Randy Wood Is Turning a 70-Year-Old Sprinkler Company Into a Subscription Business — Because a Broken Gearbox in July Is a Farmer Who Doesn't Call in November

THE CRAFT

Randy Wood took over as CEO of Lindsay Corporation in 2020. Lindsay has been making center-pivot irrigation systems — the giant rolling sprinkler arms you see stitched across Nebraska and Iowa wheat fields — since 1955. Seventy years of selling steel and gearboxes to farmers.

Think about what that business looks like on the ground. A farm manager buys a pivot. It sits in a field. It waters crops. If it breaks, a service tech drives three hours to fix it. If it breaks in July, while corn is silking and the wind is 40 mph and the farmer is already behind — that farm loses the crop. And that farmer does not buy his next pivot from Lindsay.

Wood is four years into a project most people outside the agricultural irrigation business haven’t noticed. He is turning Lindsay from a hardware manufacturer into what he now calls, without flinching, a “Tech-as-a-Service” company. Subscription revenue. Predictive AI. An installed base of steel that earns its keep in software.

Everyone in the AI newsletter economy is writing about Fortune 500 companies announcing AI committees. Meanwhile, a $620M company in Omaha quietly rebuilt its business model around an AI product that has exactly one job: prevent the service call that would have cost them the customer.

Which brings me to Randy Wood and a piece of equipment called the Smart Pivot.

THE OPERATOR

The situation

Lindsay Corporation sits at the intersection of three businesses most AI pundits will never write about: agricultural irrigation, road infrastructure, and industrial IoT. The company does about $620M in annual revenue — Q1 FY2026 came in at $155.8M and Q2 at $157.7M, which is an annualized pace of roughly that. It trades on the NYSE as LNN. The biggest business is Zimmatic, its center-pivot irrigation brand; Zimmatic machines water millions of acres globally.

Wood is not the founder. Lindsay was founded in 1955 by Art Zimmerer; Tim Hassinger ran it from 2017 to 2020; Wood took over in 2021 after running the irrigation segment for years. He is not Silicon Valley. He is not Harvard Business School. He runs the earnings calls in a Nebraska accent and talks about growers the way a person talks about people he actually knows.

When Wood took the job, Lindsay’s problem was simple and old. The company made excellent pivots. It sold them once. The warranty period ended, and the only recurring revenue was parts and service calls — which the farmer hates paying for, because every service call is a day the pivot isn’t moving water. Meanwhile, Valmont Industries was eating market share, Chinese manufacturers were racing down the price curve, and the generational knowledge inside Lindsay’s engineering team — the guys who knew why a specific gearbox fails in specific soil types — was walking toward retirement. Every new pivot Lindsay sold competed, in the farmer’s mind, with a used Valmont one county over.

That is the scene at 10pm on a Tuesday in Omaha four years ago. A great product in a commoditizing market with zero subscription revenue and institutional knowledge aging out of the building.

The move

Wood made four moves, in a specific order. The order matters more than any single piece of the stack.

First: he named the strategy out loud. Wood told the board and the market the company was pivoting from a hardware manufacturer to a “Tech-as-a-Service” provider, and called the umbrella strategy “Value Transformation.” This is unusual. Most CEOs of legacy manufacturers talk about “digital transformation” and mean, in practice, that they bought a CRM. Wood named subscription revenue as the goal, explicitly, on the record, and started publicly tying recurring software revenue to the company’s long-term valuation thesis. That framing locked the rest of the organization into a direction. You cannot walk that back.

Second: he built the AI product around the single moment that loses customers. Lindsay had two pre-existing technology assets when Wood took over: FieldNET (remote pivot monitoring and control, launched 2007) and FieldNET Advisor (irrigation scheduling recommendations that tell a farmer when to water). Wood combined them with a partnership with Taranis — an Israeli aerial imagery company that uses planes and drones to take leaf-level crop photographs — and Microsoft Azure’s machine learning stack, and shipped a product called Smart Pivot.

Smart Pivot does two things. It uses on-machine sensors — monitoring tire pressure, gearbox temperature, motor vibration — combined with predictive analytics to detect pivot failures before they happen, and notify a dealer service tech to schedule a repair in the window where the farmer isn’t watering. And it uses Taranis leaf-level imagery plus on-board sensors to detect crop health issues — nutrient deficiency, pest pressure, disease — and adjust irrigation recommendations automatically.

Read that again. The AI is built around two moments: the moment a gearbox is about to fail, and the moment a leaf shows the first signal of blight. Both are moments where traditional irrigation equipment has no voice at all, and where the cost of silence is the customer relationship.

Third: he bought the sensor network. In April 2024 Lindsay announced it was acquiring a 49.9% minority stake in Pessl Instruments, an Austrian company that makes weather stations, soil moisture probes, insect monitoring traps, and field sensors under the METOS brand. The deal closed in January 2025. Pessl brings 1 million+ sensors deployed globally, 100,000 in-field data collection devices, and 50,000 active customers. The investment has an option to acquire the remainder of Pessl at a later date. Wood has said publicly that Lindsay is “collaborating with Pessl on the development of new AI-based products to add to our suite of Smart Pivot Solutions.”

This is a move I haven’t seen another mid-market manufacturer pull off cleanly. Lindsay did not try to build a sensor network from scratch, which would have been a $200M mistake and a five-year timeline. It bought 49.9% of one that already existed and fed its data stream into an AI layer Lindsay controls. The data advantage compounds: the more pivots run Smart Pivot, the better the AI gets at predicting failures on new pivots.

Fourth: he landed a flagship subscription deal and pointed at it publicly. In 2024 Lindsay announced an $80M irrigation project in the MENA region — Zimmatic pivots plus FieldNET technology, with about $70M of that revenue realized in fiscal 2026. Wood has used that deal in every subsequent earnings call as proof that large international buyers now see FieldNET and FieldNET Advisor as core to the purchase decision, not an accessory. That language matters. The technology is no longer being cross-sold alongside steel. The steel is being sold because of the technology.

The result

The financial numbers are not the point. Lindsay’s Q2 FY2026 revenue was down 16% year-over-year, primarily due to delayed capex in North America (farmers waiting on tariff and commodity clarity) and weaker Brazil. This is an equipment cycle, not an AI story. Revenue is going to move with the ag cycle for the next several years regardless of what happens in the software stack.

The number I’m watching is the one Lindsay hasn’t yet broken out publicly: the revenue share of FieldNET and FieldNET Advisor as recurring software subscriptions. Wood has talked about subscription strategy in public-facing interviews (including a LinkedIn-posted conversation earlier this year titled “Inside Lindsay’s Subscription Strategy with CEO Randy…”), and the Tech-as-a-Service framing is now standard language in analyst notes. But the company has not yet split out software ARR as a separate line item on the P&L. When they do, that line will either vindicate the strategy or bury it. I expect it to vindicate. The MENA deal is the tell — international buyers paying a premium specifically for the software layer means there is a real, portable software business inside a steel company.

What Wood has proven, in public and on the record, is this: a 70-year-old legacy equipment maker with a commoditizing core product can graft an AI-driven subscription layer on top of installed steel without replatforming the entire business, without spinning up a “digital” subsidiary, and without hiring a Chief AI Officer. He did it by naming the strategy, building AI around the customer-losing moment, buying the sensor network instead of building it, and pointing at every subscription win publicly until the market priced in the narrative.

The Craft of AI read

Here is what I think is going on, and why it matters for a small-cap or mid-cap operator reading this on a Sunday night.

The AI story people are telling right now — the one Fortune 500 companies are selling themselves on — is that AI remakes the business. AI replaces the work. AI creates a new category.

Wood’s story is the opposite. Wood is using AI to defend an existing business. He is using AI to close the single gap in the business model that was costing Lindsay customer relationships: the broken gearbox in July. Every other piece of the Smart Pivot stack — the crop-health imagery, the irrigation scheduling, the subscription revenue — follows from that single defensive move. The AI is not there to impress. The AI is there because steel pivots are a commoditizing business and the only durable advantage a pivot manufacturer has left is uptime.

This is the ground-up frame in its purest form. Wood did not start with a boardroom PowerPoint about AI strategy. He started with the specific operational moment — a pivot that fails during the growing season — that costs the business the most over ten years. Then he built the AI to live at that moment.

Now compare that to what the typical AI strategy deck looks like in a mid-market manufacturer’s boardroom. It opens with a McKinsey chart about AI’s macroeconomic impact. It names four or five “use cases” — customer service chatbot, predictive maintenance, inventory optimization, marketing personalization. It budgets $2M across all four. Eighteen months later the company has three abandoned pilots and an AWS bill.

The difference is not about tooling. Wood used the same Azure and Taranis and IoT hardware anybody else can buy. The difference is that Wood started from a specific pain point on a specific machine in a specific field at a specific moment in the calendar. The AI had a single job. It had to earn its keep against a single alternative — the cost of losing a farmer after one bad service call.

That is what “ground-up” means. Not more voices in the room. Not employee surveys. Ground-up means you start with the specific operational moment that, when it breaks, costs you a customer — and you build the AI around that moment, not around a strategy.

If Wood had started top-down, Smart Pivot would have been a platform play. Instead, it is a product that solves one problem. The platform is something the company earns by solving enough individual problems that the product can be sold as a subscription.

The thing I would have done differently: I would have pushed harder, faster, on splitting out software ARR as a separate P&L line. The story of the pivot is in that number, and the longer it stays hidden inside total revenue, the longer the market will keep pricing Lindsay like a Midwestern equipment maker instead of like a subscription business with an installed base. That is a cosmetic complaint against a real piece of work, but it matters. Investors price what they can see.

Things to consider

• Wood named subscription revenue as the goal in public, before the product existed. That forced the organization in a direction it couldn’t easily reverse, and gave the market a narrative to track. A $200M founder who is quietly funding AI projects without naming the strategy publicly is getting none of that lock-in benefit — and is also handing the narrative to every competitor who says it out loud first. If you are building AI capability right now without saying what business model it is meant to create, you are building in the dark.

• Lindsay built the AI around the single moment that loses customers, not around an AI vision statement. The Smart Pivot product exists because a broken gearbox in July is the most expensive failure mode in the business — not because Lindsay wanted to be an AI company. If your AI portfolio has four “use cases” in four different parts of the business, you are not building AI; you are hedging. Pick the one operational moment where failure costs you the customer, and build the AI there first.

• Wood bought the sensor network (Pessl) instead of building it. This was not a build-vs-buy decision on a component. It was a decision to buy an existing asset — 1 million sensors and 50,000 customers — that would have been a five-year, $200M mistake to attempt internally. For a $200M founder, the analog is: there is an established vendor in your space that already has the data you’d need to build AI from. Buy equity in them, or buy them outright, before trying to replicate their data stream. The data compounds faster than any internal team can catch up.

• The installed base is the moat, not the software. Lindsay did not win by having the best AI algorithm. It won by owning the pivots the AI runs on — 70 years of installed Zimmatic machines in fields around the world. Your equivalent installed base is probably your customer relationships, your ERP data, or the tribal knowledge in your long-tenured operators. The AI advantage comes from running AI over the asset you already have. Not from building a new asset to run AI over.

• The financial story will lag the operational story. Lindsay’s revenue is down year-over-year right now — equipment cycles will dominate the top line for years. That does not mean the AI strategy failed. It means the AI strategy is underneath an equipment cycle the company doesn’t control, and the signal you’re watching for (subscription ARR) won’t be visible in the headline number for a long time. The corollary for your own AI work: do not measure it against the headline P&L for the first two years. Measure it against the specific operational metric it was built to move — and be prepared to explain that metric to your board and your investors.

THE WORKBENCH

Do this on Tuesday

Pick one specific failure mode in your business that, when it happens, costs you the customer. Not a broad failure category — the specific moment. For a manufacturer, it might be a delivery miss on a three-year customer’s largest SKU. For a distributor, a stockout on a margin-critical item during a promotional window. For a services firm, a missed response-time SLA on an anchor client’s escalation. You know which one it is. You’ve watched this failure cost you a relationship at least once in the last two years.

Block sixty minutes on your calendar. No vendors, no slide decks. Take three sheets of paper. On sheet one, describe the failure scene in operational detail — who, what, when, in what sequence, with what data signals available in advance. On sheet two, list every signal that was present in the 72 hours before the failure but invisible to the person who could have acted on it. On sheet three, write the AI product that would have converted those signals into a single, specific action for a single, specific person — not a dashboard, not an alert, a directive. “Call this customer now.” “Ship from this warehouse instead.” “Pull this lot.”

What you will learn: you do not need an AI strategy. You need one AI product, built around the failure mode that loses you customers. Most of what passes for AI strategy at your scale is four half-baked attempts at an AI product, none of which earn their keep against the cost of the failure they were supposed to prevent.

What it costs you if you don’t do this: another year of AI projects that sound strategic in the boardroom and do nothing to protect the customer relationships that pay your bills.

The rigorous version

The Tuesday exercise gets you a clear-eyed view of one failure mode and one AI product. It is the shape of the work, not the work.

The rigorous version is The Ground-Up Workshop.

Every AI strategy you’ve been sold starts with vendors. This one starts with the small group of people in your business who already know where the operational truth lives. Two days in person. A target operating model that has AI built in from the ground up. A 90-day starting plan. The alignment step that gets everyone on the same page about what AI can actually do for your business — before the deeper work begins.

What you get: a two-day in-person intensive with a small handful of your SMEs (the operators and frontline leaders who already know where the failure modes live); a synthesis week that turns those sessions into a coded analysis of where institutional knowledge sits, where AI can compound the highest-cost operational moments, and where you are currently hedging across four use cases instead of committing to one; a target operating model document that shows which operational moments the AI is designed to own and which it deliberately doesn’t touch; and a 90-day starting plan with specific decisions, vendors, and milestones.

Price: $10,000. I run a small number of these each quarter. What you walk out with is the document Wood would have needed four years ago — the specific operational moment the AI is built around, and the reason the AI is going to earn its keep against every alternative.

THE QUESTION

If one of your products stopped working tomorrow in the hands of your most important customer, and a service tech couldn’t get there for three days, what would happen to that customer relationship — and what specific signals, available 72 hours in advance, could have prevented it? Answer that question in your head before you finish your coffee.

If you already know the answer, that is the AI product your company should be building. Not a chatbot. Not a dashboard. Not a “transformation.” An AI that lives at the moment where a failure would cost you the customer, and that converts the signals you already have into a single specific action for a single specific person.

If you want to talk through what the rigorous version of this looks like for your company — the SME-proxy intensive, the target operating model, the 90-day plan — hit reply, or send a note to grant@thecraftofai.com. I run a small number of these conversations each quarter, and I start each one by asking you the same question I just asked above. If you can answer it cleanly, we can probably do real work together. If you can’t, the workshop is designed to get you to the answer.

— Grant grant@thecraftofai.com