AI Change Management Fundamentals

Change management can be sticky under normal circumstances, but when you throw a new technology like AI into the equation, things can get sticky-icky.

And recent research reveals that less than 20 percent of organizational change initiatives (like your AI deployment) achieve their stated objectives.

This area of AI expertise is exploding, so your expert guidance here will be in high demand.

Even though the leadership team has successfully developed the AI Business Strategy and is enthusiastic about its rollout, it may be a surprise that not every role in the org may be excited about the positive changes in process.

And it’s understandable why there could be some hesitation from the team.

According to an investment bank Goldman Sachs report, AI could replace 300 million full-time jobs.

Another report by FindWeb3 states that 85 million jobs are expected to be replaced by AI as soon as 2025!

With news stories about AI replacing jobs and the need for every employee to upskill with AI coming out daily, there’s a definite need to address those concerned about job security or who currently lack the skills to use new AI systems.

It is safe to expect that without proper change management, AI initiatives may face resistance, lack of user adoption, decreased productivity, and failure to see ROI.

As a result of what you’ll learn in this Module, you’ll be able to communicate effectively with the entire staff about the benefits of the AI deployment, how it affects their jobs, the upskilling available to them, and ultimately to have their support in rolling out the AI Business Strategy.

So let’s start with the basics – what exactly is “change management”?

Change management is the process of helping people and organizations transition from their current way of doing things to a new paradigm.

For AI projects, in particular, it means guiding employees, leaders, and the organization as a whole through significant changes to systems, processes, policies, and culture to support enterprise-wide adoption of AI.

 

Why AI Fails without Change Management

There are many reasons AI projects fail without effective change management, and the bottom line is that more than the technology alone is needed to drive real transformation.

We’re all clear that AI can transform businesses and how work gets done, but realizing the full benefits requires getting employee buy-in and adapting company culture and processes.

You need the entire team onboard to really make it stick.

So, let’s talk about some common pitfalls you and the leadership team may encounter:

 

1. Lack of Communication and Transparency

The last thing any company needs is more fodder for the rumor mill. Employees who will be impacted should be looped in early and updated on how AI will change their roles.

By developing a clear communication strategy for the AI rollout, you will help avoid uncertainty, rumors, and resistance when the technology starts popping up across the business.

In the next section of this lesson, we will walk you through the framework we use to control the conversation in the company about AI in a positive way during the initial phases and beyond.

 

2. Focusing on Technology, Not People

Plain and simple, people ultimately determine the success or failure of AI.

An AI solution can be immaculately designed and technologically impeccable, but its potential remains unrealized without the engagement and enthusiasm of those who use it.

Don’t let the leadership team fall into the rabbit hole of the technology without adequately planning for the human factors – upskilling people, addressing fears, and gaining buy-in.

Remember “human in the loop”?

 

3. Ignoring Impact on Roles and Skills

Look, AI may automate some roles completely and certainly alter many others. The company will have two choices: (1) eliminate some roles on the org chart and let go of that staff, or (2) redeploy that talent onto other areas of opportunity within the AI Business Strategy.

Either way, there may be uncertainty for the employee/team member.

Employees may view AI as a threat if they don’t understand how their responsibilities will change. They must have opportunities to gain the new skills required to fully leverage AI in their roles. Communicate that from day one.

 

4. Poor Integration with Workflows

Dropping AI tools into the business without aligning them to existing business processes and workflows is a recipe for failure.

If the team feels AI just adds busy work, you will have a hard time if they don’t see AI as helping them with their actual work. AI should integrate seamlessly into how employees currently get things done.

Using the methods you’re about to learn in the rest of the lessons, there will be minimal disruption with a fast learning curve for employees.

 

5. Lack of Executive Leadership 

AI changes many aspects of an organization.

Without committed leadership from executives who communicate its importance and allocate resources, AI projects flounder. Employees take cues from leaders – if they do not see it as a priority, neither will they.

Failure to address concerns like these can manifest in myriad ways: productivity lags, lackluster user adoption, increased turnover, and an absence of the expected return on investment.

Now, let’s talk about how we manage the changes that you will be helping deploy from the AI Business Strategy.

 

Change Management Frameworks

There are lots of proven change management frameworks that we can leverage for AI projects, but the one we find most applicable is the ADKAR model from the founder of Prosci, Jeff Hiatt, which advocates a bottom-up approach that begins with the individual role and ends up with the ideal outcome of the changes.

ADKAR stands for:

Awareness (of the need for change)

Have a well-thought-out approach to communicate the business reasons for AI. You should have a different communication plan for the different levels of the business (C-Suite, middle managers, front-line employees)

Paint the big-picture vision of productivity gains, cost savings, and improved insights, and back up your communication with numbers, stats, and visuals.

Desire (to enroll support in the change)

Get stakeholders excited by demonstrating how AI will make their jobs easier and let them focus on more strategic work.

Holding a demonstration on how to use AI in certain business tasks will put AI’s power into a context they can relate to. This is a perfect time for showing the “magic tricks” they will be capable of with Generative AI in particular.

Knowledge (of HOW to change)

Comprehensive training, clear SOPs, and access to a “help desk” for AI are crucial so employees understand how to use the AI tools and trust the technology.

Ability (to show new skills and behaviors)

Give coaching and hands-on practice time to build skills. Patience is key – it takes time to master new ways of working, but once a team member has the “A-HA” moment on how AI can support their role, they never look back.

Reinforcement (to make the change stick)

Track usage, celebrate wins, and course-correct in the departments that may be struggling. To embed AI usage into the organization’s culture, work with the leadership team to formally recognize and award team and individual use and adoption, creating a “carrot,” not a “stick,” environment within the business.

 

Stakeholder Analysis

Now that you have a framework for managing the change, the next step is mapping out all groups and individuals impacted by the AI rollout.

Key stakeholders should include:

1. Senior leadership

With their buy-in, an AI deployment can avoid an uphill grind. As the CAIO, you need to help them clearly and honestly understand AI benefits and how it aligns with the long-term viability of their business.

 

2. Middle managers

They will be the ones who will drive implementation on a day-to-day basis, so giving them a voice in the discussion is insightful and imperative. They can help predict where there will be friction, both from process and people.

 

3. Frontline staff

They will see the most impact on their daily workflow, and including their perspective will give you optics on the common concerns at this level and introduce you to the granular/micro aspects of AI usage by the largest demographic within the company.

 

4. Customers

They may experience changes to products/services as a result of the new AI-enabled processes in the company, so don’t hesitate to connect with a sample of the company’s best customers if their experience will be impacted.

 

5. IT teams

Since they will most likely be responsible for providing technical support to the staff and maintaining the AI systems, you will want their feedback and participation in the deployment of AI in the enterprise.

Analyze each stakeholder group to understand their interests, concerns, influence, and potential impacts from the AI changes.

 

Understanding Stakeholder Needs

When you engage with the different stakeholder groups, these questions should help you get insight into their needs & concerns, allowing you to better include their concerns and suggestions in the overall change management plan.

 

Here are the questions I would ask each stakeholder group as an AI strategist leading the change management process:

Senior Leadership

• • What are the 1-3 most significant business objectives you want to achieve with this AI adoption?

• • How aligned is this with our long-term strategic roadmap? Is leadership fully committed to providing resources?

• • What risks or challenges concern you the most? How can I help you feel confident we will address them?

• • How should we stage the rollout to minimize disruption but achieve quick wins?

 

Middle Managers

• • How will this impact your team’s day-to-day responsibilities and workflows?

• • What parts of your jobs do you think AI could automate? Where might you need to upskill?

• • What support can I provide to get your team excited about AI assisting their roles?

• • How will we redefine success metrics to account for AI augmentation of your team’s capabilities?

 

Frontline Staff

• • What excites or concerns you about how AI may change your job duties?

• • What type of ongoing training would help you become proficient with the AI tools?

• • How can I make this transition as smooth as possible for you?

• • What existing pain points could AI help solve? How do you think it could positively impact your work?

 

Customers

• • How will this improve your customer experience and satisfaction?

• • What risks or downsides do you see from our use of AI? How can we mitigate those?

• • What is the best way to gather feedback to continuously improve our AI systems?

• • How transparent should we be about how we are using AI to enhance our services for you?

 

IT Teams

• • Are there any technology constraints that could challenge an enterprise AI rollout?

• • How much of a lift will this be for your team? Do you foresee any roadblocks?

• • What vendor partnerships or internal skills development will be critical for you?

• • How will we build monitoring, support, and continuous improvement processes for our AI systems?

These questions aim to uncover concerns, surface risks, understand impacts to roles, align on objectives, and gain insights into how to make this a smooth AI adoption.

 

Tailored Communication

With your stakeholder analysis complete, you can now tailor communications and change plans: the more customized the communications, the better the outcomes.

 

Here are some suggested key points and KPIs to communicate for each stakeholder group based on their feedback:

Senior Leadership

• Key Points: Show how AI adoption aligns with long-term growth strategy, impress upon them the necessity of commitment to follow, and demonstrate a plan to show the AI implementation will be staged to minimize disruption.

• KPIs: It’s a must to cover AI’s forecasted impact on business metrics like revenue growth, cost savings, improved forecast accuracy, etc. Predicted impact on customer metrics like satisfaction scores and churn rate should also be discussed, as they are signs to the leadership that their customers will benefit from the AI deployment.

 

Middle Managers

• Key Points: Let the managers know that workflows will evolve but not abruptly change and that new success metrics will be adopted. It’s critical to reinforce that training and support will be provided to them and their teams and that the learning curve is manageable.

• KPIs: Address topics like impact on team productivity, the process for individual skill development, improved employee satisfaction, and expected deadlines and targets for adoption rates amongst their team.

 

Frontline Staff

• Key Points: By focusing on how AI will assist job duties rather than replace roles, you’ll address the most common concern you’ll hear from frontline staff. Ensure they know that extensive training will occur and that feedback channels are open if they want to voice opinions or feedback.

• KPIs: Individual productivity, skill growth, work output quality, and reduced pain points.

 

Customers

• Key Points: Being transparent on how AI will enhance the customer experience will put the company in good graces with their customers. Invite them to provide feedback on their customer experiences with the new systems.

• KPIs: You’ll know that the communication has landed with the company’s customers when customer satisfaction and Net Promoter Scores are increasing and product return rates start dropping from historical averages.

 

IT Teams

• Key Points: Let the tech team know they have leadership’s support when acquiring resources for the AI deployment and that you will be there to support them with vendor partnerships. Additionally, be sure to mention that technology risks will be addressed, and they will have a hand in shaping the technology risk mitigation protocols.

• KPIs: The key metrics the tech team will be interested in are impacts on system uptime, any increase in support ticket volume, and percentage rates for AI tool adoption amongst the staff.

By following the guidelines established in this section, you can effectively craft change management communication that reassures stakeholders, demonstrates a commitment to their success, and aligns everyone on metrics we’ll use to track progress.

 

Building an AI Change Coalition

Next, let’s discuss how to assemble a powerful AI change coalition across the organization.

An AI change coalition is a team of leaders across different departments and levels of the organization who come together to steer a smooth transition when adopting artificial intelligence. Ideally, the coalition should include members from departments impacted by the AI deployment.

And since you’ll need them to be the evangelists for AI within their departments, you will want to carefully select the members based on their level of enthusiasm about the benefits of AI that are in process.

With this diverse group, the coalition can spot risks early, align training and communications, anticipate concerns, gather feedback, and serve as the guiding force for the people side of AI change.

 

Recruiting Change Leaders

Implementing something as forward-thinking as AI can’t be done alone. You need a crew of influencers across the business to have your back.

To build the AI Change Coalition, identify respected leaders from different departments and levels, especially passionate early adopters who can influence peers.

You will need to coach them on advocating for the AI effort. Including them in activities like designing the training for the staff, monitoring adoption metrics, and having them provide regular progress updates to their teams will lead to them naturally being seen as the resource for their departments.

Here are some roles that will need to be filled on the Coalition:

Executive Sponsors

Start by getting some VIP sponsors who will lend credibility to the Coalition. They’ll help ensure that suggestions from the Coalition get implemented, and they can communicate the AI vision throughout the company.

 

AI Project Team

The project team will help design and deliver the rollout. Empower them as ambassadors, sharing updates and gathering feedback. Enable them to be AI experts for their peers.

 

Middle Managers

As we already discussed, middle managers will be critical allies when implementing with their teams. They make things happen on the ground floor, so loop them in early. Hype them up as hands-on coaches for the new tools, and make sure they get recognition as members of the Coalition – and don’t forget to remind them this will be great for career advancement!

Finally, sprinkle in some frontline all-stars. They’ll share crucial intel on what’s resonating and where there’s confusion.

With these influential change leaders building momentum across the business, your AI launch will have the support it needs to be successful. They become invaluable advocates for promoting understanding, enthusiasm, and momentum within the organization.

 

Mapping The Journey

Now that we’ve got a communication strategy for the rollout, have gotten input from stakeholder groups, and assembled our AI Change Coalition, it’s time to map out the actual implementation steps.

Here’s an expanded look at the steps to map out an AI change plan:

Step 1.  Assemble the A-Team

You should have recruited the AI Change Coalition members at this point. Now it’s time to hold the kickoff meeting to align on the Coalition’s purpose and vision. Ideally, you will have someone from the leadership team start the meeting and introduce you as the Coalition leader.

Once you have presented the findings from the stakeholder groups and the communication plan, it is time to start digging in on the rollout plan.

 

Step 2. Diagnose the Landscape

Starting with the departments and roles identified as priorities when you developed the AI Business Strategy, you will want to document workflows and processes that will evolve with AI integration.

Assign that to the members of the Coalition whose departments will be impacted.

 

Step 3.  Define the North Star Metrics

Next, we’ll need to set S.M.A.R.T. goals related to upskilling/training and overall AI adoption by department.

You’ll also want to determine the goals tied to business drivers like costs, revenues, or customer satisfaction.

Finally, share with the Coalition how these success metrics will be tracked using the Scorecard methodology we introduced you to in previous modules.

 

Step 4.  Map the Roadmap

Next, plot a detailed timeline of change activities matched to AI rollout phases.

This includes scheduling key milestones, user training, project communications, adoption and usage monitoring, and skills assessments for the users.

You’ll want heavy input from the tech team since they will already have projects outside the deployment in the works and must be prepared to provide feedback and user support.

 

Step 5. Spread the Word

Develop the messaging about the roadmap for each stakeholder to take back to their departments. Highlight WIIFM (what’s in it for me) for each department and role so the benefits for their teams are clearly described.

Also, establish a tempo for reporting to the teams on progress, future milestones, and lessons learned during the deployment.

 

Step 6. Skill Up for the Future

Conduct a training needs analysis, with each member of the Coalition providing input on the skill level and interest in AI.

With this analysis in hand, design role-specific programs on AI capabilities, new processes, tools, and anything else the team will need to be comfortable executing the new AI-enabled workflows.

Once the training needs have been defined, deliver training via e-learning, workshops, job aids, and/or one-on-one or small-group coaching.

 

Step 7.  Make Change Stick

Once people are trained, we have to ensure these new skills and processes truly become business as usual.

Have a way to spotlight rockstar adopters to inspire others. Make success visible, and others on the team will know what’s possible for them.

Bake metrics into individual employee performance reviews as a way to drive adoption. And when those metrics are met, celebrate those milestones as a team.

And to really make it stick, encourage the members of the Coalition to keep listening and coaching. Persistence at this stage results in people having lightbulb moments on how to optimize their workflows.

 

Step 8. Be Ready to Pivot

No battle plan survives first contact with the enemy. Expect bumps and missteps.

It’s nothing to worry about as long as the leadership team and the Coalition remain nimble and patient.

When issues come up, quickly address concerns with TLC and tweaks. And if specific changes don’t get the result we’re looking for, reassess and try again from a different angle.

 

Step 9. Test and Refine

No big bang deployments here – take an iterative approach.

Start with a pilot group to pressure test the plan and spot gaps that can be fixed before the next department’s rollout.

Before going ALL IN, keep refining the rollout plan based on feedback coming from the pilot groups. Minor hiccups are better than big snafus.

There you have it – a detailed blueprint for orchestrating people-focused change to drive AI success! Let’s do this.

Sustaining the Change

Expect to find yourself managing a successful initial phase of the AI Business Strategy.

Also, expect to encounter some bumps in the road.

As a result of poor change management practices, companies tend to regress to the old ways of doing things. It’s what is comfortable, what’s known.

So, now let’s talk about Overcoming Resistance.

Some stakeholders will not want to be held accountable for adopting the new workflows.

But with some diplomacy on your part, reluctance can pave the way for eager adoption.

It’s best to have open and honest conversations with the resistors. The stakes are too high. Their team’s representative on the Coalition should drive that conversation and correction. When the feedback loop stays that tight, you can quickly stay on top of things and course correct.

Some remedies we suggest you consider when working with resistors:

Ensure they see how AI will augment their skills rather than replace their job. If you plan to redeploy their extra bandwidth, let them know.

If people think existing processes work fine, quantify the inefficiencies and pain points AI will eliminate. Show them that AI can do the “dumb parts” of their job, the repetitive tasks that can easily be automated…that’s the part they hate anyway!

AI is kind of a black box. Institute guardrails like ethics reviews to ease concerns so that resistance goes away. You’ll learn how we do it in Module 10: AI Governance – Creating Trust, Compliance, and Data Privacy (don’t worry, it’s more fun than it sounds).

The good news is that turning someone from resistor to AI-powered isn’t difficult – listen to them and help them get what they want.

Here’s how we do it:

Reinforce with Incentives. Link AI usage and proficiency to performance reviews, monetary incentives, and recognition. Highlight contributors publicly and have the team leads find creative ways to motivate continuous adoption.

Refine Operational Standards. Intelligently redesign policies, procedures, and protocols to incorporate AI-enhanced workflows seamlessly. When you integrate AI into someone’s role, their world is never the same.

Monitor Adoption Metrics. This is important. Track leading indicators (e.g., training participation rates) and lagging indicators (productivity metrics) on usage, proficiency, and impact. Analyze where adoption is strong vs. weak, and address it with training or coaching.

Celebrate Wins. Recognition goes a long way. It shows that AI usage and AI users matter to the company. It shows those hesitant team members that people are adopting it and that it’s the way things are headed in the company.

Maintain Momentum. Keep the dialogue alive and the training wheels spinning. Collaborate closely with your AI evangelist cohort to detect any sign of waning enthusiasm, intervening promptly.

And remember, it’s a marathon, not a sprint. Though the initial impact may be small (doubtful), this deployment has prepared this company to participate in the next decade of business. They have bought a ticket to the next phase that will require intelligent deployment of AI as the price of admission.

They chose enablement over extinction.