By Charlie Rohrmann
ST. PETERSBURG, Fla., Oct. 30, 2026 — Every week, we sit down with company leaders who tell us, with real confidence, that they've figured out AI. Usually what they mean is that someone on their team has an agent triaging their inbox or a tool that builds a first draft of their PowerPoint decks. These are useful applications. But they’re also among the least interesting things AI can do for a business.
Call it the gap between AI for productivity and AI for business outcomes, and it’s a distinction that’s getting lost as these new tech tools are adopted. Productivity tools let a person do in 20 minutes what used to take two hours, whether it’s summarizing what happened in a Slack channel overnight or synthesizing a document. But it doesn't change what your business is capable of.
Business-outcome tools, by contrast, touch money and risk directly. Take, for example, an agent that researches fair pricing and negotiates on a company’s behalf. That’s not a few hours of prep saved; it’s a pricing decision that shows up on a P&L.
Or consider a tool that turns a photo of a bathroom into an instant remodeling quote. What matters here is that a sales cycle that used to take a week now closes in seconds, and that changes how much business a company can win. These systems don’t just free up an afternoon. They generate actual revenue, and also can create real liability and business risk.
The confusion between the two categories is understandable. On the surface, they look similar: in both cases, an agent is doing a task a human used to do. The underlying difference is consequence. If a productivity tool makes a mistake, someone re-reads an email or re-drafts a slide. If a business-outcome tool misprices a deal or misreads a customer's intent, the error bleeds into the business.
Moving Beyond Individual Tasks
Part of what keeps companies stuck on the productivity side of the equation with AI is that they’re still thinking about using AI on one task at a time. Once you start thinking of an agent the way you'd think about hiring a person (i.e., someone/something that can own an entire project or workflow), the question changes. Instead of asking “what can this tool do for me today,” you’re now asking “if I could hire 20 people to work on this business, what would I have them do, and how would I organize that work?” That reframing is what unlocks outcome-driven use cases.
That also explains why adoption feels more fraught this time around versus previous tech shifts. Earlier waves of software were essentially expanded memory. You gave a system information, and it stored and returned it in a useful format. Now companies are being asked to outsource reasoning, too. This new “employee” is now making decisions inside your business. That requires a bigger leap of trust, and it's fair for leaders to be cautious about it. It’s perhaps akin to the hesitation and vulnerability people feel riding in a self-driving car for the first time.
Given that hesitation, we don't think companies should hand over the keys to their agents immediately. What works, in our experience, is building trust in stages. Start with narrower, well-scoped agents that produce a fast, visible win, then expand the scope once people have seen the technology work on something that matters to them. Those early steps can't be so cautious that they’re underwhelming, though. The point of a first step is to be able to make the case for a second one.
Our advice to leaders is simple. Don’t stop at productivity, but don't skip that step either. Start with your own personal outcomes so you can experience those benefits firsthand. Once you get 10 hours of thinking time back, thanks to AI, you’ll stop treating AI sponsorship as a theoretical concept.
Image credit: Singular Innovation
Two examples of this we see working. The first is something like an executive “second brain”, a Chief of Staff-style agent that reviews a leader's calendar, meeting notes, and inbox at the end of each day and returns a single-screen brief. It surfaces the handful of decisions that actually need the leader's judgment (like a contract position or a client escalation) and creates drafts for everything else that can be delegated. Instead of two hours sorting through what matters, the leader spends five minutes approving it.
The second is a communications co-pilot trained on a leader's own voice and standing preferences. It pre-drafts responses to incoming emails and proposals. The leader stops staring at a blank reply box or writing rote follow-ups and starts operating more like an editor-in-chief of their own correspondence.
Once a founder or executive has three or four of these specialized agents protecting their time in the background, they don’t really need to be convinced about the merits of bringing that same thinking to the rest of the company. They've already experienced the upsides.
From there, the harder question then becomes what can AI be trusted to decide in the business and what oversight will that decision require. But it’s essential for companies to progress to this point as quickly as possible. The businesses working through that question now are the ones that will turn AI into a real driver of business performance.
Business-outcome tools, by contrast, touch money and risk directly. Take, for example, an agent that researches fair pricing and negotiates on a company’s behalf. That’s not a few hours of prep saved; it’s a pricing decision that shows up on a P&L.
About Charlie Rohrmann
Charlie Rohrmann is the Chief Commercial Officer at Singular Innovation, where he leads growth and partnership initiatives. He works with the team to bring the company’s AI solutions to small businesses.
