From Co-pilots to Colleagues: What Ryan Wexler Sees Coming in Enterprise AI
Ryan Wexler, Principal at SignalFire and a member of Solid’s board, shares what enterprises are actually buying today, why ROI is still hard to prove, and what he sees the future of agents to be
I recently sat down with Ryan Wexler on the podcast, and I enjoyed this one a lot. It’s available to listen to now: YouTube, Spotify, Apple.
Usually, on Building with AI: Promises and Heartbreaks, I speak with builders, operators, and data leaders who are deep in one company’s AI journey. Ryan brought a different angle. As a Principal at SignalFire, he gets a horizontal view into the market: what founders are building, what enterprises are actually buying, where the hype is ahead of reality, and where something very real is already happening.
That made for a particularly interesting conversation.
Ryan is in a unique position to see patterns early. SignalFire is not a traditional VC that just writes checks and hopes for the best. It has built a real technology and data muscle internally, including Beacon AI, to support sourcing, recruiting, GTM, and portfolio support. So when Ryan talks about how AI is changing work, he’s not speaking only from the investor seat. He is also seeing how these systems get used in practice.
Most enterprises are not refusing AI. They are stuck in the middle.
One of the most useful parts of the conversation was how Ryan broke down the market.
In his view, there are roughly three groups right now:
The disbelievers, the people who still think AI is mostly a parlor trick.
The big middle, where organizations are using AI tools and seeing value, but are not yet rethinking work around them.
A much smaller group that is fully bought in and actively redesigning workflows, teams, and products around AI.
That lines up pretty well with what I’m seeing too.
The market narrative is often too binary. Either “AI is changing everything” or “AI is overhyped and barely works.” Reality, as usual, is messier. Most large enterprises are somewhere in the uncomfortable middle. They have Copilot, ChatGPT Enterprise, Cursor, Claude Code, or some equivalent floating around the organization. Some people are getting real value from it. But the company itself is still not fully operating in an AI-native way.
This is also why I keep coming back to themes I wrote about in (Almost) no AI in production and Throwing away BI is a Bad Idea. People want the new experience, but they still need the old safety rails. They want AI, but they still need trust. They want agents, but they still keep a human in the loop.
That is not failure. It is transition.
Ryan’s point was that the biggest blocker is not disbelief. It is change management. The tools are still early. The models keep changing. The workflows are not yet stable. So even when people believe, it still takes time to move.
Founders should stop asking “what’s hottest?” and start asking “what can I win?”
Another part of the conversation that stuck with me was Ryan’s framing of the types of AI companies selling into enterprises today.
He described three broad buckets.
The first are transformation plays. These are the companies trying to help enterprises fully rethink how work gets done. Huge opportunity, obviously. Also hard. These deals involve process change, redesign, consulting, buy-in, internal champions, and patience.
The second are next-generation SaaS companies. These are applications where AI is built into the product itself. The buyer does not need to reinvent the whole company to get value. A customer support platform that now resolves tickets with agents is an example. Same budget owner, same category, new capabilities.
The third are bottoms-up prosumer tools. This is where an individual gets a tool that makes them more productive: ChatGPT Enterprise, Copilot, Cursor, Claude Code, ElevenLabs, and so forth. Ryan’s view was that this is where a lot of the fastest adoption has happened because the value is immediate and the buying motion is much simpler.
Then I asked him the natural founder question: if you were starting something new, which of the three would you build in?
His answer was excellent: “Whichever one you can win in.”
I liked that a lot.
Founders sometimes over-rotate to whatever category is currently generating the most noise on X or LinkedIn. But not every founder is built for the same motion. Some are excellent at bottoms-up adoption. Some are great at enterprise solution sales. Some can navigate long cycles and help customers rethink processes. Others cannot. And that’s okay.
The goal is not to build in the hottest category. The goal is to build in the category where your team has a real shot at winning.
That also ties directly to a point I made in Nobody cares about the efficiency of the data analyst. Buyers do not wake up wanting AI in the abstract. They want impact. They want better outcomes. They want a wedge that matters.
ROI is still messy, and that’s part of the story
We also dug into a question many founders and buyers still struggle with: how do you prove ROI?
If AI is automating a specific workflow, the math is easier. If an agent resolves a support ticket, runs a recruiting screen, or handles a voice interaction, you can start counting minutes, throughput, coverage, and outcomes.
But when AI is helping an individual employee do their job better, the math gets fuzzier.
How exactly do you measure the ROI of giving 10,000 employees access to a co-pilot? Lines of code are a bad metric. “Time saved” is often hand-wavy. Yet, clearly, something is happening.
Ryan was very honest about this. A lot of organizations are still buying these tools before they have perfect measurement. They know the direction is right, even if the spreadsheet is still weak.
That matches what I’ve been hearing from leaders as well. In my conversation with Meenal Iyer, one of the recurring themes was that the winning teams are not waiting for the world to become perfectly measurable. They are building AI literacy, experimentation muscle, and the right foundation now, so they can scale what works later.
Two areas Ryan is especially bullish on
The first is voice AI.
Ryan’s reasoning here is very pragmatic. With coding assistants or general-purpose copilots, ROI is harder to pin down. With voice, it is often much clearer. Humans can only speak so many words per minute. If AI handles some of those minutes, the value is much easier to quantify.
More importantly, voice does not only replace labor. It enables new interactions that were previously too expensive to staff.
Ryan shared a great example of a behavioral treatment organization that automated initial recruiting conversations with voice AI. The impact was not just efficiency. They were able to run more interviews, keep human recruiters focused on the more valuable parts of the process, and improve outcomes.
That’s a big deal.
The second area is self-improving agents.
This one is especially exciting to me.
Ryan described a future where one agent does the work, another evaluates the trace, another improves the prompt or rules, and the system improves over time through usage. In other words, not just agentic systems, but systems that get better at being agentic.
We’re already starting to see the early versions of this pattern. I think we’ll see much more of it over the next couple of years.
And frankly, that is where things begin to get really interesting. Not when AI merely answers a question, but when it learns how to make its own future answers better.
So where is this all going?
Ryan does not believe AI is a bubble in the simplistic sense.
Sure, individual companies will go up, down, miss expectations, or disappear. Some categories will get overcrowded. Some valuations will correct. That’s startup-land. But the broader shift is not going away.
On that, I agree with him.
We are moving from co-pilots to colleagues. From tools that wait for a prompt, to systems that can take initiative, execute tasks, and eventually coordinate with other systems. Some of this will first happen inside enterprises. Some of it will show up in consumer software. But all of it will require better context, better data, and a lot more trust than most people realize.
That’s what made this episode fun for me.
Ryan brought the market view, but the takeaways were refreshingly grounded: most organizations are still in the middle, ROI is real but often messy, founders should pick the motion they can actually win, voice AI is much bigger than many think, and agents improving agents may become one of the most interesting product patterns of the next couple of years.
I thank Ryan for taking the time to join me on the podcast. You can learn more about him on SignalFire’s team page, read about Beacon AI, and check out the AI Circle community he mentioned in our lightning round.
Or - hop straight into the episode now: YouTube, Spotify, Apple.