AI is making its way into everyday workflows, but that doesn’t mean everyone’s ready for it.

Some employees are already experimenting with new tools to speed up repetitive tasks. Others are unsure what AI means for their jobs, or whether they’re even supposed to use it.

That’s why AI adoption has to start with people, not platforms. Building trust, offering training, and creating transparency from day one are what make the difference between an unused tool and one that transforms how work gets done.

Rolling out AI is one part of the equation. Most organizations fail to help people understand where it fits, why it matters, and how it helps them.

The real work starts with clear guidelines, consistent communication, and support that meets people where they are.

Start with clear policies and practical guidance

Uncertainty slows progress. According to Gallup, 70% of employees say their company hasn’t provided any formal AI policies, and just 6% feel confident using AI in their day-to-day work.

Clarity builds momentum. Start by:

  • Defining usage guidelines: Be specific about when and how employees should use AI, and how to handle sensitive data. Guardrails matter, but so does giving people room to explore. Behind the scenes, many of these AI tools rely on APIs to integrate with scheduling systems, CRMs, and support ticketing platforms, so defining where automation should end and human touch should begin is essential.
  • Explaining the benefits: Go beyond high-level outcomes. Show how AI can help with tasks like reducing call backlogs, streamlining scheduling, or automating basic support questions.

When Axis Integrated Mental Health, a growing outpatient clinic network in Colorado, introduced RingCentral AI Receptionist, they did just that. Their team was spending high volumes of time fielding routine calls—often outside of regular business hours—to help make sure patients in crisis weren’t stuck waiting. With clear internal guidance and simple workflows, including pre-defined call flows and API-driven routing rules, the AI assistant quickly became a reliable first point of contact. It now handles multiple calls simultaneously, responds to common questions, and sends patients direct links for things like scheduling or insurance inquiries.

The impact? Weekly new patient intakes increased by 60%, contributing to a projected $1.7 million in additional revenue. More importantly, it gave staff time to focus on the people who needed them most, as Cofounder Liesl Perez says: “People who are really in crisis, they need to talk to somebody. People who need to reschedule don’t really need to talk to a person. So the AI has made it possible for us to be there more for people who really need someone to talk to.”

Bring AI into the flow of work

Even the most helpful tools will struggle to gain traction if they are difficult to access or feel disconnected from everyday work. According to McKinsey, nearly half of U.S. workers say they’d use AI more often if formal training were available. And 45% want tools that integrate into the systems they already use. That’s why seamless integrations—enabled through APIs and native platform extensions—are key to embedding AI in the flow of work.

Make it easy for employees to build confidence:

  • Start with the systems they already use. Embed AI into existing platforms (via native integrations or APIs) so it feels like an enhancement, not an extra step.
  • Offer role-based training that mirrors real workflows. People don’t need to become AI experts. They need to know how it helps with their tasks.
  • Collect feedback and refine. Use early input from employees to fine-tune prompts, workflows, or integrations before scaling more broadly.

Axis, for example, made training and integration simple from the start. One clinical supervisor said it took a single day to set up RingCentral AI Receptionist with rules based on call history, using a no-code interface that mapped directly to backend APIs. The team even added pronunciation guides so the system could greet patients naturally. That thoughtful onboarding made it easier for everyone to trust the tool and freed up time previously spent answering the same question repeatedly.

Drive adoption through participation

Adoption isn’t about forcing change. It’s about showing your team what’s possible—and involving them in the process. That means:

  • Explaining why changes are happening and how they’ll help: Tie it to real workflows and outcomes—like reducing manual triage or shortening onboarding times—so employees understand what’s in it for them.
  • Offering time and space to explore the tools: Give teams access to test environments or sandboxed features, and let them provide feedback before rolling out broadly.
  • Sharing progress openly and celebrating small wins: For example, logging AI interactions and surfacing analytics dashboards via API integrations can help teams track adoption, flag anomalies, and iterate quickly.

Adopting becomes a shared goal when employees feel like they’re part of the journey, not just subject to it.

Ready to make AI part of your culture?

Bringing AI into the business is a tech rollout, but more than that, it’s a cultural shift. It works best when teams feel informed, supported, and involved from the beginning.

To set your team up for success, focus on these three essentials:

  • Set clear, flexible guidelines so employees know when and how to use AI, with room to explore.
  • Embed training in real workflows to build confidence through everyday use.
  • Foster a feedback loop rooted in transparency so adoption becomes a shared, evolving effort.

If you’re looking to build a stronger foundation for AI adoption, we’re here to help.

Let’s start the conversation.

Updated Nov 27, 2025