Phase 1: Scoping and Baselining
Define the Business Problem: Clearly state the specific challenge the AI project will solve (e.g., reduce outage duration, predict asset failure).
Identify Key Performance Indicators (KPIs):
Operational Costs: Maintenance, time lost, crew deployments, etc.
Reliability: SAIDI, SAIFI.
Establish a Baseline: Collect and document current performance data for all identified KPIs before implementation.
Phase 2: Quantifying Benefits
Calculate "Hard" Savings (Operational Efficiency):
Predictive Maintenance Savings: (Cost of reactive repair - Cost of proactive repair) x Failures prevented.
Optimized Crew Management Savings.
Vegetation Management Savings.
Calculate Reliability Value:
(SAIDI points reduced x Value per point) + (SAIFI events avoided x Value per event).
Phase 3: Building the Financial Model
List All Costs:
Upfront Costs: Software, hardware, implementation, training.
Ongoing Costs: Licensing, maintenance, data storage, personnel.
Calculate Final ROI:
ROI (%)= [(Total Financial Gain - Total Investment Cost) / Total Investment Cost] x 100
Calculate Payback Period:
To find the payback period, take the total investment cost and divide it by the annual financial return.
Sensitivity Analysis: Test your assumptions by modeling a spectrum of possibilities, including the most favorable, least favorable, and most likely outcomes.
Phase 4: Presentation and Storytelling
Tailor the Message: Customize the presentation for your specific audience (Board, regulators, etc.).
Lead with the Business Problem: Frame the narrative around solving a key challenge.
Visualize the Data: Use simple, clear charts and graphs for key metrics.
Address Risks Proactively: Discuss potential challenges and your mitigation plans.
State the "Ask": Present the required investment and the expected return.