How to roll out AI across your team without losing momentum

Most teams aren’t struggling to adopt AI because of a lack of tools. Some employees experiment constantly, others aren't sure what's allowed, and leaders can’t see whether any of it is translating into real outcomes.

This is often the moment when AI either matures into a reliable advantage, or becomes another source of fragmentation.

Below is a practical, repeatable framework for implementing AI across teams in a way that actually sticks.

Start by assessing the current state of AI usage

Before introducing new tools or expectations, leaders need a clear, shared understanding of what is happening on their teams today.

A simple assessment should map three areas:

• Comprehension: How well do people understand AI fundamentals and apply them to their work?
• Impact: Where has AI already created value, if at all?
• Adoption: How consistently are AI tools being used across roles and workflows?

This diagnostic step may feel unnecessary , but it’s often the difference between strategic clarity and misguided investment. Despite AI usage doubling in the last year, most companies still haven’t seen meaningful gains in organizational efficiency or innovation - a signal that uncoordinated experimentation rarely compounds into enterprise-level impact.

You can’t design the right AI operating model until you understand the one you already have.

Define a clear AI baseline for your organization

A baseline sets consistent expectations for how AI is used across the company. It reduces ambiguity, improves safety, and ensures teams aren’t reinventing the wheel.

Establish your approved tool stack

A strong baseline starts with a transparent list and usage guidelines. This should include:

• Approved enterprise AI tools for work involving company or client data
• A clear policy on how to handle usage of personal accounts  that could be used for non-sensitive exploration
• A clear policy on prohibited uses such as uploading proprietary company information into public models or personal accounts
• An exception process for evaluating new tools or requesting new tools for specific use cases
• A framework to support experimentation with new tools 

One in three employees admits to using AI tools that are not approved by their organization because there are often no clear guidelines, or they can't find them. A defined stack reduces security risk and keeps teams aligned.

Publish a baseline AI skills matrix

Develop an AI skills matrix so that every employee can demonstrate a minimum set of competencies. Within the matrix, ensure there is a clear definition of what success means for the employee and a list of observable behaviors to measure the employees competency level against.

Your matrix might include competencies such as:

• Project setup and Enterprise tool comprehension, so that employees understand how to use the Enterprise tool stack correctly 
• Structured prompting and context engineering, so that employees understand prompting techniques and how to create relevant outputs
• Output evaluation using source traces, consistency checks, or stress testing, so that there is a healthy culture in place to review and comprehend outputs
• Workflow integration, so that employees understand how to apply tools within their day to day
• Knowledge sharing and experimentation, so that employees are curious about AI, seeking out new ways to use it, and sharing those learnings to enhance the entire team

Assess these skills quarterly. AI evolves quickly and your baseline should too.

Create role-specific expectations tied to outcomes

Generic AI training doesn’t work. People adopt AI more consistently when expectations map directly to how their role creates value.

Examples drawn from common practice:

• Product managers: Generate and refine user stories; synthesize research faster; summarize calls to accelerate alignment.
• Designers: Conduct rapid competitive analysis; iterate on UX copy; prototype flows earlier in discovery
• Engineers: Generate boilerplate code; debug with AI assistance; produce and update documentation with more consistency
• Researchers: Synthesize interviews; produce test plans; pre-code data into themes to shrink analysis time

Outcome-based expectations make it clear why AI matters to each discipline, not just how to use it.

Develop and maintain organization-wide AI usage guidelines

Clear guidelines are one of the most overlooked drivers of successful adoption. They reduce uncertainty, improve safety, and give teams confidence to use AI while staying within guardrails.

Below is a generalized template that any company can adapt, built from the concepts already discussed.

AI Usage Guidelines Template

1. Purpose

Explain how your organization's opinion on how to use AI. For example: "to augment human decision-making, accelerate workflows, and improve creativity."

Why it matters: When people understand why AI is being used, adoption increases and misuse declines.

2. Scope

Clarify who the guidelines apply to and in what contexts (internal projects, client work, experimentation, etc.).

Why it matters: AI risks often emerge when employees assume rules don’t apply to their specific role.

3. Approved, restricted, and prohibited tools

Define which tools are safe for work involving internal or client data, which are limited to experimentation, and which are not allowed.

Why it matters: Without definitions, teams default to convenience, which is often the least secure path.

4. Core principles

Document expectations such as maintaining human oversight, protecting confidential data, verifying outputs, ensuring transparency, and mitigating bias.

Why it matters: Principles are more durable than tool lists as technology evolves.

5. Permitted uses

Provide specific examples of appropriate use cases, such as drafting summaries, generating boilerplate code, synthesizing research, brainstorming concepts, etc.

Why it matters: People adopt AI more confidently when they see concrete, allowed behaviors.

6. Prohibited or restricted uses

List activities that carry significant legal, ethical, or security risk—such as uploading confidential data into public tools or using AI for personnel decisions.

Why it matters: Guardrails only work when they are explicit.

7. Roles and responsibilities

Clarify how engineers, PMs, designers, researchers, and operational teams each oversee safe and effective AI usage.

Why it matters: Accountability prevents guidelines from becoming “shelfware.”

8. Incident reporting

Define how to report misuse or data exposure and who triages issues.

Why it matters: Speed of response determines the impact of mistakes.

9. Review cycle

Commit to updating the guidelines on a predictable cadence (e.g., quarterly).

Why it matters: AI evolves constantly, your policies must evolve with it.

Ensure guidelines are adopted

Publishing guidelines is the easy part. Adoption requires reinforcement across practices.

Here are proven ways to help guidelines stick:

• Make them accessible: Place them in onboarding materials, internal hubs, and team playbooks
• Tie them to workflows: Build checkpoints into code reviews, design critiques, and research synthesis
• Train by role, not by tool: Show employees how guidelines apply directly to their daily work
• Share examples of good usage: Promote real prompts and workflows to model correct behavior
• Update guidelines publicly: Announce changes, explain why, and invite feedback
• Identify champions: Select individuals who can be champions for AI usage, are curious and like to experiment with new tools and workflows, can share their learnings with their teams, and can help coach or teach their teammates new ways of working

When guidelines become part of how work is done, AI adoption becomes more consistent, safer, and more effective.

Track the signals that show your rollout is working

Useful early indicators include:

• Increased number of workflows updated with AI and experiments in workflow optimization
• Fewer instances of rework due to poor prompting or inadequate output evaluation
• More cross-team knowledge sharing in team meetings, slack channels, pair sessions 
• Increase in reported time spent on valuable, high-impact work 

Teams that focus on optimizing the entire workflow and focusing on strategic bottlenecks or friction points,  over only individual productivity, will see bigger impact. In fact, companies that prioritize AI-enabled coordination are nearly twice as likely to report meaningful improvements in organization-wide efficiency. 

If your organization is defining its AI baseline, building its first usage guidelines, or designing role-specific expectations, we can help you pressure-test your structure and tailor it to your teams’ workflows. Let’s make AI adoption something your teams can trust and actually use.