In December, I had a conversation with Matthew Stein on the Agent.ai podcast about AI predictions for 2026.
The discussion ranged across models, platforms, media, economics, and some fairly bold ideas about where things might break or consolidate next. On the surface, it was framed as a set of predictions. But as I revisited that conversation, what stood out was something more practical.
Almost everything we talked about shows up directly in the day-to-day reality of go-to-market teams.
Not as abstract future bets, but as signals that are already shaping how GTM leaders think about tooling, trust, productivity, and ROI. These are the same tensions I hear in conversations with CROs, CMOs, RevOps leaders, and heads of customer success who are trying to make sense of AI while still hitting numbers.
So rather than restating the predictions, I want to reframe them for this audience. Below are five AI signals pulled from that conversation that I believe are especially relevant for GTM leaders heading into 2026.
Signal 1: AI Is Becoming an Operating Layer, Not a Feature
One of the strongest themes from the conversation was the idea that AI onboarding will become as important as employee onboarding.
For GTM teams, this is already playing out.
The teams getting value from AI are not the ones with the most tools. They are the ones that have figured out how to operationalize context. Brand voice, customer definitions, ICP nuance, pricing logic, and internal judgment calls are being treated as inputs that need to be structured and maintained.
This is not about prompt cleverness. It is about operating discipline.
As AI becomes embedded across sales, marketing, RevOps, and customer success, the question is no longer “do we use AI?” It becomes “can we onboard it consistently, safely, and repeatably?”
That is an operating maturity signal, not a tooling one.
Signal 2: Model Innovation Is No Longer the Point
Another prediction that resonated strongly was model fatigue.
New releases still matter at the margins, but for most GTM use cases, text quality has crossed the “good enough” threshold. Incremental improvements are becoming harder to feel, and excitement is flattening.
From a GTM perspective, this shifts buying behavior.
Leaders are caring less about which model sits underneath and more about where the capability lives in the workflow. Is it embedded where work already happens? Is it bundled? Is it defensible economically?
This is why we are seeing growing skepticism toward tools that simply wrap an LLM without solving a real operational problem. The differentiation is no longer intelligence. It is integration, judgment, and economics.
That compression will continue.
Signal 3: Trust Is Becoming the Scarce Resource
We also spent time talking about hallucinations, authority, and the idea that something akin to PageRank may re-emerge inside AI systems.
For GTM leaders, the implication is simple.
Intelligence without trust is unusable.
As AI-generated content, recommendations, and insights proliferate, teams are becoming more cautious about what they act on. Outputs that feel generic, ungrounded, or overly confident are increasingly ignored rather than debated.
This is where zero-party data and consent-driven context become important. Customers and buyers are more willing to share information when they understand how it will be used and when it clearly improves their experience.
Trust is not a feature. It is an outcome. And it is becoming a competitive constraint.
Signal 4: Discovery and Distribution Are Being Rewritten
Some of the more provocative parts of the conversation centered on discovery.
Zero-click search is accelerating. Short-form video and AI-generated media are exploding. And distribution is increasingly owned by platforms rather than content creators.
This is where the Gemini beating ChatGPT discussion occurred and matters.
Distribution often beats innovation. Platforms that own workflows, search, inboxes, or operating systems have structural advantages that are hard to overcome, even with better technology.
For GTM teams, this means long-held assumptions about SEO, content strategy, and funnel mechanics are under pressure. Volume is easier than ever to produce. Attention is not.
The winners will not be the teams producing the most content. They will be the teams that understand where trust, attention, and context actually converge.
Signal 5: Most AI ROI Will Remain Elusive, Quietly
One of the bolder predictions was that a large majority of companies will still struggle to show meaningful ROI from AI initiatives in 2026.
That sounds pessimistic, but it aligns with what many GTM leaders are already experiencing.
AI is delivering individual productivity gains. Faster writing. Better summaries. Cleaner follow-ups. But translating those wins into durable, organization-wide ROI is harder.
The gap is rarely the technology. It is readiness.
Data fragmentation, unclear ownership, weak onboarding, and lack of judgment frameworks all slow value realization. The result is not loud failure. It is quiet underperformance.
That pattern is likely to persist as teams continue to experiment, learn, and recalibrate.
Closing Thought
The original Podcast conversation covered a lot of ground, and not all of the predictions will land cleanly or on the same timeline.
What matters more is that the signals are already visible.
AI is no longer a novelty inside go-to-market teams. It is becoming infrastructure. And as with any infrastructure shift, the outcomes will be uneven. The teams that see durable impact will not be the ones chasing the newest model or the loudest promise. They will be the ones that operationalize context, earn trust, and make clear economic tradeoffs.
If you want the full, unfiltered conversation, you can watch the episode on Agent.ai to hear the original discussion in context. And if these signals resonate, this is not the end of the conversation.
I’ll be diving deeper into several of these themes in follow-up posts over the coming weeks and months, pulling apart where GTM teams are getting traction, where they are stalling, and what that says about readiness rather than technology.
If you’re curious about where this is heading, stay tuned. There’s a lot more to unpack.