How to Find the Right Balance in Your AI Implementation Strategy

By Rachel MulryShare

AI is reshaping workplace norms, business models and everyday behaviors at a remarkable pace. Some companies are going “all in,” others are resisting it entirely and many are trying to find a responsible middle ground.

The challenge is to pursue AI in a way that moves the organization forward while remaining aligned with the organization’s culture, mission and capacity for change. Finding that balance requires leaders to assess readiness, clarify the intended outcomes, define meaningful measures of success and create open channels for learning from both progress and setbacks.

Assess Your Organizational Readiness

Successful AI adoption requires an agile and innovative workforce. Employees must be willing to embrace new ways of working, experiment thoughtfully and discover where AI can create real value. If your employee base does not share that same level of eagerness, you will have more work to do in the areas of communication, training and change management.

The quality of your data is also a key factor in readiness. If your data repository is filled with historical information, outdated documents or content that has not been reviewed in years, be careful. If your workflows and procedures are not well documented or include too many exceptions, AI implementation will likely be more difficult. Organizational readiness is as much about the data as it is about the people who interact with it.

Clarify Your Desired Goal

AI adoption often promises productivity gains, workflow automation and faster development cycles, but those outcomes will not happen because the tool is available. Leaders need to be clear about the specific problem they are trying to solve and the behavior they expect to change.

If the goal is increased productivity, employees need to understand where newly found time should be invested: deeper customer service, more strategic work, faster response times or higher-quality outcomes. If the goal is workflow automation, leaders need a structured list of processes, a clear understanding of the exceptions and risks and a realistic view of the impact automation may have on people, service and operations. If the goal is shorter development cycles, teams need to define what “faster” means, where quality controls must remain in place and how success will be measured beyond speed alone.

Without that clarity, AI can quickly become an interesting tool in search of a purpose rather than a disciplined strategy tied to meaningful organizational outcomes.

Define Metrics and Assess Progress Honestly

Because AI investments can be significant, honest realism about the ROI may be difficult. Leaders may be tempted to overemphasize the positive shifts and explain away the negatives. “We are still learning” or “this will take time” may be reasonable statements for a period, but they should not become excuses that prevent the organization from acknowledging when something is not working.

Make sure you have a circle of advisors who can challenge the data and outcomes honestly. Evaluate the measurement criteria to ensure it will provide an honest assessment of your progress. Identifying both trustworthy advisors and measurement systems can help organizations avoid the sunk cost fallacy and make better decisions based on evidence rather than optimism alone.

Open the Lines of Honest Communication

AI is not the answer for every task, and not every employee will experience the same level of success using it. That is why open communication is essential.

Employees need a safe way to share what is working, what is failing and where AI is creating confusion, risk or unintended consequences. Those conversations should not be treated as resistance. Rather, they should be treated as an important source of learning. When leaders create psychological safety around honest feedback, organizations are better positioned to identify effective practices, correct mistakes quickly, strengthen training and discover new opportunities for innovation.

Ultimately, successful AI adoption is not defined by speed alone or by the size of the investment. It is defined by disciplined leadership, cultural alignment, clear measures of progress and the humility to adjust when results do not match the aspiration. Organizations that find the right balance will treat AI not as a one-time technology deployment, but as an ongoing opportunity to learn, improve and serve their mission more effectively.

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Bridging the Gap: 5 Tips for Cross-Functional Collaboration That Enables AI Transformation

Ask ten executives who owns AI at their company, and you’ll get ten different answers. IT says it’s not their call. Legal gets blamed for slowing everything down. HR figures it’s someone else’s department. Meanwhile, teams are buying tools nobody signed off on, duplicating work and hoping it all sorts itself out. Sound familiar?

Lisa Duerre spent the last year studying why that happens. As part of an applied research project for her leadership consulting collective, RLD Group, she studied where AI adoption breaks down inside organizations, and where it works. The findings from RLD Group’s research helped inform a collaboration on the CONVERSATIONS WORTH HAVING®: The Human Accelerator for Artificial Intelligence Quick Start Guide, which is available as a digital download.

Duerre views organizations through what she calls an I–WE–US leadership framework, defined like this:

I: individual judgment and accountability
WE: workflows and cross-functional coordination
US: governance, decision rights and organizational measures

“All three levels are contributing to the breakdown or the alignment, whether people realize it or not,” Duerre says. “AI is amplifying whatever’s already true in your system. The teams that were disconnected before AI showed up are more disconnected now. The ones that talked to each other are moving faster, together.”

If your company is ready to collaborate better with AI tools, Duerre shared the following tips. Take a look.

Form a cross-functional AI committee

Duerre’s background is in HR, and she says most HR leaders assume AI ownership belongs to IT. It doesn’t, at least not exclusively.

“Ownership needs to sit at the system level,” Duerre says. “Each function carries a piece of it, based on what they do, how well they understand that part of the business and how their work depends on everyone else’s. AI is flattening how we work. You can’t just keep it in your own business unit anymore. You have to look all around you.”

For starters, she suggests building a cross-functional AI committee instead of having one department make all the AI decisions. Legal, IT, cybersecurity and HR should be on the committee, Duerre says.

“If you have a C in front of your title, you should be on that committee,” Duerre says. “That’s how I look at it, because it’s a system-level solution.”

During these meetings, Duerre says you’ll find out that some departments are racing ahead with AI and others are holding back.

“Both sides need to name the trade-offs aloud,” Duerre says. “With teams moving too cautiously, you have to talk about the opportunity cost of falling behind. With teams sprinting ahead, you have to ask them what happens if they don’t bring everyone else along with them.”

Figure out how to use AI strategically

Most companies spent the past two years telling employees to use AI with anything, without much strategy behind it. Duerre says that’s starting to catch up with organizations as finance teams scrutinize the cost.

Her rule of thumb: if you can’t articulate the goal and how you’ll measure success, don’t roll it out yet.

“Teams that use AI well have a strategy behind it,” Duerre says. “They’ve kicked the tires on what they’re trying to solve it for. You need to ask yourself, ‘Which business outcome are we trying to improve, and what must be aligned for AI to create measurable value?’”

Here are a few examples of how to use AI strategically:

A company could select a workflow that regularly creates delays, redesign it with AI and test the new approach. Then, measure whether it improves time, cost, quality or capacity.
Use AI to support early sales outreach and qualification across markets and languages. AI can help a business reach and assess more potential opportunities, while people remain responsible for understanding the customer and building trust.
Flag patterns in customer complaints across multiple channels with AI, so leadership can see recurring problems before it shows up in satisfaction scores.

Check-in regularly during an AI rollout

Duerre recommends a minimum weekly check-in during any AI rollout, sometimes daily depending on complexity. But the format matters more than the frequency. Status updates don’t cut it.

“Ask, ‘What are we learning and what are we surprised by?’ That’s a question that helps you with your check-ins, versus, ‘It’s in three products now and we’ve tested six,’” Duerre says. “That doesn't help, because you’re having these meetings to figure out what’s working and why. If you ask more strategic questions, you can move even faster.”

Publish AI guardrails

Employees who don’t know what’s allowed with AI will either freeze or go around the system entirely, Duerre says. She recommends publishing clear, specific guardrails on what’s okay and what’s not. Come up with some real examples, and pair them with an intake process that doesn’t require writing a thesis to get an approval for using it.

"The approval path should be lightweight, not bureaucratic,” Duerre says. “Something like, ‘If you’re going to use AI, here’s the path. And if it needs approval, here’s three or four quick questions for you to answer.’”

Take employee anxiety about AI seriously

“AI is just a tool” is a phrase Duerre hears at nearly every conference she attends, but she doesn’t buy it.

“Saying it’s a tool is underselling what’s happening at companies right now,” Duerre says. “AI is changing how we work. It’s changing how we lead teams.”

Duerre wants leaders to remember that a lot of employees are fearful of AI. Pew Research Center found 52% of U.S. workers are worried about the future impact of AI in the workplace.

Employees who feel AI is being “done to them,” instead of built alongside them are especially anxious, she says.

“Leaders need to recognize that anxiety is contagious,” Duerre says. “As a leader, this is your opportunity to show up as the safe, steady person who is showing what you’re learning with AI. And don’t be afraid to show how you’ve failed using AI, too.”

Duerre asks every executive she works with: “Who am I with AI?” and encourages them to pass this mindset question along to their employees, too.

“AI is now your teammate,” Duerre says. “Phrasing it as, ‘who am I with AI?’ is different than, ‘what’s going to happen to me with AI?’ You really want your team to feel empowered with AI and show them how it can help accelerate their career.”

Put these ideas into action

Rewiring your organization for AI requires more than the right tools. It takes shared language, practical frameworks, and a willingness to keep learning. Here are a few resources to help you take the next step.

Enterprise AI Playbook: Practical frameworks and executive discussion questions to help IT, HR, and business leaders align around AI that delivers measurable value.
Work-First AI Use Case Assessment: Identify the workflows where AI can have the greatest impact before you invest in new tools.
The REWIRED Brief: Get weekly insights, real-world case studies, and practical advice on leading AI transformation.

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