At Network Show 2026, Erin Coffey joined a panel on practical AI in cannabis: what works, where it falls short, and what comes next.
Cannabis Technology in the Age of AI
AI is generating no shortage of conversation, but the useful question for cannabis businesses is more grounded: what changes when a team puts it to work? The Network Show 2026 panel was designed to move past broad predictions and into the realities of using these tools inside operating companies.
Antiem Tran of Goodfellas hosted the discussion with Charles Boldwyn of Haven and Erin Coffey of High Rise. With perspectives spanning different companies and parts of the industry, the proposed conversation focused on what teams are actually trying, what it costs to maintain, and where people should be careful about the hype.
From Experiment to Workflow
The brief started with the current state of AI in cannabis: who benefits first, a company's team or its customers? It then moved to the practical details behind adoption. Which services and departments can get the most value today? What is the smallest worthwhile move for a business without a development team or extra budget? And what time and resources does it take to keep an AI-assisted process useful after the first demo?
Those questions matter because a promising tool is not the same thing as a dependable workflow. A business still has to decide which task it is improving, who owns the output, and whether the result is worth the effort of running it.
The People Behind the Tools
Another thread in the panel plan was how AI spreads through a team. The questions asked what employees can do now that they could not do before, and what qualities become more valuable as routine work becomes easier to automate. The point was not just what the technology can produce, but how people learn to use it responsibly in their day-to-day work.
Limits and What Comes Next
The brief also made space for the less polished side of adoption: where businesses get AI wrong, what it still cannot do well, and which anticipated uses have not worked out. Those limits are as important to understand as the wins, especially when a tool touches customer experiences or decisions that need human judgment.
Looking forward, the panel was set to ask what a store, brand, or agency might do differently a year from now—and what new work may become realistic along the way. Its closing prompt was intentionally actionable: take 30 minutes this week to choose one repeatable task, define a useful output, and test a small working process rather than stopping at another chat.
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