10 Things You Can Do Today to Accelerate AI Adoption in Your Org
AI adoption is quickly becoming a litmus test for organizational agility. The faster companies move from experimentation to execution, the more competitive they become. Teams that treat AI adoption as a core capability are outpacing those still stuck in pilot mode.
The organizations that are getting ahead today aren’t just experimenting with AI—they’re making it a part of how they operate. But getting from test projects to broad, sustained adoption across teams is hard. The tech is moving fast. Policies are lagging. And many employees aren’t sure if using AI will get them promoted or fired.
At Worklytics, we’ve worked with companies at all stages of the AI Maturity Curve, from those just kicking the tires to others scaling AI across thousands of employees. The key insight: adoption isn’t something that just happens. You have to design for it.
Below are 10 proven tactics to help you move faster—and smarter—when it comes to AI adoption.
You can’t manage what you don’t measure. Start by understanding how AI is being used across your organization today—who’s using it, for what, and where.
Set clear goals for adoption, and hold teams accountable. One of our favorite examples here is Shopify, where managers now incorporate AI usage into performance reviews. It sends a strong message: AI isn't just nice-to-know; it’s how we work now.
Managers are your frontline force for change. If they’re using AI, their teams are 2–5x more likely to use it too. If they’re not? Adoption stalls.
Start by measuring manager AI adoption and influence, and invest in training and enablement. Help them model the behaviors that you want their teams to emulate.
Here’s a wild stat: 52% of people who use AI at work are reluctant to admit it—especially when it comes to critical tasks. Many worry it’ll make them look lazy or like they’re cutting corners.
This mindset is a major blocker. Leaders need to explicitly communicate that AI is not “cheating.” It’s a tool, like Excel or Google. If you’re not using it, you’re probably doing things the hard way.
Even the most AI-curious employees hesitate when they’re not sure what’s allowed. Can I use ChatGPT for client emails? Can I upload internal data into Claude? What if I make a mistake?
You need clear, practical guidance—especially around data privacy and tool selection. Keep it simple and keep it visible. If people have to dig through a SharePoint page to find the rules, they’re not going to follow them.
Here’s an AI Usage Policy example from Weidert Group. We like how they’ve clearly spelled out where AI can and cannot be used, so their employees are crystal clear on what’s expected.
Chances are, someone in your org is already getting great results with AI. Maybe a salesperson closed more deals using AI-generated outreach or a customer support rep cut response times with a helpdesk assistant.
Find these stories. Validate them with data. And then promote them internally to build momentum. The goal is to move from “this is interesting” to “this is working—and we should all be doing it.”
A lot of people are still figuring out how to use AI effectively. Help them learn faster by making it easy to share what works.
One idea we love: host a “prompt-a-thon.” Invite teams to demo their best AI prompts, swap tips, and experiment live. It’s part learning lab, part culture builder—and a great way to uncover new use cases.
AI agents—tools that take action, not just generate content—can have outsized impact in the right parts of your org. The trick is knowing where to deploy them.
Use tools like Organizational Network Analysis (ONA) to find overloaded connectors, siloed teams, or high-friction workflows. These are often the best places to insert an agent and start removing friction at scale.
(We wrote more about this in How AI Agents Will Reshape Organizational Networks).
Many orgs have been too slow to approve and roll out AI tools, which has led to a rise in “guerilla IT”where people start using whatever works, often without security guardrails.
It’s better to allow some experimentation within safe boundaries. Watch what tools people are adopting on their own, learn from those patterns, and then invest in enterprise-grade versions that can scale securely.
AI isn’t one-size-fits-all. What works for sales won’t necessarily work for engineering or HR. The best results come from building domain-specific strategies.
Sales? Think conversation intelligence and proposal generation. Engineering? Think code suggestions and test automation. Get specific and invest where the payoff is clearest.
You might feel like you’re doing fine with AI adoption—but how do you know? Benchmarking your org’s AI adoption helps you understand where you stand relative to peers and identify areas you’re falling behind.
Whether it's tooling, usage patterns, or domain-specific maturity, staying competitive means keeping tabs on what others in your industry are doing—and not getting complacent.
Long-term success with AI means investing in your people. Prioritize AI literacy as a core skill—include it in onboarding, build training programs, and update your hiring strategy to favor candidates who are fluent in modern AI tools.
Every new hire is a chance to raise the bar.
Final thought: AI adoption isn’t just about tools—it’s about habits
At the end of the day, this isn’t just a tech rollout. It’s a behavior change. And like any transformation, it takes intention, iteration, and reinforcement.
The good news? You don’t need to do all of this at once. Start with a few high-leverage plays, build early momentum, and scale from there.
AI is reshaping how work gets done. The faster you can help your teams embrace it, the more future-ready your org will be.
