He’s also seeing positive AI proofs of concept in purpose-built tools for IT help desk, customer support, and sales and marketing. “We’re experimenting with general purpose co-pilots or assistants, too,” he says. “We released a couple of options for our employees to experiment with, one commercial LLM service and one that’s open source.” Samsara employees are applying these general-purpose assistants to a variety of use cases, like writing documentation and job descriptions, debugging code, or writing API endpoints.
By using LLM capabilities for code generation, for example, Samsara engineers are more productive in generating boilerplate code, as well as in code documentation and commenting, which is a critical practice for the company. “Some of our engineers don’t have English as their first language,” adds Franchetti, “so bringing AI to commenting and documentation helps them in their work.”
Having spent a year on this bottoms-up approach to AI innovation, Franchetti offers some advice:
Don’t limit “citizen creation” to engineers: At Samsara, Franchetti estimates that 50% of gen AI usage is by engineers, but the other half is in legal, sales, marketing, finance, and customer support.
Don’t let your current architecture hold you back: Franchetti acknowledges that companies like Samsara, that were born in the cloud, have a jump in gen AI over older companies running on legacy infrastructure. But that doesn’t mean they can’t enjoy the fruits of a bottoms-up approach. “I believe your employees can experiment regardless of your architecture,” he says. “They can improve productivity by using AI for the creation of marketing collateral or even finance reconciliation. They can do this in any environment, because these specific tools don’t rely on integration with the broader architecture.”
Clean up your enterprise data: Without clean data, your AI results will be limited. “The power of AI and gen AI comes from the ability to share context with the model, so the model can understand your environment and be fine-tuned to give you better answers,” Franchetti says. “AI starts as a novice about your business, but as it gets trained on your data, the tool becomes an expert.” When you have data in various systems, and conflicting sources of truth, the AI will not have the context needed to get smarter.
