How AI is revolutionizing strategic planning in local government by making data analysis accessible to all.
Planning. Some people love it. Others hate it. When it comes to city management, there is no avoiding it. Well, maybe now you can. At least cut down the tedious parts. With AI.
While AI is imperfect, it's proven to be a force in making sense of and organizing massive amounts of data faster than you can say, “Come with me if you want to live!"
It's why AI is particularly apt at crippling your chess game (it knows all the moves) or creating art by rapidly scanning massive amounts of other online images and then spitting out its regenerated masterpiece. (Though it still struggles a bit with hands.)
Now imagine an AI data analyst fed wide-reaching community livability metrics for towns, cities, and counties across the United States. Just like an AI can succinctly summarize a scientific report in seconds, it can summarize your city’s performance and compare it to communities nationwide while you refill your coffee cup.
The thing is, for this to work, the AI needs the data in the first place. That data must be verifiable and trusted.
Polco has provided just that, with dozens of robust, accurate, and reliable datasets.
An AI Analyst for Strategic Planning Needs Good Data
So, where does the data come from then?
The Government Performance Action & Learning (GPAL) dataset. The GPAL database is composed of freely available public data like the US Census, the FBI, the Bureau of Labor Statistics, and many others. What makes GPAL so unique is that it aggregates data from all these sources.
Also data scientists from Polco’s research arm, National Research Center, and experts from High Rode Strategy Center at The University of Madison-Wisconsin, Stanford's Hoover Institution, Harvard, and other specialists hand-picked what metrics are most valuable because not all data points are created equal.
The people behind GPAL are maestros in measuring community livability and knowing what data points actually matter because they have been doing this work for decades.
The National Community Survey (The NCS) dataset. This data comes from 30 years of public opinion research on community livability, service delivery, trust in government, and all the other components that determine resident quality of life.
This data, which comes from real human opinions, ensures that the AI isn’t overlooking what community members want. This resident input data adds a human element to Polco’s AI Analyst that can confirm that the AI's suggestions are aligned with resident priorities.
Using these two massive databases, Polco created Polly, the first and only AI data analyst for local governments.
Polly can do what innovative public sector organizations are already doing, using data for planning and decision-making, but much faster and at a bigger scale.
For example, you can ask Polly, "Tell me about how housing in my city compares to the rest of the state."
Polly may respond:
- Housing Cost Burden for Homeowners (2022): Approximately 29.71% of homeowners in your city experience a housing cost burden, which is considered similar to the state average.
- Median Home Value (2022): The median home value in your city is $290,800, which is more favorable when compared to the rest of the state.
- Housing Cost Burden for Renters (2022): About 51.96% of renters in your city experience a housing cost burden, which is less favorable compared to the state average
You just saved all that time not having to look this up yourself.
But let's say you're the type that likes to do your due diligence. You want to cite your sources.
You can ask Polly: Where did this data come from?
And she will tell you:
Certainly! Here's a list of the housing-related indicators, along with the datasets they originate from:
- Housing cost burden for homeowners: US Census Bureau American Community Survey (ACS)
- Housing cost burden for renters: US Census Bureau American Community Survey (ACS)
- Median home value: US Census Bureau American Community Survey (ACS)
Through this process, you've eliminated hours of research, and you have more data to work with than you did in the past.
You can hone your strategic plan by looking at all this data. If housing is a top priority, you now know alleviating the renting housing cost burden is an issue.
You can even ask Polly for strategies on how to fix that problem. Or even create drafts for a grant application to apply for funding in this area.
Polly also levels the playing field. Now, governments of all sizes have access to these data advantages. Before, only governments with more staff and funding could comfortably dedicate the resources to data-driven decision-making. That's no longer the case. Any government can leverage data with Polly on their team.
Polly marks a significant leap forward in the efficiency and effectiveness of data-driven strategic planning.
By harnessing databases like GPAL and The NCS and Polly's analysis ability, strategic planners can focus more on essential initiatives and less on tedious data gathering. While AI may have imperfections, its transformative potential for enhancing community livability and governance is undeniable.
Are you ready for the new era of AI in governance?
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