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 amenities. 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 figure out volume.”
At present, drones glide over those self same vegetation 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 considered one of company America’s very unlikely know-how tales.
The AI revolution finds an sudden chief
Enterprise AI has been led by predictable gamers. Software program corporations with cloud-native architectures. Monetary providers companies with huge knowledge lakes. Retailers with wealthy digital touchpoints. Client 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 experiences 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 20 years in semiconductor manufacturing at Intel and Tokyo Electron, he knew the best way to apply superior know-how to complicated operations.
“I sort of initially said, ‘Why would I do this? I’m running a tech company. It’s an industry 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 “pressure from a leverage standpoint.”
His spouse challenged him with a direct immediate. If you’re not studying or placing your self in uncomfortable conditions, it’s best 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 became apparent to me whether it was on the backend with data analytics, business process transformation, and obviously now with AI being front and center of the consumer experience, a lot of opportunities are there,” he explains.
The declaration that modified every thing
The pivot started at an all-hands assembly. “I just said, you know, guys, we’re a tech company. You just don’t know it yet,” Baxter remembers. “There’s so much opportunity here to drive this company to where it needs to go.”
The primary problem was organizational. SMG had developed into purposeful silos. IT, provide chain, and model groups ran impartial techniques with little coordination. Drawing on his expertise with complicated know-how organizations, Baxter restructured the patron enterprise into three enterprise models. Normal managers turned accountable not only for monetary outcomes but additionally for know-how implementation inside their domains.
“I came in and said, we’re going to create new business units,” he explains. “The buck stops with you and I’m holding you accountable not only for the business results, for the quality of the creative and marketing, but for the implementation of technology.”
To assist the brand new construction, SMG arrange facilities of excellence for digital capabilities, insights and analytics, and artistic 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 many years 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 costly part of the migration was the business reporting layer we have in SAP Business Warehouse,” Fleites explains. “You need to uncover business logic created in many cases over decades.”
SMG selected Databricks as its unified knowledge platform. The staff had Apache Spark experience. Databricks provided sturdy SAP integration and aligned with a choice for open-source applied sciences that decrease vendor lock-in.
The breakthrough got here by means 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 data for AI consumption. The trouble lowered information articles by 30 % whereas rising their utility.
“We used Gemini LLMs to actually categorize them into topics, find similar documents,” Fleites explains. A hybrid strategy that mixed fashionable AI with strategies like cosine similarity turned the inspiration for later functions.
Constructing AI techniques that really perceive fertilizer
Early trials with off-the-shelf AI uncovered an actual threat. Normal-purpose fashions confused merchandise designed for killing weeds with these for stopping them. That mistake can spoil a garden.
“Different products, if you use one in the wrong place, would actually have a very negative outcome,” Fleites notes. “But those are kind of synonyms in certain contexts to the LLM. So they were recommending the wrong products.”
The answer was a brand new structure. SMG created what Fleites calls a “hierarchy of agents.” 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 guide.
The system additionally adjustments the dialog. When customers ask for suggestions, the brokers begin with questions on location, objectives, and garden circumstances. They slender prospects step-by-step earlier than providing options. 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, client 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 only have the ability to move marketing and promotion dollars around, but we’ve even gotten to the point where if it’s going to be a big weekend in the Northeast, we’ll shift our field sales resources from other regions up there,” Baxter explains.
Client Providers modified as nicely. AI brokers now course of incoming emails by means of Salesforce, draft responses primarily based on the information base, and flag them for temporary 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.
“Typically, if you open a prediction to a business person and you don’t say why, they’ll say, ‘I don’t believe 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 probably the most refined 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 is the additional level of [internal] knowledge we can fit to them.”
Partnerships are central. SMG works with Google Vertex AI for foundational fashions, Sierra.ai for production-ready conversational brokers, and Kindwise for laptop imaginative and prescient. The ecosystem strategy lets a small inner staff recruited from Meta, Google, and AI startups ship outsized impression with out constructing every thing from scratch.
Expertise follows impression. Typical knowledge says conventional corporations can’t compete with Meta salaries or Google inventory. SMG provided one thing completely different. It provided the possibility to construct transformative AI functions with fast enterprise impression.
“When we have these interviews, what we propose to them is basically the ability to have real value with the latest knowledge in these spaces,” Fleites explains. “A lot of people feel motivated to come to us” as a result of a lot of massive tech AI work, regardless of the hype, “doesn’t really have an impact.”
Staff design mirrors that philosophy. “My direct reports are leaders and not only manage people, but are technically savvy,” Fleites notes. “We always are constantly switching hands between developing or maintaining a solution versus strategy versus managing people.” He nonetheless writes code weekly. The small staff of 15 to twenty AI and engineering professionals stays lean by contracting out implementation whereas protecting “the know-how and the direction and the architecture” 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 autos directly with sturdy security data.
“The technology was actually really great,” Baxter acknowledges. The autos couldn’t raise sufficient weight for SMG’s heavy merchandise. The corporate paused implementation.
“Not everything we’ve tried has gone smoothly,” Baxter admits. “But I think another important point is you have to focus on a few critical ones and you have to know when something 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 problem. 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” cellular app in 2026 that identifies vegetation, weeds, and garden issues from pictures and offers on the spot steerage. A beta already helps area 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 may 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 primarily based on the interior stack. The long run imaginative and prescient pairs predictive fashions with conversational brokers so the system can attain out when circumstances counsel a buyer could need assistance.
What conventional industries can be taught
ScottsMiracle-Gro's transformation presents a transparent playbook for enterprises. The benefit doesn't come from deploying probably the most refined fashions. As an alternative, it comes from combining AI with proprietary area information that rivals can't simply replicate.
By making basic managers chargeable for each enterprise outcomes and know-how implementation, SMG ensured AI wasn't simply an IT initiative however a enterprise crucial. The 150 years of horticultural experience solely turned invaluable 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 provide one thing tech giants typically can't: fast, measurable impression. When engineers see their climate forecasting fashions immediately affect quarterly outcomes or their agent structure forestall prospects from ruining their lawns, the work carries weight that one other incremental enchancment to an advert algorithm by no means will.
“We have a right to win,” Baxter says. “We have 150 years of this experience.” That have is now knowledge, and knowledge 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 largest breakthrough is likely to be cultivating knowledge.
