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, employees walked acres of towering compost and wooden chip piles with nothing greater than measuring sticks. They wrapped rulers round every mound, estimated top, and did what firm President Nate Baxter now describes as “sixth-grade geometry to determine quantity.”
In the present day, drones glide over those self same vegetation with mechanical precision. Imaginative and prescient programs calculate volumes in actual time. The transfer from measuring sticks to synthetic intelligence alerts greater than effectivity. It’s the seen proof of one in every of company America’s impossible expertise tales.
The AI revolution finds an sudden chief
Enterprise AI has been led by predictable gamers. Software program firms with cloud-native architectures. Monetary companies corporations with huge knowledge lakes. Retailers with wealthy digital touchpoints. Shopper packaged items firms 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 studies a 90 p.c 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 tips on how to apply superior expertise to advanced operations.
“I kind of initially mentioned, ‘Why would I do that? I’m working a tech firm. It’s an trade 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 dealing with what he describes as “stress from a leverage standpoint.”
His spouse challenged him with a direct immediate. In case you are not studying or placing your self in uncomfortable conditions, it is 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 advanced programs. He additionally noticed untapped potential in SMG’s area data. 100 fifty years of horticultural experience, regulatory know-how, and buyer perception had by no means been totally digitized.
“It grew to become obvious to me whether or not it was on the backend with knowledge analytics, enterprise course of transformation, and clearly now with AI being entrance and middle of the patron expertise, numerous alternatives are there,” he explains.
The declaration that modified every thing
The pivot started at an all-hands assembly. “I simply mentioned, , 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 advanced into purposeful silos. IT, provide chain, and model groups ran impartial programs with little coordination. Drawing on his expertise with advanced expertise organizations, Baxter restructured the patron enterprise into three enterprise items. Basic 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 items,” 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 artistic capabilities. The hybrid design positioned centralized experience behind distributed accountability.
Mining company reminiscence for AI gold
Turning legacy data into machine-ready intelligence required what Fausto Fleites, VP of Information Intelligence, calls “archaeological work.” The crew excavated a long time of enterprise logic embedded in legacy SAP programs 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 have now in SAP Enterprise Warehouse,” Fleites explains. “You must uncover enterprise logic created in lots of instances over a long time.”
SMG selected Databricks as its unified knowledge platform. The crew had Apache Spark experience. Databricks provided sturdy SAP integration and aligned with a desire for open-source applied sciences that decrease vendor lock-in.
The breakthrough got here by way of systematic data administration. SMG constructed an AI bot utilizing Google’s Gemini massive language mannequin to catalog and clear inner repositories. The system recognized duplicates, grouped content material by matter, and restructured data for AI consumption. The hassle diminished data articles by 30 p.c whereas rising their utility.
“We used Gemini LLMs to really categorize them into subjects, discover comparable paperwork,” Fleites explains. A hybrid strategy that mixed trendy AI with strategies like cosine similarity grew to become the muse for later purposes.
Constructing AI programs that really perceive fertilizer
Early trials with off-the-shelf AI uncovered an actual danger. Basic-purpose fashions confused merchandise designed for killing weeds with these for stopping them. That mistake can break a garden.
“Completely different merchandise, if you happen to use one within the improper place, would even have a really adverse final result,” Fleites notes. “However these are type of synonyms in sure contexts to the LLM. So that they had been recommending the improper 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 data encoded from a 400-page inner coaching handbook.
The system additionally modifications the dialog. When customers ask for suggestions, the brokers begin with questions on location, objectives, and garden circumstances. They slim potentialities step-by-step earlier than providing strategies. 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 elements, together with climate patterns, shopper sentiment, and macroeconomic indicators.
These predictions allow quicker strikes. When drought struck Texas, the fashions supported a shift in promotional spending to areas with favorable climate. The reallocation helped drive optimistic quarterly outcomes.
“We not solely have the flexibility 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 area gross sales assets from different areas up there,” Baxter explains.
Shopper Companies modified as properly. AI brokers now course of incoming emails by way of Salesforce, draft responses primarily based on the data 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.
“Sometimes, if you happen to 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 doable 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 refined fashions. It comes from combining general-purpose AI with distinctive, structured area data.
“LLMs are going to be a commodity,” Fleites observes. “The strategic differentiator is what’s the further stage of [internal] data we are able to 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 laptop imaginative and prescient. The ecosystem strategy lets a small inner crew recruited from Meta, Google, and AI startups ship outsized affect with out constructing every thing from scratch.
Expertise follows affect. Standard knowledge says conventional firms can not compete with Meta salaries or Google inventory. SMG provided one thing completely different. It provided the prospect to construct transformative AI purposes with instant enterprise affect.
“When we have now these interviews, what we suggest to them is mainly the flexibility to have actual worth with the newest data 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.”
Staff design mirrors that philosophy. “My direct studies are leaders and never solely handle folks, however are technically savvy,” Fleites notes. “We all the time are always switching palms between creating or sustaining an answer versus technique versus managing folks.” He nonetheless writes code weekly. The small crew of 15 to twenty AI and engineering professionals stays lean by contracting out implementation whereas preserving “the know-how and the path 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 directly with sturdy 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 every thing we’ve tried has gone easily,” Baxter admits. “However I feel one other vital level is it’s a must to deal with a number of crucial ones and it’s a must 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 problem. Merchandise should adjust to EPA guidelines and a patchwork of state restrictions, which AI programs should navigate accurately.
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 photographs and offers on the spot steerage. A beta already helps area gross sales groups reply advanced product questions by querying the 400-page data base.
The corporate is exploring agent-to-agent communication so its specialised AI can interface with retail companions’ programs. 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 programs 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 recommend a buyer might need assistance.
What conventional industries can be taught
ScottsMiracle-Gro's transformation affords a transparent playbook for enterprises. The benefit doesn't come from deploying essentially the most refined fashions. As an alternative, it comes from combining AI with proprietary area data that opponents can't simply replicate.
By making normal managers answerable 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 useful when it was digitized, structured, and made accessible to AI programs.
Legacy firms competing for AI engineers can't match Silicon Valley compensation packages. However they’ll provide one thing tech giants typically can't: instant, measurable affect. When engineers see their climate forecasting fashions immediately 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 now have a proper to win,” Baxter says. “We now have 150 years of this expertise.” 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 data into an working system for progress. For an organization constructed on soil, its greatest breakthrough may be cultivating knowledge.
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