PI Firm AI Readiness Checklist

Decide whether one workflow is ready for a pilot.

A personal injury firm does not need a single readiness score for all AI. It needs a defensible decision about one task: what the assistant may do, what evidence will show that it worked, and who takes responsibility when it does not. Use this checklist with the operational owner, the people who handle the task, and whoever approves systems and data access.

For each item, mark Confirmed, Needs work, or Unknown in the downloadable worksheet. Confirmed means the evidence exists and someone has checked it. Record the evidence, unresolved gap, responsible person, and next action. A sales demonstration or a verbal assurance is not a substitute for a tested connection or a named owner.

Use our operating framework to prepare administrative intake, follow-up, knowledge and reporting workflows for implementation. Case acceptance and legal judgment remain with the firm. No email is required to read or download it.

First, define the job and the decision.

• 1. What single task should the assistant complete? Name the trigger, permitted actions, and final record. For example, collect approved intake information and create a review task for staff. Answering a chat is not completion if the inquiry never reaches its owner. Evidence to collect: A workflow map with a clear start and finish.
• 2. Does this task need AI? Compare an AI assistant with a form, a fixed reminder, an existing software feature, or a process correction. AI may be useful for varied language or approved knowledge retrieval. A predictable rule may be simpler to operate without it. Evidence to collect: A written reason for choosing AI over a simpler option.
• 3. What will remain a human decision? List prohibited actions, including legal advice, accepting or rejecting representation, and promises the firm has not approved. Define how requests outside the scope reach a person. An assistant can gather information without deciding whether the firm takes a case. Evidence to collect: An approved scope, prohibited-action list, and escalation rules.
• 4. What is the current baseline? Choose a stated observation period and count eligible tasks, incomplete tasks, staff time, and missing records. Include difficult cases. If the baseline is unreliable, first run a measurement period instead of assigning an unsupported savings target. Evidence to collect: Dated counts, definitions, and known gaps.
• 5. What would make the pilot worth continuing? Agree on acceptance criteria for completion, accuracy, human handoffs, review effort, and cost before launch. Record who may approve expansion. A faster first reply alone does not establish that the full workflow improved. Evidence to collect: A pilot brief with success measures and a decision date.

If the current process needs definition, start with law-firm intake consulting.

Then, prove the connections and human handoffs.

• 6. Which systems and permissions are actually available? Identify the CRM or case-management system of record and verify the exact connection on the firm's subscription. Separate native features, paid add-ons, third-party connectors, and custom work. Test required reads and writes with minimum necessary permissions. Evidence to collect: A verified integration and permission map, including extra costs.
• 7. What information may the workflow access? Have the firm approve permitted data, users, providers, retention, deletion, and relevant training-use terms before connecting sensitive records. Limit what appears in logs. Use synthetic or appropriately authorized test data and document access removal. Evidence to collect: Approved data boundaries, provider terms, and access owners.
• 8. Who approves the assistant's answers and messages? Use a maintained set of firm-approved procedures, scripts, and answers. Name an approver and effective date. Confirm communication, identification, consent, and opt-out requirements for the intended channels with the firm before launch. Do not let the assistant improvise legal answers. Evidence to collect: Versioned knowledge and approved communication rules.
• 9. Can a person reliably take over? Define the receiving person or staffed queue, the context transferred, coverage hours, and fallback route. Test a request for a human, an urgent concern, an unsupported language, and an unanswered transfer. A sent alert is not evidence of an accepted handoff. Evidence to collect: An observed end-to-end handoff and coverage plan.
• 10. What happens when the workflow fails? Test missing information, duplicate records, conflicting answers, expired access, failed writes, and interrupted conversations. Prevent repeated messages or duplicate actions during retries. Ensure staff can see incomplete tasks and recover them without silently losing an inquiry. Evidence to collect: A test log showing expected behavior, failures, fixes, and retests.

Compare system requirements in the PI software decision center.

Make ongoing ownership part of the purchase.

• 11. Is the first deployment small enough to supervise? Limit the pilot to an approved channel, task, and population with available human coverage. Identify a pause control and manual fallback. Start with internal or staff-reviewed output when external communication is not yet ready. Set boundaries before selecting volume. Evidence to collect: A bounded rollout plan with a named launch approver.
• 12. Who reviews quality and closes exceptions? Assign conversation or output review, error triage, and escalation closure. Review all flagged serious issues plus a stated sample of routine work. Record what was reviewed and what was not. Adjust review frequency to the workflow's risk and observed errors. Evidence to collect: A review schedule, error log, and accountable owners.
• 13. Who maintains the system after launch? Assign knowledge updates, integration maintenance, access changes, and retesting after material changes. State support coverage and response expectations. A bot that is always available does not imply that a human support team is staffed around the clock. Evidence to collect: A written management scope and change approval process.
• 14. Can you calculate the full operating cost? List platform subscriptions, communication and model usage, integration charges, management, and staff review. Show setup costs separately or state how they are allocated. Compare that total with successfully completed tasks, not messages sent or vendor-reported activity alone. Evidence to collect: A cost model with usage assumptions and budget alerts.
• 15. Can the firm pause, improve, or exit the workflow? Agree on stop conditions, such as unauthorized disclosure or repeated incorrect routing, and who acts on them. Define how records, configurations, accounts, and documentation are handed over. Keep a usable manual process and remove access when it is no longer needed. Evidence to collect: A pause and recovery procedure plus a handover plan.

Measure completed work, including review and recovery.

Choose measures that match the task and publish their definitions internally before the pilot. Keep the observation window, eligible population, missing records, and review method visible. Separate fully automated completion from completion that required staff intervention. A small pilot can reveal defects; it does not establish a national benchmark or guarantee results at higher volume.

• Completion rate: successfully completed eligible tasks divided by all eligible tasks due in the period. Record failures and unresolved tasks separately.
• Handoff success: required transfers with a confirmed receiving owner and usable context divided by all required transfers. Track time to human contact separately from the first automated response.
• Review effort: total staff minutes checking, correcting, and escalating output, with the number of tasks reviewed. Include unsuccessful attempts.
• Error rate: reviewed tasks with a defined error divided by all tasks reviewed. Break out severity and explain how the review sample was selected.
• Cost per completed task: the agreed operating cost base divided by successfully completed tasks in the same period. State whether setup costs are included, allocated, or excluded.

Connect task outcomes to the wider law-firm marketing attribution process.

Example: test an inquiry handoff before adding more channels.

For a website assistant that collects initial information and creates a staff review task, we test both the conversation and the receiving system. A rejected CRM update must create a visible exception with a recovery owner before the workflow is ready for client-facing use.

The next test is specific: interrupt the CRM connection, confirm that the assistant does not claim the task was saved, verify that a staff member receives enough context to recover it, and check that a retry creates only one record. Separately test how it responds to a request for legal advice and how the person reaches a human. Record the outcome and retest the failures before considering a controlled launch.

Use the evidence to choose the next step.

Pause client-facing deployment when data permission, the human handoff, failure visibility, or accountable ownership remains unknown. Resolve those gaps first. A high number of completed checklist items cannot compensate for an unresolved critical control.

Choose a controlled pilot when the scope and data use are approved, integrations and fallback paths have been tested, staff can supervise the work, and acceptance and stop criteria are documented. The firm sets the review date and approves any expansion based on the actual results.

Choose a simpler process when a rule, form, or existing software feature addresses the problem with less operating burden. Choose further measurement when the baseline is too weak to judge value. The checklist should make the next action clearer, even when that action is not to buy AI yet.

Bring one workflow to a consultation.

Gadient Consulting creates, integrates, deploys, and manages AI assistants and automated workflows for law firms. The work can be a focused implementation engagement or part of broader fractional CMO support. We assess scope, test realistic failures, establish a controlled pilot, and define ongoing quality, maintenance, escalation, and cost responsibilities.

Bring the task you want to improve, the systems it touches, the current owner, and the evidence you already have. Share business process information through the consultation form, without client records or confidential case details. For broader voluntary AI risk-management guidance, consult NIST's framework alongside your firm's applicable requirements and professional judgment.

Explore AI agent implementation and ongoing management.

Reference: NIST AI Risk Management Framework. Voluntary guidance; not a certification of this checklist or your firm.

Ready to scope the work? Request an AI workflow consultation.