The “AI Trust Barrier” is real – and it’s costing companies their competitive edge
- Data and security concerns are the top factors eroding trust (34%), followed by a lack of explainability (30%) and AI model transparency (28%)
- The ability to articulate AI outputs and data guardrails has emerged as a non-negotiable requirement for solution providers
- Nearly half (46%) of planned AI investments are currently stalled due to trust concerns—providing a clear blueprint for how vendors must evolve to win
SAN FRANCISCO and LONDON, April 15, 2026 /PRNewswire/ — Gong, the leading revenue AI company, today released new research revealing an AI trust barrier that stands between companies innovating with AI and those stuck in exploration and confusion. The study, which was supplemented by findings from Gong Labs, showed that 58% of companies (US: 63% | UK: 52%) had stalled AI projects. Here’s why: it’s not a lack of budget – it’s a deficit in trust. The research explored how concerns about data privacy, security, and transparency lead to a lag in adoption.
Gong understands how to help revenue leaders get the assurance they need to drive AI adoption success. Gong Labs data, based on aggregated and de-identified signals derived from over 25 million sales interactions processed within Gong’s platform, found that one in four calls referenced security, with uncertainty over AI’s foundational data and learning mechanisms the most commonly discussed topics.
A study of over 2,000 US and UK leaders reveals that transparency in how AI generates outputs is essential for them to trust, invest in, and deploy AI tools. As it stands, 75% (US: 80% | UK: 70%) of leaders feel their organizations are falling behind when it comes to realizing AI’s most powerful benefits, giving solution providers an opportunity to allay concerns and drive their success.
“Security and AI trust are no longer back-office conversations; they are revenue conversations,” said Chris Peake, Chief Trust Officer, Gong. “Gong’s research found that trust can be a performance multiplier when applied as part of an AI strategy. The competitive advantage delivered by AI is no longer up for debate, but the trust barrier remains for those using tools that have yet to establish this trust. By embedding enterprise-grade governance directly into the Revenue AI OS, we’re helping the world’s most successful teams bypass the doubt and move straight to acceleration.”
Trust barriers preventing adoption of unproven AI tools identified
The survey uncovered several barriers preventing companies from adopting AI solutions from providers that had failed to allay their concerns:
| Trust Barrier | Overall | US | UK |
| Data Privacy & Security | 34 % | 36 % | 31 % |
| Explainability | 30 % | 30 % | 31 % |
| Model Transparency | 28 % | 28 % | 28 % |
| Regulatory Uncertainty | 27 % | 27 % | 27 % |
With more than half of companies having delayed or cancelled their plans to adopt an AI tool, the data suggests that organizations are under-utilizing powerful AI capabilities and leading these companies to miss out on transformational gains. What’s more, this same lingering uncertainty had meant 46% (US: 44% | UK: 47%) of their planned AI investments had been paused, on average.
Barriers can be overcome through enterprise-grade guardrails and transparency standards
The top assurances leaders said would help them confidently adopt AI solutions were:
| Assurance | Overall | US | UK |
| Explainability, or the ability to articulate AI-derived outputs | 26 % | 24 % | 27 % |
| Ability to articulate AI model guardrails protecting data | 25 % | 24 % | 25 % |
| Security guarantees built into solutions | 23 % | 25 % | 22 % |
| Third-party audits or certification | 23 % | 23 % | 23 % |
| Transparency into how training data is used | 22 % | 22 % | 22 % |
| Transparent model logic | 22 % | 22 % | 22 % |
With the right guardrails, trust can be an enabler of a successful AI strategy. Gong works with customers to address the trust gap by building enterprise-grade security and data governance within its Revenue AI Operating System, producing explainable and relevant insights using its context-aware AI, grounded in patterns observed across aggregated customer usage, and providing built-in governance and insight. Gong enables custom redaction capabilities, enabling organizations in highly regulated industries to safely leverage AI insights that previously couldn’t be monitored or analyzed due to data privacy risks.
To learn more about the report’s findings and how Gong safeguards companies’ data, standardizes AI trust, and embeds assurances directly within the Revenue AI OS, read the blog.
Methodology
The research was conducted by Censuswide, among a sample of 2,056 business leaders at medium and large businesses across the UK and US. The data was collected between January 6 – 9, 2026. Censuswide abides by and employs members of the Market Research Society and follows the MRS code of conduct and ESOMAR principles. Censuswide is also a member of the British Polling Council.
Additional research was conducted by the Gong Labs research team by analyzing aggregated, de-identified metadata and topic-level signals derived from sales interactions with Gong from January 1 – December 31, 2025. Gong Labs research does not involve reviewing or exposing individual customer conversations and is designed to reflect broad, industry-level trends.
About Gong:
Gong harnesses the power of AI to transform how revenue teams win. The Gong Revenue AI Operating System unifies data, insights, and workflows into a single, trusted system that observes, guides, and acts alongside the world’s most successful revenue teams. Powered by the Gong Revenue Graph, AI-powered intelligence, specialized agents, and trusted applications, Gong helps more than 5,000 companies around the world deeply understand their teams and customers, automate critical sales workflows, and close more deals with less effort. For more information, visit www.gong.io.
SOURCE Gong
