4 GTM trends for AI startups to watch in 2026
Customer activation, tangible ROI, brand building & GEO
The 4 GTM trends I’m seeing across early-stage AI startups are:
Customer success as a pre-sales function
ROI tied to real cost savings
Brand building starting very early
LLMs as the starting point for discovery
Customer success as a pre-sales function
SaaS customer success has traditionally been a post-sales function, servicing customers in a mostly reactive manner after they’ve bought the product. This has changed for AI-native startups because the buying process for 99% of AI tools involves an evaluation (PoC / trial / pilot) which needs to be managed proactively to ensure prospects see the value and convert into paying customers.
Running a PoC for an AI-native tool is time consuming. At a minimum you need to define success criteria, onboard users (often individually), monitor usage, keep the buying committee engaged with weekly calls, produce ROI metrics and run a decision call to get a yes/no decision on a purchase.
If you also need to integrate with existing systems you’ll need at least one IT or infra team call and if the customer is also evaluating multiple vendors you’ll need extra 1:1 calls with end users to maximize usage and ensure they vote for you over your competitors. It all adds up to a lot of work.
The trend I’m seeing across the board is bringing customer success (or “customer activation”) managers into the pre-sale to run pilots from start to finish, with the salesperson joining only for the final call. Several of my clients are filling these roles from outside the traditional CS talent pool, bringing in folks from consulting and banking backgrounds who are accustomed to being dropped into unfamiliar situations, having to learn quickly and think on their feet.
ROI tied to real cost savings
The last six months have seen buyers taking a more considered approach to evaluating and purchasing AI tools, baking off multiple vendors and taking a harder look at the projected ROI. It’s no longer enough to counter on early adopter exuberance, CEO mandates or simple time savings. You need to show how investing in AI results in getting more productivity out of an existing workforce without adding more headcount.
For example, several of my clients sell AI products that reduce the time taken to perform repetitive tasks; Scope reduces the time to produce industrial inspection reports, Fleetcraft reduces the time to complete aircraft maintenance paperwork, Henry reduces the time to produce commercial real estate pitch decks, Tracelight reduces the time to produce financial models in Excel.
For all of these companies the ROI story has a similar structure:
Without their product, the customer can complete X tasks per month with their existing team.
With their product in place they reduce the time spent per task by T minutes, saving a total of X*T minutes per month, which in turn creates X*T minutes of additional monthly capacity for the existing team.
As long as their product costs less than the cost of hiring additional people, its ROI positive for their customer to buy their product.
Brand building starting very early
The barrier to building an AI product is very low, which is why there are so many AI startups cropping up in every category. Standing out from the crowd has become less about differentiating on product features and more about building a brand that drives inbound interest.
Early-stage startups are building brands by positioning their founders as the go to AI expert in their space. Founders like Cecilia Ziniti at GC AI, Sammy Greenwall at Henry, Peter Fuller at Tracelight and Simba Jonga at Laborup have all been doing this aggressively since they were seed or even pre-seed with a handful of customers and are all growing quickly as a result.
From a tactical standpoint this can be as simple as posting regularly on LinkedIn and speaking at events (all of the above do a great job of that), or taking it a step further and running full AI classes for your target market as GC AI has been doing since day one to fill the top of their funnel. You no longer need to dump money on ads and sponsorships to build a brand.
LLMs as the starting point for discovery
Customers looking for AI solutions are increasingly using LLMs as their starting point for discovery, forcing vendors to adapt their existing content to appear in the results — a process known as Generative Engine Optimization or GEO.
The basic principles for GEO are:
Optimize for prompts (questions) not keywords. Phrases like “What is {Category}”, “Best tools for {Category}”, “Comparison {VendorA} vs {VendorB}”, “Is {Vendor} secure?”, “Best alternatives to {Vendor}”.
Less fluff, more substance. Remove all jargon and hyperbole from your content and ensure you are using plain language with short paragraphs, bullet points, and explicit definitions.
Build out pages that LLMs love. Documentation, FAQs, help centers, feature pages and comparison pages, using consistent phrasing and definitions that LLMs can converge on. Educational content gets citied more than sales pages so be fair and factual.