The 2026 midterm elections arrive at a time when government policy and technology strategy are increasingly intertwined.

AI governance, cybersecurity, privacy, semiconductor supply chains, data-center expansion, energy policy and trade policy are all influenced by decisions in Washington and state capitals.

But waiting for political clarity is not a winning strategy. The more useful question is, “What technology decisions make sense under multiple policy scenarios?”

The enterprises best positioned after Nov. 3 will be those that build technology strategies flexible enough to succeed across several possible outcomes.

Here are six decisions worth making now.

1. Build AI governance that can survive regulatory change

AI regulation may evolve differently depending on election outcomes, but one trend is consistent: greater scrutiny of how AI systems are governed, documented and validated.

States continue advancing their own AI rules. For example, California recently enacted SB 813 and AB 1405, establishing a framework for independent verification organizations and standards for AI auditors. At the federal level, proposals range from limiting some state AI rules to requiring independent assessments of frontier AI developers.

Regardless of where policymakers land, enterprises should expect increasing pressure to demonstrate that AI systems are properly governed and controlled.

Your decision: Build governance capabilities that remain useful regardless of how regulation evolves.

That includes:

• Maintaining an inventory of AI models and where they're used.
• Classifying AI systems by risk.
• Documenting controls and maintaining audit trails.
• Defining when human review is required and who is accountable.
• Preparing for independent assessments of AI systems and controls.

If regulations become more stringent, you'll be ahead. If they don't, you'll still have stronger visibility and control over AI across the business.

2. Design data architecture for regulatory flexibility

The bigger challenge may not be federal regulation. It may be dozens of different state requirements.

Twenty-three states now have comprehensive privacy laws, while additional legislation continues to emerge around AI, biometrics, automated decision-making and health data.

For enterprises, regulatory compliance increasingly depends on data architecture as well as legal policies.

Your decision: Design systems for regulatory flexibility.

Data governance platforms, identity systems, consent management tools, retention policies and AI controls should accommodate jurisdiction-specific requirements without major redesigns.

Organizations that build that flexibility now will likely spend less time and money reacting later.

3. Fold AI security into cybersecurity strategy

Many organizations still treat AI governance and cybersecurity as separate programs. That distinction is becoming harder to maintain.

The Congressional Research Service has identified growing policy concerns around agentic AI, including AI-enabled attacks, model testing and information sharing. Meanwhile, the World Economic Forum’s Global Cybersecurity Outlook 2026 found that 94% of surveyed leaders expect AI to be the most significant driver of cybersecurity change in 2027.

As AI gains access to workflows, enterprise systems and decision-making processes, governance and security increasingly overlap.

Your decision: Integrate AI security into your enterprise cybersecurity program.

AI risk assessments, red teaming, monitoring, identity controls, incident response planning and model-security reviews should become part of the core cybersecurity program rather than a standalone AI effort.

4. Identify technology supply chain risks and vendor dependencies

Technology supply chains remain exposed to geopolitical and trade-policy shifts.

The administration has imposed tariffs on certain semiconductor imports and outlined the possibility of additional semiconductor-related trade actions. Beyond tariffs, concentration risk poses another significant challenge.

Many organizations depend heavily on a small number of cloud providers, AI platforms, chip suppliers and networking vendors.

Your decision: Identify the dependencies that could materially affect your business.

Determine which vendors, platforms or suppliers could create operational, financial or regulatory risks if costs rise, availability declines or policies change.

Assess where alternative vendors or sourcing arrangements may be needed to reduce exposure without disrupting critical partnerships.

5. Account for power availability in AI infrastructure planning

Power availability is becoming a technology issue.

Congress is considering proposals related to data-center energy use and infrastructure, while IDC recently increased its forecast for 2026 AI infrastructure spending to $497 billion.

As AI investment accelerates, infrastructure decisions increasingly depend on energy availability, utility costs, geography, permitting and transmission capacity.

A company may have the budget for additional AI capacity but discover that power availability or utility costs become the real constraint.

Your decision: Incorporate energy planning into infrastructure planning.

Ask questions such as:

• Could power availability limit future AI initiatives?
• Are utility costs affecting long-term infrastructure economics?
• Does geography restrict expansion options?
• Could local permitting or community concerns affect future projects?

Infrastructure strategy and energy strategy are becoming increasingly difficult to separate.

6. Build your 2027 technology portfolio around scenarios, not forecasts

Many enterprises still build plans around a single expected future. That's becoming harder to justify.

A change in congressional control doesn't automatically reverse tariffs, cybersecurity priorities or technology-related executive actions. Likewise, unified government doesn't guarantee consensus on AI regulation, privacy or technology policy.

Planning for multiple political outcomes helps IT leaders reduce risk and avoid overcommitting to a single policy scenario.

Your decision: Stress-test major investments against several plausible scenarios.

Identify where changes in regulation, trade policy, operating costs, energy availability or geopolitical conditions could affect the 2027 technology portfolio.

Effective scenario planning establishes which developments would warrant reassessing an investment or changing course. The strongest investments can continue delivering value across multiple potential outcomes.

How can IT leaders prepare for post-election technology uncertainty?

IT leaders can prepare for post-election uncertainty by stress-testing their 2027 technology plans, identifying investments vulnerable to policy changes and building flexibility into major decisions.

The easiest mistake IT leaders can make before the midterm elections is treating the election as a reason to delay decisions.

But waiting isn't a strategy. Clarity rarely arrives on schedule.

Whether Congress changes hands or not, organizations will still face decisions about AI governance, cybersecurity, privacy, infrastructure, energy and supply-chain resilience. The policy landscape may shift, but the need to make technology investments won't.

The organizations best positioned to navigate the post-election environment will be those that have already accounted for uncertainty in their technology strategies.