The AI Go-to-Market Gap: Why the Biggest Problem in AI Isn't the Technology
WRITTEN By Fluvio Founder & Managing Partner, Devon O’Rourke
AI is the dominant force in tech right now. New AI companies are launching daily, traditional SaaS companies are pivoting to roll out AI offerings or reposition entirely, and AI leaders are shaping the foundation of a new market. Investment is at an all-time high.
Yet adoption and ROI are not following the money. Many companies assume this is a technology problem, that more dollars into the product and underlying models will sort it out. What we're actually seeing is a go-to-market problem.
The go-to-market is broken
AI go-to-market is broken. Traditional product marketing frameworks were built for SaaS products with stable features, deterministic outputs, established categories, and buyers who know how to effectively compare options. AI is disrupting all of these.
Products in this era are harder to explain. They're built on opaque models with probabilistic approaches and variable outputs dependent on user skill. This creates a trust bottleneck that slows adoption before the product ever gets a real chance to prove its value. New categories are being created. Existing categories are being contested. And sales cycles are adding layers of evaluation that didn't exist before: AI governance councils need to be involved, buyers require more education to understand both products and competing options, and many of the standard go-to-market motions like PLG and demos don't address these changes.
Product-led growth depends on users discovering value through self-service, but when the product is probabilistic and the experience is a blank text box, users churn before they ever see what the product can actually do. Someone signs up for a free trial, types in a prompt they haven't thought through, gets a mediocre response, and concludes the product doesn't work. The product might be exceptional for their use case. They'll never know because nobody guided them to the right starting point.
Demos face the parallel problem: they depend on predictable, repeatable outcomes, and AI products are non-deterministic by nature. A sales engineer runs a live demo and the AI generates something different from what it produced in rehearsal. The "wow moment" doesn't appear on cue, or at all. The prospect leaves underwhelmed, not because the product is bad, but because the sales motion wasn't designed for how the product actually behaves. We saw this play out publicly when Meta's AI showcase produced inaccurate and embarrassing outputs on stage. If one of the most well-resourced AI companies in the world can't guarantee a clean demo, the format itself is the problem.
These aren't edge cases. They're the standard experience for companies trying to sell AI products with motions built for a different era.
The flattening
The result is that AI products are converging into indistinguishable commodities. Ask the average customer what the difference is between Claude, ChatGPT, Gemini, and other AI products and you probably won't get a meaningful answer. The experience itself is flat; AI tools are often just blank text boxes with logos, or hidden and embedded in the background of other software with no distinct identity.
Then there's the use case confusion. Beyond which AI product is better at which tasks, AI has been sold as a do-it-all silver bullet. Users don't actually know what AI is good at or bad at until they have poor experiences and lose trust. They ask an LLM to do math and get confident-sounding wrong answers. They ask it to cite sources and get fabricated references. They ask it to build a financial model and get something that looks polished but doesn't hold up under scrutiny. These aren't model failures. LLMs are word-probability machines. They're exceptional at synthesis, pattern recognition, and language tasks. They're unreliable for precise calculation, factual claims, and tasks that require deterministic accuracy. But nobody told users that, or at the very least, they haven't been loud enough. The industry marketed omniscience and delivered probability, and the gap between expectation and reality is where trust goes to die.
This flattening of product experience and differentiation has pushed AI into CPG-style brand competition. But expensive, high-complexity, high-consideration products aren't designed for that. Nor have tech companies built the muscle and strategy for CPG-style brand warfare. Instead they lean into the classic tech solution of rolling out more features, and this furthers the flattening rather than solving it. More capabilities that users can't distinguish and don't know how to use doesn't solve a narrative and trust problem. It deepens it.
The identity crisis
All of this is exacerbated by boards and executives rushing to rebrand their organizations as AI companies to position for an AI era that's still in flux. They're asking "how do we become an AI company?" rather than asking the right question: "how does AI make us the best version of what we already are?"
This is a critical issue for SaaS companies making the pivot. Rather than solidifying their positioning in the category they've established and making clear how AI takes them to the next level, they're abandoning the identity they've built and entering a fight for category position that might not exist in a year or two, for buyers whose needs are still unclear. A CRM company adding AI-powered features isn't becoming an AI company. It's becoming a more intelligent CRM. A project management tool with AI capabilities isn't an AI platform. It's a better project management tool. But the market pressure to claim the AI label is so intense that companies are surrendering the category specificity that buyers actually need in order to understand what the product does and why they should care.
Many are making this bet while simultaneously cutting the marketing and communications teams that would be responsible for making any positioning work at all. The strategy and the staffing decisions are moving in opposite directions, and the companies doing this will pay for it. Either the AI pivot doesn't land and they face a painful correction, or they rebrand again in a few years when the market demands clarity over buzzwords. Both paths are expensive and avoidable.
The investment paradox
All of this points to a fundamental misunderstanding and divergence in strategy between the companies building AI and the companies buying it. The companies building AI are heavily investing in human-centric marketing, communications, and strategy roles. The companies buying AI are cutting those teams and trusting AI to replace them. The people who understand the technology best don't trust it to do this work. That should tell everyone something.
For AI investments to land, companies need to be more in tune with their customers than ever before. Feedback from the customer has to flow quickly to product and sales. Sales needs support on how to frame and drive AI conversations. The market needs constant education, messaging, and GTM execution that differentiates solutions, tells buyers what the product works best for, and shows them how to get the most out of it. This is where many AI companies have fallen into a trap.
The consumer-grade trap
Rather than building that clarity, they've shipped consumer-grade tools into business environments. Tools designed to drive engagement, tell users what they want to hear, avoid transparency about capabilities and limitations, and prioritize confidence over accuracy.
This is a design philosophy problem, not a bug. And it isn't getting fixed quickly. The major AI models are tuned through feedback loops that reward responses that feel helpful and sound confident (we’ve all experienced the sycophantic, falsely empowering tone). The models learn to agree with the user, validate their assumptions, and produce polished-looking output regardless of whether it's accurate. Ask a model to review a flawed strategy and it will tell you it's great. Ask it to analyze data and it will produce charts with fabricated numbers that look completely real. Ask it a question it can't answer and it will answer confidently anyway.
For consumers, this might be inconsequential and even pleasing. For business users, it leaves them sour. AI doesn't help them. It wastes their time and erodes their trust. In high-stakes contexts, the consequences are worse than wasted time. Lawyers have submitted AI-generated briefs citing fabricated case law and nonexistent legal precedents, facing sanctions and professional embarrassment because the tool sounded authoritative and they didn't verify. This is what happens when consumer-grade confidence meets professional-grade stakes. It makes the case that human expertise is more valuable than ever, not less. AI doesn't replace the need for people who actually know their domain. It amplifies the cost of not having them. And yet they're told by their managers they still need to use it. Meanwhile, AI founders are publicly messaging that AI will replace workers, reinforcing a competition based on fear that nobody wants to live in. Resentment grows. Adoption stalls. The core problem is that the personas, the ICPs, and their unique needs were not in the room when the product was being built.
Cutting product marketing and other human-centric roles with proximity to users only reinforces this loop. The companies that understand their customers best will win. The companies that replace that understanding with AI-generated assumptions will build products the market doesn't want, position them in ways that don't resonate, and wonder why adoption never materializes.
The path forward
Technology has never been the hero. It's always been a tool. Unfortunately, the excitement over the potential of AI seems to have made everyone forget that. We don't cheer for the Iron Man suit. We cheer for the person in it.
Every company right now is leading with "we use AI" or "AI will take over this" or "AI will change that." The buyers and users on the other end are wondering where they fit. Is AI going to replace them? Make them vulnerable? We've seen founders pivot their messaging to "the workers who use AI will replace the workers who don't," reinforcing a work competition rooted in fear rather than potential.
The dream of AI is abundance. The message should be "here's how AI helps you become better at what you already care about."
Both AI-native companies and SaaS companies navigating their AI pivot need to dig into the specificity of AI's best fit. Not "our AI helps with sales" but "here's exactly how a regional sales manager uses this to prep for their Monday pipeline review, here's what it does well, and here's where they should apply their own judgment." That level of specificity is what creates differentiation in a flattened market. It's what builds trust and drives real adoption.
They need to be honest over agreeable. When a company tells users what their AI is not good at, it paradoxically increases trust in what the AI is good at. Setting boundaries isn't a weakness. It's a competitive advantage that almost nobody is leveraging. Think about the professionals you trust most in your own life. Your doctor doesn't say "I know exactly what's wrong." They say "based on what I'm seeing, here's what I think, here's what I'm less sure about, and here's what we should test." That transparency is what creates trust. AI companies should be doing the same thing.
They need to build the human-AI partnership explicitly. Not assumed, not implied, not buried in a tooltip. Users need to know: here's what the AI will do for you, here's what it needs from you, here's how you'll know when to trust the output and when to verify. This isn't just a messaging exercise. It should shape onboarding, product experience, sales conversations, and customer success. The companies that build this clarity into every touchpoint will earn the trust that everyone else is losing.
And they need to ground all of this in real research. What do buyers actually think about AI in your category? How do they evaluate it? What are their trust barriers? What use cases matter most to them? What are their challenges, pain points, and unmet needs? What goals are they trying to reach, and where does AI actually help them get there? You can't answer these questions from a conference room. The companies getting AI GTM right are talking to their market, not guessing at it.
Marketing is an AI investment
Marketing should be considered an AI investment. The best AI companies in the world are proving this with their hiring decisions. The companies whose AI bets are paying off are the ones investing in the human-centric strategy, positioning, research, and go-to-market execution that makes AI products feel unique, not commodities. The companies whose AI investments are stalling are the ones that built the product, skipped the go-to-market, and expected the technology to sell itself.
This is where product marketing becomes the connective tissue that makes everything else work. Product marketing is the function that sits between what the company builds and how the market receives it. It translates customer insight into product strategy so engineering builds what the market actually needs. It equips sales teams with the language, tools, and frameworks to have conversations that educate and build trust rather than just pitch features. It gives customer success the narrative to help users get real value and stay. It ensures that positioning, messaging, and competitive strategy are grounded in evidence from the market, not assumptions from the leadership team. In an AI world where products are harder to explain, trust is harder to earn, and adoption is harder to drive, this connective role isn't a nice-to-have. It's the difference between an AI investment that pays off and one that doesn't.
Every problem outlined in this article traces back to the same root: the voice of the customer and the voice of the market were not present when critical decisions were being made. Products were built without understanding how buyers would evaluate them. Positioning was created without knowing what language resonates. Sales teams were sent into conversations without the tools to educate and build confidence. Use cases were assumed, not researched. Trust was expected, not earned. Strategic product marketing fixes all of this, not by doing one thing, but by being the function that connects market reality to every team that needs it.
The AI industry has a choice. Keep applying old playbooks and watch adoption stall, or recognize that the go-to-market challenge is fundamentally different and invest in solving it. The technology is ready. The go-to-market needs to catch up.