Table of contents
Highlights
- Adoption is not the same as usage intensity — you can have broad access and still see low task completion because the agent is not embedded in real workflows.
- The first deploy-to-use gap often shows up in high-frequency support journeys like password resets, PTO requests, and purchase approvals when the entry point adds extra steps.
- A 30-day adoption scorecard is more useful than quarterly ROI narratives because it links engagement, work completed, and escalation rates to specific workflows.
- Trust tends to break at action time, not chat time, so governance needs to cover access boundaries, action policies, and auditability for multi-step tasks.
- Daily use scales when you run a closed-loop operating model, route failures to owners, and activate managers to reinforce the new default way of getting work done.
- Moveworks AI Assistant meets employees where they already work — in Teams, Slack, and email — and Agent Studio gives teams the tools to build, govern, and measure the workflows that drive daily usage.
Your team just deployed an AI agent to handle PTO and short-term leave requests, but once they pull up the analytics, something feels off. Nothing is wrong with the workflow, but the adoption across the organization is, for lack of a better word, dismal.
A lot of companies fail to recognize the stark reality of implementing agentic AI systems. If employees don’t like the experience of a new technology, they're likely to fall back on their old workflows — or worse.
Nearly half of employees admit to using shadow AI, and 58% of them say they’ve fed it sensitive company information. That puts a lot more pressure on the teams executing the AI deployment to get it right from the start.
And while many employees are already comfortable using generative AI (genAI), orgs are now shifting toward agentic AI that supports multi-step orchestration across systems. Instead of just generating answers, teams are designing AI agents to plan, make decisions, and take action to achieve more complex-looking goals.
That can feel like a big jump for non-technical employees. So strong agentic AI adoption requires a realistic, thoughtful approach to common barriers like trust, integration drag, and unclear ownership.
What is agentic AI adoption?
Agentic AI adoption is the integration of autonomous AI systems that independently plan, make decisions, and take action to achieve complex goals without constant human supervision. Moving beyond basic generative AI prompts, these systems coordinate multiple tools to execute full workflows on your behalf.
Say an employee initiates a laptop access request in Teams. The agent can check eligibility and work through the necessary steps, like routing approvals and updating the status, to provision access as needed.
As organizations move from experimenting with genAI to deploying agents, the adoption of AI technologies becomes more about reliability and whether employees can trust agents to complete work on their behalf.
Adoption vs usage intensity
Most teams make the mistake of tracking adoption like it's an either/or equation. Either people are adopting the agent, or they’re not.
The thing to keep in mind is that adoption numbers can look healthy while usage stays flat. If someone signs up to use the tool once and doesn't feel like it's worth their time, they’ll likely go back to the portals, ticketing systems, and emails that they know.
The implementation team sees skewed numbers because that one login still counts as an "active user."
A better way to measure AI usage breaks down into a concrete formula that’s easy to remember:
- Breadth - Percentage of eligible users who are active users
- Depth - Number of tasks completed per active user
- Repeat - Weekly active usage by workflow
It takes measurable results across all three to reach real AI adoption. When breadth is high, but depth and repeat are low, for example, you have a tool people tried once and shelved.
Getting the agent in front of people is the easy part. Making them feel empowered to use it is where things get complicated. Make them reach for the AI agent in the same way they’d reach for a popular app or dashboard they use every day.
Why successful deployment doesn’t equal adoption
Even if the deployment goes off without a hitch, adoption doesn't always show up in your daily usage numbers. If the entry points where work starts don't change during rollout, employee behavior won't either.
Here are the four places that agentic AI usage usually breaks down:
- Friction is the obvious one. If using the agent means navigating somewhere employees don't already go, most of them won't bother. The bar for a new AI tool is often higher than most people realize, especially when the old way works well enough to get the job done. If someone is completing an HR onboarding task and has to leave Teams to find a portal they used once, that’s friction, and it kills adoption.
- Trust at action time is another place where AI adoption can start to break down. Approving a software request or submitting an invoice often feels higher-stakes than just waiting for a response to an inquiry. Distrustful user behavior often shows up as a high volume of questions with low completion rates.
- Integration drag happens when the agent doesn't connect to the systems people need across IT, HR, and finance systems. It answers correctly, but it can't actually do anything beyond that to help complete the task. So the employee ends up opening a ticket with support anyway.
- Unclear ownership is another issue that can come up frequently. Something goes wrong, and nobody knows if they should fix it in the agent config, escalate to IT, or loop in the software provider. Now your helpdesk is stuck fixing the AI’s mistakes instead of addressing the tickets that need their expertise, leading to high rates of rework.
These friction points aren’t unsolvable. But you have to be willing to look at the telemetry honestly before declaring your AI deployment successful.
How to measure adoption: A 30-day scorecard
Most teams measure adoption right after launch and treat a healthy number of sign-ups as the one-and-only green light. Instead, a 30-day scorecard gives your team something tangible to work with.
This way, your leadership team can get a factual read on whether the use of artificial intelligence tools is compounding or just spiking and fading.
Here are some of the metrics to focus on and why they matter:
Metric | What It Measures | Why It Matters |
Eligible users | Employees with access to the agent and a relevant workflow | It’s the baseline denominator |
Weekly active users | Consistent return visits | It measures tool adoption |
Task completion rate | The workflow is updated in the system of record or closed within the approval chain | If agentic workflows don’t close the loop, that’s a gap that can hurt trust (and compliance) |
Escalation rate | The number of times a task is handed to an employee to intervene and finish | High escalation typically means an user trust gap or an integration problem |
Median time to completion | The average amount of time to complete a task | Stalled progress compromises your investment in the tool |
Repeat usage by workflow | Return rate broken down by specific task | Can calculate which workflows have genuinely changed user behavior |
Start measurement in the channels where work actually happens. If your telemetry lives only in the system of record, you're missing everything that happened upstream, including all the moments when employees started a task and gave up before it was completed.
Once you've segmented everything by channel, workflow, and user group, you can analyze whether an agent works well for one group or task over another, giving you better insight into how to move forward with your AI-driven solution.
Design agents for real workflows and govern the risk
When you start deciding which workflows you want agentic AI to tackle first, it’s usually recommended to begin with high-frequency, high-friction ones. Access requests, onboarding tasks, procurement intake, and time-off approvals are common but high-value tasks that can be taken off your employees’ plates.
The most impactful workflows often meet these four criteria:
- High-volume: Start where the work is — the constant, repetitive, but routine tasks that eat up teams’ time and keep them from more valuable projects.
- Clarity of success: You need a clear definition of what “finished” looks like to measure workflow completion. Can an agent fully close the loop on this process? A task like updating an address in Workday has a definite end point.
- Ability for end-to-end task completion in the primary channel: If the agent can start the task but the employee will have to open another tool to finish it, you’re inviting friction that can put adoption at risk. A workflow like creating a Jira ticket from Teams keeps the full user experience in one place.
- Low-to-medium risk: Workflows that touch sensitive data add more governance and compliance concerns. The last thing you want is critical mistakes that poison adoption before it even starts.
But even low-risk AI-powered workflows required strong governance, both for security and compliance purposes and for employee trust. Guardrails like role-based access, action policies, and audit trails are what give employees enough confidence to let the agent act as intended.
In IT, this looks like clear thresholds for when the agent approves versus escalates. For HR, it means an audit trail on profile changes that satisfies compliance and builds employee trust. In finance, it looks like explicit sign-off requirements are in place before anything touches vendor records.
You’ll also need to consider where to deploy agents. A communication tool like Teams or Slack can give employees a familiar, chat-based experience in the place they’re already working, making them more likely to keep coming back.
Run a closed-loop improvement engine
To make the most of agentic AI investments, many teams create an ongoing adoption engine: monitor outcomes, examine failures, and optimize workflows in short cycles.
When the system breaks down, it’s crucial to your deployment's success to figure out where and why. Common causes include:
- Missing data
- Permission denials
- Unclear user intent
- Policy conflicts
- System outages
- User abandonment
Segmenting failures based on these categories at triage makes it easier to assign them to the right owner. But you’ll need to clearly define routing rules here.
For example, a request that fails due to missing content gets routed to knowledge base owners, while integration teams fix the platform, and policy snafus are handled by governance owners. Without these routing rules in place, failure reports pile up without accountability.
After you figure out who owns what, it’s often best to run small, controlled experiments to continually optimize your usage of the platform.
You might change where the agent lives in Teams and see whether repeat usage grows. Maybe you could add two or three examples to a confusing workflow to build familiarity and improve completion rate, or adjust handoff messages for approvals to shorten time to completion.
Scale through operating model, not just tooling
Scaling this improvement engine is an operating model challenge. The technology rarely fails in isolation. Failures typically go hand-in-hand with gaps in role clarity, inconsistent manager reinforcement, or an unclear rollout sequence.
A phased automation rollout by workflow can help you measure success, establish the feedback loop, then expand once you have evidence the model holds. But that rollout depends on well-defined roles:
- Executive sponsor: sets strategic direction, removes organizational blockers
- Platform owner: manages the technical environment (integrations, permissions, system health)
- Workflow owners: accountable for individual use cases end-to-end, from prompt design to completion rate
- Security partner: ensures compliance controls are in place before each workflow goes live.
- Analytics lead: owns the instrumentation — defining success metrics, surfacing failure patterns, and generating the insights that drive the next cycle
Managers should be responsible for reinforcing behavior at the team level by modeling usage, normalizing AI-assisted workflows, and flagging when adoption is slipping. Without manager-led activation, even well-designed workflows can revert.
Accelerate daily adoption with Moveworks
Leaders are being pressured to prove the value of AI initiatives, but without significant adoption and consistent usage, it’s hard to achieve successful outcomes. Getting there typically takes a well-defined adoption playbook, clear metrics, and an agentic solution that meets employees where they already are.
Moveworks is designed to help reduce adoption friction by acting as a unified conversational front door to work.
Deployable right in web, Teams, or Slack, Moveworks AI Assistant gives employees one entry point to ask questions, search for information, and take action without needing to know which system, agent, or workflow to use.
Behind that front door sits the Reasoning Engine, built to understand what an employee is trying to do, plan the multi-step work required, and execute across systems, not just surface documents. Instead of stopping at answers, it can often complete tasks with little or no manual intervention.
Instead of just surfacing relevant content, Moveworks can search across multiple enterprise systems and take governed action. When tools stop at retrieval, teams can’t complete work. Moveworks' Search + Action can help close that gap, while Agent Studio extends agentic capabilities across the enterprise.
It provides the governance and extensibility layer teams need to define what agents are allowed to do, enforce access boundaries, and measure outcomes. Pre-built plugins and agent templates may also accelerate time to value, since teams don't have to build integrations from scratch.
Role-based permissions, policy enforcement, and auditability are built in to support autonomous action at scale without losing oversight, encouraging employee trust at action time.
As more automated interactions and autonomous resolutions flow through a single entry point, the more fulfillment data feeds back into the system so it can improve over time. It becomes a scalable operating model that can help you move from daily use to compounding value.
If you’re ready to close the adoption gap with an agentic solution employees actually want to use, it’s time to explore Moveworks.
Frequently Asked Questions
Reported adoption rates vary by study, industry, and how researchers define AI agents versus broader AI tools. Some executive surveys suggest a meaningful minority of organizations have already adopted AI agents in some form, while many more are planning expanded deployments in the next one to two years. For leaders, the more useful benchmark is often internal: how many employees are eligible, how many are active weekly, and how much work is actually completed. That lens keeps focus on usage intensity, not just deployment announcements.
Common blockers include workflow friction, trust concerns at the moment an agent takes action, and integration drag from legacy systems and permissions. Many programs also slow down due to unclear ownership, nobody owns outcomes for a workflow once the pilot ships. Finally, weak feedback loops make it harder to learn from failures and improve quickly. The strongest programs treat these as operating-model issues, not only technical ones.
Adoption typically answers “Who has tried it or used it at least once?”, while usage intensity answers “How often are people using it, and how much real work gets completed?” A practical approach is to measure eligible users, weekly active users, tasks completed, completion rate, escalation rate, and time to completion by workflow. Segment results by channel such as Teams or Slack and by workflow, because usage often varies widely. Over time, repeat usage and work completed are usually more telling than initial activation.
Scaling usually benefits from a workflow-first rollout sequence, clear roles, and a manager activation plan that reinforces the new default behavior. Start with high-frequency workflows that can be completed end to end in existing channels, then expand autonomy as governance and trust mature. Instrument outcomes early with a 30-day scorecard, and keep improving through a closed-loop process that routes failures to owners. This approach tends to create a repeatable path from pilot excitement to daily use.
Responsible adoption often includes role-based access controls, defined data boundaries, and explicit action policies for what an agent is allowed to do. Enterprises also tend to benefit from audit trails that show what happened, which system was updated, and who approved key steps. Escalation paths matter too, when an agent fails, the right human team should receive the context to resolve it quickly. These controls can support both risk management and user trust, which can influence adoption.
