Regulating Smarter: How AI Can Transform Cannabis Oversight
“In an industry shaped by decades of disproportionate enforcement and cultural stigma, regulators were tasked not only with creating rules but also with repairing harm.”
When I served as Executive Director of the Cannabis Control Commission in Massachusetts, we approached our work with a clear philosophy: if we were building government from scratch, we could show the world what 21st-century bureaucracy might look like. From the outset, we pursued a digital strategy, implementing electronic applications, an open data platform, and tools like a case management system. We even (regrettably) embraced Microsoft Teams before COVID made it necessary. We worked hard to minimize physical paperwork while ensuring accessibility for every applicant and stakeholder.
Even with these efforts, the sheer volume of information revealed the limits of human capacity. Our staff spent countless hours sifting through spreadsheets, reconciling inventory logs, and juggling inspection schedules. At times, I wondered if artificial intelligence could have helped us step back and reimagine our options before the flood of operational demands consumed our bandwidth. In an industry shaped by decades of disproportionate enforcement and cultural stigma, regulators were tasked not only with creating rules but also with repairing harm. That mission demanded creativity, and it demanded resources. For many agencies working under budget constraints, AI could represent a way to stretch those resources and empower teams to focus on the work that matters most.
AI as a New Opportunity for Government
Artificial intelligence is not a science-fiction gadget for the private sector; it can become one of the most powerful tools in a regulator’s toolkit. Picture an AI system that tirelessly scans applications for missing fields, flags inconsistencies in dense data reports, organizes inspection schedules for fairness, and spots unusual trends in inventory that might signal a bigger issue. These tools should not replace the human judgment that regulation depends on. They can give regulators the space to think strategically, focus on nuanced issues, and connect with the communities they serve. When state budgets are tight and getting tighter, AI is a resource agencies can shape and deploy to maximize limited bandwidth.
Smarter Licensing and Compliance
Cannabis license applications often run hundreds of pages, each packed with dense details and attachments. Reviewing them line by line to ensure completeness would take our staff hours upon hours to review, sometimes days. The FDA recently tested generative AI for similar work and found it could flag gaps and errors in minutes, shaving days off their review timelines. A similar approach for cannabis regulators could clear backlogs and free staff to focus on substantive reviews to assess whether applicants are truly ready to operate responsibly.
The logic extends to audits and inventory oversight. Seed-to-sale tracking systems, like Metrc, produce massive amounts of data. Sorting through it manually overwhelms staff and slows down oversight. Colorado’s Marijuana Enforcement Division has started using analytics tools to flag anomalies such as sudden spikes in production or unexplained inventory losses. In Monterey County, California, AI-driven analysis exposed unreported sales and increased cannabis tax revenues significantly. For regulators facing growing responsibilities and shrinking budgets, these tools reflect pragmatic solutions that improve oversight and strengthen public trust.
“Human schedules, no matter how well intentioned, sometimes gravitate toward familiar or convenient stops. They are also ripe for accusations of favoritism or retaliation.”
Building Fairness, Equity, and Predictive Power
AI can also bring fairness to inspections. Human schedules, no matter how well intentioned, sometimes gravitate toward familiar or convenient stops. They are also ripe for accusations of favoritism or retaliation. Chicago’s food inspection agency solved that problem by adopting AI to plan visits strategically and evenly. Inspectors found critical health code violations sooner, and the system reassured businesses that inspections were impartial. Cannabis regulators could adapt the same approach, giving operators confidence that oversight is both fair and consistent.
Equity remains a cornerstone of cannabis legalization. For years, communities disproportionately impacted by prohibition bore the brunt of enforcement while others profited from the shift to a legal market. Regulators must ensure AI systems support this mission rather than undermine it. Predictive tools that detect early signs of diversion or fraudulent lab testing can help regulators act proactively. Without careful oversight, algorithms risk embedding historical disparities into modern oversight. Datasets that overrepresent compliance actions in certain neighborhoods or demographics could lead AI to replicate past injustices. Regulators must audit these systems rigorously, ensuring they elevate equity alongside efficiency.
The Human Element
Using AI in regulation comes with responsibilities. Algorithms reflect the biases of the data they are trained on. The IRS learned this the hard way when an audit-selection model unfairly flagged Black taxpayers at disproportionate rates. Cannabis regulators need to ensure transparency in how AI systems make decisions and conduct regular checks for fairness. AI models should be stress-tested for unintended consequences, particularly around license denials or enforcement priorities, to safeguard social equity goals.
AI does not have to be a threat to regulatory jobs. It is a chance to reassign talent and expertise to higher-value work. Instead of poring over paperwork or spreadsheets, regulators can spend more time in the field, resolving complex issues and building trust with stakeholders. That shift would make agencies more nimble and effective, especially in a regulatory space as young and dynamic as cannabis.
Leading the Way
Cannabis regulation is still a young field, and that is an advantage. Agencies are not weighed down by decades of legacy systems and data. They can pilot AI tools and set an example for other regulators in how to use them responsibly. By integrating AI thoughtfully, agencies can protect public health and safety while advancing the promise of social equity in the industry.
Good regulation has always depended on expertise and judgment. Artificial intelligence can strengthen those qualities by giving regulators the bandwidth to use their skills where they matter most. The future of cannabis oversight does not belong to robots; it belongs to agencies bold enough to use technology wisely and keep human values, including fairness, equity, and trust, at the center of every decision.
Sources, Resources, and Suggested Reading:
Sources
“Metrc – Seed‑to‑Sale Tracking System,” Metrc, 2023
“Predictive Analytics Optimize Chicago’s Food Inspection Process,” GovTech, 2022
“Delivering Faster Results with Food Inspection Forecasting,” Data-Smart Harvard, 2023
“Hindsight Analysis of Chicago Inspection Model,” arXiv, 2019
“AI Accountability Policy Request for Comment,” U.S. Department of Commerce (NIST), April 2023
Suggested Reading
“The Coming Wave: Technology, Power, and the 21st Century’s Greatest Dilemma,” Mustafa Suleyman, 2023
“Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy,” Cathy O’Neil, 2016
“Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence,” Kate Crawford, 2021
“Artificial Intelligence and Life in 2030,” One Hundred Year Study on Artificial Intelligence (Stanford University), 2016
