Measuring AI ROI

ai_roi_challenges:
  attribution:
    - AI assists, humans decide
    - Multiple factors in outcomes
    - Hard to isolate AI impact

  measurement:
    - Quality is subjective
    - Long-term benefits
    - Productivity is complex

  comparison:
    - What's the baseline?
    - Would humans do better?
    - Opportunity cost of not using AI

ai_value_categories:
  cost_reduction:
    examples:
      - Reduced support tickets
      - Automated processing
      - Fewer manual reviews
    measurement: "Direct cost comparison"

  time_savings:
    examples:
      - Faster content creation
      - Quicker code development
      - Reduced research time
    measurement: "Time studies, surveys"

  quality_improvement:
    examples:
      - Better customer responses
      - Fewer errors
      - More consistent output
    measurement: "Quality metrics, error rates"

  revenue_enablement:
    examples:
      - New product features
      - Better personalization
      - Faster time to market
    measurement: "Revenue attribution"

class AIROICalculator:
    """Calculate AI initiative ROI."""

    def calculate_roi(
        self,
        initiative: AIInitiative
    ) -> ROIAnalysis:
        # Costs
        costs = self._calculate_costs(initiative)

        # Benefits
        benefits = self._calculate_benefits(initiative)

        # ROI calculation
        roi = (benefits.total - costs.total) / costs.total * 100

        return ROIAnalysis(
            costs=costs,
            benefits=benefits,
            roi_percentage=roi,
            payback_period=self._calculate_payback(costs, benefits)
        )

    def _calculate_costs(self, initiative: AIInitiative) -> Costs:
        return Costs(
            api_costs=initiative.monthly_api_cost * initiative.months,
            development_cost=initiative.dev_hours * initiative.hourly_rate,
            infrastructure=initiative.infrastructure_cost,
            maintenance=initiative.monthly_maintenance * initiative.months,
            training=initiative.training_cost,
            total=sum([
                initiative.monthly_api_cost * initiative.months,
                initiative.dev_hours * initiative.hourly_rate,
                initiative.infrastructure_cost,
                initiative.monthly_maintenance * initiative.months,
                initiative.training_cost
            ])
        )

    def _calculate_benefits(self, initiative: AIInitiative) -> Benefits:
        time_savings = self._measure_time_savings(initiative)
        cost_avoidance = self._measure_cost_avoidance(initiative)
        quality_value = self._measure_quality_improvement(initiative)
        revenue_impact = self._measure_revenue_impact(initiative)

        return Benefits(
            time_savings=time_savings,
            cost_avoidance=cost_avoidance,
            quality_value=quality_value,
            revenue_impact=revenue_impact,
            total=sum([
                time_savings,
                cost_avoidance,
                quality_value,
                revenue_impact
            ])
        )

class TimeSavingsTracker:
    """Track time savings from AI assistance."""

    async def measure_task(
        self,
        task: Task,
        ai_assisted: bool
    ) -> TaskMeasurement:
        # Track actual time
        start = time.time()
        result = await self._execute_task(task, ai_assisted)
        duration = time.time() - start

        # Compare to baseline
        baseline = await self._get_baseline(task.type)

        return TaskMeasurement(
            task_type=task.type,
            ai_assisted=ai_assisted,
            duration=duration,
            baseline=baseline,
            time_saved=baseline - duration if ai_assisted else 0,
            percentage_improvement=(baseline - duration) / baseline * 100
        )

    def calculate_value(
        self,
        measurements: list[TaskMeasurement],
        hourly_rate: float
    ) -> float:
        total_hours_saved = sum(m.time_saved for m in measurements) / 3600
        return total_hours_saved * hourly_rate

ab_testing_roi:
  approach:
    - Control group without AI
    - Treatment group with AI
    - Measure same metrics

  metrics:
    - Task completion time
    - Output quality
    - User satisfaction
    - Error rates

  duration:
    - Long enough for significance
    - Account for learning curve

before_after:
  baseline_period:
    - Measure current state
    - Document processes
    - Track relevant metrics

  implementation:
    - Deploy AI solution
    - Training period

  measurement_period:
    - Same metrics as baseline
    - Same time duration
    - Account for other changes

roi_reporting:
  executive_summary:
    - Total investment
    - Total return
    - ROI percentage
    - Payback period

  detailed_breakdown:
    - Cost categories
    - Benefit categories
    - Assumptions stated

  qualitative_benefits:
    - Employee satisfaction
    - Customer experience
    - Strategic positioning