Your AI Agent Should Find Customers, Not Replace Employees
Agentic AI should go beyond automating tasks and cutting costs: it can identify the right customers, partners, and opportunities, qualify business matches, and let humans focus on trust-building and closing deals.
One of the defining stories of the AI revolution in 2026 is the drive for efficiency. Companies are examining which tasks can be handed over to AI, how much time can be saved, and how smaller teams can do work that once required more people. The wave of layoffs across the tech industry has made this debate even sharper, and at some companies the connection between AI and workforce reductions is already being stated explicitly. But focusing on cost savings frames the question too narrowly. Before a company asks which role an Agent can reduce, it should ask which customer, partner, or business opportunity that Agent can find for it.
This is especially important for startups selling to large corporations. From the outside, a major corporation may look like a single customer. In practice, it can include dozens of business units, subsidiaries, countries, and managers across innovation, technology, operations, procurement, and finance. So even a startup that has managed to ‘get into the corporation’ has not necessarily reached the right person. It still needs to understand who owns the problem, who controls the budget, who can approve a pilot, who will actually use the product, and who might stop the process. Sometimes the person who can move a deal forward is two divisions and three job titles away from where the search began.
That is why business development with large corporations is such an expensive and slow process. Startups spend months identifying contacts, attending conferences, traveling, reaching out, and holding countless introductory calls, only to discover later that they approached the wrong business unit, there is no budget, the need is not urgent enough, or the organization is already evaluating another solution. At conferences, companies can invest in a booth, design, travel, and full days of business-development work, yet still speak with only a tiny fraction of the relevant people. The problem is similar on the other side: a corporate executive may be exposed to hundreds of companies promising to solve a particular challenge, but has no practical way to evaluate all of them in depth.
This is where, in my view, one of the most interesting uses of Agentic AI emerges. Not an Agent that writes yet another sales email, but an Agent that knows the company it represents in depth: what the product can do, which systems it integrates with, which markets it operates in, what its limitations are, and what kind of customer it is looking for. On the other side, an Agent representing the corporation can understand the needs of its various business units, the challenges they are trying to solve, and the conditions under which they are willing to evaluate a new technology. Instead of starting with an introductory call, both sides can arrive at that conversation after much of the preliminary discovery has already been done.
The next step is even more interesting: Agents representing different companies can conduct the initial screening between themselves. They can ask questions, compare needs with capabilities, identify gaps, and rule out matches that have no real basis before two executives ever open their calendars. This is a fundamental shift from the most common use of AI in sales today, which focuses mainly on accelerating the existing process. Being able to draft an outreach message faster is useful, but the greater value lies in determining whether that message should be sent at all, who within the organization should receive it, and what business reason would make the other side respond.
This is a well-known gap in the work of matching the needs of industrial companies with startup technologies. The most effective starting point is usually not, “We have an interesting startup. Who can we sell it to?” but the other way around: What is the corporation trying to solve? Who within the organization owns that need? And what needs to happen for the company to be ready to evaluate a solution? Only then does it make sense to look for the right technology. Agents can scale this model, making it possible to evaluate many more potential matches before investing valuable human time.
This does not mean sales and business-development professionals are about to disappear. In complex deals, especially with large corporations, the human element remains critical. People need to build trust, understand organizational interests, handle objections, negotiate, and persuade others to take a risk on a new product. The Agent should not replace the people who do those things well. It should spare them the part where they spend hours searching for the right person, sit through conversations that go nowhere, and only afterward try to figure out whether there was ever a reason to meet in the first place.
Perhaps it is also time to change the way organizations measure the value of AI. It is easy to measure how many hours were saved and how many actions were automated, so the discussion naturally gravitates toward the cost line. But a company cannot optimize forever it also needs to grow. The more meaningful measure will be how many new opportunities were created, how many markets were opened, how many partnerships were found, and how many customers came from connections that would not otherwise have existed. Your first AI Agent does not have to be the one doing an employee’s job. Its greatest value may be in finding that employee their next customer.
Liav Ben-Rubi is the CEO of Quantum Hub and Managing Partner at Q Fund
First published: 15:35, 28.08.26
