained knowledge base, drafts the authorization letter and routes it to a nurse case manager for review inside the existing workflow tool. The nurse approves, edits or rejects in seconds rather than the minutes a full draft would take.4. InsuranceA 2025 BCG survey found that 67% of responding insurers were piloting a GenAI system. One area where AI can be particularly useful is the inbox, which commercial underwriters live and die by. Every broker submission arrives as some combination of email, PDF, spreadsheet and loss-run report, and the first hour on each one is data entry.The AI architecture I've seen work best here is a triggered workflow tightly integrated with the policy admin system. A broker email arrives. The agent classifies the line of business, parses every attachment, extracts the structured risk data, validates it against required fields, writes the submission into the policy admin system and drafts an acknowledgment for the underwriter to send. Underwriters get a fully formed submission in their queue instead of being bombarded with the contents of their raw inbox.5. BankingAn April 2026 MIT article discussed five core banking functions AI is transforming. One of these areas is back-office compliance, including internal audit. Internal audit must prove that controls held, which means an auditor combing a loan file that can run hundreds of pages, finding the one field that matters and placing a tick mark on it as evidence.I've helped build an agentic workflow specifically for this use case, which reads a given program, works out which fields to check and runs several specialist sub-agents. Some extract facts and record where each was found, while others place the tick mark on the source document, proving the review at the same time it pulls the data. What used to take hours of review per file becomes a first cut in minutes ready for human second-level review, with coverage no longer capped at just a few samples.What The Winners Have In CommonThere are a few key architectural points to take away.First, nearly all of the highest-value agents are triggered, not chat-based. This means the AI can work seamlessly in the background rather than forcing users to talk to it each time they need results.Second, each use case translates the process from the real world into the AI platform, with a mix of deterministic actions executed across different tools, combined with large language model (LLM) reasoning at specific points as well as human-in-the-loop steps for business-critical actions. Third, integrations matter. The most useful AI agents have access to every system that allows them to close the loop of a given task or process: EHRs, Microsoft Office, Gmail and more.The AI agent landscape will keep getting noisier. The most useful thing CIOs and tech leaders can do right now is ignore most of it and study the patterns that work in reality to build the systems best suited for their teams.Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?