Authored by the LynixAI operations collective • Updated: 2025-11-18
This checklist distills lessons from LynixAI pilots with founders, operations leaders, and revenue teams. Work through each section to verify that your organisation is ready to deploy a privacy-first meeting assistant such as Meet.AI.
1. Define your objective
Identify the meetings that suffer from fragmented notes, slow follow-up, or inconsistent coaching. Be specific—“weekly sales stand-up” is stronger than “meetings.”
Document the pain metrics you want to improve (for example, prep time, call summaries delivered, customer response latency).
Decide how you will measure success within 30, 60, and 90 days of deployment.
2. Stakeholder alignment
List the teams that need visibility (security, legal, compliance, IT, business sponsor).
Nominate an executive sponsor who can unblock resources and approve policy updates.
Assign an internal project manager responsible for coordinating the rollout and ongoing governance.
3. Policy and compliance readiness
Review existing data-handling policies to confirm AI assistants are covered.
Identify regulations that apply to the meetings in scope (GDPR, CPRA, HIPAA, FINRA, etc.).
Draft or update privacy notices informing participants that AI assistance is available and how their data will be handled.
4. Technical prerequisites
Confirm supported operating systems and browsers for the LynixAI capture method you plan to use.
Test network routes to ensure the assistant can reach LynixAI processing clusters without latency spikes.
Decide whether you will use LynixAI-managed infrastructure or host Meet.AI within your cloud environment.
Security note: When deploying to customer-managed infrastructure, request LynixAI’s Terraform modules and architecture diagrams. They include sample IAM policies, network segmentation guidelines, and log shipping options.
5. Data minimization controls
Create a redaction dictionary that matches your industry vocabulary.
Set retention windows for transcripts, prompts, and audit logs.
Define procedures for purging data when an employee leaves or when customers request deletion.
In-article ad placement for engaged readers.
6. Workflow integration
Decide where summaries will live (CRM, ticketing system, shared drives).
Plan notifications or automations triggered by Meet.AI outputs.
Document the “human-in-the-loop” steps that validate AI-generated insights before they become customer-facing.
7. Training plan
Develop enablement sessions that cover activation boundaries, privacy responsibilities, and best practices for prompting.
Create quick-reference guides for meeting hosts and note-takers.
Schedule office hours during the first month of rollout to answer questions.
8. Pilot execution
Select a diverse pilot cohort (varied roles, seniority, and meeting types).
Track baseline metrics before enabling Meet.AI.
Run the pilot for at least three weeks to capture a full cycle of planning, meeting, and follow-up.
Collect qualitative feedback via interviews or surveys. Focus on trust, usability, and perceived value.
9. Review and iterate
Compare pilot metrics against baseline. Highlight quick wins and gaps.
Adjust prompts, retention settings, and redaction dictionaries based on feedback.
Review audit logs to confirm that privacy controls performed as expected.
10. Scale responsibly
Roll out in waves, starting with teams that have clear success metrics.
Provide a channel for ongoing questions and incident reporting.
Schedule quarterly governance reviews to assess adoption, control effectiveness, and regulatory updates.
Readiness scorecard
Use the scorecard below to evaluate progress. Each item should be marked complete before a full production rollout.
☐ Objectives documented and approved
☐ Stakeholder roster created with roles assigned
☐ Privacy notices drafted and legal review complete
☐ Capture architecture chosen and tested
☐ Redaction dictionary and retention policy finalized
☐ Workflow integrations mapped and tested
☐ Enablement materials delivered
☐ Pilot metrics reviewed and adjustments made
☐ Governance cadence established
What success looks like
Teams that follow this checklist report faster follow-up, improved coaching, and higher trust in AI outputs. More importantly, they can demonstrate to auditors and partners—like Google—that automation operates within clearly defined privacy guardrails.