Economic development is a data-rich profession, and the development and use of artificial intelligence (AI) by economic development organizations (EDOs) will provide ample opportunities to transform and enhance the delivery of economic development programs and initiatives. An EDO’s inability to understand, fund, and fully adopt AI tools can make the process more difficult and slower.
The following are seven potential barriers to AI adoption that EDOs will need to overcome to take full advantage of the benefits AI tools can provide:
1. Lack of Resources
The expertise needed to organize data, sort through various software platforms, and/or build AI agents to support internal operations and external economic development efforts comes at a cost. Like any organization, EDOs have limited budgets and staffing. Hiring an in-house AI-level coder or contracting with an outside expert may not be financially feasible for smaller EDOs.
Even larger organizations that may be utilizing a platform like SalesForce will need to contemplate the additional cost associated with building and operating new agentic AI tools.
2. AI Technology Skills Gap
Like other industries that have been transformed through technology and automation, EDO leaders who are unwilling to build their organization’s AI technology skills could eventually be left behind.
While this isn’t likely to occur overnight, once agentic AI tools begin to become commonplace within organizations, economic developers will need to be capable of building and enhancing data models, operating software tools at a high level, and learning to build and work alongside automated processes and agents that can carry out unguided tasks.
Organizations that can successfully build a culture of AI learning and adoption will excel.
3. Organizing and Building Unique Data Sets
Data is what fuels AI. Fully optimizing all the capabilities AI has to offer will require that EDOs utilize not only information available through an LLM, but also its own closed-source or unique datasets.
These closed-source models may include one or a combination of data points, including local property and tax information, existing business data (CRM), local labor data, target industry trends, available sites, demographic trends, and other community data.
While many EDOs already value and access a variety of these information sources, they typically do not maintain this information in one data model. This will make it difficult to implement advanced AI tools, which will operate more efficiently and seamlessly when all data and information are contained within one or more easily accessible systems.
Like the race to create LLMs, EDOs that can obtain and maintain data and information within one or more closed-source, proprietary models capable of working seamlessly with larger open systems will quickly outperform their peers. Building and maintaining your own unique data about the local economy, businesses, or community is vital for two reasons:
First, your organization’s competitive edge will come from having exclusive data that isn’t available through public AI models. If an EDO only uses an open model chatbot to query data, it’s getting the same information as anyone else who wants to utilize the same.
Second, it’s critical to make sure that chatbots don’t gain access to sensitive internal information. For example, if a chatbot inside your business system can add new info to its AI model, it could put your proprietary data at risk. Therefore, public agencies, banks, and healthcare providers must maintain the security of both their personal and organizational data.
That’s why it’s so important for organizations to protect their own information systems and combine their unique data with what’s available from open source LLM. This mix will help you automate tasks that matter most to your organization, create unique insights, and possibly even develop new software or tools that offer special value to your clients/businesses. In short, this approach will help your organization stand out, especially if you’ve relied on people and data for creative ideas and strategies in the past.
When these various sources are combined into a single model and aided by AI, they would offer even greater insights for users. For example, what might AI learn if it could access a region’s existing business data, target industry trends, labor force skills, real estate market data, demographics, industry multipliers, and export data all in one place? Might it help provide some unique insights and relationships between data points that only deep and time-consuming human research would otherwise reveal?
4. A Multitude of Existing Software Platforms
Many EDOs also subscribe to multiple software platforms. While each provides a unique value proposition, they all tend to operate independently and require individual users to access the information through unique platforms.
Camoin Associates, as an example, operates a large CRM and subscribes to approximately 10 different economic development software platforms that cover FDI, real estate, industry reports and trends, labor, economic data, business databases, and more. Each of these sources is likely to develop AI tools to assist users with information analysis in the near future.
Integrating all of these independent systems will require obtaining permission, paying fees, and enhancing software programming capabilities.
5. Outdated Systems and Data
Government-led economic development organizations often have legacy IT infrastructure, which will hinder interoperability and innovation. Critical data exists in fragmented systems, where internal or proprietary data sets are provided through a variety of sources.
Examples may include sources provided by the Department of Commerce BEA, US Census, State labor departments, as well as local property, tax, zoning and permitting data. Just as the source is siloed, so too is the analysis of these datasets; rarely, if ever, is there an opportunity to understand how these disparate sources may be related. Again, this leads to difficulty with data integration without software and IT expertise.
6. Fear of Job Elimination
EDOs are in the business of job creation, and the adoption of tools that reduce and replace human labor runs contrary to our mission. Investments in AI initiatives that result in lost jobs for humans will not go without scrutiny and criticism. If an EDO’s efforts to support new businesses whose mission is to improve organizational efficiency through increased human productivity result in lower employment demand or no job creation, there may be pushback.
7. Bureaucracy
For EDOs tied to a governmental body by funding, law, or policy, getting approvals to access, extract, and utilize data will be time consuming in an environment where moving and innovating quickly is competitively advantageous. This bureaucracy is likely to reveal itself through data privacy concerns, perhaps slowing or eliminating opportunities for innovation.
Part 4 of this series of articles will be published during the week of January 26-30, 2026. It will explain the action EDOs should take to begin the adoption and integration of AI.
If you would like a free preliminary AI organizational assessment or want to discuss how your organization might get started, please contact Rob Camoin, CEcD, at rcamoin@camoinassociates.com.
View The AI-Ready EDO series home page
About the Author
Robert Camoin, CEcD, is founder and current President of Camoin Associates, an economic development consulting firm that works with EDOs across the nation, and is currently leading the firm’s effort to build its first AI agent. He is also President and CEO of ProspectEngage®, which provides digital business retention and prospecting platforms supported by a closed-source data platform that now incorporates AI for user insight and efficiency.
