What Workato’s AI Agent Survey Reveals About Business Trust
The recent survey by Workato and Harvard Business Review exposes a striking gap in how companies use AI agents. While 86% of over 600 tech leaders plan to increase investments in agentic AI, only 6% fully trust these systems with core business processes. This reluctance signals a deeper structural constraint in enterprise automation.
Many organizations deploy agents for simple tasks like IT ticketing or email summarization but hesitate to hand off complex, multistep workflows. Workato CIO Carter Busse calls this out as early-stage adoption: true “real work agents” that operate autonomously on end-to-end processes remain two to three years away. Cybersecurity, data quality, and automation readiness top the blockers.
The real story isn’t just skepticism but how trust shapes automation strategies. Companies are not underinvesting in AI agents; they’re strategically confining use to contained, non-core workflows. This approach repositions the constraint from AI capability to trust governance—a shift that fundamentally changes organizational deployment.
“Trust is the new bottleneck in scaling AI agents,” Busse’s insight nails it succinctly.
Why Trust, Not Technology, Is the Real Constraint
Conventional wisdom credits AI adoption hurdles to immature technology or poor integration. The survey flips this assumption. Even with mature platforms like Salesforce and orchestration from Workato, business leaders hesitate to automate end-to-end processes. The sticking point is confidence in agent reliability and security.
This manifests as nuanced risk management: 43% delegate only routine tasks; 39% limit agents to supervised, complex non-core activities. Entire business operations are trusted to agents by a mere 6%. This selective trust shapes phased rollouts and cautious scaling, not wholesale adoption. See a similar dynamic in how AI changes workforce leverage.
How Orchestration Platforms Build Leverage Despite Distrust
Workato itself uses AI agents internally to prep sales teams leveraging Salesforce and Gong data, creating automated nudges without human intervention. This highlights a key leverage mechanism: orchestration coordinates specialized, single-purpose agents within trusted boundaries.
Unlike approaches demanding an all-in autonomous agent, orchestration protocols let discrete agents communicate securely and manageable. This reduces the risk of cascading failures that come with end-to-end automation and gradually earns trust. Compare to platforms without standardized agent protocols, which face higher integration friction. Related analysis on shifting system constraints here: operation docs best practices.
The Silent Leverage of Deliberate AI Trust Boundaries
This careful delegating of AI agents reveals a silent mechanism: trust constraints define how automation scales. Businesses don’t lack the ambition or funds—86% intend bigger AI investments—they lack confidence in multi-step complex agents handling mission-critical workflows autonomously.
Recognizing trust as the primary gating factor reframes strategy. Leaders must design automation systems around layered verification, fallback controls, and gradual agent escalation paths. This reduces human intervention over time while keeping risk manageable.
Why Operators Should Watch This Shift Closely
As AI agent orchestration matures over the next two to three years, the constraint will move from trust to capability. Early adopters investing in **trust-building mechanisms** like secure protocols and transparent outputs will gain durable leverage.
Operators need to shift focus from just technology adoption to embedding **trust governance**. This creates compounding advantages by unlocking multi-step, autonomous workflows that currently stall due to skepticism. Organizations ignoring trust will waste capital on underused AI.
Trust unlocks leverage: automated workflows compound only when leaders fully hand off control. The companies that master this system design will outpace peers and reshape business operations.
Related Tools & Resources
For organizations grappling with trust and scalability in AI, tools like Blackbox AI can empower developers by streamlining coding processes and enhancing the reliability of AI implementations. As businesses look to build trust in their AI agents, leveraging automation in coding could be a key strategy to unlock greater efficiency and confidence. Learn more about Blackbox AI →
Full Transparency: Some links in this article are affiliate partnerships. If you find value in the tools we recommend and decide to try them, we may earn a commission at no extra cost to you. We only recommend tools that align with the strategic thinking we share here. Think of it as supporting independent business analysis while discovering leverage in your own operations.
Frequently Asked Questions
What percentage of tech leaders plan to increase investments in AI agents according to Workato’s survey?
The survey found that 86% of over 600 tech leaders plan to increase investments in agentic AI to enhance their business operations.
Why do so few companies fully trust AI agents with core business processes?
Only 6% of companies fully trust AI agents with core processes due to concerns over cybersecurity, data quality, and automation readiness, creating a trust barrier to full automation.
What tasks are AI agents commonly assigned in enterprises?
Most organizations use AI agents for simple tasks such as IT ticketing and email summarization, avoiding complex, multistep workflows for now.
How does trust affect AI automation strategies in companies?
Trust governs how widely AI agents are deployed; companies strategically limit AI to non-core workflows until trust governance and reliability improve.
What role do orchestration platforms like Workato play in AI agent deployment?
Orchestration platforms help coordinate specialized AI agents within secure boundaries, enabling automation of parts of workflows while reducing cascading failure risks.
What is considered the main constraint to scaling AI agents according to Workato’s CIO?
Trust is seen as the new bottleneck in scaling AI agents, not technology capability, requiring layered verification and gradual agent escalation.
How might the focus of AI adoption shift in the next few years?
The constraint is expected to move from trust to capability as trust-building mechanisms mature, enabling multi-step autonomous workflows.
What strategies should operators use to gain leverage with AI agents?
Operators should embed trust governance, secure protocols, and transparent outputs to build confidence and unlock complex AI-driven workflows.