Agentic AI adoption in enterprises is accelerating. The models are smarter. The workflows are faster. The automation is deeper.
Here’s the uncomfortable truth: the biggest barrier to enterprise AI isn’t technical. It’s trust.
According to PwC, only 39% of employees trust how their organisation deploys AI. Meanwhile, Microsoft’s Work Trend Index shows over half of employees worry AI could replace parts of their job. That’s not a technology problem. That’s a leadership one.
And if trust collapses, adoption stalls. Fast.
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TL;DR: Agentic AI Adoption in Enterprises
- Agentic AI systems act autonomously — setting goals, making decisions, executing tasks
- The biggest barrier to adoption is employee trust, not model accuracy
- Human-in-the-loop governance protects transparency and accountability
- Cross-functional oversight (HR, IT, Legal, Security) is essential
- Enterprises that prioritise clarity over speed see stronger adoption and ROI
What Is Agentic AI?
Agentic AI refers to artificial intelligence systems designed to act as autonomous agents – capable of reasoning, planning, making decisions, and executing multi-step actions across systems with minimal human intervention.
Unlike traditional automation, agentic AI doesn’t just follow predefined rules. It interprets context, adapts to new inputs, and drives outcomes.
In enterprise environments, that means AI can:
- Execute multi-step service requests
- Orchestrate workflows across HR, IT, and finance systems
- Interpret natural language and take action
- Operate continuously across connected platforms
And yes – that changes the psychological contract at work.
How to Measure the ROI of Agentic AI Adoption (Simple Formula)
If trust drives adoption, and adoption drives outcomes, then ROI isn’t abstract.
Agentic AI ROI Formula:
ROI = (Productivity Gains + Cost Savings + Cycle Time Reduction – Implementation Costs) ÷ Implementation Costs × 100
Measurable enterprise indicators include:
- IT ticket resolution time reduction
- Lower support headcount scaling costs
- Employee self-service adoption rate
- Reduced workflow cycle times
- Improved employee satisfaction scores
- Retention improvements linked to digital experience
Let’s talk numbers: when AI reduces friction, the gains show up in hours reclaimed, escalations avoided, and operational overhead contained.
To read more on measuring the ROI of employee engagement – read our article.
Why Is Trust the Central Barrier to Agentic AI Adoption?
As AI begins to “think” and act across systems, employees aren’t just evaluating outputs. They’re asking:
- What does it see?
- What decisions does it influence?
- Who is accountable?
When those boundaries aren’t clear, fear fills the gap.
The issue isn’t that AI is too powerful. It’s that its guardrails are invisible.
Enterprise leaders must recognise that AI governance is no longer an IT checkbox. It’s an employee experience strategy.
Why Do Employees Resist Workplace AI?
The resistance is rational.
Microsoft Work Trend Index (2024) findings:
- 50%+ worry AI may replace aspects of their role
- Nearly half question how their data is being used
PwC findings:
-
Only 39% trust organisational AI deployment
Common employee concerns include:
- Job displacement
- Opaque data usage
- Algorithmic bias
- Surveillance fears
- Lack of human oversight
Fear doesn’t slow innovation. Silence does.
If AI is introduced without explanation, transparency, or governance clarity, adoption becomes performative rather than productive. To read more on the success stories of companies who have successfully implemented AI integrated employee engagement platforms, click here.
What Does Responsible Agentic AI Deployment Look Like?
Some organisations are taking a people-first approach that balances innovation with transparency and accountability.
Coca-Cola Consolidated: Scaling With AI Without Scaling Support Burden
For Coca-Cola Consolidated – North America’s largest independent Coca-Cola bottler with over 17,000 employees – rapid growth posed a support challenge: how to empower employees while keeping support teams lean.
The solution was deploying Moveworks, an agentic AI assistant platform that automates employee support and routine requests directly within familiar tools such as Microsoft Teams and Slack.
According to Retha Summers, IT Director at Coca-Cola Consolidated:
“Our biggest challenge was giving everyone a better experience with technology – and making our people more productive.”
By automating tasks like password resets and enabling a wider range of employees to build AI-assisted workflows using Moveworks’ Agent Studio, Coca-Cola Consolidated maintained support capacity even as its workforce tripled.
This deployment wasn’t just about automation – it was about shifting mindset and capability. Business users without technical backgrounds participated in creating solutions, which fostered ownership and trust rather than fear of surveillance or replacement.
Coca-Cola Consolidated’s experience provides a practical model: start small, target real pain points, and use AI platforms that invite participation, not just execution.
What Makes Agentic AI Different from Traditional Automation?
Traditional automation:
- Rule-based
- Single-task focused
- Reactive
Agentic AI:
- Goal-oriented
- Context-aware
- Multi-step orchestration
- Cross-system reasoning
Platforms like Moveworks combine:
- Natural language understanding
- Context interpretation across enterprise systems
- End-to-end workflow execution
- Low-code agent building (e.g., Agent Studio)
This transforms AI from assistant to orchestrator.
But orchestration requires oversight.
How Should Enterprises Govern Agentic AI?
Technology alone doesn’t create trust. What matters is how you govern it.
Moveworks’ own Trustworthy AI in HR guide (co-authored with enterprise leaders and HR practitioners) outlines core principles for responsible deployment:
- Build Cross-Functional Governance
AI governance should not live in IT alone. Effective oversight requires collaboration between HR, IT, Legal, and Security. Establishing a small working group or AI council ensures regular review of tools, outcomes, and standards.
- Safeguard Employee Data
Teams must be clear about what data AI systems access, where it is stored, and how long it is retained. Role-based access controls and transparent policies help employees see privacy as protection, not surveillance.
- Embed Fairness and Transparency
Fair AI practices require both technical and human checks. Simple, human-centered questions — such as “Can we explain how the AI makes decisions?” — reveal whether a system is ready for responsible use.
- Empower People Through AI Literacy
Employees adopt more readily when they understand why and how AI is used. Clear messaging (“AI makes work easier, not monitors you”) reduces anxiety and reinforces agency.
- Measure Trust and Adoption
Continuous evaluation of how AI impacts adoption and sentiment helps organisations refine governance and communication strategies over time.
These principles reinforce that responsible AI is not just a technical implementation; it’s an organisational practice that must align with values, transparency, and human-centered governance.
How Can Leaders Introduce Agentic AI Without Breaking Trust?
Leadership still determines success.
- Explain why AI is introduced and what it will do
- Address surveillance fears openly
- Reinforce augmentation, not replacement
- Train managers to contextualise AI insights
- Keep accountability human
How to Improve Agentic AI Adoption Now
For organisations at any stage of AI adoption:
- Make AI visible: Employees should always know when AI is acting
- Design for oversight: Human accountability must be explicit
- Communicate data boundaries: Silence fuels fear
- Position AI as a partner: Not a performance judge
- Link trust to outcomes: Adoption and retention depend on it
Deploying agentic AI is ultimately about shaping experience, not just capabilities.
The Real Competitive Advantage of Agentic AI
Agentic AI will redefine how work gets done. But organisations that succeed won’t be the ones moving fastest — they’ll be the ones moving clearest.
Trust, transparency, and communication are as critical as automation. By learning from real deployments like Coca-Cola Consolidated and aligning with governance frameworks like those outlined in Moveworks’ guide, leaders can ensure that agentic AI amplifies human potential rather than undermining it.
The future of AI at work isn’t autonomous – it’s accountable.
FAQs: Agentic AI Adoption in Enterprises
What is agentic AI in the workplace?
Agentic AI refers to autonomous AI systems that can reason, plan, and execute multi-step tasks across enterprise systems with minimal human input.
Why is trust critical for AI adoption?
Without trust, employees hesitate to use AI tools. Low adoption directly reduces ROI and slows digital transformation.
What does human-in-the-loop AI mean?
Human-in-the-loop AI ensures that critical decisions include human oversight, accountability, and review mechanisms.
How do you measure agentic AI ROI?
Track productivity gains, ticket resolution time, workflow automation rates, support cost reductions, and employee satisfaction metrics.
What are the biggest risks of agentic AI?
Risks include opaque decision-making, data misuse concerns, bias, and insufficient governance.
Can agentic AI replace employees?
Most enterprise deployments focus on augmenting roles by automating repetitive workflows rather than replacing strategic human functions.
To read more on how AI is transforming employee engagement, read our master guide here.
