Grainger Innovation & Technology Culture

How do your teams stay ahead of emerging technologies or frameworks? 

At Grainger, we foster a culture where learning and exploration are built into our workdays and continuous improvement is encouraged. Forums across our Grainger Technology Group function provide spaces for learning and growth. For example, the ML organization hosts monthly demo hours and academic lab sessions where team members come together to share new ideas, review the latest literature and discuss upcoming projects. Additionally, our broader communities of practice empower engineers to experiment with emerging frameworks and build targeted skill sets across the organization. 

Further, we ‘compete with urgency.’ My team, the ML platform and operations team, makes intentional updates to our technology roadmap quarterly and continuously evaluates our stack to ensure we’re providing best-in-class tools to our users. It’s a balance of keeping our eyes on new emerging technology while staying very focused on our day-to-day work. While we love getting our hands on new tech, we ground ourselves by talking with our users about real needs and running early proofs of concept before committing them to the platform. By combining structured learning with hands-on exploration, we ensure our talent and our roadmaps stay in step with the next wave of AI innovation. 

Can you share a recent example of an innovative project or tech adoption?  

Driving innovation is core to our operating principles. During Grainger’s annual hackathon — a week-long sprint that encourages teams to move quickly and experiment — our ML platform & operations team built TicketSmith, an agentic support bot designed for internal user support channels. Leveraging cutting-edge frameworks like LangGraph, TicketSmith connects with GitHub, analyzes user logs, and uses large language models to provide instant, actionable support directly in chat. Since our team receives dozens of support tickets each week, a thoughtful and performant use of AI has the potential to drive meaningful time savings for both our team and our users. We also see clear potential for teams across Grainger who manage similar support workflows to benefit from a tool like this. What began as a proof of concept is now being woven into our roadmap for broader deployment. In each of the three years our team has participated in the hackathon, we’ve built PoCs that later matured into platform tools leveraged by users, demonstrating that the hackathon enables teams not only to experiment, but to deliver real, lasting impact. 

How does your culture support experimentation and learning?  

Grainger is a place where technologists thrive. Our culture is the engine behind our innovation — and it’s built on the Grainger Edge Principles including embracing curiosity and competing with urgency. These principles shape every aspect of how our teams work, learn and grow. Our leadership actively urges team members to pursue new knowledge and upskill, whether through formal education, online courses or hands-on workshops. For example, on my team we encourage dedicating 10 percent of working hours for personal development like diving into new AI/ML frameworks and building proofs of concept. Our larger GTG function also hosts shared learning forums my team participates in, such as focused communities of practice and monthly sessions where we review emerging academic research. These spaces help us maintain a collective pulse on both industry and academic trends. The annual GTG Technology Conference and Hackathon provide more opportunities to connect, learn and grow. Our leadership supports career advancement through mentorship, access to conferences and offers a tuition reimbursement program for continuing education. 

What educational opportunities have you leveraged to deepen your understanding of AI?

Grainger has a strong commitment to team member growth and development. Internally, Grainger has provided me the time and space for a variety of learning opportunities including conferences focused on the Grainger Technology Group with TEDx-style learning conversations, collaborative lunch-and-learn sessions spanning various tech topics and open forums — inclusive of leadership — to discuss emerging technology. 

I’ve also been given the opportunity to spend time taking LinkedIn Learning courses to stay up to date on new technology capabilities, and Coursera courses, which have allowed me to hear from and interact with industry leaders. Technology is always changing, and I feel equipped with the right tools, resources and support to be a continuous learner and explore new technologies when they’re announced.

How has Grainger encouraged you to apply AI at work? What impact has that had on your productivity or the business itself?

Grainger’s purpose, “We Keep The World Working,” is apparent in the way we design, deliver and operate digital experiences, tools and information that solve customers’ needs. My job requires me to leverage new and emerging technologies to implement solutions that make the lives of our team members’ and customers’ easier. Grainger has encouraged me to find these solutions through hosting open forums, encouraging cross-collaboration and employee advocacy for new solutions, and providing the latest and greatest AI models to help our teams achieve their goals. 

We are building a digital edge by using technology like machine learning, IoT, cloud automation, data-driven customer insights, continuous delivery and more to help our customers rapidly find and receive the products they need.

Generative AI is the latest frontier we are using to make our processes smarter. Our leadership is making AI a priority to help us achieve three main goals: to create solutions to better serve our customers, to evolve our digital landscape by team members learning new technologies and to develop efficient processes while documenting data.

How has a stronger understanding of AI helped you grow as a tech professional? Please provide specific examples.

Using cutting edge technology to solve problems for people who are not enmeshed in the technology space is what excites me about my work. 

AI is only the latest frontier of technology, and the technology landscape is in a continuous state of change and evolvement. Understanding AI has allowed my teams and I to institute what’s considered best-in-class technology solutions available today. Simplifying a basic users’ understanding of our technology enhances the user experience. 

What practices does your team employ to foster innovation?

One of Grainger’s company principles is to embrace curiosity. This value is encouraged at the companywide level and put in practice by teams across the organization. On the machine learning side, we’re encouraged at a company level to attend conferences to expand our knowledge base, share insights when applicable and network with others in the industry.

At a team level, we host all-hands meetings, journal clubs to discuss research papers about emerging technologies and biweekly demos both on applicable solutions and new technology functionality.

Finally, within my team, as an effort to promote personal development while also aligning to team goals, we build in time during our working hours to learn. Personal development learning could include diving deeper into research papers, exploring new solutions, learning about new technology, etc.

With such a cross-collaborative team that takes the time to learn with and from one another, I feel we are better able to gain new perspectives that help us find the best solutions to meet our customer needs. People — customers or team members — are at the center of our work, and brainstorming, networking and conversing with peers often paves the way to the best solution we can find to solve our problem.

How has a focus on innovation increased the quality of your team’s work? 

I’ve been in a manager role for about two years, and I feel that the knowledge we share with one another allows us to be creative in identifying a solution, while also remaining focused on our business objectives. 

A key Grainger principle is to win as one team, and the cross-collaborative elements of our team do exactly that. Presentations, journal sharing, demos, etc., stimulate us to design multiple innovative solutions that may solve the problem, all while building confidence in our team members to feel comfortable sharing their ideas. Creating a psychologically safe environment where team members feel they can have influence, even if it’s not directly the solution, allows us to get a full scope of a problem, and identify pros and cons to a solution. It’s often in these brainstorming sessions where we can best understand the pain-points of a customer and help discover the root of a problem.

One example of how we do this is by hosting an annual Grainger hack-a-thons where teams participate in creating innovative prototypes to solve a business case. Learning in this way makes us stronger engineers and encourages out-of-the-box thinking. Sometimes we come up with applications that may solve another problem entirely.

How has a focus on innovation bolstered your team’s culture? Do these different practices give team members greater chances to bond and have fun?

A focus on innovation has allowed our teams to bond more. We take the time to listen to unique perspectives on a solution and try to see through other eyes how a team member’s background or industry knowledge may make them approach a solution differently. Having this understanding of how others think allows us to make better decisions. Cyclically, when we’re able to understand each other better and spend time getting comfortable ideating, we feel encouraged and comfortable to share more. This mentality is already enmeshed in our culture, as we’re encouraged to in as one team, through the Grainger Edge principles, driving us to share ideas, cross-collaborate and feel like we can rely on one another.

What is the unique story that you feel your company has with AI? 

Grainger is nearly 100 years old and serves a very large customer base. What makes us stand out is our ability to balance established industry leadership with continually innovating technology solutions that aid our core business processes. My team maintains a fast-paced work environment while striving to meet business objectives that help drive the solutions we’re looking for. One of our core principles at Grainger is “start with the customer,” which, for our team, means understanding the root of the problem and finding a quality solution while working fast and completing projects at scale. AI also helps us deliver on Grainger’s purpose to keep the world working by making our processes more accurate and leaving space to continuously make improvements that allow us to solve customer needs.

What was a monumental moment for your team when it comes to your work with AI?

Every day has the potential to be monumental; some interesting moments come out of places we least expect them. One of Grainger’s principles is to “embrace curiosity,” so finding how to best apply new technology to solve business cases is exciting and our values directly encourage exploring new solutions. Simultaneously, we are intentional about remaining customer centered, finding the root of a problem and learning how to solve it. I believe that any company that wants to outlive their competitors needs to be customer-focused. Grainger’s principle of “start with the customer” enables us to understand the root of a problem so we can find the best solution.  

I am leading a team of exceptionally talented people who can work laterally across various applications of AI, yet they also have their own specializations. Being able to cross collaborate, make practical connections between customers, the problem and technology, and then adapt the tech quickly keeps me engaged. Our team expects new things to happen and stays agile to shift direction when a new solution comes along. Staying closely connected to our product team also allows us to adopt best-in-class AI technology.

What challenges did your team overcome in AI adoption? 

Proving or disproving a case is easy but getting it to work at the scale of Grainger’s business is challenging and requires a lot of data. My team has developed exceptional capabilities in simulating real-world data on which to train our models. We started with no actual data for training models and have since built models that allow us to deliver successful projects. With AI as a continual emerging technology, there’s still a challenge to pave the way in a lot of AI applications which can be a valuable learning experience. We understand that success comes from taking intentional steps and making positive contributions while learning new things. 

As for continuous learning, Grainger’s culture encourages team members to be curious and eager to learn. We’re urged to take time to develop or learn new skills, participate in weekly cross-team learning sessions, work on journal collaborations, attend conferences and share knowledge as much as possible. This culture of learning allows us to be both students and teachers, experiment with tools to discover outcomes — and, ultimately, feel like we have unlimited solutions to tap into.