How Does a Governed AI Workflow Pilot Work? One Workflow, Thirty Days, Three Numbers

Chris Sowa · Founder, RedactSure
Published September 26, 2026 · Last updated September 26, 2026

One workflow, fixed scope, fixed timeline, a named approver on the customer’s side, and success criteria agreed before anything runs. A governed AI workflow pilot is built so the decision at the end is already made at the start: the numbers both sides signed are either met or they are not, and the record shows which. The pilot runs the workflow inside the governed environment under Least Exposure, so the agent receives exactly the data the task requires and nothing more, enforced before any model reads the screen, and a named person approves every consequential action. This page lays out what the customer supplies, what happens each week, what the three numbers are, and what the scale decision looks like when the thirty days are up. Corral first, automate second. The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.

Key findings

Gartner projects that over 40% of agentic AI projects will be canceled by the end of 2027, with unclear business value and weak risk controls among the causes. A pilot that fixes the value measure and the control evidence at signature removes both causes before they can act.
The pilot is not a demonstration. It runs the customer’s real workflow, against the customer’s real applications, with the customer’s own approver, and it produces the AI Control Record from day one.
Three numbers go to the board at day thirty: shadow-AI attempts redirected, hours returned by the governed workflow against the week-one baseline, and sensitive-data exposure events, which should read zero with the audit trail to prove it.
The security review happens once, at the platform boundary, before anything runs on customer data. Each later workflow inherits it, which is what makes the second and third workflows faster than the first.
The scale terms are agreed at signature, so meeting the criteria starts the rollout without a second selling cycle, and missing them ends the pilot cleanly: environment removed, keys revoked, findings handed over.

Why one workflow?

Because the failures in the published numbers come from the opposite design. Enterprise AI programs that start wide, with a platform, a committee and a roadmap, produce Theater AI: activity that looks like transformation and touches nothing that changes an outcome. Programs that start with an agent wired into everything hit the PII Wall the first time security asks what the model can see. One workflow, chosen because it is valuable and because it runs on records a model must not hold, is the smallest thing that proves both halves at once: the work gets done, and the data stays hidden.

The right first workflow has four properties. It crosses at least two applications, because single-application work is what embedded AI already does. It carries sensitive values on its screens, because a workflow with nothing to protect proves nothing about protection. It has a person who owns it and can approve its consequential steps. And it has a baseline: someone can say how many hours or cases it takes today. A claim from first notice to a queued payment, a patient account worked to a payer submission, a district budget transfer prepared for a principal’s approval, an invoice matched and queued for payment: each qualifies.

What does the customer supply?

Five things, all of which exist already.

A named approver: the person who owns the workflow, who will confirm the exposure policy and approve the consequential actions. Not the technology office, which sets the policy with them and receives the audit trail.

Access under existing permissions: the agent works under the approver’s own access to the applications. Nothing is provisioned for the AI and no permission changes.

A baseline: the week-one measure of the workflow as it runs today, in hours, cases or cycle time, so the day-thirty number has something to stand against.

A security contact: the person who receives the architecture, the tokenization rules, the logging design and the deployment configuration before anything runs on customer data, and who wires the SIEM export.

Success criteria: numeric targets set jointly in week one from the baseline. Not “did we like it” but numbers both sides sign.

What happens each week?

Week one: baseline and policy. The workflow is walked with the approver and mapped screen by screen. The exposure policy is written field by field: which values the task needs in clear, which are replaced by tokens (USER_001, SSN_001, ACCT_001), and the destinations where a real value may resolve. The approver confirms it. Allowed actions per system and the approval points are set: payments, submissions and record changes stop for the approver, on the record. The baseline is measured. The security contact receives the platform documentation and the review of the platform boundary happens here, once.

Weeks two and three: sandbox against the real applications. The workflow is built in the governed environment and run against the customer’s real application set in a sandbox, with the approver watching the same tokenized stream the agent sees, able to pause or take over. The blast-radius test runs on purpose: a prompt injection is fired at the sandboxed task and the record is checked for three things, tokens in model context, the resolver declining the unauthorized destination, and the consequential step stopped for approval. The audit export is wired to the SIEM and confirmed to hold tokens, not plaintext. Go or no-go at the end of week three.

Week four: close the doors for the pilot group. The workflow runs live with a named approver on every consequential action, for the fifteen to twenty-five people in the one department who do this work. Generative-AI category blocking is turned on at the web gateway for the pilot group only, so unsanctioned AI attempts are logged and redirected instead of silently succeeding. That is the corral: the sanctioned path becomes the only path for the people who have it.

Day thirty: three numbers. Shadow-AI attempts redirected, from the gateway logs. Hours returned by the governed workflow, against the week-one baseline. Sensitive-data exposure events, which should read zero, with the run history to prove it. The AI mandate delivered and the stack repaired in the same motion.

Longer pilots follow the same shape stretched: a twelve-week version adds a second queue, payer or environment in the final weeks to test reuse, with a go or no-go at week four and at the end.

What does “governed” mean during the run?

Four things are true of every run, and the record shows each.

The model received tokens where the identifiers were, because render-layer tokenization replaced them before any model read the screen. The agent worked under the approver’s existing permissions, tethered to that person for the whole run under Supervised Delegation. Every payment, submission and record change stopped for the approver, with their name and time on the record. And every screen and action landed in the AI Control Record as tokens, exported to the customer’s SIEM under the customer’s custody.

The real values live in hardware-encrypted enclaves with keys the customer holds; RedactSure stores ciphertext it cannot decrypt. The environment deploys into the cloud the customer’s compliance posture requires. Permissions stay exactly as they are. What changes is what the AI can see.

What does the decision look like at the end?

The decision was made at signature; day thirty reads out the result.

Criteria met: the scale phase begins on the pre-agreed terms. Those terms are written into the pilot agreement so nobody sells anything twice: per-seat pricing for the rollout, the order in which teams, queues or environments come on, the security review that carries over because the platform boundary was reviewed once, and a named owner for each new workflow. The pilot price credits against the first scale period.

Criteria not met: the pilot ends cleanly. The environment is removed, the keys are revoked, and the findings, including the run history and what it showed about the workflow, are handed to the customer. Either way the organization made one decision, at the start, and has the record to show why.

The second workflow, whichever one was not chosen first, is scoped during the scale phase using what the first taught both sides about the environment. It inherits the security review and the operating model, which is why the second is measured in weeks and the first in a month.

What the record shows

A governed AI workflow pilot is one workflow, fixed in scope and timeline, with a named approver on the customer’s side and success criteria signed before it runs. It supplies what the published numbers say failing programs lack: a value measure fixed against a baseline, and control evidence produced by the architecture rather than promised by a policy. Week one writes the exposure policy and the baseline; weeks two and three run the workflow in a sandbox against real applications and fire the injection test on purpose; week four closes the doors for the pilot group; day thirty reads three numbers to the board. The scale terms were agreed at signature, so the result starts a rollout or ends cleanly, and the AI Control Record shows which. Corral first, automate second. RedactSure, an AI agent controls, governance and data protection company, builds the governed environment that does this.

Frequently asked questions

How long is a pilot?

Thirty days for a single workflow in one department; twelve weeks when a second queue or environment is added to test reuse. Both have a go or no-go before live work begins.

What does it cost?

Fixed for the scope chosen, with the pilot price credited against the first scale period. The number depends on the workflow and the environment; the structure does not.

Do we need to integrate our applications?

No. The agent operates the applications through the governed environment at the render, so nothing is installed in the applications and nothing runs on employees’ endpoints. The workflow moves into the environment; the applications stay where they are.

Who at our organization runs it?

The approver runs the workflow; the security contact runs the review and the SIEM wiring; RedactSure installs, configures, drafts the policy, reports weekly and writes up results. Named owners on both sides are in the statement of work.

Can the pilot run on a workflow that touches PHI, student records or cardholder data?

That is the workflow to choose. A pilot on records a model must not hold is the only kind that proves the control, and the exposure policy is written for exactly those fields. Whether a given deployment satisfies a given regime is the organization’s determination with counsel; the pilot produces the evidence that determination needs.

What if security has questions before week one?

Bring them. The architecture review with the founders, threat model, token design, egress paths and audit schema, comes before the pilot and is the platform-boundary review each later workflow inherits.

What Should a Security Review of an AI Agent Vendor Cover? · What Should the AI See for This Piece of Work? · Does Banning AI Tools Stop Employees From Using Them? · What Is an AI Control Record? · On the RedactSure blog: The Two Gaps AI Agents Opened in Your Security Stack

Sources

Research and industry data

Gartner, “Applying Uniform Governance Across AI Agents Will Lead to Enterprise AI Agent Failure” (May 2026). https://www.gartner.com/en/newsroom/press-releases/2026-05-26-gartner-says-applying-uniform-governance-across-ai-agents-will-lead-to-enterprise-ai-agent-failure
Microsoft and LinkedIn, Work Trend Index, “AI at Work Is Here. Now Comes the Hard Part.” https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part
IBM, Cost of a Data Breach Report 2025. https://www.ibm.com/reports/data-breach

RedactSure documents

RedactSure, “The Two Gaps AI Agents Opened in Your Security Stack” (2026). https://redactsure.com/blog/two-gaps-ai-agents-opened-in-your-security-stack/
RedactSure, “Your AI Strategy Is Probably Wrong” (2026). https://redactsure.com/blog/your-ai-strategy-is-probably-wrong/
Pilot structure and product behavior described on this page reflect RedactSure’s current practice and design.

Bring your hardest questions.

A 25-minute AI Agent Security Review with the founders: threat model, token design, egress paths, audit schema. Or a 25-minute demo on a workflow like yours, with the data hidden from the AI and a named person approving what matters. We come with diagrams, not a pitch deck.

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About the author

Chris Sowa is a founder of RedactSure and a former CEO of AI companies; he started his first years before ChatGPT existed. He previously led AI at Accenture, served as Global VP of Strategy & Innovation at Schneider Electric, was CCO of Sovos, and spent more than a decade at Oracle, with earlier roles at SAP and IBM.