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The AI Legal Program Manager's Operating Playbook

$199.00
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Run an AI legal review program that ships product on time without becoming the bottleneck the engineering org routes around. You own the AI legal review queue. Product is shipping faster than the legal review can clear, and every missed launch date lands on your operating metric. Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course?

An AI legal program manager sits between three groups that all move at different speeds. Product wants a yes or no in days. Outside counsel wants a full memo per matter. Privacy, security, policy and the model risk team each want their own intake form and their own SLA. Meanwhile regulators in the EU, the UK, Brazil, California, Colorado and Texas have.

A documented intake form that captures the eight inputs every reviewer needs to make a decision. A written risk tiering rubric that routes launches to the right reviewer pool with predictable SLAs. An audit trail design that survives a regulator question about how a specific launch was cleared. A reviewer capacity model that tells leadership exactly when to hire the next AI.

What you get with this course?

Twelve written modules in the Art of Service learning environment. Intake form, risk tiering rubric, model card template, impact assessment template, training data sheet, SLA template, audit record schema, metrics pack template, outside-counsel brief template, regulator inquiry runbook. Hand-built implementation playbook tailored to the recipient's current intake volume, reviewer mix and primary jurisdictions. Thirty-day money-back if the playbook does not match the.

What you will have in hand by Day 1, Week 1, Month 1?

Day zero: course access in the Art of Service learning environment. Day zero: hand-built implementation playbook delivered alongside course access. Week one: intake form, risk tiering rubric and reviewer pool stood up from the playbook templates. Week two to four: SLA published, audit trail design wired, metrics pack drafted. Days thirty to ninety: stabilise, calibrate the rubric against actual decisions, deliver the.

Launch dates slip because legal review is the named blocker on the tracker. Reviewers do duplicative work because there is no shared intake or routing rule. Audit trail is a thread of emails and meeting notes nobody can reconstruct. Quarterly leadership review surfaces queue length, not decision quality. Launches hit the legal gate with a defined SLA that the program clears predictably.

What happens if you do not address this?

The window where AI legal review is a respected new function is closing. If the operating model is not standing up, product teams build the workaround. Once the workaround is normal, the legal program is not a gate, it is paperwork. The same window applies in reverse for the program manager personally: this is the role where an operating system either gets.

Who it is for?

Program manager inside an in-house legal team at a large technology company, responsible for the operating model of AI legal review. Owns intake, triage, reviewer assignment, SLAs, escalation, metrics and the audit trail. Reports to a senior counsel or AI legal lead. Works daily with product counsel, privacy counsel, policy, security review and outside counsel on novel matters. Not a practising lawyer.

Closely related courses: Legal AI Integration Playbook, Legal Operations Efficiency Playbook, Legal Diversity Compliance Efficiency Playbook, Immersive Media Legal Compliance Playbook.

More answers: what you get with every course, refund policy, all help answers.

A focused course, tailored for you

Run an AI legal review program that ships product on time without becoming the bottleneck the engineering org routes around.

You own the AI legal review queue. Product is shipping faster than the legal review can clear, and every missed launch date lands on your operating metric.

$199 one-time
Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

An AI legal program manager sits between three groups that all move at different speeds. Product wants a yes or no in days. Outside counsel wants a full memo per matter. Privacy, security, policy and the model risk team each want their own intake form and their own SLA. Meanwhile regulators in the EU, the UK, Brazil, California, Colorado and Texas have all issued AI-specific guidance in the last twelve months and your in-house lawyers expect you to translate that into reviewer checklists they can actually use. The failure mode is predictable: legal becomes the named blocker on the launch tracker, product starts shipping without legal sign-off and calling it a documentation gap, and the next audit surfaces decisions nobody can explain. This course rebuilds the operating model so the legal review program clears launches on schedule with defensible records, instead of becoming the function engineering quietly routes around.

What you walk away with

  • A documented intake form that captures the eight inputs every reviewer needs to make a decision.
  • A written risk tiering rubric that routes launches to the right reviewer pool with predictable SLAs.
  • An audit trail design that survives a regulator question about how a specific launch was cleared.
  • A reviewer capacity model that tells leadership exactly when to hire the next AI counsel or contract out.
  • A quarterly metrics pack that shows product partners the queue is clearing, not becoming a backlog.

The 12 modules

Module 1. The AI legal intake form that actually works
Walks through the eight fields every AI launch intake form needs before a reviewer can decide anything: training data provenance, deployment geography, deployment surface, intended use case, user population, model provider, retention posture and rollback plan. Includes the form template, the routing logic that sends each intake to the right first reviewer, and the integration pattern into the engineering project tracker so intake is not a separate workflow product teams forget to start.
Module 2. Risk tiering: which launches need what review
The tiering rubric that decides which launches need a five-minute reviewer sign-off, which need a model card and impact assessment, and which trigger a full cross-functional review with privacy, security and policy. Includes the decision table that maps deployment surface and user population to required artefacts. Calibrates against EU AI Act prohibited and high-risk categories, the Colorado AI Act consumer-facing rule, and CFPB and EEOC guidance. Includes the rubric, three worked examples, and the quarterly recalibration cadence.
Module 3. The reviewer pool: who reviews what and when
Designs the reviewer pool the program runs against: which decisions sit with product counsel, which need specialised AI counsel, which need privacy counsel, which need outside counsel, which can be cleared by a trained program manager directly. Covers the SLA per tier, the rotation policy that prevents reviewer burnout, the coverage matrix for time zones and absences, and the escalation chain when a reviewer will not clear a launch without senior sign-off.
Module 4. Model cards and impact assessments without the homework problem
Most model cards and impact assessments stall because product teams treat them as a homework assignment due at launch. This module redesigns the workflow so the model card builds as the model is built. Includes the model card template scoped to legal-relevant fields only, the impact assessment template tuned to EU AI Act high-risk obligations and the Colorado AI Act consumer impact rule, the weekly prompt list the program manager runs against engineering, and the sign-off checklist counsel uses.
Module 5. Training data provenance: the question reviewers can actually answer
Training data provenance is the question that breaks most AI legal reviews because the engineering team cannot answer it in the form the lawyer needs. This module gives the program manager a structured data sheet that asks engineering for exactly the fields legal cares about: source, licence, third-party scraping exposure, opt-out signal handling, synthetic share, evaluation set separation. Includes the template, the engineering counterpart's quick-start guide, and the reviewer checklist for spotting gaps.
Module 6. The SLA you can actually commit to
Most AI legal programs commit to an SLA that is aspirational, then quietly miss it, then lose product trust. This module builds the SLA from capacity data: reviewers available per tier, average review time per tier, queue arrival rate, peak season multiplier. Includes the capacity spreadsheet, the SLA agreement template you publish to product partners, the dashboard that shows current SLA performance, and the escalation pattern when a launch needs to break SLA for a credible reason.
Module 7. Audit trail design that holds up when a regulator asks
Builds the audit record every AI launch needs: who reviewed, what they reviewed against, which version of the model and which version of the policy applied, what evidence they considered, what they decided, why. Covers storage location, retention schedule aligned with statute of limitations across jurisdictions, immutability posture, and access control. Includes the records template, the integration pattern with the existing matter management or DMS, and the litigation hold workflow when a regulator opens an inquiry.
Module 8. Working with privacy, security, policy and product counsel as one pipeline
An AI launch typically needs privacy review, security review, policy review and product counsel review in addition to AI-specific legal review. Most programs run these as four parallel queues that surprise each other. This module builds the unified pipeline: shared intake, joint triage meeting cadence, defined handoffs, a single decision record. Includes the joint operating agreement template, the weekly triage agenda, and the metric set that tracks where launches stall across functions.
Module 9. External counsel: when, why and the brief that makes them useful
When to route a matter to outside counsel versus clearing internally, the brief format that gets a useful response on a budget, the per-firm specialty map, the cost tracking and the post-matter knowledge capture so the same novel question does not re-bill the firm next quarter. Includes the outside-counsel brief template, the matter intake form, the budget approval pattern, and the quarterly outside-counsel spend review.
Module 10. The metrics pack product and leadership actually read
Replaces the dense ops report nobody opens with a one-page pack the program manager publishes monthly: queue throughput, SLA performance, top decisions by tier, top blocking matters, capacity utilisation and forecast, regulatory developments that changed the rubric. Includes the slide template, the data sources, and the script for presenting the pack in product partner reviews so the legal program is read as enabling launches, not blocking them.
Module 11. When the regulator letter arrives
The operating runbook for the day a regulator inquiry lands. Covers immediate steps: scope confirmation, litigation hold, evidence preservation across the model registry and the decision records, internal escalation, outside counsel engagement, response timeline. Tuned for an inquiry under the EU AI Act, the FTC Section 5 unfair practices authority, a state AG consumer protection action, or a CFPB or EEOC bias inquiry. Includes the runbook, the contact tree, the evidence index template, and the response coordination checklist.
Module 12. The first ninety days of the operating model
A day-by-day implementation plan to stand up the operating model from scratch or to migrate from an existing informal model. Week one inventories every AI matter currently open. Week two stands up the intake form and the routing rules. Week three onboards reviewers to the new SLA. Week four publishes the metrics pack. Days thirty through ninety stabilise the model, train product partners, calibrate the rubric against actual decisions, and produce the first quarterly retrospective for leadership.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Module 1 to 3 stand up the intake, the risk tiering and the reviewer pool that the program runs on.
Module 4 to 7 cover the artefacts each launch produces and the audit trail that survives a regulator question.
Module 8 to 10 wire the AI legal program into privacy, security, policy, product counsel, outside counsel and the leadership metrics pack.
Module 11 to 12 are the failure-mode runbook and the ninety-day implementation plan.

What you get with this course

  • Twelve written modules in the Art of Service learning environment.
  • Intake form, risk tiering rubric, model card template, impact assessment template, training data sheet, SLA template, audit record schema, metrics pack template, outside-counsel brief template, regulator inquiry runbook.
  • Hand-built implementation playbook tailored to the recipient's current intake volume, reviewer mix and primary jurisdictions.
  • Thirty-day money-back if the playbook does not match the actual operating reality.

What you will have in hand by Day 1, Week 1, Month 1

Day zero: course access in the Art of Service learning environment.

Day zero: hand-built implementation playbook delivered alongside course access.

Week one: intake form, risk tiering rubric and reviewer pool stood up from the playbook templates.

Week two to four: SLA published, audit trail design wired, metrics pack drafted.

Days thirty to ninety: stabilise, calibrate the rubric against actual decisions, deliver the first quarterly leadership pack.

Before and after

Before

Launch dates slip because legal review is the named blocker on the tracker. Reviewers do duplicative work because there is no shared intake or routing rule. Audit trail is a thread of emails and meeting notes nobody can reconstruct. Quarterly leadership review surfaces queue length, not decision quality.

After

Launches hit the legal gate with a defined SLA that the program clears predictably. Reviewers see only the matters that match their tier. Every decision has a record that survives a regulator request. The quarterly pack shows throughput, decision quality and forecast capacity, and product partners route to legal earlier instead of around it.

What happens if you do not address this

The window where AI legal review is a respected new function is closing. If the operating model is not standing up, product teams build the workaround. Once the workaround is normal, the legal program is not a gate, it is paperwork. The same window applies in reverse for the program manager personally: this is the role where an operating system either gets built or the role gets absorbed into broader legal operations.

Who it is for

Program manager inside an in-house legal team at a large technology company, responsible for the operating model of AI legal review. Owns intake, triage, reviewer assignment, SLAs, escalation, metrics and the audit trail. Reports to a senior counsel or AI legal lead. Works daily with product counsel, privacy counsel, policy, security review and outside counsel on novel matters. Not a practising lawyer providing legal advice, but the person who decides how the work flows and who sees what.

Who this is NOT for. Not for a practising counsel who personally drafts every AI-related opinion. Not for a general legal operations manager whose portfolio spans contracts, e-billing and matter management broadly. This is specifically for the person whose operating metric is throughput and quality of the AI legal review queue.

How it arrives

Text-based course in the Art of Service learning environment, plus downloadable templates and worked examples for every module, plus the hand-built implementation playbook delivered alongside course access.

Time investment. About eight hours across the twelve modules at a comfortable pace. The intake form, risk tiering rubric and SLA template can be in production within the first week. The full operating model stabilises across about ninety days.

Why $199 is the right number

Hiring an AI legal program management consultant on a short engagement typically costs in the high tens of thousands and leaves the operating model behind as a slide deck, not a working system. A vendor-built AI governance platform addresses the tooling layer but does not produce the SLA, the reviewer rubric or the regulator runbook. This course produces the operating model directly, tuned to the recipient's actual queue, and the playbook stays with the recipient.

FAQ

Does this give legal advice?
No. The course is the operating model for a program. The legal substance of any review still sits with qualified counsel. The course is for the person who runs the program around them.
Is this tuned to a specific jurisdiction?
The implementation playbook is tuned to the primary jurisdictions of the buyer. The course content covers EU AI Act, the Colorado AI Act, FTC Section 5, CFPB and EEOC sector guidance, the UK regulator approach and the Brazilian AI bill posture. Other jurisdictions are added in the playbook if relevant.
What if the operating model has to coexist with an existing legal operations team?
The course is designed for that case. The unified pipeline module covers how the AI legal program integrates with broader legal operations rather than replacing it.
What does the implementation playbook actually contain?
A document specific to the buyer's reviewer count, queue volume, primary jurisdictions and reporting line. It picks the templates from the modules that apply, customises the SLA to the buyer's actual capacity, and gives the ninety-day plan a real start date.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

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