Right now, it's easy for a food or ag company to say it's 'doing AI.' It's much harder to point to where AI has actually changed how the commercial team works.
The pattern we see is consistent. Licenses for AI tools have been purchased. Someone on the team ran a few experiments. There was a demo for leadership. It made it into a board deck or an investor update. And then... not much changed.
The BD team still runs the same process. Content still gets produced the same way. Competitive intel still lives in someone's inbox or a shared drive nobody checks. The tools are sitting there, but the daily work looks identical to what it looked like eighteen months ago.
That's not a technology failure. It's an implementation failure. And it's fixable, but not by buying more tools.
What AI adoption actually looks like inside most commercial teams
The honest picture in most food and ag commercial teams right now is that AI is present but not operational.
Someone has a ChatGPT account. Maybe the company bought a Copilot license or a specialized tool for content or prospecting. A few people on the team use it occasionally for drafting emails or summarizing documents. It's helpful when they remember to use it, which isn't consistently.
At the organizational level, there might be an AI initiative or a set of pilots. These tend to focus on use cases that are technically interesting but disconnected from the daily rhythm of commercial work. Someone built a prompt library. Someone else tested a tool for market research. But none of it is woven into the workflows the team actually runs every day.
The result: AI shows up in strategy conversations and slide decks, but it doesn't show up in pipeline numbers, content velocity, BD output, or the speed at which the team learns from the market. The metrics that actually matter to a commercial team haven't moved.
Why AI experiments don't change behavior
AI adoption doesn't fail because the tools are bad. It fails because the implementation skips the hardest step: changing how people actually work.
The first failure mode is that nobody decided which workflows AI should own or augment. Tools were purchased broadly ('everyone gets access') without identifying the two or three specific workflows where AI would make the biggest difference. When everything is a use case, nothing gets done well.
The second is that AI experiments run in isolation from GTM strategy and revenue priorities. The AI pilot lives over here. The commercial team's actual workflow lives over there. They never connect. So the pilot produces a nice report but doesn't change how deals get done.
The third is that the organization's processes, incentives, and review rhythms stay the same. If the weekly pipeline review still asks for the same inputs produced the same way, the team has no reason to change how they work. AI gets layered on top of old processes instead of being used to redesign them.
The fourth is that success gets measured in AI activity rather than commercial output. Number of prompts run. Number of tools deployed. Number of team members 'trained.' None of those metrics matter. What matters is whether pipeline moved, whether content velocity increased, whether BD output improved, whether the team is making better decisions faster.
What an AI-upgraded commercial workflow actually looks like
A practical AI implementation for a commercial team doesn't start with technology. It starts with picking the right workflows and redesigning them.
Take BD outreach. In most food and ag companies, a BD person spends significant time researching prospects, drafting initial outreach, and personalizing messages. An AI-upgraded version of that workflow has the AI handling prospect research and first-draft outreach while the human focuses on personalization, relationship context, and follow-through. The work that requires judgment stays with the person. The work that's repetitive and time-consuming gets handled by AI. The BD person's output goes up measurably without working longer hours.
Take content production. A commercial team that needs case studies, competitive briefs, or market updates can use AI to generate first drafts from existing data, competitive intel, and prior content. The human's job shifts from writing from scratch to editing, positioning, and ensuring the voice is right. Content velocity increases because the bottleneck, the blank page, gets removed.
Take knowledge management. In most companies, institutional knowledge about deals, competitors, and market patterns lives in people's heads or scattered across email and Slack. AI can surface patterns across CRM data, call notes, and deal history so the team learns from its own experience systematically instead of anecdotally.
In each case, there's simple governance around when AI should be used, how outputs get reviewed, and how patterns get updated. And everything is tied to a commercial outcome: time saved, pipeline created, deals moved, decisions improved.
Where to start with AI implementation for commercial teams
You don't need an AI roadmap for the entire business to get started. That's actually one of the biggest traps. Companies try to boil the ocean with a comprehensive AI strategy and end up in analysis paralysis.
What works is picking one or two commercial workflows where the team spends significant time on repeatable, structured tasks. BD research and outreach is usually the strongest starting point because the workflow is consistent, the time savings are measurable, and the team sees the benefit quickly.
Define the outcome you're trying to hit. Not 'adopt AI' but something concrete: cut prospect research time by 50%, increase outbound volume by 3x, produce competitive briefs in hours instead of days.
Redesign that specific workflow with AI built into it. Not AI as an optional add-on that people might use if they remember. AI as a required step in how the work gets done.
Then embed it. Work with the team until the new workflow becomes the default, not a side experiment. This usually takes 4-6 weeks of active reinforcement before the habit sticks.
Once one workflow is operating and the impact is visible, expand to the next. That's how AI moves from 'interesting' to 'obviously working.'
How 9 North implements AI for food and ag commercial teams
9 North's AI practice treats AI as an upgrade layer to your commercialization system, not as a standalone technology project. We're not building models or running IT implementations. We're redesigning how commercial teams work.
The work starts with identifying the commercial workflows where AI can materially change output or quality. We focus specifically on BD, content, competitive intelligence, and knowledge management because those are where commercial teams in food and ag spend the most time on structured, repeatable work.
For each selected workflow, we design and test AI-augmented patterns. These aren't theoretical. We build the actual prompts, templates, and review processes, then test them with the team under real conditions against real deals and real content needs.
Then we embed them. We work alongside the team until the new patterns become the default way work gets done. We set simple review cycles so the patterns keep improving and don't decay.
All of this ties back to revenue, productivity, or strategic traction. We don't measure success in AI adoption rates or tools deployed. We measure it in pipeline movement, content output, time saved, and commercial decisions improved.
Questions we hear
Q: How should a commercial team in agriculture start using AI?
Start with one or two workflows where AI can measurably improve output, such as BD research, content drafting, or competitive intelligence. Redesign those specific workflows with AI built in, train the team, and measure against commercial outcomes before expanding.
Q: Why do AI pilots fail to scale in food and ag companies?
Most AI pilots stall because they're treated as experiments disconnected from daily workflows. Without redesigning the actual work process and embedding new habits, the tools sit unused after the pilot ends.
Q: What commercial workflows benefit most from AI in agriculture?
BD outreach and prospect research, content production, competitive intelligence, knowledge management, and deal analysis tend to show the fastest measurable impact for commercial teams in food and ag.
