Define provider governance before signing, not after the first failure. An effective model connects every expected outcome to a controllable metric, evidence source, accountable owner on each side, tolerance, decision cadence, and consequence. For AI, the scorecard must combine business value, system quality, reliability, risk, delivery, economics, internal capability, and continuity. Uptime or completed tasks alone cannot show whether the service remains useful, safe, and affordable.
The buyer should retain authority over priorities, data, risk acceptance, deliverable acceptance, and budget. A provider may operate measurements and recommend actions, but it should not unilaterally set the baseline, change KPI formulas, or declare its own success. Put forums, data access, audit rights, escalation, and remedies into the operating agreement.
The AI Provider Governance Loop-8: 32 points
Score each dimension from zero to four: zero is absent or contradictory; one is a promise without a mechanism; two is partially defined; three is operable with controllable gaps; four is reproducible evidence with an owner, limit, and consequence. As an illustrative threshold, require 24 of 32, no zero, and at least three for outcome, AI quality, risk, and continuity. Set weights before reviewing finalists.
- Outcome and adoption — value, eligible population, baseline, real usage, counterfactual, and benefit owner.
- AI quality — task, evaluation set, error, safety, human review, drift, and acceptable boundary.
- Operational reliability — journey availability, latency, dependencies, queues, recovery, and capacity.
- Risk and compliance — data, access, third parties, incidents, controls, exceptions, and evidence.
- Delivery and change — roadmap, milestones, backlog, dependencies, decisions, changes, and current forecast.
- Economics — cost per useful unit, consumption, rework, human review, cloud, models, and price variance.
- Capability and transfer — named team, allocation, substitution, documentation, training, and client autonomy.
- Continuity and exit — concentration, backups, portability, transition assistance, data, and exit timing.
Turn KPIs into decision rights
For every metric, document its purpose, formula, unit, baseline, segmentation, source, frequency, owner, tolerance, cure period, and associated decision. Separate leading indicators from final outcomes. Adoption may predict value; completion rate does not prove quality; endpoint availability does not represent the end-to-end journey.
Freeze data definitions and versions. Changes to models, prompts, knowledge sources, policies, or populations can break comparability. Require history, release annotations, and reconciliation when parties use different sources. If a material characteristic cannot be measured, document the gap, residual risk, and alternative review.
Use four cadences and one escalation path
- Weekly operations — incidents, quality, consumption, blockers, changes, and dated actions.
- Monthly performance review — trends, outcomes, risk, capacity, financials, roadmap, and improvement plan.
- Quarterly steering — realized value, priorities, investment, concentration, architecture, and scale-correct-exit decisions.
- Event-driven review — critical failure, quality breach, data exposure, subcontractor change, material cost increase, or regulatory change.
Specify who decides, recommends, executes, and receives notice. Escalation needs levels, response times, and named alternates; it cannot rely on goodwill. A repeated problem should change class—from operational action to corrective plan, payment holdback, scope reduction, or transition—as proportionate and contractually agreed.
Six minimum operating artifacts
- Outcome and service scorecard using controlled sources.
- AI evaluation pack with versions, samples, failures, and thresholds.
- Integrated risk, incident, dependency, and exception register.
- Decision and change log with owner, due date, and impact.
- Consumption, unit-cost, forecast, and variance report.
- Improvement and transition plan with entry, exit, and verification criteria.
Test governance during provider selection
Run a 75-minute simulation with finalists. Give each the same facts: quality drops for a priority segment, consumption rises 35%, a subcontractor changes, and a milestone is at risk. Ask the named team to conduct the review, surface evidence, classify severity, propose options, record the decision, and update the forecast. Score clarity, traceability, candor, and the ability to challenge the buyer with evidence.
Implement the model in three stages
Before signature, approve the decision map, metric dictionary, and minimum evidence set. During discovery or pilot, freeze baselines, test instrumentation, simulate escalation, and confirm that the buyer can access the underlying data. Before production, run a complete review with actual signals, named owners, calendar, permissions, and continuity plan. In the first 90 days, treat performance targets as hypotheses that can be calibrated; keep safety limits and acceptance conditions firm.
For U.S. delivery, map the contracting entity, data locations, regulated workflows, subcontractors, support coverage, and state or sector obligations into the operating model. Do not turn regional oversight into disconnected dashboards: preserve a comparable core and add local risks, evidence, and accountable owners where requirements genuinely differ.
Red flags before signature
- The dashboard tracks only activity, uptime, or closed tickets.
- The provider controls the source, formula, and approval of its own performance.
- AI quality is not segmented by task, risk, or relevant population.
- Metrics lack baselines, tolerances, owners, or consequences.
- Governance meetings produce slides but no decision record.
- Model or third-party changes can occur without notice and revalidation.
- Improvement plans lack deadlines, efficacy tests, or exit triggers.
Connect governance to the rest of the deal
Use https://makinai.co/insights/en/ai-project-acceptance-criteria-payment-milestones to distinguish acceptance from ongoing control, https://makinai.co/insights/en/ai-service-levels-support-incident-response-provider for incident operations, https://makinai.co/insights/en/ai-project-scope-change-control-before-hiring-provider for change, and https://makinai.co/insights/en/evaluate-ai-consulting-roi-business-case-before-hiring for benefit realization. Governance brings these decisions into one operating rhythm.
When to involve MAKINAI
MAKINAI can design the governance model, normalize metrics, structure decision forums, and test how finalists respond under pressure before signature. Explore https://makinai.co/services/en/ai-strategy-transformation-consulting. Align rights, remedies, and obligations with legal, finance, security, privacy, procurement, and relevant sector specialists.