The Corporate AI Paradox

In today’s rapidly evolving digital landscape, agentic AI promises significant gains in efficiency, productivity, and innovation. C-suite executives are eager to champion this transformation—almost two-thirds believe AI is critical to driving revenue, competitiveness, and customer success. Yet on the ground level, a quiet resistance is brewing. More than half of employees report struggling to find AI tools they trust.

This gap between executive optimism and employee skepticism forms a critical challenge for businesses looking to harness the power of artificial intelligence. As Shane Smyth, CTO of Saltbox Management, noted at TDX 2025, “If you don’t trust something when you’re using it, then you’re not going to use it.” Trust isn’t just a technical issue—it’s a human one.

Understanding the Trust Gap in Agentic AI

Across industries, from healthcare to tech consulting, concerns around AI bias, misuse, and transparency are common. For Alex Waddell, CIO of Adobe Population Health, the fear that AI systems could unintentionally recommend harmful health advice was a real barrier.

Raju Malhotra, Chief Product and Technology Officer at Certinia, echoes these sentiments, asserting that skepticism is not only expected but necessary. “Being aware of potential risks is very important, especially as we go into this uncharted territory.”

The crux of employee hesitation lies in the unknown. Employees want assurance that AI will not only perform effectively but do so without compromising privacy, accuracy, or job integrity.

Operational Strategies to Foster AI Trust

To address this disconnect, forward-thinking companies are implementing structured approaches to AI deployment that prioritize transparency, security, and user involvement. Here are some key strategies leaders have used effectively:

1. Adopting a “Crawl, Walk, Run” Rollout

Waddell’s team introduced AI through a controlled, incremental process. By:

– Starting with a single use case
– Involving clinical leadership and compliance from the outset
– Ensuring user relevance and clear communication

They were able to build credibility early and gradually scale adoption.

2. Partnering with End Users Early

User involvement is essential. IT leaders must collaborate closely with customer-facing or operations teams to co-develop AI implementations. This builds internal champions who not only understand the technology but can also vouch for its integrity and value.

3. Employing Rigorous Testing and Phased Deployment

Smyth emphasized the importance of secure, well-tested AI systems. Companies should:

– Run pilot programs with limited exposure
– Implement Minimum Viable Product (MVP) cycles
– Ensure results align with user expectations and key KPIs

Trust becomes tangible when users see AI outputs that are valuable, consistent, and safe.

Educating to Empower: Closing the Gap through Learning

One of the most potent tools to overcome AI fear is education. Kelly Bentubo, Director of Architecture at Alpine Intel, believes hands-on exposure demystifies AI. “When you get under the hood of Agentforce, you’ll see that it is simply doing everything that you’ve given it access to do,” she said.

Encouraging AI Literacy across Teams

To facilitate learning, organizations should provide access to learning paths like:

– Salesforce Trailhead’s Agentforce Trailmixes
– Internal sandbox environments for experimentation
– Regular workshops and knowledge-sharing sessions

Dan O’Leary of Box advocates not just personal exploration, but making these resources widely accessible across teams. “Go earn some badges, take some classes, get involved.”

Fostering a Culture of Curiosity

Beyond formal training, companies must encourage a mindset of experimentation. When employees are free to explore AI tools in low-risk environments, uncertainty gives way to confidence.

Governance and Transparency: The Foundation of Trusted AI

A trustworthy AI strategy isn’t built on technology alone; it requires governance. This includes:

– Clear data privacy policies
– Transparent decision-making processes
– Documentation of model outputs and limitations

Organizations must also create feedback loops for users to flag unexpected behavior. This openness fosters accountability and continuous improvement.

Unlocking AI’s True Potential Requires Trust

Agentic AI has the power to transform business operations, elevate customer experiences, and drive revenue growth. But these benefits will remain out of reach unless employee confidence is earned and sustained.

To close the AI trust gap, organizations must:

– Embrace transparent education
– Execute methodical, secure rollouts
– Include diverse teams in AI lifecycle discussions
– Continuously refine based on user feedback

Trust is not a one-time achievement—it’s an ongoing commitment.

Next Steps for AI Trust Building

If your organization is on the path to implementing agentic AI, consider these actionable tips:

– Launch small-scale, cross-functional pilot projects
– Involve end-users in early testing and decisions
– Provide open-access learning resources like Salesforce Trailhead
– Regularly solicit feedback and iterate solutions
– Align AI performance metrics with business and user expectations

Final Thoughts

Agentic AI, when properly implemented and trusted, can drastically enhance how work gets done. But executive vision alone cannot fuel its adoption. Employees need to feel safe, informed, and empowered. Bridging that trust gap is the linchpin for realizing the full potential of digital labor—where humans and AI confidently work side-by-side to shape the future.

Let’s move forward not just with code, but with conviction.