We surveyed state commerce departments, regional innovation hubs, and ecosystem builders across the country to understand how they’re using AI and what they’re planning for 2026.
Organizations have moved past the “let’s try this AI thing” phase. Now they’re asking harder questions about implementation, data infrastructure, and where AI can solve problems they’ve been struggling with for years.
Here’s what we heard.
1. The Data Struggle Continues
Manual tracking remains one of the biggest operational challenges for EDOs.
Organizations are managing 990s, grant applications, and job postings outside of their main systems. Most rely on spreadsheets or separate databases that don’t talk to each other. This creates gaps in reporting and makes it harder to show impact to funders.
Alumni tracking came up repeatedly as a pain point for innovation hubs. These organizations need to demonstrate follow-on funding and job growth for the businesses they’ve supported, but most don’t have a reliable way to track those outcomes over time. When a founder moves on to raise a Series A or hire their tenth employee, the hub that helped them get started often doesn’t know about it unless the founder volunteers the information.
That missing data makes it difficult to prove ROI to boards, elected officials, and funders who want to see long-term outcomes.
2. Generative AI: Internal Efficiency and External Education
Most organizations are using generative AI tools like ChatGPT and Gemini on a daily basis, but how they’re using them varies.
Internal applications focus on efficiency. Teams are drafting emails, proofreading content, preparing presentations, and summarizing meeting notes. These uses save time but don’t fundamentally change how the organization operates.
External applications are more strategic. Some organizations are using AI to build educational programming for their communities. They’re creating courses that teach local business owners how to use these tools, lowering the barrier to entry for entrepreneurs who might otherwise be intimidated by the technology.
This positions EDOs as technology translators. They’re not just adopting AI for themselves. They’re helping their communities adopt it too.
3. Top Priorities for 2026: Reporting and Automation
Automated reporting for funders was the most common answer. Organizations want to generate impact reports without rebuilding them from scratch every quarter. Right now, most teams are pulling data from multiple sources, cleaning it manually, and writing narrative summaries based on anecdotal evidence. It’s time-consuming, and it makes reporting feel like a burden instead of a strategic exercise.
The second priority was automated guidance for entrepreneurs. There’s growing interest in tools that can provide real-time support to business owners without requiring staff to be available around the clock. This could mean chatbots that help founders find funding opportunities, tools that guide them through loan applications, or systems that suggest the right resources based on where they are in their business journey.
Both priorities point to the same need. Organizations want AI to handle repetitive, manual work so their teams can focus on relationship-building and strategic decision-making.
In 2026, success will come down to whether EDOs can pull together fragmented data (market trends, funding, policy changes, engagement metrics) and turn it into useful reports that serve both their boards and the businesses they support.
Beyond the immediate priorities, three patterns emerged that point to where the field is headed.
1. EDOs as Educators
Organizations are using generative AI to build training programs for their communities. They’re not just using the technology to improve their own workflows. They’re teaching business owners how to use it, creating curriculum, and positioning themselves as guides for entrepreneurs navigating new tools.
This builds trust, demonstrates value, and ensures that local businesses aren’t left behind as technology evolves.
2. Most Leaders Are Unsure What Data They Need
When we asked which new data types would give organizations a strategic advantage in 2026, the most common answer was “Unsure.”
Leaders know they need better data infrastructure. They know AI can help. But they’re not yet clear on what specific data inputs would make the biggest difference for their decision-making.
This suggests a capabilities gap. Organizations understand the problem (fragmented data, manual reporting, limited visibility into outcomes), but they’re working through what the solution looks like.
3. Advanced Segmentation Is Already a Priority
Some organizations are focused on basic automation (drafting emails, generating summaries). Others are already thinking about advanced demographic and geographic segmentation as a competitive advantage for 2026.
These organizations want to understand which neighborhoods have the highest concentration of early-stage startups, which industries are growing fastest in their region, or which demographic groups are underserved by existing programs. That level of insight requires sophisticated data infrastructure, and it positions these EDOs to make more strategic, targeted investments in their ecosystems.
What This Means for 2026
The organizations that succeed in 2026 will be the ones that stop treating AI as a novelty and start treating it as infrastructure.
That means investing in systems that can aggregate data from multiple sources, automate repetitive reporting tasks, and provide real-time insights to staff and entrepreneurs. It means moving away from manual tracking and toward platforms that can capture outcomes automatically. And it means being willing to experiment, even when the path forward isn’t perfectly clear.