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 peak, and did what firm President Nate Baxter now describes as “sixth-grade geometry to determine quantity.”
At the moment, drones glide over those self same crops with mechanical precision. Imaginative and prescient methods 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 know-how tales.
The AI revolution finds an sudden chief
Enterprise AI has been led by predictable gamers. Software program firms with cloud-native architectures. Monetary providers companies with huge information 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 experiences a 90 % enchancment in customer support response occasions. Its predictive fashions allow weekly reallocation of selling sources 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 how one can apply superior know-how to complicated operations.
“I form 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 recollects 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, 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 methods. 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 totally 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 middle of the buyer expertise, numerous alternatives are there,” he explains.
The declaration that modified every thing
The pivot started at an all-hands assembly. “I simply mentioned, you realize, guys, we’re a tech firm. You simply don’t understand it but,” Baxter recollects. “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 useful silos. IT, provide chain, and model groups ran impartial methods with little coordination. Drawing on his expertise with complicated know-how organizations, Baxter restructured the buyer enterprise into three enterprise items. Normal managers grew to become accountable not only for monetary outcomes but in addition for know-how 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 know-how.”
To help the brand new construction, SMG arrange facilities of excellence for digital capabilities, insights and analytics, and inventive capabilities. 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 crew excavated a long time of enterprise logic embedded in legacy SAP methods 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 expensive a part of the migration was the enterprise reporting layer now we have in SAP Enterprise Warehouse,” Fleites explains. “It’s essential uncover enterprise logic created in lots of circumstances over a long time.”
SMG selected Databricks as its unified information platform. The crew had Apache Spark experience. Databricks supplied sturdy SAP integration and aligned with a desire for open-source applied sciences that reduce 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 matter, and restructured data for AI consumption. The trouble diminished information articles by 30 % whereas rising their utility.
“We used Gemini LLMs to truly categorize them into matters, discover related paperwork,” Fleites explains. A hybrid strategy that mixed fashionable AI with methods like cosine similarity grew to become the inspiration for later functions.
Constructing AI methods 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 smash a garden.
“Completely different merchandise, for those who use one within the unsuitable place, would even have a really unfavourable end result,” Fleites notes. “However these are sort of synonyms in sure contexts to the LLM. In order that they had been recommending the unsuitable 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 slender 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, client 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 sources from different areas up there,” Baxter explains.
Shopper Companies modified as nicely. AI brokers now course of incoming emails by means of Salesforce, draft responses based mostly on the information base, and flag them for transient human evaluation. Draft occasions 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, for those who open a prediction to a enterprise individual and also you don’t say why, they’ll say, ‘I don’t consider you,’” Fleites explains. Transparency made it potential 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’s the extra stage 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 laptop imaginative and prescient. The ecosystem strategy lets a small inner crew recruited from Meta, Google, and AI startups ship outsized influence with out constructing every thing from scratch.
Expertise follows influence. Standard knowledge says conventional firms can’t compete with Meta salaries or Google inventory. SMG supplied one thing totally different. It supplied the prospect to construct transformative AI functions with instant enterprise influence.
“When now we have these interviews, what we suggest to them is mainly the flexibility to have actual worth with the newest information in these areas,” Fleites explains. “Lots of people really feel motivated to come back 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 experiences are leaders and never solely handle individuals, however are technically savvy,” Fleites notes. “We at all times are always switching palms between creating or sustaining an answer versus technique versus managing individuals.” He nonetheless writes code weekly. The small crew of 15 to twenty AI and engineering professionals stays lean by contracting out implementation whereas protecting “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 sturdy security information.
“The know-how was truly actually nice,” Baxter acknowledges. The automobiles couldn’t elevate sufficient weight for SMG’s heavy merchandise. The corporate paused implementation.
“Not every thing we’ve tried has gone easily,” Baxter admits. “However I believe one other vital level is you must give attention to just a few essential ones and you must 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 methods 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 crops, weeds, and garden issues from pictures and gives immediate steering. 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’ methods. 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 methods 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 could need assistance.
What conventional industries can study
ScottsMiracle-Gro’s transformation provides a transparent playbook for enterprises. The benefit would not come from deploying probably the most refined fashions. As a substitute, it comes from combining AI with proprietary area information that opponents cannot simply replicate.
By making common managers answerable 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 grew to become useful when it was digitized, structured, and made accessible to AI methods.
Legacy firms competing for AI engineers cannot match Silicon Valley compensation packages. However they will supply one thing tech giants usually cannot: instant, measurable influence. When engineers see their climate forecasting fashions immediately affect quarterly outcomes or their agent structure forestall clients from ruining their lawns, the work carries weight that one other incremental enchancment to an advert algorithm by no means will.
“We’ve a proper to win,” Baxter says. “We’ve 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 most recent AI mannequin. It turned information into an working system for development. For a corporation constructed on soil, its greatest breakthrough could be cultivating information.
