Erik Salazar, Founder and CEO of Iconic – Interview Series

Erik Salazar, Founder and CEO of Iconic is a serial entrepreneur and technology executive with more than two decades of experience spanning mergers and acquisitions, enterprise infrastructure, healthcare technology, and startup leadership. Prior to launching Iconic in 2023, he served as COO and Co-Founder of Kit.com, a healthcare testing platform that was later acquired by Ro Health Ventures. Earlier in his career, Salazar co-founded Unitas Global, helping scale the company into a major cloud and infrastructure services provider before its acquisition by Digital Alpha. He also held leadership roles at CoreSite and began his career in finance with The Carlyle Group, where he focused on underwriting, modeling, and acquisitions. Drawing on this combination of financial expertise, operational leadership, and startup execution, Salazar founded Iconic to modernize the traditionally fragmented M&A industry through technology-driven advisory services, AI-powered buyer matching, and business valuations.

Iconic is an AI-powered M&A platform designed to help business owners sell their companies more efficiently while improving outcomes for both sellers and buyers. The company combines proprietary software with experienced advisors to streamline every stage of the transaction process, from valuation and buyer discovery to deal management and closing. By leveraging artificial intelligence, automation, and data analytics, Iconic aims to bring greater transparency, speed, and precision to lower-middle-market M&A transactions, an industry that has historically relied on manual processes and fragmented networks. The platform’s focus on technology-enabled advisory services positions it at the intersection of fintech, AI, and business brokerage, seeking to modernize how private companies are bought and sold.

Your father’s experience as a small business owner who was unable to fully realize the value of what he spent decades building became the inspiration for Iconic. Can you share that story and explain how it influenced your decision to modernize the M&A process for small businesses?

My father spent decades running a travel agency. He built something real with customers who trusted him, employees who depended on him, and a business that worked. Then he got sick. There came a point where he couldn’t operate it anymore, and he had no idea what to do next. He didn’t know there was a process for selling a small business. He didn’t know there was a market for it. He assumed that if he couldn’t run it, and none of his kids could step in, it was just over.

So that’s what happened. The business wound down, and it wasn’t because it failed. It was because nobody knew how to save it.

That stayed with me. When my co-founder Roberto and I started digging into the small business M&A market, the professional opportunity was obvious. But underneath the market analysis was always something more personal – the memory of watching my father’s business disappear because the right infrastructure didn’t exist for someone like him. Iconic is, in some ways, the outcome he never got to have.

Nearly six million small businesses are expected to change hands over the coming decade. Why do you believe the current M&A infrastructure is unprepared for this wave of ownership transfers?

The infrastructure was never built for the small business M&A market. Investment banks, M&A advisors, and institutional transaction support have traditionally been designed for deals above a certain size. Small businesses on Main Street have been left out of the equation.

What exists for them is mostly manual and includes spreadsheets, email threads, local brokers working in isolation with no shared data, and no standardized processes. This setup won’t hold up when millions of transactions are moving through simultaneously.

You have a background spanning private equity at Carlyle, data center infrastructure, healthcare startups, and now M&A technology. What lessons from those industries have proven most valuable in building Iconic?

The most honest answer I can give comes from personal experience. When Kit was acquired, I was on the selling side of a transaction with an army of attorneys and professional M&A executives sitting across the table. I can tell you that if I didn’t have the right team around me, people who actually knew what they were doing in M&A, the outcome would have been very different.

That experience made one thing clear: the people with the right infrastructure and access win. The people without it leave money on the table, or worse, get taken advantage of at the most important financial moment of their lives.

I saw the same pattern in private equity. The firms with the best deal flow and the best data compound their advantages over time. Everyone else fills the gaps they’re allowed to fill. That asymmetry exists just as starkly on Main Street as it does on Wall Street. The difference is that on Main Street, we’ve just normalized it.

Small business owners have spent decades building something real. They’ve served their customers and communities with their bare hands. And when the time comes to sell, they’re navigating a foreign process with no roadmap. That’s the problem Iconic is built to fix.

Small-business transactions often rely on spreadsheets, emails, and manual processes. What are the biggest inefficiencies that AI can realistically eliminate over the next five years?

Deal preparation is the first real bottleneck. Getting a business ready to go to market takes weeks, and most of it is organizing financials, building a CIM, and establishing a buyer profile. This is all structured, repeatable work that AI is genuinely good at.

Buyer matching is also vital to ensuring good outcomes. Right now, the process is basically a broker’s contact list. AI can pull signals that no individual broker has access to, including acquisition history, stated investment criteria, roll-up patterns in a sector, and search behavior that indicates active deal-hunting. And due diligence is fundamentally a document management and cross-referencing problem. That’s exactly the kind of work where AI can help cut close times in half.

Five years out, the front end (preparation) and back end (diligence) of a transaction will look very different from today. The part in the middle, where a seller is deciding whether to trust someone with what they built, will still be human. Leveraging AI for the grunt work will free advisors up to do the most impactful part of the job.

Many AI startups focus on replacing human expertise. Iconic appears to be augmenting advisors rather than eliminating them. Why do you believe the future of M&A is human-plus-AI rather than AI-only?

M&A is a trust transaction at its core. A seller is handing over the thing they spent their life building. That moment doesn’t automate. The second a seller feels like they’re dealing with a system rather than a person who actually understands what’s at stake, you’ve lost the deal.

The challenge with small business M&A today is that brokers have been asked to do everything by hand, which caps how many deals they can run and limits how much time they can actually spend with each seller. That’s the real cost. These owners are navigating the biggest financial decision of their lives, often for the first time, with no roadmap and limitations on the quality of representation. In larger transactions, investment banks solve this by throwing a team of analysts at the problem. On Main Street this is not possible, but what is possible is what current tech now allows us to do. We can close the gap now. When a broker is buried in spreadsheets and buyer emails, that’s time they’re not spending in the room with the person who needs them most.

What we’re building removes that ceiling. The AI handles the operational load. The broker handles the relationship, the trust, the human judgment that no model can replicate.

There’s also a practical reality. These transactions carry real legal, financial, and tax complexity that requires accountable professionals. But beyond the mechanics, a seller deciding whether to hand over their life’s work to a stranger needs to feel genuinely seen and guided through that process. That’s not a software problem. That’s a people problem. And it always will be.

One of Iconic’s core promises is reducing the time required to close a transaction. What are the specific bottlenecks that most often delay deals, and how does AI help address them?

Sellers often come to market underprepared. Getting financials organized, building presentation materials, and landing on a realistic valuation range can add weeks before the actual process even starts. Then buyer outreach is too broad because the targeting data is too thin. Brokers compensate for bad matching by running wide, which means spending time on buyers who were never right for the deal. Once someone is under LOI, due diligence turns into an email chain with shared drives and version confusion. Slow and manual, every time.

AI compresses each of these stages. Preparation gets faster when tools can pull and organize financial data automatically, outreach gets tighter when you’re working from behavioral signals and transaction history rather than a contact list, and diligence moves when documents are organized and cross-referenced in real time instead of reviewed in sequence by hand.

Business valuation remains one of the most debated aspects of any acquisition. How do you see AI changing valuation methodologies, particularly for smaller businesses that often lack extensive comparable transaction data?

The challenge here is really about data. The comparable transaction data that investment bankers rely on for mid-market deals doesn’t exist at scale here. Deals don’t get reported, terms don’t get disclosed, and sample sizes are thin. So valuations end up inconsistent, and that inconsistency creates friction on both sides of every deal.

AI helps by pattern-matching across inputs that manual analysis can’t process efficiently. This includes industry benchmarks, local market conditions, owner dependency, customer concentration, and recurring revenue. You get a more defensible starting point for the negotiation, which is genuinely useful even if it doesn’t resolve everything. The models also get better as more transaction data gets captured over time.

AI will struggle most with businesses where the majority of the value lives in the owner’s relationships. How do you comp that? You mostly can’t, and an algorithm that pretends otherwise is going to mislead people.

Buyer matching is another area where Iconic is applying AI. What signals or data points can machine learning uncover that traditional brokers may miss when identifying the best strategic or financial buyer? 

A traditional broker works from a network they’ve built over years. That network can be excellent. It’s also local and finite. A broker in the Midwest running a manufacturing deal knows the buyers in their region. They probably don’t know about the PE firm doing a quiet roll-up in that sector, or the family office that just exited a similar business six months ago and is actively looking.

Machine learning runs on a different signal set entirely, with active acquisition searches, historical transaction patterns, stated investment mandates, web behavior that indicates deal-hunting mode, and industry adjacencies that a human might not think to check. That surfaces buyers no individual broker finds through their contact list.

It also does something subtler, which is weighting. Not every qualified buyer is the right buyer. The best match is usually the one where synergies are real, cultural alignment is there, and the seller’s people and legacy are likely to be preserved. Those signals exist in data. They’re just not being used yet.

The lower-middle market has historically been underserved by investment banks. Why has this segment been overlooked for so long, and what economic opportunity does that create for technology-driven platforms?

Pure unit economics. Investment banks are built around fee structures that work on large deals. A $500 million transaction generates the revenue that justifies the team and the timeline. A $3 million transaction doesn’t, at least not without a completely different cost structure. So the banks went upmarket, and Main Street got whatever local brokerage infrastructure already existed, which was fragmented and mostly undercapitalized.

That’s not a failing of the banks. It’s just math. And technology changes the math. When AI handles the work that used to require significant labor hours, the economics of a smaller deal become viable. You can run more transactions with better outcomes at a fraction of the previous cost. That’s what actually opens this market.

The stakes are real. There are trillions of dollars in small business equity sitting in this segment. Most of those owners have never had access to real transaction infrastructure. That’s the opportunity we’re after.

Looking ahead, do you envision a future where AI agents can independently handle significant portions of the M&A process, from sourcing and diligence to negotiations, or will human judgment remain the decisive factor in major transactions?

AI agents will handle more of this process than most people expect, and faster. Sourcing, deal preparation, data room management, diligence review, and early term structuring. The underlying capabilities are developing fast enough that significant AI involvement in all of those areas is a near-term reality.

Where human judgment stays is in the moments that actually decide whether a deal happens. A seller deciding if they trust this buyer with what they built. A negotiation that comes down to something no model is going to capture, like whether the new owner will keep the 30 people who’ve been there for 15 years. These are judgment calls made by people with real stakes.

The version of this I’m building toward isn’t AI running the process while humans observe. It’s AI absorbing the operational grind so that advisors can spend their time on the part of the job that only they can do. This is the largest transaction in a business owner’s life, typically. Having a credible human guiding them throughout and helping to uncover nuance in offers that AI cannot catch is why this will always fundamentally be a human endeavor augmented by AI.

Thank you for the great interview, readers who wish to learn more should visit Iconic.