As I’ve connected with people in both my personal and professional lives recently, Artificial Intelligence (AI) adoption keeps coming up—and not because I’m the one raising the topic. In these conversations, a consistent theme has emerged: the biggest challenge lies in helping people form new habits, which most of us find difficult. If you feel like you’re still trying to find your footing with AI, you’re not alone. As technology continues to evolve, so do the questions around it.
In part 1 of this series, I shared high-level practices to make your AI strategy successful by focusing on the human side of change. Now it’s time to see these practices in action.
When I’m guiding a client through an AI-driven transformation, I keep coming back to our tried-and-tested change curve: Awareness, Understanding, Adoption, and Sustainment. These four stages create the rhythm that shapes real behavior change and help smooth any bumps when bringing AI adoption to life.
Here’s what artificial intelligence adoption looks like in real-world projects at each step of the change curve.
Step 1: Awareness
AI adoption starts with awareness, focusing everyone on a sharp vision and shared goals. My team helps customers identify “quick-win” use cases and stand up a clear communications plan supported by a resource hub, which provides the foundation for conversations and learning. Network building is also crucial in this awareness phase. I encourage clients to build a change agent network full of influencers. Choose people who are both natural early adopters and connectors—people who already bridge departments by engaging in cross-functional initiatives, lunching with different groups, and popping into other teams’ events. These informal influencers will help anchor change in every corner of an organization.
Example: A financial services team I worked with had a vision to increase artificial intelligence use across the entire workforce, with a goal of increasing productivity by 10%. To do so, they assembled a task force of AI early adopters from across the organization—essentially creating the first version of their change agent network. They asked the task force to identify ways that they might make AI more approachable for their workforce and the team decided to highlight ways that they could use AI outside of work. This unlocked an understanding of how AI could be useful, enabling a path to seeing how even small tasks at work could be made easier with the help of artificial intelligence.
Step 2: Understanding
This stage is where you shift into execution: build role-based training paths, run hands-on labs and support clinics, refine processes, ship practical playbooks, and embed fast feedback loops. This is also when I like to remind everyone that no one likes surprises. Equip your change agents with core messages, then let them deliver those messages in the channels and styles their audiences trust. Move quickly as you respond to feedback, iterate, and fine-tune to reach more people faster.
I’ve found that once people start using artificial intelligence—even a little—it’s like an onramp to a Formula 1 track: everything they do can be made easier with AI. A friend of mine described making a doctor’s appointment with the help of AI in two words: “mind blown.” She received more information from AI than she expected to get from her medical practitioner.
Once people reach the stage of understanding, the power of the AI prompt becomes evident. Learning to prompt effectively to get desired action from artificial intelligence is a way for people to invest in themselves. An Oracle AI World 2025 main stage presentation highlighted an example of using AI agents within Oracle Fusion applications to identify variances, drill down into details, and take action. At the crux of reducing variances is the quality of the prompt. We recommend making a concerted effort to help people learn how to craft strong prompts—it is a skill that we are all learning as we go, but if you get it right, the effects can be remarkable.
Remember to keep your AI learnings timely, relevant, and easy to digest. Incremental steps help people learn faster so make sure that your change agents can retell the success stories.
Step 3: Adoption
It’s go time! In this phase, real habits around AI start to take hold. We typically launch short pilots with clear KPIs, track the metrics, fold AI into existing workflows, and lock down governance for data sensitivity and auditability. At this juncture, provide just-in-time performance support and celebrate early wins to fuel momentum. This is where new habits solidify, when people have truly progressed from Awareness to Understanding. However, you should anticipate some friction: task your change agents to coach teams in small groups or even one-on-one sessions so the new way of working becomes the norm.
With strong AI prompts, team members can create wins they can showcase for others. In a recent project at a healthcare system in North America, the client team identified testing scenarios, and we asked AI to compare them to standard testing scenarios for the processes they were implementing. This enabled the team to review a complete list of applicable scenarios and, after agreeing on which to prioritize, define testing steps for each one. This example highlights a key benefit of artificial intelligence: instead of collaboration focusing on testing steps, the discussion centered on how the team’s testing activities could provide the most insights and confirm processes and ways of working. Time was spent on high-value, high-return discussions rather than on tactical steps.
Step 4: Sustainment
Change may take hold faster than you anticipate, especially as artificial intelligence picks up pace and reshapes your organization. Those change agents you’ve relied upon? They will likely morph into long-term AI-savvy ambassadors who will normalize change and help latecomers through their change curves. Update policies and processes, nurture communities of practice, and amplify usage with clear metrics that prove impact. Keep telling the story: showcase outcomes and make the organization’s AI-driven transformation visible and repeatable.
Your biggest challenge? Staying ahead of AI
Move through these stages in weeks, not months. AI use cases evolve rapidly, and those available today far surpass those from just 90 days ago. Stay focused on your sharp AI vision and strategy, build a strong team, and maintain steady execution. Do this, and your organization will consistently gain AI successes that will drive value and differentiate you from your competitors.
Worried about holdouts or slow adopters? You are not alone. Part 3 of Change Management – AI’s Secret Weapon will share practical tactics to overcome resistance, shift negative mindsets, and turn fear into momentum.
Learn more
Looking to accelerate AI adoption in your organization? Connect with Holly Nelson, Change Management Senior Practice Director, to discover how we can support your strategy. For more details now, explore our AI Services page or try our online AI Maturity Assessment.