The AI opportunities for EDOs are twofold. First, there is the existing opportunity to attract data center investment, which many organizations have already been doing for the last 20 years. However, the key ingredients for attracting AI data centers are changing, and latency is quickly being trumped by affordable power, labor availability, and a welcoming environment.
The second opportunity, and the focus of this white paper (article series), lies in your EDO’s ability to become a more efficient, effective, productive, and smarter organization. Understanding, adopting, and implementing AI initiatives to improve productivity within your EDO will feel overwhelming, and learning the backend technology, software integration, data organization, and terminology will be no small task. But that is where organizations need to start.
Here is a step-by-step process EDOs and economic development professionals can use to lay the foundation for long-term development of AI technology and ongoing adoption of the tools that will lead to a higher-performing organization:
Step 1: Educate Your Leadership
Successful AI adoption must be led from the top. Leadership needs a clear understanding of the technology, data, skills, and capacity required to use AI effectively. For adoption to last, AI must become part of the organization’s culture—from the boardroom to frontline staff.
Because AI is still in its infancy, resources explaining how organizations have adopted and use it (beyond generative AI/chatbots) remain scarce. Scaling AI requires a leadership team with a shared understanding: not just of AI terms, but the technology, and how AI creates value, supports strategy, and drives performance. When leaders understand the possible uses of AI and how they link to organizational performance, they can confidently assess investments, guide teams, and avoid both underreach and overreach.
To start with, consider reading The Executive AI Playbook by Scott Schobert. Develop a structured training curriculum for your leadership that takes them from AI basics (generative AI) to agentic AI capabilities. Developing an early understanding of AI will also help you create an information technology plan for your organization.
To realize AI’s full value and help your organization improve its competitive positioning for optimal economic growth, leadership must also understand the data and software that power it. It is also your leadership team that will need to develop and then ensure your AI initiatives are supported and aligned with your organization’s overall strategy. Understanding its capabilities and then establishing and monitoring its KPIs will be critical to measuring AI’s ROI.
If you already work in the Microsoft Office environment, start with their free online AI and Copilot and Power app training courses, and develop a training curriculum for your team using them.
Step 2: Inventory Your Data Assets
Inventory the data sets and subscriptions you have access to and currently use and need. In addition, understand how that data is stored—e.g., in spreadsheets, CRM, dashboards, etc. Then begin identifying the local, state, regional, and other federal data available to your organization. Some additional non-traditional datasets you should also consider including are:
- Zoning and land use regulations
- Building permits
- Property tax by use
- Existing businesses
- Local employment forecasts via BR&E CRM survey results
- Historic marketing campaign data
- Lead generation data
- Website visitation data
- State labor reports
- US Census and employment trends by industry
- Industry outlook reports
- Proprietary information sources available through JobsEQ, Lightcast, Co-Star, or Implan, etc.
- Information obtained from prior reports and studies
Once inventoried, evaluate the internal and external data sources available to your EDO and how they are obtained (accessing a software platform, downloading a spreadsheet, or via an API connection), and begin organizing them into structured vs. unstructured data. Determine if there are opportunities to convert unstructured data to structured data. Understanding that this data will be derived from numerous sources, be sure to establish clear rules for data ownership, privacy, use, and sharing.
Once data sets have been inventoried and organized, begin to think about how your unique data sets might lead to exclusive insights about your economy or organization, as well as how you will maintain and store that data as part of a closed-source model (versus it becoming a part of an LLM). Layering AI tools will then provide automated analysis and insight and allow for the development of other automated tasks and processes previously completed by staff.
Step 3: Inventory Your Software
Are you using Salesforce, Microsoft Dynamics, HubSpot, or other database/CRMs for business monitoring? What about PowerBI or Tableau dashboards, documents, or presentation and spreadsheet tools?
Start with the tools you currently use. For smaller EDOs, once you have inventoried your software, consider which ones are introducing new AI capabilities. Of those, which are more likely to support your team’s effort to utilize those programs?
Microsoft is the leading tool for small to mid-size organizations that are incorporating data and AI into daily practice. They are already offering generative AI integration into their traditional and commonly used software tools such as Dynamics CRM, PowerBI, Word, PowerPoint, and Excel. In addition, their newer workflow, data illustration, productivity, and other low-code software (Power apps) also allow users to work seamlessly across platforms.
Microsoft’s 365 Agent is soon to be released. This tool will help users build and manage AI agents and the supporting data sets that will accomplish tasks autonomously and act as digital employees. Organizations already operating in the Microsoft environment will find this tool to be a cost-effective way to begin building and managing their data and agentic AI capabilities.
Larger EDOs may be using software like Salesforce, HubSpot, and others, which are rapidly developing AI tools to aid in automation, predictive analytics, and agent development. Without software coding expertise, however, continuing to use these data platforms will be limited if your data remains siloed and disconnected. Consider which platforms may allow for API connection and storage within an independent CRM as part of your inventory process.
Step 4: Form Your AI Team
Establish a cross-disciplinary AI team charged with inventorying the economic development initiatives and processes involved in the administration and delivery of your organization’s services.
This team should include individuals in the organization who are familiar with internal administrative processes and software, as well as with economic development program delivery and/or data and analysis. It should include administrative staff, management, software developers, data analysts, economic development program managers, and IT staff.
This team’s objective would be to identify, develop, and execute priority AI initiatives, with their first task being to identify internal and external organizational and programming processes that could benefit from automation.
This can include tasks related to an EDO’s members or board, monthly report preparation, bill processing, or client communication. Once this list is crafted, the team would prioritize it by assessing which tasks are easiest to automate and which would yield the greatest time savings.
Some criteria that could be used to evaluate and select potential tasks for automation or an AI agent development initiative include:
- Is data available to support an AI effort?
- Does the initiative require multiple software connections or API?
- What is the estimated ROI?
- What is the estimated savings in labor/time?
- How complex will the initiative be to develop?
- What is the estimated development cost?
- Will the effort solve an organization-wide problem?
- Will the effort improve client/member/prospect experience and/or satisfaction?
Step 5: Begin Building, or Build Upon, Your CRM
As previously mentioned, AI needs data and information to work. Information about your community, businesses, target industries, marketing campaigns, and organization, to name a few, is unique. In and of itself, an LLM is not going to capture and maintain your unique information, such as your organization’s financial performance, existing businesses, local real estate market, sites, etc. You will need to create a unique set of information that will serve as the basis for your organization’s future AI adoption initiatives.
Structured data is most often stored in a CRM or spreadsheets. While data models may rely on multiple sources, the development of a CRM tool is most beneficial when considering development cost, annual license and storage fees, ease of use, and AI tool development. After evaluating numerous options, Camoin Associates chose Microsoft’s Dynamics CRM for affordability, ease of use, and cross-platform integration.
Once you have selected the CRM right for your organization, use the data inventory list your AI team generated and begin building your unique data set.
Step 6: Develop Your First AI Agent
Once you have data to use and have inventoried all the software and organizational processes, hire an outside programmer to help you build your first AI agent. Start simple and look for processes to automate that will save time, save money, or improve a program’s output or performance, and are more likely to be utilized by your team.
After building and refining your first AI agent, build another that handles a more complex task. BR&E and investment attraction are good places to start. This is where your CRM will be able to, for example, take your existing business data and use AI to analyze trends, provide recommendations for scheduled outreach, and create tasks and reminders for direct outreach and assistance.
Step 7: Develop Your Long-Term AI Strategy
Develop a formal AI strategy and integrate it into your organizational and community strategic plans. Once you have inventoried and categorized available data and information, advanced team member skills, established a vision for how your organization is going to begin utilizing AI to become a more effective and efficient entity, and developed your first AI agent, it’s time to really dive into developing a long-term organizational AI strategy.
Here are some key topics or issues your AI strategy should address:
- Supporting the evolution of an AI adoption culture within the organization.
- Creating or expanding an existing closed-source data model that eventually connects a variety of data through public and proprietary sources.
- Building the software and technology skill capacity necessary through structured and ongoing training.
- Identifying more complex internal administrative and operational tasks for automation consideration. These might include:
- Employee onboarding
- Vendor contracting and communication
- Project management
- Hiring and employee evaluations
- Board reporting and communication
- Loan due diligence
- Program performance data gathering
- Identifying processes that, with AI automation, will improve economic development initiative performance and efficiency. These might include:
- Identifying business prospects
- Querying existing businesses for expansion assistance
- Running automated business marketing campaigns
- Conducting data analysis
- Developing organizational strategy
- Constituent communication
- Ensuring privacy, security, and data protection. As your data sets grow, it will be critical to ensure that they are protected and secure. Many of the software tools you are already using, along with your internal network, already have the necessary software security. Nevertheless, your strategy will need to address any vulnerabilities as you capture increasing amounts of information and create data unique to you, but valuable to others.
As with other organizational and economic development strategic planning efforts, your AI strategy will eventually become an important component of your broader planning initiatives. Your new AI experience and burgeoning expertise will prepare you for tackling more complex efforts.
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.
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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.
