Most software and AI companies are making the same go-to-market (GTM) mistake without realizing it. They are layering new AI capabilities onto existing GTM strategies instead of rethinking how those strategies should evolve. The tools are changing. The approach is not. Mike Peroni, a revenue and GTM leader who helped engineer a $1.2 billion acquisition at ETQ, has a precise view of where that breaks down and what companies need to do differently.
“AI is not just an efficiency layer,” Peroni says. “It is a competitive divider. You can use it to create separation, or you can treat it as an add-on and slowly fall behind. The companies that win will continuously adapt how they go to market as the technology evolves, not just once, but as an ongoing discipline.”
How a $1.2 Billion Acquisition Gets Built
At ETQ, Peroni built the company’s first indirect sales channel and strategic alliance program. The goal was not only to drive revenue but to expand market perception and create a credible path to acquisition. Rather than treating the partnership as a theoretical exercise, he took it directly into a live customer environment. That decision changed everything.
The feedback loops he established quickly exposed what resonated, what did not, and most importantly, what had not been considered at all. Machine utilization and visibility into the hidden factory emerged as critical value drivers, insights that were not part of the original pitch but became central to the value story.
“The biggest value did not come from validating the proposition,” Peroni says. “It came from what we discovered in the field.” That real-world validation shaped a market message strong enough to support a $1.2 billion valuation. The most powerful value propositions are not built in boardrooms. They are forged in the field.
The Differentiation Problem AI Has Created
The most urgent GTM challenge today is one that most leadership teams are still underestimating. Features that once took months to build can now be replicated in days. As access to AI tools becomes widespread, product-based differentiation is eroding faster than most competitive strategies were designed to handle. What used to be a moat is becoming table stakes.
“If your differentiation is features, you do not have a moat anymore,” Peroni says. “The only defensible advantage is proprietary insight, data, or execution velocity that compounds over time.” Organizations now face a clear choice: move faster than competitors can replicate, or build an advantage in ways that cannot be easily copied.
This shift is also changing how organizations need to think about automation. Most companies still treat it as a one-time transformation initiative. The ones pulling ahead are operationalizing continuous automation as a core capability, which requires new roles, structures, and teams embedded close to revenue and customer workflows, whose job is to identify and execute incremental automation opportunities in real time rather than waiting for centralized initiatives.
Immediate opportunities are not in short supply: automating customer relationship management (CRM) updates improves data accuracy and frees sales capacity, real-time deal insights can be generated from activity signals, and AI can be applied to legal and contract review workflows. More advanced use cases, including AI-driven sales development representative (SDR) functions powered by intent data, show promise but warrant disciplined experimentation rather than aggressive scaling, given current limitations in data quality and signal reliability.
The Frontier Problem Nobody Has Solved
Beyond near-term GTM execution, a larger, less-understood challenge is emerging. Traditional search engine optimization (SEO) was built for search engines like Google. Large language models operate differently, surfacing answers through mechanisms that are far less transparent and constantly evolving. At this point, there is no reliable method to ensure a company appears in AI-generated answers. “We do not yet know how to consistently influence what AI surfaces in buying moments,” Peroni says. “And that is where decisions are increasingly starting.”
Companies are no longer just competing for rankings. They are competing for inclusion in the answers themselves, at the exact moment buyers are forming opinions and building shortlists. Organizations that begin investing now in understanding this shift through content strategy, authority building, and structured experimentation will develop a compounding advantage. Those that wait may find themselves invisible precisely when it matters most. The companies that treat AI as an add-on will keep pace. The ones that rethink how they go to market and build the capability to continuously adapt will pull ahead. The gap between the two is only beginning to form.
Follow Mike Peroni on LinkedIn for more insights on GTM strategy, revenue architecture, and competing effectively in the AI era.
