AI in Cultivation: MJBizCon Panel Stresses Fundamentals First

Artificial intelligence (AI) officially has entered the cannabis grow room.

At MJBizCon 2025 in Las Vegas, a panel about AI in cultivation drew
operators, technologists, and curious skeptics looking for clarity on what
is real, what is hype, and what might actually move the needle in an
oversupplied, margin-squeezed market.
Why environmental control comes before AI

On stage, the technology spanned everything from canopy-scanning robots to
tissue-culture automation and leaf-level plant monitoring. But throughout
the conversation, Cannatrol founder and Chief Executive Officer Jane
Sandelman served as a kind of counterweight, repeatedly reminding the room
that no amount of AI can compensate for poor environmental control.

“Our core competency is environmental control,” she said early in the
session. “If your HVAC and room conditions aren’t consistent, AI can’t fix
that for you. You’ll just end up in a ‘garbage in, garbage out’ situation.”

Her point landed, because the other panelists’ success stories depended on
exactly the kind of stability she described.
How data-driven cultivation is rewriting SOPs

Gary Holland, chief innovation officer at Endless Biotech, described a
data-heavy approach that would have been unthinkable in the early medical
days. His team invested in scientific instrumentation that clips onto
individual leaves, measuring what is happening inside the plant almost down
to the cellular level and generating 45 different data points per sample.

Those thousands of data points are fed into AI systems that analyze growth
across propagation, veg, and flower. Studying how plants respond to
different stimuli — light spectra, irrigation patterns, environmental
changes — helped the team rewrite the company’s standard operating
procedures. The result, he said, was the ability to consistently hit
roughly 100 grams of flower per square foot, up from the 25–55 grams per
square foot he sees in many of the facilities with which he consults. At
the same time, the operation pushed terpene levels from the 2–3-percent
range into the 4–6-percent range, with occasional runs approaching 9
percent.

Holland explained AI’s biggest impact is coming through robotics. Working
with a company called Bud Scout, his team is bringing an automated robot
into vertical racking systems to continually scan the canopy. Rather than
relying on two or three wall-mounted sensors, the robot reads environmental
and plant data at the level of individual square inches, and it does so for
the full life cycle of each plant. Over time, that dataset reveals patterns
that would be nearly impossible to keep straight in a human-managed logbook.

He also described a tissue-culture lab where robots, trained using a
combination of virtual-reality sessions and human demonstrations, are
learning to recognize explants, understand plant polarity, and perform
repetitive lab tasks around the clock. The next phase is even more science
fiction–adjacent: semi-mobile robots with two arms, designed to move
through flower rooms and eventually handle defoliation and trimming.
Why stability matters more than advanced sensors

Against that backdrop, Sandelman’s focus sounded almost deceptively simple.

In her view, the industry’s rush toward AI can succeed only if facilities
solve long-standing environmental issues first. It does not matter whether
the AI is optimizing irrigation, suggesting setpoints, or issuing commands
to a post-harvest system. If the room cannot hold the set conditions, the
algorithm is just shouting into the void.

“I think a lot of people think AI is a magic bullet, and it is certainly
not,” she said. “If you have an environmental situation — if your HVAC
isn’t up to snuff and your rooms are not consistent either in drying or
cultivation — you’re going to have problems. The AI can tell your room to
be at a certain [vapor-pressure deficit], but if your room cannot achieve
that consistently, it’s not going to make things better. It could create
more problems.”

Sandelman emphasized that post-harvest is where product value can evaporate
if rooms drift in and out of target conditions. In a market where
oversupply is common and inventory often sits longer than intended, keeping
flower in a stable environment is less a luxury and more a survival
strategy.

Environmental discipline, she argued, is also the bridge between
cultivation and branding. As regulations evolve toward eventual federal
oversight, she expects consumer expectations to shift from fixation on THC
percentage to a demand for consistent performance and experience from SKUs.
AI and machine learning can help identify the inputs that drive
repeatability, but only if the underlying systems do what they are told.
What AI can reveal that growers can’t see

Across the panel, there was broad agreement that AI’s real strength is not
in replacing growers, but in seeing patterns they cannot.

Holland shared how AI-driven analytics helped him uncover the root causes
of issues like bud rot and powdery mildew. By analyzing temperature swings,
vapor-pressure deficit (VPD), and root-zone moisture, AI models highlighted
factors such as sudden drops in temperature that pushed rooms to the dew
point, leaving leaves and buds wet long enough to fuel botrytis. The same
tools pointed to overly aggressive VPD targets that were stressing plants,
closing stomata, and creating conditions where powdery mildew spores could
take hold.
Building cultivar-specific roadmaps with machine learning

Corey Waggoner, CEO of Higher Yields Consulting, stressed how AI changed
the way his team approached “genetic roadmapping.” By correlating plant
performance with the number of days in veg, defoliation intensity,
transplant timing, substrate choice, and lighting technology, his group is
building what he described as cultivar-specific “recipes” that can be
adapted across facilities. For multistate operators juggling single-tier
high-pressure sodium rooms, four-tier LED builds, soil, rockwool, and
everything in between, that level of insight has become essential to any
serious attempt at standardization.

Sandelman listened to these examples and consistently brought the crowd
back to the precondition for all of them: good data. Without
well-maintained rooms, calibrated sensors, and clear targets, she said, AI
cannot sort signal from noise.

“Go slow,” she cautioned. “The computer is not going to fix your world. You
need to teach the computer — you need to teach the robot — to fix your
world. You need to lead the machine, not let the machine lead your
business.”
Why AI won’t replace growers anytime soon

The panel did not sidestep the question most employees worry about when
they hear the letters “AI”: Will this take my job?

Sandelman’s answer was quick and unequivocal. She does not believe AI will
trigger mass layoffs in cultivation facilities. Instead, she sees the
technology shifting people away from repetitive tasks and into more
valuable, and more interesting, work.

She pointed to the dry room as an example. In traditional workflows, a
staff member might spend an entire shift manually burping jars. Automation
and better environmental control can take over that job, freeing the
employee to work in quality control, data collection, or higher-skill plant
work.

Holland offered a longer historical view, comparing today’s anxiety
circulating around AI to the fear that greeted early tractors. Blacksmiths
and field hands worried mechanization would erase their livelihoods.
Instead, agricultural output soared and new categories of work emerged
around machinery, logistics, and agronomy. In cannabis, he suggested, AI
likely will reduce the need for brute-force labor while creating demand for
data-literate head growers, systems integrators, and specialized
technicians.

That does not mean adoption will be painless. Capital expenditure remains a
hurdle in a tight funding environment, and several panelists acknowledged
that operators are understandably skeptical after years of vendors
overpromising and underdelivering. There is also cultural resistance: One
technologist described winning grants that would have placed dozens of
robots in facilities at no cost, only to have operators ask not how much
data they would receive, but how much money they would be paid to
participate.
A practical roadmap for adopting AI in cultivation

By the end of the session, a loose roadmap for AI in cultivation had
emerged.

Panelists urged operators not to “go all in” overnight. Instead, they
suggested piloting a single solution in a specific niche, watching how it
performs, and then iterating. That might mean trying a canopy-scanning
system in one flower room, using AI-driven analytics to refine standard
operating procedures in a single facility, or adding smarter controls to a
post-harvest space where environmental drift has been a recurring headache.

Moderator David Johnson, chief commercial officer at Chorus, closed by
noting one practical entry point is to bring in an expert — whether a
consultant focused on AI in cultivation or a vendor solving a well-defined
problem — and test one system at a time. That incremental approach echoed
Sandelman’s philosophy: Fix the fundamentals, define what success looks
like, then layer in automation and intelligence where they actually can
deliver.

For now, AI in cannabis cultivation is in its early days. The technology on
display at MJBizCon suggested a future of highly instrumented rooms,
robotic tissue-culture labs, and genetic roadmaps tailored to each
facility. But if the panel made anything clear, it was that the operations
most likely to benefit are not the ones that chase every shiny new tool.

Successful operations will do what Sandelman kept emphasizing: Get the
house in order first. Then let machines help keep it that way.
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The Truth About AI in the Grow Room

1. Can AI improve cannabis cultivation if a grow room isn’t stable?

No. Panelists at MJBizCon emphasized that AI depends on consistent
environmental control. If HVAC, humidity, or setpoints drift, the data
becomes unreliable and automation can’t correct underlying issues. AI only
works when the room can hold the conditions it’s being asked to maintain.
2. What cultivation challenges is AI actually good at addressing?

AI excels at pattern recognition. It can analyze thousands of data
points to identify the causes of issues like bud rot, powdery mildew, and
inconsistent yields. It also improves SOPs by revealing how plants respond
to lighting, irrigation, and environmental adjustments across their full
life cycle.
3. Do AI and robotics replace growers in cannabis facilities?

Not according to the MJBizCon panelists. They agreed AI won’t eliminate
jobs; instead, it shifts labor away from repetitive tasks and into
higher-skill roles such as data analysis, environmental oversight, and
quality control. Automation handles burping, monitoring, or scanning, while
people manage strategy and decision-making.
4. How should cultivators start using AI in their grow rooms?

Experts recommend starting small: pilot a single tool in one room, test
its impact, refine SOPs, and expand only after environmental fundamentals
are stable. A crawl-walk-run approach avoids costly mistakes and ensures AI
delivers measurable improvements.
5. Why is environmental control so important before adding AI?

Because inconsistent rooms produce “garbage in, garbage out” data. If
temperature, VPD, and airflow fluctuate, AI can’t accurately interpret
plant responses or optimize conditions. Stable environments are the
foundation for reliable analytics and meaningful automation.
6. What types of AI tools are cultivators adopting today?

Examples discussed at MJBizCon included canopy-scanning robots,
leaf-level sensors that collect dozens of physiological parameters,
AI-guided tissue-culture robotics, and systems that analyze environmental
and genetic performance to build cultivar-specific “recipes.”
7. How is AI helping with genetic selection in cannabis?

Consultants described using machine-learning models to correlate plant
performance with variables like veg time, defoliation, light spectrum,
substrate, and room design. The output is a data-driven roadmap that helps
operators standardize recipes across different facilities.