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Utility CFO

Manage fuel and purchased power cost recovery

Enhances✓ Available Now

What You Do Today

Track actual fuel costs against recovered amounts, file fuel adjustment clauses, manage the regulatory true-up process, and hedge fuel price risk through forward contracts.

AI That Applies

Fuel cost AI monitors actual vs. recovered costs in real-time, predicts under/over-recovery trends, optimizes hedge timing from price models, and generates regulatory filing data.

Technologies

How It Works

For manage fuel and purchased power cost recovery, the system monitors actual vs. The automation engine executes each step in the process sequence — validating inputs, applying business rules, generating outputs, and routing exceptions to human review queues. The output — regulatory filing data — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Cost tracking and recovery projections are real-time. AI optimizes hedge execution timing against price forecasts, reducing the fuel cost volatility that creates rate pressure.

What Stays

You still set the hedging policy, manage the regulatory relationship around fuel cost recovery, and make the strategic calls about risk tolerance.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for manage fuel and purchased power cost recovery, understand your current state.

Map your current process: Document how manage fuel and purchased power cost recovery works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: You still set the hedging policy, manage the regulatory relationship around fuel cost recovery, and make the strategic calls about risk tolerance. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Commodity Analytics AI tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long manage fuel and purchased power cost recovery takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your CFO or VP Finance

Where are we spending the most time on manual budget reconciliation or variance analysis?

They're prioritizing which finance processes to automate first

your ERP or finance systems admin

What spending patterns would we want to detect early that we currently only see in quarterly reviews?

They know what automation capabilities exist in your current stack

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.