How a semiconductor veteran turned over a century of horticultural knowledge into AI-led aggressive benefit
For many years, a ritual performed out throughout ScottsMiracle-Gro’s media services. Each few weeks, staff walked acres of towering compost and wooden chip piles with nothing greater than measuring sticks. They wrapped rulers round every mound, estimated peak, and did what firm President Nate Baxter now describes as “sixth-grade geometry to determine quantity.”
Right now, drones glide over those self same crops with mechanical precision. Imaginative and prescient techniques calculate volumes in actual time. The transfer from measuring sticks to synthetic intelligence indicators greater than effectivity. It’s the seen proof of one in every of company America’s very unlikely expertise tales.
The AI revolution finds an sudden chief
Enterprise AI has been led by predictable gamers. Software program corporations with cloud-native architectures. Monetary companies companies with huge information lakes. Retailers with wealthy digital touchpoints. Shopper packaged items corporations that deal with bodily merchandise like fertilizer and soil weren’t anticipated to guide.
But ScottsMiracle-Gro has realized greater than half of a focused $150 million in provide chain financial savings. It stories a 90 % enchancment in customer support response instances. Its predictive fashions allow weekly reallocation of selling assets throughout regional markets.
A Silicon Valley veteran bets on soil science
Baxter’s path to ScottsMiracle-Gro (SMG) reads like a calculated pivot, not a company rescue. After twenty years in semiconductor manufacturing at Intel and Tokyo Electron, he knew learn how to apply superior expertise to complicated operations.
“I type of initially mentioned, ‘Why would I do that? I’m working a tech firm. It’s an business I’ve been in for 25 years,’” Baxter remembers of his response when ScottsMiracle-Gro CEO Jim Hagedorn approached him in 2023. The corporate was reeling from a collapsed $1.2 billion hydroponics funding and going through what he describes as “stress from a leverage standpoint.”
His spouse challenged him with a direct immediate. If you’re not studying or placing your self in uncomfortable conditions, you need to change that.
Baxter noticed clear parallels between semiconductor manufacturing and SMG’s operations. Each require precision, high quality management, and the optimization of complicated techniques. He additionally noticed untapped potential in SMG’s area information. 100 fifty years of horticultural experience, regulatory know-how, and buyer perception had by no means been absolutely digitized.
“It grew to become obvious to me whether or not it was on the backend with information analytics, enterprise course of transformation, and clearly now with AI being entrance and heart of the buyer expertise, a whole lot of alternatives are there,” he explains.
The declaration that modified all the pieces
The pivot started at an all-hands assembly. “I simply mentioned, you understand, guys, we’re a tech firm. You simply don’t realize it but,” Baxter remembers. “There’s a lot alternative right here to drive this firm to the place it must go.”
The primary problem was organizational. SMG had developed into useful silos. IT, provide chain, and model groups ran unbiased techniques with little coordination. Drawing on his expertise with complicated expertise organizations, Baxter restructured the buyer enterprise into three enterprise models. Normal managers grew to become accountable not only for monetary outcomes but additionally for expertise implementation inside their domains.
“I got here in and mentioned, we’re going to create new enterprise models,” he explains. “The buck stops with you and I’m holding you accountable not just for the enterprise outcomes, for the standard of the artistic and advertising and marketing, however for the implementation of expertise.”
To assist the brand new construction, SMG arrange facilities of excellence for digital capabilities, insights and analytics, and inventive features. The hybrid design positioned centralized experience behind distributed accountability.
Mining company reminiscence for AI gold
Turning legacy information into machine-ready intelligence required what Fausto Fleites, VP of Information Intelligence, calls “archaeological work.” The staff excavated a long time of enterprise logic embedded in legacy SAP techniques and transformed submitting cupboards of analysis into AI-ready datasets. Fleites, a Cuban immigrant with a doctorate from FIU who led Florida’s public hurricane loss mannequin earlier than roles at Sears and Cemex, understood the stakes.
“The pricey a part of the migration was the enterprise reporting layer we now have in SAP Enterprise Warehouse,” Fleites explains. “You might want to uncover enterprise logic created in lots of circumstances over a long time.”
SMG selected Databricks as its unified information platform. The staff had Apache Spark experience. Databricks supplied robust SAP integration and aligned with a desire for open-source applied sciences that reduce vendor lock-in.
The breakthrough got here by way of systematic information administration. SMG constructed an AI bot utilizing Google’s Gemini giant language mannequin to catalog and clear inner repositories. The system recognized duplicates, grouped content material by subject, and restructured info for AI consumption. The hassle diminished information articles by 30 % whereas growing their utility.
“We used Gemini LLMs to truly categorize them into matters, discover comparable paperwork,” Fleites explains. A hybrid strategy that mixed fashionable AI with strategies like cosine similarity grew to become the muse for later purposes.
Constructing AI techniques that really perceive fertilizer
Early trials with off-the-shelf AI uncovered an actual danger. Normal-purpose fashions confused merchandise designed for killing weeds with these for stopping them. That mistake can spoil a garden.
“Totally different merchandise, for those who use one within the flawed place, would even have a really unfavorable end result,” Fleites notes. “However these are form of synonyms in sure contexts to the LLM. In order that they have been recommending the flawed merchandise.”
The answer was a brand new structure. SMG created what Fleites calls a “hierarchy of brokers.” A supervisor agent routes queries to specialised employee brokers organized by model. Every agent attracts on deep product information encoded from a 400-page inner coaching handbook.
The system additionally adjustments the dialog. When customers ask for suggestions, the brokers begin with questions on location, targets, and garden circumstances. They slim prospects step-by-step earlier than providing recommendations. The stack integrates with APIs for product availability and state-specific regulatory compliance.
From drones to demand forecasting throughout the enterprise
The transformation runs throughout the corporate. Drones measure stock piles. Demand forecasting fashions analyze greater than 60 components, together with climate patterns, shopper sentiment, and macroeconomic indicators.
These predictions allow sooner strikes. When drought struck Texas, the fashions supported a shift in promotional spending to areas with favorable climate. The reallocation helped drive constructive quarterly outcomes.
“We not solely have the power to maneuver advertising and marketing and promotion {dollars} round, however we’ve even gotten to the purpose the place if it’s going to be an enormous weekend within the Northeast, we’ll shift our discipline gross sales assets from different areas up there,” Baxter explains.
Shopper Providers modified as effectively. AI brokers now course of incoming emails by way of Salesforce, draft responses based mostly on the information base, and flag them for transient human overview. Draft instances dropped from ten minutes to seconds and response high quality improved.
The corporate emphasizes explainable AI. Utilizing SHAP, SMG constructed dashboards that decompose every forecast and present how climate, promotions, or media spending contribute to predictions.
“Usually, for those who open a prediction to a enterprise particular person and also you don’t say why, they’ll say, ‘I don’t consider you,’” Fleites explains. Transparency made it attainable to maneuver useful resource allocation from quarterly to weekly cycles.
Competing like a startup
SMG’s outcomes problem assumptions about AI readiness in conventional industries. The benefit doesn’t come from proudly owning essentially the most subtle fashions. It comes from combining general-purpose AI with distinctive, structured area information.
“LLMs are going to be a commodity,” Fleites observes. “The strategic differentiator is what’s the extra degree of [internal] information we will match to them.”
Partnerships are central. SMG works with Google Vertex AI for foundational fashions, Sierra.ai for production-ready conversational brokers, and Kindwise for pc imaginative and prescient. The ecosystem strategy lets a small inner staff recruited from Meta, Google, and AI startups ship outsized affect with out constructing all the pieces from scratch.
Expertise follows affect. Standard knowledge says conventional corporations can not compete with Meta salaries or Google inventory. SMG supplied one thing completely different. It supplied the possibility to construct transformative AI purposes with fast enterprise affect.
“When we now have these interviews, what we suggest to them is principally the power to have actual worth with the most recent information in these areas,” Fleites explains. “Lots of people really feel motivated to return to us” as a result of a lot of huge tech AI work, regardless of the hype, “doesn’t actually have an effect.”
Group design mirrors that philosophy. “My direct stories are leaders and never solely handle folks, however are technically savvy,” Fleites notes. “We at all times are continuously switching arms between creating or sustaining an answer versus technique versus managing folks.” He nonetheless writes code weekly. The small staff of 15 to twenty AI and engineering professionals stays lean by contracting out implementation whereas holding “the know-how and the route and the structure” in-house.
When innovation meets immovable objects
Not each pilot succeeded. SMG examined semi-autonomous forklifts in a 1.3 million sq. foot distribution facility. Distant drivers within the Philippines managed as much as 5 automobiles without delay with robust security information.
“The expertise was really actually nice,” Baxter acknowledges. The automobiles couldn’t carry sufficient weight for SMG’s heavy merchandise. The corporate paused implementation.
“Not all the pieces we’ve tried has gone easily,” Baxter admits. “However I believe one other essential level is you need to give attention to a number of important ones and you need to know when one thing isn’t going to work and readjust.”
The lesson tracks with semiconductor self-discipline. Investments should present measurable returns inside set timeframes. Regulatory complexity provides issue. Merchandise should adjust to EPA guidelines and a patchwork of state restrictions, which AI techniques should navigate appropriately.
The gardening sommelier and agent-to-agent futures
The roadmap displays a long-term view. SMG plans a “gardening sommelier” cell app in 2026 that identifies crops, weeds, and garden issues from pictures and offers prompt steering. A beta already helps discipline gross sales groups reply complicated product questions by querying the 400-page information base.
The corporate is exploring agent-to-agent communication so its specialised AI can interface with retail companions’ techniques. A buyer who asks a Walmart chatbot for garden recommendation might set off an SMG question that returns correct, regulation-compliant suggestions.
SMG has launched AI-powered search on its web site, changing key phrase techniques with conversational engines based mostly on the inner stack. The long run imaginative and prescient pairs predictive fashions with conversational brokers so the system can attain out when circumstances recommend a buyer might need assistance.
What conventional industries can study
ScottsMiracle-Gro's transformation presents a transparent playbook for enterprises. The benefit doesn't come from deploying essentially the most subtle fashions. As a substitute, it comes from combining AI with proprietary area information that rivals can't simply replicate.
By making basic managers liable for each enterprise outcomes and expertise implementation, SMG ensured AI wasn't simply an IT initiative however a enterprise crucial. The 150 years of horticultural experience solely grew to become helpful when it was digitized, structured, and made accessible to AI techniques.
Legacy corporations competing for AI engineers can't match Silicon Valley compensation packages. However they will supply one thing tech giants typically can't: fast, measurable affect. When engineers see their climate forecasting fashions straight affect quarterly outcomes or their agent structure stop prospects from ruining their lawns, the work carries weight that one other incremental enchancment to an advert algorithm by no means will.
“We have now a proper to win,” Baxter says. “We have now 150 years of this expertise.” That have is now information, and information is the corporate’s aggressive edge. ScottsMiracle-Gro didn’t outspend its rivals or chase the latest AI mannequin. It turned information into an working system for progress. For a corporation constructed on soil, its greatest breakthrough is likely to be cultivating information.
