Your Business Has the Same AI Problem Enterprise Giants Do — Here Is What to Do About It

Your Business Has the Same AI Problem Enterprise Giants Do — Here Is What to Do About It

Most business owners assume the AI adoption problem is a big-company problem. Too much legacy tech, too many departments, too much red tape. But a $20 million funding round that didn't even need a pitch deck to close suggests the problem is more universal than you think — and the solution it is funding has direct implications for any business owner trying to make AI actually work.

A startup called June emerged from stealth on August 3, 2026, with $20 million in pre-seed funding led by Marc Benioff's Time Ventures. The round also drew backing from Michael Dell, Aaron Levie, and George Kurtz — a who's-who of enterprise tech. The company was founded by Efrat Rapoport and three cofounders who previously built Bonobo AI, a pre-transformer language model company that launched a voice-to-text service in 2017 and was acquired by Salesforce two years later. The team spent years inside Salesforce working on its AI initiatives before going out on their own again, motivated by a specific frustration: watching enterprise customers struggle to get AI working inside their existing platforms.

June's platform scans a company's existing systems to understand its business processes, identifies bottlenecks, and then builds more optimized, agent-powered workflows to replace them. The key insight from Rapoport is that "building an agent template is the easy part." The hard part is working with fragmented data, duplicate database fields, complex workflows, and years of technical debt. June addresses that by giving teams a step-by-step roadmap — "remove these duplicates, connect to this data source" — and then builds each piece automatically. The platform also notifies teams through their existing communications channels, so adoption happens in context rather than in isolation. One early customer, Paul Akinmade, chief strategy officer at CMG, a major U.S. mortgage lender, described his team spending weeks hitting a wall trying to integrate Claude Code with Salesforce before June cleared the path forward — even before the official kickoff call between the two companies.

For small and mid-size business owners, the lesson here is not that you need June specifically. The lesson is that the problem June was built to solve exists at every scale. When AI tools do not perform the way they were promised to, the default answer from the vendor ecosystem is to hire more specialists. Rapoport puts it plainly: "AI, paradoxically, increases the demand for professional services. The industry's answer to AI implementation is, 'let's hire more and more and more people.'" That is fine if you are a Fortune 500 company with the budget to bring in forward-deployed engineers. It is not fine if you are a 15-person business trying to automate lead follow-up or client onboarding.

The reason AI tools underperform for most small businesses is not the AI itself. It is the data and workflow mess underneath it. If your CRM has duplicate contacts, your email sequences are disconnected from your intake forms, or your team is operating across three platforms that do not talk to each other, no AI tool will save you. The AI will be operating on bad inputs and fragmented logic, and you will blame the technology when the real culprit is the infrastructure it is sitting on top of. This is exactly what Akinmade was experiencing at the enterprise level, and it is what small business owners experience every time they install a "magic" AI tool and wonder why it is not producing results.

The actionable insight here goes further than diagnosis. What the June story confirms is that before layering AI onto your operations, you need visibility into how your existing systems are actually being used. Pick one workflow in your business this week — lead capture, client follow-up, appointment scheduling, or proposal delivery — and map it out from trigger to outcome. Identify every handoff, every tool involved, and every place where data sits in two places and could conflict. That mapping exercise is not busywork. It is the prerequisite that separates businesses that get results from AI from businesses that get frustrated by it.

The businesses that win with AI in the next two years will not be the ones that bought the most tools. They will be the ones that understood their own operations clearly enough to deploy AI where the leverage actually lives.

Originally inspired by: A Marc Benioff-backed startup thinks AI can solve the AI deployment problem (https://techcrunch.com/2026/08/03/a-marc-benioff-backed-startup-thinks-ai-can-solve-the-ai-deployment-problem/) See how Leads to Conversion can help you deploy AI where it actually moves the needle. Get your free AI audit