By leveraging Artificial Intelligence (AI), economic developers and their organizations can significantly improve their impact in their communities. What does it take for these organizations to embrace and fully leverage AI in supporting and attracting businesses?
This article is the second in an ongoing series exploring how advanced analytical approaches employing machine learning and big data can improve the practice of economic development and the use of incentives. The first article explored how economic developers can compete, bid, and win intelligently by harnessing the power of AI. Here, we turn to the organizational side.
The most advanced data and analytics are useless if organizations—the people—do not have the time, capacity, or commitment to change, learn new techniques, and build and maintain a continuously-learning culture.
There are four criteria that are essential to successfully integrating AI and advanced analytics in the practice of economic development:
- Data to power the analytics
- Capability to analyze the data
- Capacity to leverage analytics to enhance decision-making
- Commitment to creating a culture that embraces change, dropping ineffective tactics and improving existing ones
1. Data – Finding and organizing the data to power the analytics
The data exists to power the analytics—the challenge is finding, accessing, and organizing it so your organization can efficiently pull this data into new analyses.
In many ways, the field of economic development was an early pioneer in the collection and analysis of large datasets. Economic development relies on two primary sets of data:
- Demographic – Who are our residents?
- Economic – What do they produce? What do they earn?
Until the near ubiquitous use of the internet, the most reliable and comprehensive demographic datasets were gathered through the U.S. Census, which dates back to 1790.
The concept of economic output as measured by Gross Domestic Product (GDP) dates back to a report by the economist Simon Kuznets in 1934 as the “the ultimate measure of a country’s overall welfare, a window into an economy’s soul, the statistic to end all statistics” (Foreign Policy).
However, there is such a thing as data overload. The figure below presents a cursory picture of the data sources and types that a typical economic developer may use in their daily work. Within each of these data sources and/or portals, there are dozens if not hundreds of datasets, greatly expanding the information overload.
A few questions to check your data readiness:
- What are the top 10 or 20 questions that you routinely field from stakeholders (public officials, existing businesses, or prospects)? Is that information easy to find? Can everyone in your organization access the latest data or report?
- What data (public and subscription-based) does your organization typically use? Is your organization using 20% or 80%+ of your data subscription’s capabilities? Why or why not?
- How does your organization typically pull and use data? Do all requests go to one division or one person? Could you empower more people (like your sales team) to use data more efficiently and effectively to both improve their efforts (for example, make more compelling pitches) and increase capacity? Said explicitly, if 80% of the research requests in your organization relate to the same topics and/or data, you should probably invest in automating that information.
2. Capability – Developing data analysis capabilities
Value comes from the insights drawn from analytics, not the data. Without analytics, the data is relatively useless. However, across industries, organizations are reluctant to invest in research and analytical capabilities, viewing these divisions as “back office” roles that are cost centers rather than profit generators.
In the case of economic development, one scholar has explored the implications of incentives bidding for communities that have low information costs because they have invested in analytical staff and capabilities versus those that have not, whether due to constrained resources or willful ignorance (she explores the political economy of underinvesting in economic development research capabilities in some detail) (Patrick, 2014).
Under-investment in research and analytics has significant competitive consequences, as other companies and organizations find ways to compete more effectively and efficiently, harnessing AI to build a formidable digital operating model (see Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World for an interesting read on how leaders can build organizations that thrive in this new era).
The reality is that most economic development organizations (EDOs) are small, and most have very limited budgets. Thus, not every EDO needs a robust research team. As in the rest of life, quality (of capabilities) is more important than quantity (of researchers).
A few questions could help identify if your EDO is analyzing its data to the fullest extent:
- What sectors and types of firms are driving growth (defined however you want) in your community? Most likely, more than 80% of your growth is coming from less than 10% of your firms. Have you built relationships with those firms?
- Have you analyzed your lead generation pipeline? How long does it typically take to close a project? Why have you won or lost recent projects?
- Who are your competitor communities (not just geographically)? Why are they considered your competition (i.e., reasons beyond anecdotes)? What are their advantages over your community? What are your community’s advantages over your competitors?
3. Capacity – Creating capacity to leverage AI in decision-making processes
The most robust dataset means nothing without helpful analytics, and the best analytics mean nothing if decision-makers do not have the capacity to integrate new insights into their decision-making processes.
Nearly every organization recognizes they could improve the way they integrate analytics into their existing decision-making processes but just as many say they just do not have the capacity to do so “right now.”
Businesses and organizations don’t have to majorly change their operations in order to create this capacity. But organizations have to be razor-focused on how they deliver on their objectives. In my scholarly research, I have written extensively about how the lack of defined objectives within the practice of economic development severely limits the field’s impact.
Every stakeholder identifies different objectives for the same field:
- Scholars – Improved standards of living or per capita incomes
- Public officials – Increased project announcements and promised jobs
- Practitioners – More promised jobs and capital investment
- Residents- Actual jobs for residents distributed equitably
Furthermore, common practical objectives concerning jobs and capital investment are increasingly becoming a tradeoff between jobs or capital investment (see Carlianne Patrick’s “Jobless Capital? The Role of Capital Subsidies”).
With this in mind, the first step for any EDO to create analytics capacity is to start with your objectives. Capacity and efficiency are positively correlated: organizations create capacity by devoting their energies only to activities that contribute to desired outcomes and objectives.
A few questions to get started with building more capacity:
- What are your objectives? How would your community prioritize projects based on promised jobs counts, wages, and capital investment? For example, are projects promising lots of jobs but average or slightly below-average wages better than projects promising few but higher-paying jobs? Would your community prefer capital-intensive projects with fewer jobs or projects with plenty of jobs but lower capital intensity?
- Are your outcomes aligned with your objectives? This question is critical. An easy example would be a community that wants (needs) jobs for its residents but consistently devotes the bulk of its incentives to capital-intensive projects with relatively low demands for labor.
- Are your organization’s activities aligned with those objectives? The flip side of this question is, “Which activities are not aligned with those objectives?” Stop these activities to free up some capacity.
- What steps can your organization take to align activities > objectives > outcomes? This alignment (and prioritization of high-impact activities) must be driven from the top.
4. Commitment – Committing to a culture that embraces change and continuous improvement
Every leader knows that organizational culture has the potential to enable or hinder progress.
A McKinsey article, “How the implementation of organizational change is evolving,” compared “top digital implementers” against all other respondents in their successes (or challenges) in driving organization-wide digital transformations. Of the seven categories assessed:
- Sufficient resources and capabilities to execute changes (i.e., the data, analytics, and capacity to leverage AI in economic development) was the least important capability for successful implementers.
- The top two categories were “planning from day 1 for long-term sustainability of changes” and “clear, organization-wide ownership of commitment to change across all levels of organization.”
If your economic development organization struggles with change generally, integrating AI into your daily routines is probably not the best next step.
A few questions to assess your culture and ways to improve:
- What was the last successful “big change” for your organization? Similarly, what was the last failed “big change” for your organization? What worked? What could you have done differently?
- If you have a strategic plan, how much of it is devoted to “strategy” vs. “implementation”? I was always amazed at how our consulting decks for clients were 98% about the strategies and less than 2% about implementation. That probably explains why so many consulting decks end up in a quickly forgotten file folder or recycle bin.
- Are you adequately resourcing the implementation plans? Resourcing and budgets are indicators of an organization’s priorities; and a plan without resourcing is just a document likely heading for the trash bin.
Change is hard and the media has done a great job of scaring people into thinking the machines will take everyone’s jobs. Yes, jobs will change and organizations will need to change. Successful ones will embrace this trend and become more effective in delivering value to their customers.
Economic development is a very traditional and conservative field: we are still using the same tactics like property tax abatements used during Alexander Hamilton’s day (New Jersey Treasury). But economic developers are tasked with supporting and attracting businesses that are integrating cutting-edge innovations like AI into their organizations.
The best economic developers will embark on a similar journey as the (businesses they seek to support and entice, learning about these innovations, integrating these approaches in their daily operations, and in the process improving their organizations’ ability to adapt and respond to change.