In a remarkable transformation that blends over a century of horticultural expertise with cutting-edge artificial intelligence (AI), ScottsMiracle-Gro (SMG) has emerged as an unexpected leader in the tech-driven landscape of enterprise operations. The company, known for its lawn and garden products, has successfully harnessed AI to achieve significant operational efficiencies, resulting in targeted savings of $150 million in its supply chain. This shift not only underscores the potential of AI in traditional industries but also highlights the importance of leveraging proprietary knowledge to drive innovation.
For decades, the process of measuring compost and wood chip piles at SMG was labor-intensive and rudimentary. Workers would traverse vast expanses of land armed with measuring sticks, estimating volumes through basic geometry. However, this outdated method has been replaced by advanced drone technology and sophisticated vision systems capable of calculating volumes in real time. This transition from manual measurement to automated precision exemplifies a broader narrative of technological evolution within the company.
Nate Baxter, the president of ScottsMiracle-Gro, is a semiconductor veteran who previously spent two decades at Intel and Tokyo Electron. His journey to SMG began when he was approached by CEO Jim Hagedorn in 2023, during a tumultuous period for the company marked by a failed $1.2 billion hydroponics investment. Initially hesitant, Baxter was encouraged by his wife to embrace new challenges and explore opportunities outside his comfort zone. He recognized parallels between semiconductor manufacturing and SMG’s operations, both of which demand precision, quality control, and the optimization of complex systems.
Baxter’s vision for SMG was clear: the company needed to pivot from its traditional operational model to one that embraced technology as a core component of its business strategy. During an all-hands meeting, he boldly declared, “We’re a tech company. You just don’t know it yet.” This declaration set the stage for a comprehensive restructuring of the organization, which had become mired in functional silos where IT, supply chain, and brand teams operated independently with minimal coordination.
To address these challenges, Baxter restructured the consumer business into three distinct units, each led by general managers who were held accountable not only for financial results but also for technology implementation within their domains. This shift ensured that technology was no longer viewed as a separate initiative but rather as an integral part of the business strategy. To support this new structure, SMG established centers of excellence focused on digital capabilities, insights and analytics, and creative functions, fostering a hybrid design that combined centralized expertise with distributed accountability.
One of the most significant hurdles in this transformation was the need to convert decades of legacy knowledge into machine-ready intelligence. Fausto Fleites, the Vice President of Data Intelligence, described this process as “archaeological work.” The team undertook the daunting task of excavating business logic embedded in legacy SAP systems and converting extensive filing cabinets of research into AI-ready datasets. This migration was not without its challenges; uncovering business logic created over decades proved to be a costly endeavor.
To facilitate this transformation, SMG selected Databricks as its unified data platform, capitalizing on the team’s expertise with Apache Spark. Databricks offered robust integration with SAP and aligned with the company’s preference for open-source technologies that minimize vendor lock-in. The breakthrough came through systematic knowledge management, where SMG developed an AI bot utilizing Google’s Gemini large language model (LLM) to catalog and clean internal repositories. This innovative system identified duplicates, grouped content by topic, and restructured information for AI consumption, ultimately reducing the number of knowledge articles by 30 percent while enhancing their utility.
However, the journey was not without its pitfalls. Early trials with off-the-shelf AI models revealed a critical risk: general-purpose models often confused products designed for killing weeds with those intended for preventing them. Such errors could have disastrous consequences for customers’ lawns. To mitigate this risk, SMG developed a new architecture known as a “hierarchy of agents.” This system features a supervisor agent that routes queries to specialized worker agents organized by brand, each drawing on deep product knowledge encoded from a comprehensive 400-page internal training manual.
The AI agents not only provide recommendations but also engage users in a dialogue, asking questions about location, goals, and lawn conditions to narrow down possibilities before offering suggestions. This approach integrates seamlessly with APIs for product availability and state-specific regulatory compliance, ensuring that customers receive accurate and relevant advice.
The impact of these innovations extends beyond product recommendations. Drones now play a crucial role in measuring inventory piles, while demand forecasting models analyze over 60 factors, including weather patterns, consumer sentiment, and macroeconomic indicators. These predictive models enable SMG to make agile decisions, such as reallocating marketing resources in response to changing weather conditions. For instance, when drought struck Texas, the models supported a strategic shift in promotional spending to regions experiencing favorable weather, ultimately driving positive quarterly results.
In addition to enhancing operational efficiency, SMG has transformed its customer service processes. AI agents now handle incoming emails through Salesforce, drafting responses based on the knowledge base and flagging them for brief human review. This automation has drastically reduced draft times from ten minutes to mere seconds while improving response quality. The company places a strong emphasis on explainable AI, utilizing SHAP (SHapley Additive exPlanations) to build dashboards that decompose each forecast and illustrate how various factors contribute to predictions. This transparency fosters trust among business stakeholders, allowing for more frequent resource allocation adjustments.
ScottsMiracle-Gro’s success challenges conventional assumptions about AI readiness in traditional industries. The company’s advantage does not stem from possessing the most sophisticated AI models but rather from effectively combining general-purpose AI with unique, structured domain knowledge that competitors cannot easily replicate. As Fleites aptly notes, “LLMs are going to be a commodity. The strategic differentiator is what is the additional level of [internal] knowledge we can fit to them.”
Partnerships have played a pivotal role in SMG’s transformation. The company collaborates with Google Vertex AI for foundational models, Sierra.ai for production-ready conversational agents, and Kindwise for computer vision. This ecosystem approach allows a small internal team, recruited from leading tech companies like Meta and Google, to deliver outsized impact without the need to build everything from scratch.
Talent acquisition has also shifted in favor of SMG. While traditional companies often struggle to compete with the compensation packages offered by Silicon Valley giants, SMG presents a compelling alternative: the opportunity to create transformative AI applications with immediate business impact. As Fleites explains, many engineers are motivated to join SMG because they see the tangible effects of their work, whether it’s influencing quarterly results through weather forecasting models or preventing customers from making costly mistakes with their lawn care.
The design of SMG’s AI team reflects this philosophy. Fleites emphasizes that his direct reports are not only leaders but also technically savvy individuals who can navigate the complexities of both strategy and implementation. The small team of 15 to 20 AI and engineering professionals remains lean by contracting out implementation while retaining the essential know-how, direction, and architecture in-house.
Despite the successes, not every pilot project has yielded positive results. For example, SMG tested semi-autonomous forklifts in a massive 1.3 million square foot distribution facility, where remote drivers in the Philippines controlled multiple vehicles simultaneously. While the technology demonstrated strong safety records, the forklifts ultimately lacked the capacity to lift the heavy loads associated with SMG’s products, leading the company to pause implementation. Baxter acknowledges that “not everything we’ve tried has gone smoothly,” emphasizing the importance of focusing on critical initiatives and knowing when to pivot.
This disciplined approach mirrors the principles of semiconductor manufacturing, where investments must demonstrate measurable returns within defined timeframes. Additionally, the regulatory complexity surrounding SMG’s products necessitates that AI systems navigate a patchwork of EPA rules and state restrictions accurately.
Looking ahead, SMG has ambitious plans for the future. The company aims to launch a “gardening sommelier” mobile app in 2026, which will leverage AI to identify plants, weeds, and lawn problems from user-submitted photos, providing instant guidance. A beta version of this app is already assisting field sales teams in answering complex product questions by querying the extensive knowledge base.
Furthermore, SMG is exploring the potential for agent-to-agent communication, enabling its specialized AI to interface with retail partners’ systems. For instance, if a customer asks a Walmart chatbot for lawn advice, it could trigger an SMG query that returns accurate, regulation-compliant recommendations. The company has also implemented AI-powered search functionality on its website, replacing traditional keyword systems with conversational engines based on its internal stack. This future vision pairs predictive models with conversational agents, allowing the system to proactively reach out to customers when conditions suggest they may need assistance.
ScottsMiracle-Gro’s transformation serves as a powerful case study for traditional industries seeking to embrace digital transformation. The key takeaway is clear: success does not hinge on deploying the most advanced AI models but rather on effectively combining AI with proprietary domain knowledge that competitors cannot easily replicate. By empowering general managers to take responsibility for both business results and technology implementation, SMG has ensured that AI is not merely an IT initiative but a fundamental business imperative.
As Baxter succinctly puts it, “We have a right to win. We have 150 years of this experience.” That experience has now been translated into data, positioning SMG to leverage its competitive edge in an increasingly data-driven marketplace. In a world where many companies chase the latest AI trends, ScottsMiracle-Gro has demonstrated that true innovation lies in cultivating knowledge and transforming it into actionable insights that drive growth and efficiency. For a company built on soil, its most significant breakthrough may very well be its ability to cultivate data.