7 trends from the field
At this point, everyone’s tired.
Tired of LinkedIn thought leadership, empty AI promises, and tools that do half the job and vendors who claim they’ll 10x output when they can’t even write a decent subject line.
In 2023, AI was exciting. In 2024, it was chaotic. In 2025, it got… boring.
The AI curve is flattening, and the dust is settling. Teams are starting to focus less on “using AI” and more on building workflows, processes, and systems that actually generate outcomes.
Below are seven trends we’re watching closely as we move into 2026. Some are obvious and others under-discussed. All of them are grounded in what we’re seeing on the inside, from GTM teams who are trying to scale and not get left behind.
The internet is now mostly AI-generated. Content, emails, internal docs all blur together.
In theory, AI made things faster. In practice, it made them flatter. Everything sounds and looks the same. Every graphic and illustration has a tepid, off-white background color. As a result, audiences have gotten sharper. They’re asking: Did a person make this? Does this sound like you? Do I trust it?
2026 will reward teams who lead with storytelling and specificity. Not just in brand, but in product launches, in sales decks, in how you pitch a partnership.
Storytelling isn’t fluff, it’s functional. It’s what moves deals forward, aligns internal teams, and creates momentum when everyone else is stalled.
Expect a premium on human input, brand voices that actually feel like someone is behind them, and authentic messages versus LLM fluff.
In October, Aerie publicly vowed to never use AI in ads. It wasn’t a gimmick. It was a signal. Consumers and buyers are reaching their saturation point, and companies are starting to respond.
AI saturation and skepticism is no longer fringe. It’s growing in tech, in retail, in education, in creative sectors. The lack of regulation, rising job loss, and “fake content” fatigue are fueling it.
We’re seeing brands position themselves against AI as a way to build trust. Not just with customers but with investors and employees. Expect this to show up more prominently across campaign creative, hiring messaging, and product marketing.
You can expect a reversion to genuine assets such as real photos, real teams, real expertise. Not because it’s trendy but because it works.
The erosion of digital trust due to AI isn’t just a marketing problem, it’s a go-to-market problem.
As buyers grow more skeptical of everything online, the value of human-to-human interaction increases. Events won’t be nice-to-haves. They’ll be increasingly at the center of your motion.
But it won’t be the old model of giant booths, fake conversations, and QR-code badge scans. We’re talking smaller, high-trust formats:
Trust is harder to fake in real-time and face-to-face.
The gray areas around AI are shrinking.
States are already making moves. California passed new laws requiring AI developers to disclose risk assessments and show how their models operate. They’re forcing companies to document real safety practices, which means governance is no longer something that can be ignored or deferred. What used to be abstract is now showing up in contracts, audits, and customer questions.
GTM teams won’t be exempt. Companies will need to show which models are powering what, where human review happens, and how outputs are audited. Not just to clear legal hurdles, but to build trust with buyers, partners, and internal teams.
The smart GTM orgs won’t wait to be told. They’ll get ahead of it. In 2026, governance won’t just be about compliance, it’ll be a trust signal and a competitive edge.
Most teams have no idea which model their AI tools are using. That was fine in 2023 but won’t be in 2026.
The market is getting smarter. Leaders are asking: Where was this trained? Can I trust the output? Can we audit this?
One reason for the shift is performance. New models like Gemini and Claude are passing law and professional exams that earlier versions failed outright. It’s forcing teams to see model choice as a strategic lever tied to accuracy, risk, and credibility, not just a backend decision.
Expect more scrutiny, not less. Enterprise buyers are demanding documentation. Operators are leaning toward open-source or fine-tuned models where trust and control matter.
This doesn’t mean everyone needs to become a machine learning expert, but it does mean GTM teams will need to evaluate AI vendors like they would any other platform based on actual performance, not branding.
AI was supposed to clean up workflows but did the opposite.
Teams raced to plug in AI tools without rethinking how work actually gets done. Now there’s bloated tech, overlapping automations, and broken processes everywhere.
In 2026, the smartest teams will go into cleanup mode. They’ll deprecate the tools that never got past the demo stage. They’ll build simpler, cleaner systems that make fewer promises and actually deliver.
AI will stay. But it will get quieter, less performative, and more pragmatic.
Forget the prompt engineer hype. The real AI-native roles are hybrid:
These aren’t innovation roles. They’re operational ones.
In the same way Salesforce created the need for CRM admins, AI will create demand for GTM pros who can operate inside AI-enhanced workflows without slowing things down or introducing risk.
Hiring for these roles now gives teams a head start. Ignoring them means watching someone else scale faster with the same headcount.
The fundamentals still matter.
AI doesn’t replace the need for GTM discipline. It just raises the stakes for doing it right.
At F3, we sit inside the teams doing this work, not advising from the sidelines. We’ve helped companies shrink bloated tool stacks, build real AI systems, and rethink go-to-market execution from the ground up.
If that’s what you’re aiming for in 2026, we should talk.
Let’s build something that works.
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