InstaLILY announced a $60 million Series B on July 14, 2026, led by Energize Capital with participation from Home Depot Ventures, United Rentals, and Insight Partners. The company has raised nearly $100 million total. Revenue grew five times over the past year. The product is called Lily, and it does something most AI companies refuse to talk about: it stays.

Lily is what InstaLILY calls an AI forward deployed engineer. It goes into a business, learns how the work actually happens, builds custom software around those workflows, and ships it live in days. Then it keeps running, adjusting, and rebuilding as the business changes underneath it.

That last sentence is the entire pitch. And it is the part that should concern you if your organization bought AI and then watched it slowly stop working.

The Decay Problem

Most AI deployments follow the same arc. An engineer or consultant builds the system. It works in the demo. Leadership signs off. The builder moves on to the next project. And the system starts drifting the day they leave.

Data formats change. Business rules shift. New tools get added. The model that powered the original workflow gets updated or deprecated. Nobody on the team knows how the system was wired together. Six months later, it still technically runs, but it produces answers nobody trusts, so the team routes around it and does the work manually.

McKinsey has documented this pattern repeatedly. Forty-three percent of AI initiatives fail to move past pilot. Gartner reported in early 2026 that 85 percent of enterprises pursuing agentic AI are not ready for production deployment. Those figures give leaders a reason to examine the operational support required to keep a deployment working.

InstaLILY built that layer and turned it into a product.

What the Numbers Actually Show

The company’s customer results tell you more than the funding round does.

At a national distributor, Lily built software that finds and pursues revenue across the entire customer base. The reported result: more than $200 million in new annual sales identified, beyond what a human sales team could cover by hand. That is a claim about identified opportunities from AI-built software deployed into the sales workflow.

At a field service company, a technician used to spend 15 minutes diagnosing each problem before starting the repair. Lily cut that to under 10 seconds. The cost to serve a single call dropped 98 percent.

A logistics operator that planned routes in 15 minutes now does it in three. A services company cut the time to train a new field rep by more than 60 percent.

InstaLILY attributes these results to software built around the way each business operates, deployed quickly, and kept running.

The Strategic Investors Tell the Real Story

Home Depot Ventures and United Rentals are investing in a company whose product they also use. Those investments have an operational dimension: the investors can assess the tool through their own use of it.

Patrick Garcia, the Chief Digital and AI Officer at SRS Distribution (part of the Home Depot family), said it directly: “Lily has helped us build the software our teams need while working within the enterprise platforms, governance, and processes we’ve already established.”

Garcia describes AI building directly inside SRS Distribution’s existing infrastructure. That offers a deployment approach worth examining if your team faces a six-month integration project after its sandbox pilot.

Why This Matters for Your Organization

Apply the lesson to whichever product your team uses.

Budget for the ongoing operational work of keeping AI connected to how your business runs, adapting it when the business changes, and making sure someone owns the system after the initial build. Model selection, a proof of concept, and budget approval all need that follow-through.

Treat deployment as the start of ongoing maintenance. A CRM still needs an owner a year after installation, and AI adds changing models and business context to the data and system changes a team already manages. Assign responsibility for absorbing those changes and keeping the workflow useful.

InstaLILY’s $60 million bet is that the operations layer has a large market. BCG estimates that roughly 5 percent of companies generate outsized value from their AI investments. Close to 60 percent report little material return despite real spending. Ongoing ownership after installation is one practical requirement leaders can examine when trying to improve their returns.

The Question You Should Be Asking

If you run a team that uses AI in any form, name the person responsible for the system six months from now. Confirm who will keep it connected, accurate, and useful as the business changes, including after the builder or budget sponsor moves on.

If the answer is nobody, you already know what happens next. The system drifts. The team loses trust. The work goes back to manual. And six months after that, someone in leadership asks why the AI investment did not produce results.

The investment produced exactly what it was set up to produce. A demo. The part that produces results is the part that comes after, and it is the part that $60 million just went to fund.