Texas Wildfire Rules, One Year Later: Can AI Turn Risk into Action?

Since the start of the year, nearly 65,000 wildfires have burned more than nine million acres across the U.S. Texas now requires utilities in fire-prone areas to show how they will manage wildfire risk, but many still face challenges as they put those requirements into practice.

As of September, only eight of 160 utilities operating in fire-prone areas had filed wildfire mitigation plans required under House Bill 145, according to the Texas Tribune. Most others told regulators their plans were not yet complete.

At the same time, utilities such as Southern California Edison and SDG&E continue expanding their use of AI-enabled cameras, sensors, and other wildfire technologies. But technology doesn't answer operational questions: When risks compete for limited resources, how do you decide what needs attention first and where your investment will make the greatest difference?

Is Using AI for Risk Prevention in Utilities a Market Disruption or Hype?

AI becomes meaningful when better information improves an operating decision.

Operators already work with substantial amounts of data, alarms, forecasts, and applications. AI needs to help them connect that information to equipment and network conditions, so teams can evaluate their options.

A practical place to start:

• Start with one meaningful operational problem
• Establish a baseline
• Demonstrate measurable value before expanding

Learn more: How utilities modernize grid planning with digital twins for risk mitigation

What’s Being Overlooked in the Wildfire Technology Conversation?

Utilities need to detect fires, and at the same time they need to keep their own equipment from becoming the ignition source. They cannot harden everything at once, so the question is where the next dollar and the next crew should go first.

For example, combining vegetation and weather conditions with inspection findings, asset condition, and network configuration can help utilities prioritize inspection, trimming, repair, or replacement.

For a recommendation to be useful, it needs to name:

• The equipment or location involved
• The supporting evidence
• The available actions

Utilities need to understand the likelihood of an ignition, its potential consequences, and which interventions could reduce that exposure. Engineers and operators also need to see the evidence, assumptions, and uncertainty behind the assessment so they can choose an appropriate action.

How can Utilities Improve Resilience?

Start by connecting risk information to the decisions your operators and engineers already make. A digital twin can represent the network, assets, and relevant conditions to help your teams evaluate alternatives and understand potential consequences before acting.

The information needed depends on the decision:

• Near-term operating decisions: Require sufficiently current information
• Longer-term hardening and investment decisions: Require planning scenarios and assumptions

Whether the threat is wildfire, hurricanes, or ice, evaluate it against a defined operational problem and a baseline.

Measures could include:

• Time from a risk flag to a completed inspection
• The share of inspections that show actionable defects
• Completion of priority corrective work within the agreed time
• Customer disruption associated with mitigation actions

Track projected risk reduction separately from demonstrated results. A functioning model alone does not prove that resilience has improved.

How Should Utilities Prioritize Wildfire Investments?

Wildfire mitigation plans put greater attention on how utilities manage risk. A practical risk-based approach brings together asset condition, network topology, environmental conditions, operating constraints, and available mitigation options. You should compare mitigation options against the same factors:

The business case should include integration, training, and ongoing support costs, with projected avoided losses distinguished from realized savings. At PG&E, I helped deploy its advanced distribution management system and build a wildfire mitigation module around its operational needs. That experience showed me what this looks like in practice.

What Restricts Proactive Wildfire Risk Management?

The gaps can be technical, procedural, or policy related. Utilities need to connect information to the people and systems responsible for acting on it. Common hurdles include:

• Inconsistent asset identifiers, locations, and timestamps across source systems
• Incomplete or outdated information about equipment and network conditions
• Integration with existing work management and operating processes
• Cybersecurity and access requirements
• Clear ownership for reviewing recommendations and carrying out actions
• Clear rationale behind recommendations
• Your goal is to reduce the effort needed to make a sound decision, rather than create another application or alert that operators must interpret.

Where is Utility Wildfire Risk Management Headed in 2027?

In 2027, I expect utilities to focus less on prediction alone and more on whether recommendations lead to measurable action. The next step is to make recommendations easier to evaluate and act on. That means keeping a clear record of:

• The information used
• The alternatives considered
• The decision made
• The resulting action

AI and digital twins can support that process, while engineers and operators keep decision authority within the utility’s established approval procedures.

FAQ

How is AI used to reduce wildfire risk for utilities?

AI can help utilities bring different sources of risk information together and surface areas that may call for attention. The value comes from helping teams make more informed choices about where and when to intervene.

Can AI predict utility-caused wildfires?

AI can help find patterns and conditions associated with elevated risk, but predictions should not be treated as certainty. Utility experts still need to assess the context and decide what action is appropriate.

What data can help utilities assess wildfire risk?

Useful inputs can include information about assets, vegetation, weather, inspections, and electrical networks. Bringing those inputs together can offer a more complete view of where exposure may exist.

How can digital twins support wildfire mitigation?

Digital twins can give utility teams a model of physical infrastructure and its surrounding conditions. Teams can use that representation to explore potential responses before making changes in the field.

What is risk-based wildfire mitigation?

Risk-based mitigation focuses resources where they can have the greatest impact instead of treating every asset or location equally. It gives utilities a way to weigh potential exposure alongside the effort and cost of addressing it.

How can utilities decide which assets to inspect first?

Utilities can prioritize assets by considering multiple signals rather than relying on a single risk indicator. The objective is to direct limited inspection capacity toward areas where additional evaluation may be most useful.

How can utilities measure the value of wildfire technology?

Measurement should connect technology investments to observable changes in utility operations. Teams can establish a starting point, monitor what changes after deployment, and distinguish anticipated benefits from results they have actually achieved.

What prevents utilities from acting on wildfire risk insights?

The challenge is often turning information into action across existing systems and processes. Data gaps, technology integration, security requirements, and unclear accountability can all add friction.