6 best practices for adopting AI in Spatial Analytics
AI is no longer just a buzzword; it's a practical tool that changes how organizations work with spatial data. By integrating AI into daily workflows, you can automate routine tasks, create predictive workflows, and involve all team members to make faster, more confident decisions.
But for AI to deliver real value, you need a strong foundation. This blog post, inspired by CARTO's report "Applied AI for Spatial Analytics," outlines six best practices to help you establish a secure and scalable AI-driven spatial analysis practice.
Do: Establish robust data governance.
Make sure your spatial data is clean, reliable, and well-documented. Assign data stewards and track data lineage so everyone knows where the information is coming from and how it's being used.
Don't: Assume AI will “fix” bad data.
While AI and machine learning are great for data cleaning, this should be an explicit part of your workflow, not an afterthought.
Do: Invest in your people.
Upskill them in both spatial and AI literacy. Analysts and experts are crucial for guiding, troubleshooting, and validating AI outputs.
Don't: Rely solely on AI to replace technical expertise.
AI can assist, but it can't replace the domain knowledge and decision-making abilities of experienced spatial analysts.
Do: Start with real problems.
Use AI to streamline repetitive tasks, enhance predictions, or reduce manual analysis in actual business workflows like logistics planning or climate risk assessment.
Don't: Treat AI as a "shiny add-on".
It only provides value when it's integrated into how your teams already work, not as an isolated experiment.
Do: Build safeguards into your AI strategy.
Secure sensitive data, validate model outputs, and ensure transparency in how AI is applied, particularly with proprietary data.
Don't: Ignore ethical, security, or oversight concerns.
Addressing them before implementation can prevent long-term liabilities and slow adoption.
Do: Start with why.
Clarify what success looks like and how it will be measured before you choose specific tools or platforms.
Don't: Rush into tools without a strategy.
You risk investing in technology that doesn't solve a real problem or deliver a measurable impact.
Do: Clearly define the specific task the AI Agent is built to solve.
For example, "generate retail site suggestions based on demographic and POI data" or "forecast neighborhood-level mobility patterns". Specify the business questions it answers and the data it uses.
Don't: Leave the agent's scope too broad.
An agent that tries to answer everything is likely to underperform. The more tightly scoped its mission, the more useful it will be in real-world workflows.
AI is already here, transforming how organizations make decisions every day. The biggest gains will come from thoughtful integration and a strong foundation in how teams actually work.
Download the full practical guide “Applied AI for Spatial Analytics” to learn how AI is already solving real-world spatial problems across industries.
The report features 10 examples of AI in action, from validating flood insurance claims to optimizing site selection for retail. You'll also discover the value of using AI to accelerate insights, empower non-technical users, and enable deeper decision-making through predictive spatial analysis.
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