Leaders' One-Page Playbook — Funding, KPIs & Operating Model

Purpose

This one-page playbook helps leaders decide how to fund AI work, how to measure progress, and which operating model fits your organization’s maturity, risk tolerance, and strategic priorities. Use it as a consistent decision reference to avoid fragmented programs and underfunded pilots.

How to use this page

1. Match funding style to scale and strategic alignment.
2. Pick a small set of KPIs (leading + lagging) and a reporting cadence.
3. Choose an operating model that fits skills, data ownership, and pace.
4. Apply the vendor oversight checklist for procurement or partnerships.

1) Funding options — quick guide

• Central (centralized budget / COE funded)
  • Pros: economies of scale, consistent standards, shared expertise.
  • When to use: early-stage, high-risk or enterprise-level platforms, cross-cutting opportunities.
  • Example: central AI/Platform team funds common data infra and pilots; business units fund specific apps.
• Distributed (business-funded)
  • Pros: fast local decision-making, closer to domain expertise and ROI ownership.
  • When to use: mature AI competence in business units, clear ROI per unit, low cross-unit risk.
• Hybrid (cost-share / funded platform + local delivery)
  • Pros: balance of governance and speed; shared platform with local feature funding.
  • When to use: scaling organizations that need standards but also speed.

2) Suggested KPIs and cadence

Choose 3–6 meaningful measures mixing adoption, impact and health. Report monthly to operational owners and quarterly to the executive steering group.

• Adoption & usage – active users, tasks automated, percent of workflow using AI (weekly/monthly)
• Business impact (lagging) – cost saved, revenue influenced, time saved per process (monthly/quarterly)
• Quality / Risk – error rate, human override rate, safety incidents, model drift alerts (weekly/monthly)
• Delivery velocity – time to production, cycle time for model updates (monthly)
• Compliance & privacy – data access violations, audit findings, consent coverage (quarterly or on-demand)
• ROI proxy – payback period or NPV for funded initiatives (quarterly)

Reporting cadence recommendation

• Operational owners: weekly or biweekly highlights for active projects.
• Program leads / COE: monthly consolidated dashboard with trend lines.
• Executives / Board: quarterly strategic review focused on outcomes, risk and funding decisions.

3) Operating model choices — pick what fits

• Center of Excellence (COE)
  • Role: central standards, shared platform, specialist talent pool.
  • Fit: when you need governance, reuse, and shared infrastructure.
• Federated / Hub-and-spoke
  • Role: COE provides platform & guardrails; business units own delivery.
  • Fit: scaling across units that need autonomy with central controls.
• Fully distributed (embedded teams)
  • Role: AI specialists embedded in business teams who own products end-to-end.
  • Fit: high domain specificity, fast product cycles, units able to hire/retain AI talent.
• Product-oriented platform team
  • Role: platform as product, cross-functional teams operate AI products for internal customers.
  • Fit: organizations moving toward product thinking with defined internal customers.

Choosing by context (short flow)

1. If data and AI skills are concentrated and you need standards → COE or platform team.
2. If multiple units need autonomy but must share guardrails → Federated hub-and-spoke.
3. If each unit owns distinct products and can fund talent → Distributed/embedded.

4) Quick checklist for vendor oversight & compliance

Use this checklist when acquiring models, platforms, or managed services.

• Business case & funding owner identified
• Data access & lineage documented; PI/PHI handled per policy
• Model validation plan, performance thresholds and monitoring defined
• Security assessment completed (SaaS, on-prem, hybrid implications)
• Contract clauses for IP, model updates, liability, exit & data return
• Regulatory & ethical review completed where applicable
• Escalation path and SLAs for incidents and vendor failures

Decision anchors — practical examples

Three snapshot recommendations:

• Pilot / proof-of-value: short-term central funding, 1–2 KPIs (impact & quality), COE supports but business leads delivery.
• Scaling cross-unit: hybrid funding (platform + local delivery), federated model, KPIs include adoption, platform health, and ROI per unit.
• Productized internal service: platform-as-product with dedicated funding, embedded product teams, KPIs include uptime, user satisfaction, and cost-per-transaction.

Recommended next steps

1. Pick funding model and operating model for the next 12 months and document owners.
2. Agree 3–5 KPIs and reporting cadence; set up a one-page dashboard template.
3. Run a vendor oversight checklist before signing or renewing major contracts.