Creating a Pathway for AI Implementation

How to thoughtfully introduce AI into your organization.

When it comes to AI, most teams fall somewhere on a spectrum.

Some people are energized and curious, eager to explore what’s possible. Others are

cautious, concerned about privacy, ethics, or the potential for misuse.

Both perspectives are valuable.

If you’re thinking about bringing AI into your organization, the key is to design a process that includes both ends of that spectrum, curiosity and care, innovation and integrity.

This work also calls for two different kinds of leadership challenges, as Ronald Heifetz describes:

• Technical challenges — the kind that can be solved with expertise, training, and the right tools.

• Adaptive challenges — the kind that require people to shift how they think, work, and collaborate.

AI implementation involves both. There’s the technical learning, figuring out which tools to use and how they work, and the adaptive work of reimagining workflows, roles, and what “good work” looks like in a changing landscape.

Below is a simple pathway to help your team navigate both kinds of challenges — so you can explore AI thoughtfully, without losing your bearings.

1. Start with Green Lights and Guardrails

Before diving into tools or workflows, create shared clarity around where AI can and cannot be used.

Think of “green lights” as areas where AI can safely add value, things like writing support, process automation, or idea generation.

And “guardrails” as the boundaries that protect your organization, like not using AI for confidential client information or personnel decisions. And if you don't have a no-personal-AI-account policy and/or an enterprise AI account, you should!

Your AI policy can serve as a guide here. The goal is to build trust and alignment before experimentation begins.

This step addresses both sides: a technical understanding of what’s safe, and an adaptive conversation about what aligns with your mission and values.

2. Identify Early Adopters (and Their Counterparts)

Every organization has a few people who are naturally curious and comfortable testing new tools. Invite them to pilot new AI workflows in their roles. These can be within departments or across departments. There is value in both.

Encourage them to document what they learn and focus on open learning rather than perfection.

Then, pair them with more AI-cautious colleagues, people who can ask thoughtful

questions and keep the work grounded.

This pairing helps build a balanced culture of exploration: learning that’s both creative and careful. It’s a small but powerful example of adaptive leadership — helping people learn from one another and navigate uncertainty together.

3. Create The Sandbox

Give your early adopters a safe space to play, test, and learn.

In this sandbox:

• Use non-sensitive data only.

• Provide clear privacy reminders tied to your AI policy.

• Offer example prompts, a few recommended tools, and set aside time each week for experimentation.

The technical side here is about building comfort with new tools. The adaptive side is about cultivating curiosity and psychological safety, helping people see experimentation as part of how the organization learns.

4. Design 2–3 Targeted Experiments

Start small, specific, and time-bound, for example, a two-month trial.

Choose areas that could free up time or reduce repetitive work, such as:

• Drafting meeting summaries or newsletters

• Translating documents

• Automating simple data categorization or form responses

• Generating options for storytelling or grant framing

Track results using meaningful metrics: time saved, quality improvements, or lessons learned.

These small tests let people practice new technical skills and begin to question long-held habits about how work gets done. That’s where adaptive learning happens

5. Capture and Share Learnings

Make learning visible across your organization.

Encourage the team to share both wins and fails, the experiments that didn’t go well. Discuss them in team meetings or short debriefs.

Invite leadership to sit in occasionally. Their presence signals support and curiosity, and helps them see the potential of AI in real time.

When early adopters share examples with colleagues they already trust, it helps others see both the benefits and limitations of AI in context.

This visibility turns individual learning into organizational learning, a cornerstone of adaptive change.

6. Build Reflection and Feedback Loops

Keep the process alive through regular check-ins. Ask:

• What’s saving time?

• What feels risky or unclear?

• What new ideas or needs are emerging?

Use that feedback to update your green lights and guardrails.

And always bring it back to your mission, voice, and values, your organization’s north star.

AI adoption isn’t a one-time rollout. It’s an ongoing conversation about how people, technology, and purpose evolve together.

The Bigger Picture

Implementing AI isn’t just about learning a new tool. It’s about rethinking what effective, values-aligned work looks like.

The technical work — understanding the platforms, prompts, and privacy settings, is the easy part.

The adaptive work — helping people redefine their roles, trust the process, and stay aligned with purpose is where the real leadership challenges are.

When you approach AI as both a technical and adaptive challenge, you give your organization room to learn, experiment, and grow, without losing sight of what matters most.