A Marc Benioff-Backed Startup Thinks AI Can Solve the AI Deployment Problem

The Paradox of AI Adoption

It's so hard for big businesses to get AI tools working reliably that whole new organizations of forward-deployed engineers, or FDE specialists, who drop into a company to get its AI systems up and running, are springing up to help them.

This is the paradox at the heart of the AI revolution. The technology that promises to automate complex tasks and streamline operations is itself so complex that it requires a new class of highly skilled specialists to implement. The solution to the AI deployment problem, it seems, has become hiring more people to solve it.

"AI, paradoxically, increases the demand for professional services," says Efrat Rapoport, a former Salesforce executive whose new company, June, emerged from stealth Monday morning. "The industry's answer to AI implementation is, 'let's hire more and more and more people.'"

Rapoport and her three co-founders, Ohad Hen, Barak Goldstein, and Idan Tsitiat, have a different idea about how to bring AI into broader use. Rather than throwing more human capital at the problem, they've built a platform that uses AI to solve the AI deployment problem itself. It's a meta-approach that reflects the recursive nature of the technology they're working with.

The Team Behind June

The four founders previously started Bonobo AI, a pre-transformer language model company that launched a voice-to-text service in 2017. Bonobo AI was snapped up two years later by Salesforce, and the team worked for several years on the tech giant's AI initiatives before setting out on their own again after watching customers struggle to bring AI into their existing platforms.

Their experience at Salesforce gave them a front-row seat to the challenges enterprises face when trying to implement AI. They saw customers with ambitious plans and massive budgets hitting the same walls over and over again. The technology was promising, but the integration was a nightmare.

Their potential was clear enough to their investors, Rapoport says, that "we didn't even have a deck for this raise." The company raised $20 million in pre-seed funding led by Marc Benioff's Time Ventures, with additional backing from tech luminaries like Michael Dell, Aaron Levie, and George Kurtz. The company declined to share its valuation.

The investor lineup is notable. Benioff, the CEO of Salesforce, has been one of the most vocal advocates for AI in the enterprise space. His backing of June suggests he sees the startup as solving a problem that his own company has been grappling with. Dell, Levie, and Kurtz bring similar credibility from their respective domains.

The Problem: Legacy Systems and Technical Debt

While the so-called SaaSpocalypse has software firms fearing that AI might replace them, thus far no one is vibe-coding a CRM for a Fortune 500 company. Any AI model brought into a corporate setting still has to work with Salesforce, ServiceNow, Databricks, Workday, or any of a dozen other data-management platforms.

This is the reality that the hype often ignores. AI models don't exist in a vacuum. They need to connect to existing systems, access existing data, and fit into existing workflows. And those systems are almost always a mess.

"Before AI can create value, someone has to deal with legacy systems," Rapoport says. "You have fragmented data across these platforms. You have complex workflows. You have years of technical debt."

Building an agent template is the easy part, she says. The hard part is getting it to work with the mess underneath. "How does an agent know how to operate when you have 10 duplicate [database] fields that say the same thing, and different teams are using them?"

This is the problem that forward-deployed engineers are supposed to solve. They parachute into an organization, spend weeks or months untangling its data architecture, and then build custom integrations to make the AI work. It's expensive, time-consuming, and not scalable.

How June Works

June's platform scans a company's existing systems to understand its business processes, find bottlenecks, and then build more optimized, agent-powered processes to replace them, automatically notifying teams through the company's comms channels.

The platform acts like a digital consultant, analyzing the organization's data architecture and workflows to identify where AI can add value. But unlike a human consultant, it can do this analysis at scale, across hundreds or thousands of processes simultaneously.

"We give you the full roadmap automatically of what needs to happen step by step for you to actually implement this agent successfully in an enterprise environment, which is often very complex," Rapoport said. "We give you a step by step guide. 'Remove these duplicates. Connect to this data source.' And then you click on 'build' on each task, and June starts building it for you in the organization."

The platform is designed to be accessible to non-technical users. It doesn't require a team of FDEs to implement. Instead, it guides users through the process, handling the technical complexity behind the scenes.

A Real-World Test: CMG

Paul Akinmade, chief strategy officer at CMG, a major U.S. mortgage lender, moved his company's software engineering over to Claude Code quickly, but hit roadblocks trying to integrate it with Salesforce. That was a problem because he'd promised at Salesforce's annual conference the year before that he'd return with 100 agents running, and it wasn't looking like he'd hit that target.

The mortgage industry is notoriously complex, with layers of regulation, legacy systems, and manual processes. Automating any part of it is a significant challenge. Akinmade had made a public commitment to his peers and competitors, and he was staring down the barrel of failure.

Akinmade says his team spent weeks hitting a wall meeting with architects, talking to forward-deployed engineers, consulting everybody they could without making progress. June changed that, he says, giving his team a clear view of where to deploy agents and letting them do so safely, even before the official kickoff call between the two companies.

The ability to get started before the formal engagement began was crucial. In the fast-moving world of AI, weeks matter. June allowed CMG to move quickly, without waiting for consultants to get up to speed.

Complement or Replacement?

Rapoport sees June as a tool that complements FDEs and consultants, but her customers may be drawn to it for the opposite reason: it lets them avoid FDEs altogether.

When Akinmade was first considering piloting the tool at CMG, he says he told her: "If your product requires FDEs, I don't want your product. I've already done that and I'm getting annoyed by it. I don't want a black box. I don't want something only certain people can figure out. I want an easy-to-use tool."

This sentiment is likely to resonate with many enterprise leaders. The FDE model works for the largest companies with the deepest pockets, but it's not scalable. There simply aren't enough forward-deployed engineers to go around, and the ones that exist are expensive and in high demand.

June's value proposition is that it democratizes AI deployment. It puts the power of AI implementation into the hands of the people who understand the business best the employees who work there every day.

The Broader Context: AI Deployment as a Bottleneck

The emergence of companies like June reflects a broader trend in the AI industry. The technology itself is advancing rapidly, but the ability to deploy it effectively is lagging behind. This creates a bottleneck that limits the value organizations can extract from their AI investments.

The problem is particularly acute in large enterprises, where legacy systems, regulatory requirements, and organizational complexity make implementation challenging. These organizations have the data and the budgets to deploy AI at scale, but they often lack the technical expertise to do so effectively.

This is where June sees its opportunity. By automating the deployment process, the company hopes to make AI accessible to a wider range of organizations and use cases. The goal is not just to help companies deploy AI, but to help them deploy it faster, cheaper, and with less risk.

The Future of AI Deployment

Evidently, June cleared the bar for CMG. Akinmade was able to meet his commitment of 100 agents running, and the company is now exploring broader uses of the platform.

For June, the real test will be whether it can scale its approach to a wide range of industries and use cases. The platform needs to be flexible enough to handle the idiosyncrasies of different organizations while remaining simple enough to be used by non-technical staff.

The company's $20 million pre-seed round gives it runway to build out its product and customer base. The backing of high-profile investors like Benioff, Dell, and Levie provides credibility and access to networks that could accelerate its growth.

But the competition is heating up. Other startups are tackling similar problems, and the major enterprise software vendors are investing heavily in their own AI deployment tools. June will need to differentiate itself and prove that its approach delivers real value.

The paradox of AI is that the technology designed to automate complexity has itself become a source of complexity. June's approach using AI to solve the AI deployment problem is a recursive solution that reflects the nature of the challenge.

The company's success will depend on whether it can deliver on its promise of making AI deployment simple and accessible. If it can, it could unlock significant value for enterprises struggling to get their AI initiatives off the ground.

In a world where the potential of AI is widely recognized but its deployment remains a challenge, June is betting that the solution to the problem is the problem itself and that AI, properly applied, can solve the very bottlenecks it has created.