AI Feature Lifecycle Management

Why AI Features Need a Different Lifecycle

The Seven Lifecycle Stages

1. 1

   Stage 1: Ideation

   Define the problem, assess AI feasibility, identify available data, and establish success metrics. The key exit criterion is a one-page brief that answers: What problem does this feature solve? Why is AI the right approach? What data is available? What are the success metrics and minimum acceptable quality thresholds?

2. 2

   Stage 2: Experimentation

   Build a prototype and evaluate it offline. Run the model against evaluation datasets and measure quality against the thresholds defined in Stage 1. The exit criterion is an evaluation report showing that the model meets minimum quality thresholds on the evaluation dataset, with documented failure modes and an honest assessment of readiness.

3. 3

   Stage 3: Pilot

   Deploy to a small group of real users (internal team, beta users, or a small percentage of traffic). Collect qualitative and quantitative feedback. The exit criterion is evidence that the feature works in production conditions: quality metrics from live traffic, user feedback, and no blocking issues identified.

4. 4

   Stage 4: Gradual Rollout

   Expand from pilot to a larger percentage of users using feature flags. Run A/B tests comparing the AI feature against the baseline experience. Monitor quality metrics, user engagement, and business outcomes. The exit criterion is statistically significant evidence that the feature improves the target metric without regressing other metrics.

5. 5

   Stage 5: General Availability

   Full production deployment with SLA commitment. Monitoring dashboards operational. On-call runbooks documented. The feature is now a production service with reliability expectations and incident response procedures.

6. 6

   Stage 6: Maintenance

   Ongoing operation: model retraining, drift monitoring, cost tracking, periodic evaluation against current data. The maintenance stage has its own cadence: monthly quality reviews, quarterly cost reviews, and retraining triggered by drift detection or scheduled cadence.

7. 7

   Stage 7: Sunset

   Principled retirement when the feature no longer meets quality thresholds, when the cost exceeds the value, or when the business need changes. The sunset process includes deprecation notice to users, migration path to alternatives, data retention and deletion per policy, and model decommissioning.

Stage Gate Criteria

Feature Quality Dashboard

/**
 * AI Feature lifecycle tracking.
 *
 * Tracks the current stage, gate criteria status,
 * and quality metrics for each AI feature.
 */

type LifecycleStage =
  | "ideation"
  | "experimentation"
  | "pilot"
  | "gradual_rollout"
  | "general_availability"
  | "maintenance"
  | "sunset";

interface AIFeature {
  id: string;
  name: string;
  description: string;
  currentStage: LifecycleStage;
  owner: string;
  createdDate: string;
  stageEntryDate: string;
  successMetrics: {
    metric: string;
    threshold: number;
    current: number;
    passing: boolean;
  }[];
  rolloutPercentage: number;
  monthlyCostUsd: number;
  lastEvaluationDate: string;
  nextReviewDate: string;
  modelVersion: string;
  featureFlagKey: string;
}

function shouldSunset(feature: AIFeature): {
  recommend: boolean;
  reasons: string[];
} {
  const reasons: string[] = [];

  // Quality below threshold for all success metrics
  const allFailing = feature.successMetrics.every(
    (m) => !m.passing,
  );
  if (allFailing) {
    reasons.push(
      "All success metrics below threshold",
    );
  }

  // Cost exceeds reasonable per-interaction budget
  // (this threshold would be set per-org)
  if (feature.monthlyCostUsd > 10000) {
    reasons.push(
      `Monthly cost ($${feature.monthlyCostUsd}) exceeds review threshold`,
    );
  }

  // Not evaluated recently
  const daysSinceEval = Math.floor(
    (Date.now() - new Date(feature.lastEvaluationDate).getTime())
    / (1000 * 60 * 60 * 24),
  );
  if (daysSinceEval > 90) {
    reasons.push(
      `${daysSinceEval} days since last evaluation (max: 90)`,
    );
  }

  return {
    recommend: reasons.length >= 2,
    reasons,
  };
}