62% of B2B SaaS teams say data trust is their #1 limitation with AI.
Nearly two-thirds of teams have invested in AI tools but can't rely on them for decisions. They're stuck in a never-ending state of "trust but verify," turning what should be efficiency gains into extra work for their teams..
When we used Wynter to survey 100 sales and marketing leaders at $50M+ SaaS companies, we uncovered a trust crisis that goes beyond just accuracy concerns. It's about integration nightmares, hallucinating outputs, and AI tools that create as much work as they eliminate.
Our research revealed three interconnected issues destroying trust in AI tools:
"The biggest lesson is that how good your outputs are is 100% dependent on how good your inputs are. Whether it's to ensure you have all the relevant data, APIs, background information, etc, the quality is based on how good it is."
The math is simple but brutal: Bad data + AI = automated mistakes at scale.
Teams are discovering their CRMs are messier than they thought:
"We've never quite been able to master lead scoring. There have been too many drivers and nuances that we haven't been successful in accurately labeling a lead with a measurable measurement."
18% of teams specifically called out AI hallucinations as a trust killer. And they're not wrong to worry.
"Always double check AI output. Hallucinations are a real problem, and it is your job to check the accuracy of output."
The challenge gets worse in specialized industries:
"I work in an industry where it's important to make accurate claims, or where we need to be sensitive about claims we're making because of competitive product and feature overlaps with some of our partners, and AI hasn't picked up on that level of nuance yet."
This forces teams into a constant verification loop:
"Current solutions still require a lot of handholding. Even sophisticated AI solutions are still hallucinating and have to be checked by humans before they can be trusted."
28% of teams are fed up with tools that don't connect to anything else. Instead of streamlining workflows, they're creating new ones.
"One of the biggest gaps we've seen is integration. Many AI tools don't connect well with our existing tech stack, making it hard to embed them into real workflows."
The daily reality looks like this:
"Currently, our AI solution is a standalone product that has not been integrated with any other piece of our martech stack. So, users are doing a lot of copying and pasting between systems. While it's saving time - and providing great intel for our marketers - there are so many more places we could be leveraging AI if our tech stack were better aligned."
Teams report spending hours on:
While 62% struggle with trust, 7% of teams report clear ROI from their AI investments. What are they doing differently?
Successful teams treat data quality as a prerequisite, not an afterthought:
"Data quality is important for AI to work properly. CRM data, including validations and flows, needs to be structured in order for AI to deliver productivity improvements."
Before implementing AI, they:
The 7% design for human oversight.
"I would expect them to either have human input and approval built into their workflow, or at a conceptual level, the solution has been trained effectively by humans to act as human as possible."
Instead of adding another standalone solution, successful teams prioritize tools that play well with others:
"We've had much more success with platforms that allow us to develop prompts that truly meet our needs and provide flexibility to provide whatever we need."
Based on our research, here's how to move from the skeptical 62% to the successful 7%:
Before touching any AI tool:
Pick one narrow use case where:
Common starting points that work:
Accept that trust is earned over time:
Address integration issues head-on:
When we asked leaders to define what would make them trust AI tools, three themes emerged:
Transparency: "Show me why you made this recommendation"
Consistency: "Give me the same quality output every time"
Control: "Let me override when my human judgment says otherwise"
Together, these three qualities signal a clear direction: the future of AI tools is human-in-the-loop by design. The strongest results come when human input is built into the foundation
The trust gap isn't permanent. But closing it requires acknowledging that AI is a tool that's only as good as its foundation.
"I've learned to absolutely trust the process and realize that you likely will not see results right away. There's trial and error with AI but if you put the time and effort I believe most companies will reap the benefits."
The teams seeing ROI built better foundations:
Until AI tools can guarantee accuracy and seamlessly connect to your tech stack, trust will remain the biggest barrier to adoption. But for teams willing to do the foundational work, the payoff is real.
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