Phase 3: ACT

Measurable Implementation

Prove value quickly and scale responsibly. Implement AI solution with measurable success metrics and continuous improvement approach.

The Goal

Shift focus from output to outcome. Many AI implementations fail because they are cool demos but add no business value. The KDA framework requires "Measurable Success Metrics" before scaling.

The Pilot Protocol

Never roll out globally immediately. Select a "Pilot Workflow"—a contained, high-friction task with a small user group. This approach minimizes risk and maximizes learning.

Select Pilot Workflow

Choose a contained, high-friction task that represents a real business problem.

Small user group (5-10 people)
Clear success criteria
Measurable outcomes

Contained Scope

Limit the pilot to a specific workflow or department to minimize disruption.

Single workflow or process
Isolated from critical systems
Easy to roll back if needed

Time-Boxed

Set a clear timeline for the pilot to ensure focused execution and evaluation.

30-90 day pilot period
Regular check-ins
Clear go/no-go decision point

ROI Measurement

If a tool generates text but doesn't save time or improve quality, it is discarded. This ensures only high-impact tools survive the selection process.

Time Savings
80% reduction in time on task
Quality Improvement
80% reduction in error rates
Efficiency Gain
75% reduction in revision cycles
Customer Impact
23% increase in satisfaction scores

Time Savings

Measure "time on task" before and after AI implementation. This is the most straightforward ROI metric.

Baseline Measurement
Record time spent on task without AI
Post-Implementation
Measure time with AI assistance
Target: 50%+ reduction
Significant time savings justify scaling

Quality Metrics

Error rates, revision cycles required, or customer satisfaction scores. Quality improvements often matter more than time savings.

Error Rate Reduction
Track mistakes and corrections needed
Revision Cycles
Count iterations before final approval
Target: 30%+ improvement
Measurable quality gains

Success Metrics Checklist

Time Savings Achieved
Minimum 50% reduction in time on task
Quality Maintained or Improved
Error rates reduced, satisfaction increased
User Adoption
80%+ of pilot users actively using the tool
Integration Stability
No critical failures or security issues
Cost Efficiency
ROI positive within 90 days
Scalability Validated
Pattern can be replicated to other workflows

The Scalability Loop

If the pilot meets success metrics, codify the prompt engineering and integration patterns into a playbook for the rest of the organization.

1

Document Success Patterns

Capture the prompt engineering techniques, integration approaches, and workflow modifications that led to success. Create a reusable playbook.

2

Identify Similar Workflows

Find other workflows in the organization that share similar characteristics with the successful pilot. These are your next scaling targets.

3

Replicate and Adapt

Apply the playbook to new workflows, adapting as needed. Each replication should be faster and more successful than the last.

4

Continuous Improvement

Refine the playbook based on new learnings. Build organizational AI capability systematically, one workflow at a time.

Implementation Steps

Start with pilot workflow

Measure time savings and quality improvements

Evaluate against success metrics

If successful, scale pattern to enterprise

If not successful, refine prompts or re-select tool