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The Question Everyone's Asking
I've heard this question frequently from Agtech companies over the last two weeks at World Agritech and the Center for Digital Agriculture GENAI Conference: "Where is everyone else on AI? Are we behind? Ahead? About average?"
Compared to last year's conferences, the industry has made real progress. The conversations have shifted from "What is GenAI?" and "Could this work in agriculture?" to more sophisticated questions: "Which use cases deliver ROI?" "How do we move from pilots to production?" "What organizational design is required?" “Which vendors/partners are proven?” “How do we build trust in the tools?”
That shift matters. It means we're past the hype phase and into the hard work of actual implementation.
But here's what the data shows: while understanding has progressed, companies are stalling at deployment. Most companies remain stuck in awareness and experimentation, unable to bridge the gap to production operations that deliver measurable business impact.
Where Are Most AgTech Companies on AI Today?
- 75–95% of agtech companies are still in awareness, education, or personal experimentation
- Only 5–7% have deployed AI into production workflows with measurable business impact
- The gap is not technology, it’s organizational execution.
The Real Constraint: Execution, Not Understanding
Across every data source, a consistent pattern emerges:
- Most companies understand AI’s potential
- Many have active experimentation and pilots
- Very few achieve production deployment with measurable impact
This is not a knowledge gap.
It’s an execution gap.
The difference between the 5% and the 95% isn’t better technology.
It’s:
- Ownership and accountability
- Workflow integration
- Resource commitment
- Change management
The 9 Phases of GenAI Adoption in Agtech Companies
Phase 1: Awareness (AI Is “Not for Us—Yet”)
What’s happening:
AI is acknowledged, but deprioritized. The business is focused on core operations, and AI is seen as something to revisit later.
What we see in the field:
- No budget, no ownership, no urgency
- Assumption that the path will become obvious over time
What this sounds like:
- “We’re focused on core operations. Maybe we’ll look at AI when things settle.”
The real constraint:
This isn’t a technology decision; it’s a prioritization decision. Companies underestimate how quickly capability gaps compound.
Phase 2: Education (Building a Common Language)
What’s happening:
Organizations invest in learning (courses, conferences, workshops) to understand what AI is and what it could do.
What we see in the field:
- Shared vocabulary begins to form
- Teams start separating hype from real applications
- External experts drive most of the thinking
What this sounds like:
- “We’re investing in education and bringing in experts to help us figure out what AI means for us.”
The real constraint:
Education creates alignment but not action. No clear link yet between learning and business outcomes.
Phase 3: Personal Experimentation (The “Shadow AI” Phase)
What’s happening:
A small group of power users unlocks significant productivity gains, while most of the organization remains passive and some actively resist.
What we consistently see:
- High-value use cases emerging informally
- Knowledge trapped at the individual level
- Growing tension around risk, accuracy, and governance
What this sounds like:
- “I use AI every day, it saves hours. But when I showed it to my manager, it turned into a risk conversation. Now I just use it quietly.”
The real constraint:
Individual productivity is not translating into organizational capability.
Why companies stall here:
- No mechanism to capture and scale use cases
- No business ownership
- Cultural resistance outweighs demonstrated value
Phase 4: Strategy Formation (The “Board-Level Question” Phase)
What’s happening:
AI becomes a strategic topic often triggered by board or investor pressure.
What we see in the field:
- Cross-functional task forces form
- Early strategy decks and roadmaps emerge
- Discussions around ownership begin
What this sounds like:
- “We’re taking this seriously. We’ve set up a task force and are building a roadmap.”
The real constraint:
Strategy exists but without committed resources, ownership, or execution pathways.
Phase 5: Data Readiness (The “We Need Better Data” Trap)
What’s happening:
Focus shifts to data infrastructure investment as the perceived prerequisite for AI.
What we consistently see:
- Heavy debate around data quality and architecture
- IT-led initiatives around data lakes and restructuring
- Delays tied to “getting the data right first”
Where leading companies differ:
They shift from generic data debates to use-case-driven data needs including capturing tacit expert knowledge.
What this sounds like:
- “We thought we needed a perfect data warehouse. Turns out we needed our senior agronomist’s decision frameworks.”
The real constraint:
Confusing data perfection with value creation.
Phase 6: Pilots (The “Danger Zone”)
What’s happening:
Proof-of-concept use cases are running but most never scale.
What we consistently see:
- Working pilots with no path to production
- Symbolic progress without real commitment
- “Let’s refine the pilot” becomes a stall tactic
What this sounds like:
- “The technology works. We’re still figuring out how to scale it.”
Why companies fail here:
- IT-led instead of business-led
- No workflow integration
- No change management strategy
- No measurement of business impact
- Unclear ownership and resource commitment
The real constraint:
Pilots test technology. Scaling requires organizational redesign.
Phase 7: AI Operations (Engineering for Learning)
What’s happening:
AI systems are deployed with infrastructure to improve continuously.
What we see in leading companies:
- Model performance monitoring and guardrails
- Telemetry feeding product improvement
- Measurement systems tied to business outcomes
What this sounds like:
- “We’re learning from 30 users this quarter—100 next quarter. That’s driving continuous improvement.”
The real shift:
From static tools → learning systems that compound value over time
Phase 8: Workflow Redesign (Where Real Value Unlocks)
What’s happening:
Core workflows are restructured around AI capabilities.
What we see in leading companies:
- Decision rights shift between humans and AI
- Junior employees operate at higher levels
- Experts focus on edge cases and system improvement
What this sounds like:
- “Our advisors now serve more growers at higher quality. The system handles routine decisions, we focus on complex cases.”
The real constraint (for everyone else):
Most companies try to layer AI onto existing workflows instead of redesigning them.
Phase 9: Agentic Deployment & Scaled Advantage
What’s happening:
AI systems operate across workflows with targeted human intervention.
What we see in top performers:
- Multi-agent systems managing end-to-end processes
- New business models emerging
- Compounding advantage from continuous learning
What this sounds like:
- “We’re offering services that didn’t exist before. The system improves with every interaction.”
The outcome:
- Faster execution
- Higher quality
- Lower training costs
- Stronger customer retention
Where Are You? A Quick Diagnostic
If you’re honest:
- Still discussing AI strategy? → You’re in Phase 4
- Running pilots without measurable ROI? → You’re in Phase 6
- Seeing real impact inside workflows? → You’re in Phase 7+
If you’re not in Phase 7, you’re not competing yet.
Where the Market Actually Is (2026 Data Snapshot)
I synthesized data from five independent sources and used personal insight to estimate where non-farm agribusiness companies currently sit. This is distinct from farm use. For this analysis I am focusing on ag company use:
Most companies are not competing on AI yet. But they will be soon.
What We Can Know With Confidence
1. AI Pilots Are Where Most Companies Fail
Multiple independent sources (MIT, Purdue, Gartner) converge on the same number:
only 5–7% of companies reach production with measurable impact.
This isn’t sampling error—it’s a consistent, cross-industry pattern.
What this means:
The divide isn’t between companies who understand AI and those who don’t.
It’s between companies that execute organizational transformation (5–7%)
and those that treat AI as a technology experiment (95%).
2. Agriculture Is Not Behind—It’s Stalled in the Same Place
Purdue’s ag-specific data (5.5% integrated) aligns closely with MIT’s cross-industry findings (~5% with P&L impact).
Agriculture is not lagging in production adoption.
But it is lagging in progression.
Fewer ag companies appear to reach the pilot stage (8–15% vs. ~20% cross-industry), suggesting many are stuck in early experimentation without a path forward.
What this means:
The issue isn’t industry capability—it’s lack of structured progression from experimentation to deployment.
3. The “Shadow AI” Economy Is Already Here
MIT found over 90% of employees use AI tools, while only ~5% of companies have production systems.
Purdue shows a similar gap:
80% recognize the opportunity. 5.5% have integrated it.
What this means:
AI is already creating value inside your company.
You’re just not capturing it.
Individual productivity (Phase 3) does not become enterprise value (Phase 7+) without:
- Workflow integration
- Business ownership
- Adoption systems
4. Build vs. Buy Is a Growth Decision—Not a Technical One
MIT data shows:
- 67% success with specialized vendors
- 33% success with internal builds
This is not a marginal difference, it’s a doubling of success probability.
What this means:
The build vs. buy decision is one of the highest-leverage choices a company makes.
For most organizations, especially without prior AI deployment experience,
execution speed and proven use cases matter more than internal control.
5. Rapid Transformation Is Possible, When Organizations Commit
Healthcare moved from 3% to 22% adoption in 24 months, with 73% reporting positive ROI.
Agriculture shares similar characteristics:
- Biological complexity
- Field-based validation
- Heavy reliance on expert knowledge
What this means:
The limiting factor isn’t technology.
It’s urgency, ownership, and organizational design.
Where We Still Need Clarity
While the macro patterns are clear, execution-level insight is still missing in key areas:
- Phase 4–6 Precision
Where exactly are companies getting stuck—strategy, data, or pilots? - Use Case Distribution
Internal vs. external, R&D vs. commercial, admin vs. domain-specific - Knowledge Shelf-Life
How defensible is proprietary knowledge over time? - Vendor Landscape
Who is actually delivering results vs. repackaging generic AI? - Fast-Follower Triggers
What forces companies to move from waiting → deploying?
Across every dataset, the conclusion is the same: AI adoption is not a technology problem, it’s an execution problem.”
What This Means for Ag Companies
If you’re asking “Where are we?”—start here:
Look at what you’ve deployed, not what you’re planning.
The gap between:
- Phases 1–3 (75–95% of companies)
- Phases 7–9 (5–7%)
…is not technology.
It’s execution:
- Ownership
- Decision rights
- Workflow integration
- Resource commitment
- Change management
- Political alignment
The real question isn’t: “Where is the pack?”
It’s: Are you building toward the 5%—or drifting with the 95%?
Moving from Pilot to Production Is Where Most Companies Fail
If you’re in Phase 4–6, you’re not alone.
But this is where the path diverges.
Most companies:
- Stay stuck refining pilots
- Debate data strategy
- Delay decisions on ownership and workflow redesign
A small group does something different:
- Assigns clear business ownership
- Redesigns workflows around AI
- Measures impact from day one
- Commits resources to scaling—not experimenting
That’s what moves companies into the top 5%.
Where We Partner
At 9 North Group, we don’t focus on AI theory.
We work with ag and food tech companies on Practical AI Implementation:
- Start with commercial workflows and identify bottlenecks
- Build go-to-market and adoption strategies that drive usage
- Align teams around ownership, execution, and measurable outcomes
- Turn pilots into scalable revenue and operational advantage
We embed with your team to make AI actually work in your business.
Because execution—not strategy—is what separates leaders from the rest. If You’re Stuck in Phase 4–6, This Is the Moment.
You don’t need more ideas.
You need a path to production.
Let’s turn your AI efforts into real business impact.
Schedule a Growth Strategy Session. Email hello@9northgroup.com or just send me a message on LinkedIn.
References
1. MIT NANDA. (2025). "The GenAI Divide: State of AI in Business 2025." Massachusetts Institute of Technology. Available at: https://www.nanda.mit.edu
2. Purdue Agribusiness Review. Why most agribusiness AI strategies never get past pilots
3. PwC. (2026). "29th Global CEO Survey." PwC Global. Available at: https://www.pwc.com/ceosurvey
4. Gartner, Inc. (2024-2025). "AI Maturity Model Survey and Hype Cycle for Artificial Intelligence, 2025." Available at: https://www.gartner.com
5. Google Cloud. (2025). "AI in Healthcare and Life Sciences: 2025 Industry Report." Available at: https://cloud.google.com/healthcare
6. McKinsey & Company. (2024). "From bytes to bushels: How gen AI can shape the future of agriculture." Available at: https://www.mckinsey.com/industries/agriculture
7. Bayer AG. (2025). "How AI is Reshaping Agricultural Innovation." Bayer Global. Available at: https://www.bayer.com/en/agriculture/ai-for-agriculture
