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AKKR Ascend · Capstone · Deep-Dive Transformation Process

AI Knowledge Exchange

A governed repository and community of practice that turns private AI experimentation into shared, vetted, reusable assets — the bridge that moves a GTM org from Stage 2 (Emerging) to Stage 3 (Standardized).

01 — Overview

Summary

  • A governed, central repository plus a community of practice that captures, vets, and redistributes the AI prompts, playbooks, tools, and workflows people are already using.
  • Turns fragmented, private AI experimentation into shared, trusted, reusable assets.
  • Moves a GTM organization from “everyone builds their own way” to standardized, discoverable AI practice.

Functions Impacted

Strategy & LeadershipM&A & IntegrationProductEngineeringMarketingSalesImplementCSSHRFinance

Applicability & Impact

Moves the organization from Defined ➞ Managed.

Emerging
Ad hoc, private experimentation
Current
Defined
Some standards, usage varies
Target
Managed
Standardized, discoverable, reusable
Strategic
Woven into the operating model

Level of Effort

Low
Medium
High

Standing up the repository, a vetting council, and an active community is sustained, cross-functional effort.

Signals & Pre-requisites

Signals it's a fit
  • High share of staff building their own prompts/tools; duplicate solutions across teams.
  • “I can’t find what others built” (62% in survey) and quality/accuracy is the top worry about self-built AI (61%).
Pre-requisites
  • An existing AI governance policy and current tool inventory.
  • A named owner (cross-functional team + Enablement) and a hosting home (LMS / intranet / repository platform).

02 — Process & Execution

Process Overview

1
Harvest
Run structured “show your best play” sessions per role to surface prompts, workflows, agents, and tools already producing wins.
2
Vet & approve
A cross-functional council scores each asset for accuracy, security, and compliance, then tags it Approved with an owner and review date.
3
Publish to one home
A single searchable repository — playbooks by role, prompt library, approved-tool catalog with peer ratings — replaces scattered docs.
4
Activate the community
AI champions per function curate, run office hours, and route new plays back into the harvest queue.
5
Measure & prune
Track reuse, time saved, and win-rate lift; retire stale assets quarterly so the repository stays trusted.

Examples / Output

Central repository home
Prompt library + ratings
Role playbook template
Approved-tool catalog
  • Live screenshots of the repository, prompt library entries, and the approved-tool catalog drop in here.
  • Sample artifacts: per-role playbook template, asset intake/vetting form, office-hours cadence.

Who's Responsible

Executive sponsor
Funds the program, removes blockers, models reuse.
Enablement (lead)
Owns the exchange end-to-end and the operating cadence.
Vetting council
RevOps, IT/Security, Product Marketing — approves assets for accuracy, security, compliance.
AI champions
One per function; harvest plays, curate, run office hours.
All GTM staff
Submit plays; rate and comment on shared assets.

Required Inputs & Tools

AI governance policyCurrent tool inventoryRepository / LMS platformAsset intake templateThis survey as baseline

03 — Impact & Takeaways

Expected Outcomes

  • Shared, standardized AI practice; assets are discoverable and reusable across teams.
  • Trusted quality through a vetting lane; far less duplicate work.
  • Self-funding on efficiency (~$3.8K per GTM user/yr) with a ~75% token reduction on standardizable tasks; revenue upside as a share of pipeline (each +1 pt win rate ≈ 1% of pipeline).

Metrics to Track

  • Discovery/reuse “easy” rating vs. baseline (survey).
  • % of teams reporting standardized approaches (up from ~15%).
  • Asset reuse rate; time saved; token reduction.
  • Drop in duplicate-tool and quality/accuracy concerns at re-survey.

Challenges & Considerations

  • Repository becomes a graveyard — champions own curation; quarterly prune; reuse metrics kept visible.
  • Vetting bottleneck — lightweight rubric, review SLA, tiered approval for low-risk assets.
  • Low contribution — tie champion role to recognition; celebrate high-reuse authors; feed graduates from the enablement engine.
  • Shadow tools persist — make the sanctioned path the easy path via the approved-tool catalog + governance lane.

How to Get Started

  • Name the sponsor, program lead, and AI champions per function.
  • Define the intake template and repository taxonomy.
  • Run the first per-role harvest sessions and convene the vetting council.
  • Stand up the repository with seed content (≥3 plays/role + prompt library); launch office hours.