Why 90% of AI tools fail due to lack of visibility and marketing and not because of a bad product?

Read the report done by Rank of AI that analyses why 90% of AI tools fails including the causes and how to not fall in the same traps.

Why 90% of AI tools fails due to lack of visibility.

The startup landscape is notoriously harsh and unforgiving. Around 80-90% overall of SaaS / AI startups mirror the trend of failure. We have done a deep research on the topic on why this happens and how you can save your AI Tool from failing.

How many AI/Saas Startups fails?

As mentioned briefly in our introduction the order of 80-90% overall including AI tools/SaaS and AI startups often follow the trend of failure. Nearly 90% of all startups fails, and roughly 70% of SaaS companies close within five years from their formation. In a recent study done by AI Gravity Labs of AI-focused ventures reported that on 200 AI startups tracked between 2023–2025, about 90% eventually failed, with an average lifespan of just ~18 months. These analyses underlines that most of AI startups/AI tools don't survive for long.

Marketing vs. Product
One case study done by CB Insight and related surveys suggests that the market and marketing issues are the cause of these failures, particularly the "no market need" is the cause of 35%-42% of AI startup failures. At the same time around 14-22% of startups fails due to inactive marketing or go-to-market strategy. In simple terms even the best products ofter struggle because of lack of visibility. In short lack of visibility can doom AI tools and AI startups: the usage of superior technology alone is rarely enough to guarantee the success.

Why so many AI/Saas Startups fails?

As mentioned before product-market fit and marketing are tightly connected. To give an idea of the main causes of failures of a startup we are using the following example of a report that aggregates key failures reasons, which was done by Founders Forum:

42% of startups never meet a real customer need,
28& of startups or AI tools run out of funding,
23% fails for team issues,
19% of AI SaaS get outcompeted,
18% fails due to pricing/costs problems,
17% have a poor product quality,
14% for poor marketing

While another report's dataset finds that:

34% of AI startups failures are due to poor market fit,
and 22% due to marketing strategy.

Top reasons why AI SaaS and AI Tools fails

Top reasons on why AI Tools fails

These numbers suggests that roughly half of AI Tools and startups involve either a market fit problem or not adequate marketing. In other words, it doesn't matter how innovative the AI tool is, without effective outreach the product fell unseen. This pattern is confirmed by surveys done to tech founders which found that 22% attribute their startup's collapse to 'lousy marketing strategy'.

Case studies for AI Saas products that flopped

There are many, numerous real-world examples that illustrates the marketing trap. Let's take Notion as our first example. Notion for many years remained really niche while being a technically impressive workspace tool. Only after refining its marketing and message it exploded in popularity.

Cumulative AI SaaS Failure Rate Over 5 Years

Here's more examples of case studies on why and how AI SaaS product flopped:

Legalmind -- an AI legal startup achieved 94.7% of accuracy on benchmark tasks, but learned in the hard way that lawyers didn't trust their AI. As the founder Sarah Kim reflected, “We thought superior performance meant automatic market domination… we forgot that lawyers don’t want black boxes making decisions about their clients’ lives.”. In brief, LegalMind's technical quality and results couldn't overcome a misalignment with what customers actually needed.
Two more cases are ImageForge vs Midjourney (both AI art generators). The story behind both is that ImageForge invested heavily in training advanced models but spent only a few thousand dollars on marketing; its user base never exceeded ~500. By contrast, Midjourney built a “good enough” model ahd focused obsessively on community growth (notably using Discord), ultimately reaching ~200 million users and $200M+ in revenues. In shorts, distribution won the battle.
Jibo, Anki, and Kuri are some other examples of a field adjacent to AI: consumer robotics. These companies raised tens of millions and wow audiences, yet “none found a sustainable business model”. Their products were high-quality machines, but these companies failed to target the right market or communicate value.
Another example is Builder.ai -- an app-development-as-a-service platform that marketed an AI assistant called Natasha, but in reality the tasks were routed to hundreds of human engineers. When their clients discovered the true model, trust evaporatd, Builder.ai declared bankruptcy despite raising a sum of $1.3B. This is a telling example of how overpromising on AI while neglecting honest marketing leads to failure.

All these examples of case studies of AI tools/SaaS share one common theme: the products were technically viable or even innovative, but failed to gain traction because the didn't have the right messaging, positioning, or their go-to-market were weak. On the other side success stories often are related to strong marketing as for the following examples:

Figma succeeded not just on their tools designs but also on viral community marketing and a clear "collaboration" narrative.
Zapier invested heavily in educational content (tutorials and webinars) to show their value to users.
Discord targeted a specific user segment (gamers) with a laser focus on community-buildining.

Each on of these companies matched its messaging to a real audience need, contrasting with failed peers that pitched features to everyone.

What are some of the common marketing pitfalls for AI Tools?

AI SaaS Failure Attribution

The analysis of failed AI Tools (in tech and SaaS overall) reveals recurring marketing mistakes:

Feature-focused messaging: The most common error is that many founders list technical features (algorithms, models, APIs) instead of articulating user benefits or problems solved. This “feature dump” approach can confuse potential customers.
Unfocused targeting: AI tools founders often says that their product is “for everyone” or “for all industries,” which dilutes their message. Without a defined ideal customer profile (ICP), marketing campaigns fail to resonate
Under-investment in marketing: Early AI SaaS ventures frequently ignores marketing budgets. Industry benchmarks (Gartner) show mature SaaS spend ~45% of revenue on sales/marketing, yet many startups operate on <10%. Insufficient spending makes user acquisition painfully slow or nonexistent.
Misaligned branding and messaging: When founders are deeply technical, they may use jargon or emphasize wrong value drivers. If marketing narratives don’t match what customers care about, adoption stalls Inconsistent or overly technical brand voice confuses buyers.

Founders often admit being "hyped by the technology” and neglecting outreach. The net effect is a sophisticated product that nobody hears about. As analysts note, this “distribution delusion” – building a great solution but failing to distribute it – is fatal. In one survey, poor distribution/marketing was an explicit cause in ~22% of post-mortems. Put simply, ignoring the go-to-market plane leaves even excellent tools stranded.

How to improve visibility and traction for AI Tools and AI SaaS Startups?

There are many ways to avoid the “good product, no audience” trap, we have included the best ways that AI SaaS startups should adopt for marketing-oriented strategy from day one:

Validate and segment your market: First thing first, AI Tools founders should start with customer development. They should conduct user interviews and surveys focused on real problems (not product features). It's important to identify the smallest viable niche that has a pressing need(known as the "minimum lovable product" customer). Then build detailed personas and refine the value proposition in their terms. This counters the common risk of creating a solution for a problem that customers aren't asking for.
Position the product as a solution to a problem: Secondly, it is fundamental to craft a message, that, emphasizes outcomes, not tech features. For example, rather than saying "powered by AI neural nets" say "automates sales workflow to save managers hours of work per week". Ensure that all the marketing copy (website, pitch, ads) highlights clear benefits and examples.
Submit it on Rank of AI: We're building a platform for AI Tools, to improve visibility and gain traction. Rank of AI is an AI Tools directory that aggregates together new AI tools every day so users online can find those of their interest in one single platform. Here's how Rank of AI helps AI Tools / SaaS to gain visibility and traction:

Curated visibility: Our traffic comes mainly from AI enthusiasts who are finding their next AI Tools to use, which means your customers are already available on our platform.
Brand Authority: You can create a social proof and edorsement effect by being on Rank of AI alongside thousands of other AI tools. It will allows your leads to find you and trust your product quickly.
High Quality SEO Backlink: Rank of AI provides a dofollow backlink that allows your website to rank higher on Google and search engines and increase organic visibility/traffic over time.

Build a marketing roadmap: Everything related to the product should be related also to marketing. It's fundamental to plan a content calendar for each feature release, update or information. Early-stage AI Tools founders can also leverage low-cost tactics like thought-leadership posts, content focused on SEO, do webinars, and partnership with other brands. These kind of methods can generate initial awareness without huge budget.
Allocate adequate budget and resources: AI founders should gradually increase marketing spend as traction grows. Ignoring to fund basic outreach almost guarantees obscurity. If resources are really limited, prioriting one or two high-impact channels ( for example: content marketing and targeted ads ) and excel at them is reallu fundamental.
Iterate on positioning and strategy: Founders should be prepared to pivot marketing tactics if initial assumption fails. It is also important to monitor customer feedback and adapt the message. In short don't treat marketing as an afterthought.

Conclusion

As a conclusion of our research we can confirm that the myth that “90% of AI/SaaS tools fail due to poor product quality” ignores a crucial reality: marketing matters. Empirical data and case histories suggest that many AI and SaaS startups fold not because their algorithms were bad, but because few customers knew or cared about them. In fact, building traction is where “companies live or die” – technical merit alone is “table stakes”. Startups with sound products can still fail if they ignore visibility and alignment with real customer needs. Conversely, those that prioritize clear messaging, targeted outreach, and community engagement often thrive.

In practice, founders should front-load go-to-market planning in parallel with development. Statistical reports and expert opinions consistently show that a sizable share of failures is avoidable by improving marketing efforts. By learning from past failures and embracing a market-driven approach, AI/SaaS entrepreneurs can dramatically improve their odds of success.

References for this research:

FAQs

Why do most AI SaaS startups fail?
Most AI SaaS startups fail not because of poor product quality but due to ineffective marketing and lack of visibility. Studies show that around 90% of startups fail, and a large portion do so because they never reach their target audience or fail to validate market demand.

What is the main reason AI tools go unnoticed in the market?
The leading reason AI tools go unnoticed is poor go-to-market strategy. Founders often focus too much on building advanced technology without investing in marketing, branding, or customer education—resulting in a great product that no one knows about.

How many AI startups fail due to marketing, not product issues?
Industry data suggests that over 50% of AI SaaS startup failures are tied to marketing and product-market fit issues, while only 17% fail due to poor product quality. This shows that obscurity, not performance, is often the downfall.

Can good marketing save a mediocre AI product?
Yes—strong marketing and positioning can make a technically average product succeed if it resonates with user needs and builds trust. On the flip side, even the most advanced tools can fail if they lack audience awareness and proper targeting.

What are common marketing mistakes AI SaaS startups make?
Common marketing mistakes include:

Targeting too broad an audience
Using overly technical language in messaging
Underfunding marketing efforts
Launching without a clear brand story or customer persona
Focusing on features instead of benefits

What percentage of SaaS startups fail within five years?
According to industry benchmarks, around 70% of SaaS startups fail within the first 5 years, with the highest risk in the first 2 years due to weak customer acquisition strategies and lack of market traction

Are there examples of AI startups that failed due to obscurity?
Yes. Startups like ImageForge and LegalMind had strong products but failed to scale due to poor marketing or customer alignment. Conversely, Midjourney and Figma succeeded by focusing heavily on community building and clear value communication.

What can founders do to avoid failure from obscurity?
To avoid failure, founders should:

Validate market demand early
List their AI product on Rank of AI to get traffic that comes mainly from AI enthusiasts, brand authority and high quality SEO backlink.
Develop a go-to-market plan alongside product development
Invest in content marketing, SEO, and community engagement
Focus on solving specific user problems, not just building features
Start marketing before the product is fully built

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