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Role Based Path - AI Enablement Lead/Manager

This guide is for AI Enablement Leads / Managers who need a practical way to drive AI adoption across engineering without creating noise, policy confusion, or “metrics theater.” It shows where to spe…

heather.hazell
Updated by heather.hazell

This guide is for AI Enablement Leads / Managers who need a practical way to drive AI adoption across engineering without creating noise, policy confusion, or “metrics theater.” It shows where to spend time in LinearB to track AI usage and standards adoption (if enabled), connect adoption to delivery outcomes, and build a repeatable weekly operating rhythm with Tech Leads, EMs, and Platform.

Time Required: 10–15 minutes to orient, 30–45 minutes to set a weekly adoption cadence
Difficulty: Easy

TL;DR

  • Use AI Insights (if enabled) to monitor adoption patterns and surface where enablement is needed most.
  • Use AI Tools Metrics (if enabled) to track usage trends by team and detect drop-offs or uneven adoption.
  • Use gitStream (if enabled) to standardize low-noise guardrails that make AI use safer and more consistent (without adding process overhead).
  • Use Delivery and Iterations to connect AI adoption to measurable outcomes (review flow, predictability, scope stability) instead of treating adoption as the goal.
  • Use AI Code Review (if enabled) as a “quality + consistency” lever, then validate impact using trend shifts (not one-off examples).

Overview

Your job is to help engineering teams adopt AI in a way that is useful, safe, and scalable. LinearB can help you track AI adoption signals (if enabled), identify where teams are stuck, and connect adoption to delivery outcomes (flow, predictability, quality signals where configured).

This guide helps you:

  • See where AI adoption is happening (and where it isn’t) across teams.
  • Spot enablement bottlenecks (training gaps, inconsistent standards, workflow friction).
  • Standardize “safe defaults” through automation and guardrails (where enabled).
  • Run a weekly adoption operating rhythm that produces real improvement, not vanity dashboards.

What you likely care about

  • Which teams are adopting AI effectively, and which teams are stuck or skeptical?
  • Is adoption consistent, or concentrated in a few power users?
  • Are we using AI in a way that improves outcomes (faster reviews, fewer bottlenecks, clearer PRs) without increasing risk?
  • What standards or guardrails do we need to scale to keep usage safe and low-noise?
  • Where should we invest next: enablement, platform workflows, or policy?

Where to spend time in LinearB

AI Insights (if enabled)

Your primary view for AI adoption and AI-related signals surfaced by LinearB.

  • Look for adoption gaps across teams (who is active vs. inactive).
  • Identify “drop-off points” (teams start experimenting but don’t sustain usage).
  • Use insights as a starting point — validate using Delivery/Iterations before making program decisions.
AI Tools Metrics (if enabled)

Your usage trend view: adoption, engagement, and consistency over time.

  • Track usage by team and time period to see where enablement is working.
  • Watch for uneven adoption (one team surging while others remain flat).
  • Use this to prioritize coaching, office hours, and workflow improvements.
AI Code Review (if enabled)

A practical lever for quality and consistency when rolled out with clear expectations.

  • Use it to standardize review “basics” (clarity, obvious risks, missing tests) in a repeatable way.
  • Pair rollout with lightweight guidelines (what AI should flag vs. what humans must decide).
  • Validate impact by watching review flow and rework trends (not anecdotal wins).
gitStream (if enabled)

Your scale lever for low-noise standards and guardrails that make AI adoption safer.

  • Apply guardrails that reduce risk and friction (for example: required checks, labeling, approval flows, safe-change patterns).
  • Keep policies minimal and outcome-linked: standards that improve review quality without creating noise.
  • Use adoption trends + Delivery/Iterations shifts to prove ROI and refine policies.
Metrics → Delivery

Connect adoption to flow outcomes.

  • Use stage/phase bottlenecks to identify where AI enablement could help (e.g., review delays, large PR patterns).
  • Compare teams to see whether adoption correlates with healthier flow (while controlling for context).
Teams → Iterations (Completed)

Connect adoption to planning and execution stability.

  • Check whether teams with stronger AI adoption show better scope stability (carryover, unplanned work patterns).
  • Use this to prioritize enablement in teams under the most delivery pressure.
Developer Coaching / Surveys (if enabled)

Use these to capture workflow friction and sentiment that pure usage metrics cannot explain.

Start here in 15 minutes

AI Enablement quick-start checklist
  1. Open AI Tools Metrics (or AI Insights) for the last 30–60 days.
  2. Identify:
    • top 2 teams with highest adoption
    • top 2 teams with lowest adoption
    • any teams with a noticeable drop-off
  3. For those teams, open Delivery and look for one flow constraint that is plausibly addressable via enablement or guardrails.
  4. Open Iterations (Completed) and check whether instability (carryover/unplanned) is a factor that might block adoption.
  5. Pick one adoption move to run this week:
    • enablement (short training + examples), or
    • workflow fix (reduce friction), or
    • guardrail standardization (gitStream), or
    • quality consistency (AI Code Review rollout)

A small AI enablement metrics set to align on

  • AI adoption trend by team (usage direction over time).
  • Adoption distribution (is usage broad or concentrated in a few people?).
  • Delivery flow signals you’re trying to improve (for example, review friction/bottlenecks, PR size patterns).
  • Planning stability signals (carryover / unplanned work) to explain why adoption may stall.
  • Guardrail coverage (if using gitStream): how consistently standards are enforced with low noise.

Recommended AI enablement operating rhythm

Weekly
  • Review AI Tools Metrics (or AI Insights) for trend shifts and adoption gaps.
  • Select 1–2 target teams and capture:
    • what’s blocking adoption (workflow friction vs. trust vs. unclear policy)
    • one enablement action for the week
    • one measurable outcome signal to watch (Delivery/Iterations)
  • Meet with DevEx/Platform or EMs for a 15-minute “adoption unblock” check.
Monthly
  • Summarize adoption by org area (who moved, who stalled, why).
  • Decide whether the next lever is training, workflow investment, or standardization via guardrails.
  • Publish 1 short, reusable artifact (example prompts, review guidelines, safe-use checklist).
Quarterly
  • Pick 1–2 standards to scale (guardrails) and 1 adoption goal tied to outcomes (not just usage).
  • Align on investment needs (platform work, enablement capacity, or policy changes).

Common pitfalls

  • Tracking usage without outcomes (adoption is only meaningful if it improves work).
  • Over-standardizing too early (teams need room to learn before policies calcify).
  • Rolling out AI without guardrails (risk spikes, trust collapses, adoption reverses).
  • Turning adoption metrics into performance scoring instead of enablement support.
  • Trying to change everything at once instead of running small, measurable weekly experiments.

How did we do?

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