AI Enablement
Case Studies: AI Change Impact in Action
AI is transforming businesses, but success hinges on more than just technology. Companies like IBM, Johnson Controls, and McKinsey are proving that aligning people, processes, and tools is key to achieving results. Here's what you need to know:
91% of executives are driving AI adoption, but only 25% of projects meet ROI expectations.
Success depends on addressing workflow challenges, team dynamics, and skills gaps - not just deploying tools.
Examples include IBM saving 40% in HR costs with AskHR, Johnson Controls cutting HR call volumes by 30%-40% with Omni, and McKinsey saving 50,000 hours monthly with Lilli.
Key lessons:
Start with small, focused pilots.
Engage leadership to model AI use.
Prioritize early governance and clear policies.
AI adoption isn't just about software - it's about empowering teams and improving efficiency through structured, people-first approaches.
Best AI Change Management Strategies for AI Rollout
Johnson Controls – HR AI Assistant Omni
Johnson Controls, a $25 billion global leader in smart building technologies, was struggling with an overwhelming number of repetitive HR requests from its 100,000 employees worldwide. Questions about time-off, benefits, and payroll were eating up valuable time that could have been spent on more strategic priorities.
In August 2025, under the guidance of CHRO Marlon Sullivan, the company introduced Omni - an AI-powered assistant developed using the Moveworks platform in collaboration with Deloitte. The initial focus was on handling high-volume, straightforward tasks like policy clarifications, vacation requests, and benefits inquiries. By embedding Omni into platforms like Microsoft Teams and Workday, the company ensured that workflows remained smooth and familiar for employees.
To prepare for this shift, Johnson Controls worked closely with Deloitte to assess its AI readiness and create a detailed change management plan. Early feedback from employees played a critical role in shaping Omni’s evolution from a reactive tool into a proactive assistant. For instance, Omni now sends reminders about performance reviews and training deadlines, actively supporting employee engagement.
"The AI Assistant is now seen as integral to the employee journey." - Moveworks
Omni’s success also aligned with Johnson Controls’ broader automation goals, delivering $10 million in enterprise-wide value within just six months of its launch. Reflecting on this achievement, Ramnath Natarajan, Director of Global Intelligent Automation & Integration, remarked:
"Often, big companies spend too much time looking inward. AI-powered automation helps us spend less time looking inward and more where it's going to matter." - Ramnath Natarajan, Johnson Controls
This shift allowed HR to transition from routine case management to a more strategic role, setting a strong example of how to effectively integrate AI into business operations.
Results
Omni’s impact has been impressive. The AI assistant now manages tens of thousands of interactions every month, reducing HR call volumes by 30% to 40%. This has freed up the HR team to focus on consultative support and talent development without the need to increase headcount. Employees benefit from shorter wait times, as routine questions are resolved instantly via Microsoft Teams, while only complex issues are escalated to live HR staff.
The company is also expanding Omni’s capabilities to address manager self-service queries, performance goal management, and even provide access for frontline manufacturing teams. These enhancements aim to bring Omni’s benefits to even more user groups, further streamlining operations.
Ciena – Unified Support System with Navi
Ciena, a global networking company with 8,000 employees, faced significant challenges due to fragmented systems across HR, IT, legal, facilities, and finance. These disconnected tools slowed down workflows and hurt productivity. This case sheds light on how Ciena tackled these inefficiencies during its journey into AI integration.
In August 2025, Ciena launched Navi, an AI assistant powered by Moveworks and seamlessly integrated into Microsoft Teams. Spearheaded by Head of Employee Experience Marjukka van Mameren-Ahonen and Senior Analyst Lalit Kumar, Navi was built to act as a single conversational hub for all support requests. Employees could interact with Navi using natural language to handle tasks like submitting timesheets, requesting equipment, or accessing policy documents. By simplifying these processes, Navi transformed how employees engaged with internal systems.
The rollout began with a pilot involving several hundred users, allowing the team to gather feedback before scaling Navi company-wide. Ciena focused on automating high-impact workflows, including approvals, access requests, and knowledge management. Over 100 workflows were automated during this phase. To ease the transition, Ciena hosted frequent webinars to help employees understand and adapt to working alongside AI. By linking Navi to existing platforms like ServiceNow and Workday, the company eliminated the need to switch between tools, cutting down on what they referred to as "tool travel."
One notable success came in May 2025, when Ciena extended its laptop refresh cycle from three to five years. Using Navi, the customer enablement team automated responses to hundreds of monthly queries about the change. Navi pulled accurate, real-time information directly from the HP website, significantly reducing the IT team’s manual workload. Enterprise Solution Architect Berney Gehring highlighted the importance of quick implementation:
"Everything moves so fast that if I have an idea today, I can't wait four days, three days, before I can apply that idea."
Navi’s capabilities extend to managing complex, multi-step workflows autonomously. For example, during employee onboarding, Navi handles provisioning, scheduling, and follow-ups, freeing support teams to focus on more strategic initiatives instead of routine tasks.
Results
The results of implementing Navi were immediate and striking. By streamlining access across multiple systems, Ciena saw an 85% reduction in approval times - bringing the average wait down from three days to just 30 minutes. Today, about 4,000 employees, half of the company’s global workforce, rely on Navi for 24/7 support, no matter their time zone. The assistant’s ability to process requests in over 100 languages ensures it meets the needs of a diverse, global team. Adoption has climbed steadily, with usage increasing by 20% each quarter since launch. Lalit Kumar shared his enthusiasm for the results:
"Our adoption and growth are tremendous - it's increasing every quarter by 20%. Employees are asking more questions, and the AI Assistant keeps up with every one."
These outcomes showcase how Ciena effectively leveraged AI to streamline operations and improve employee experiences.
loanDepot – Onboarding Acceleration with ElleDee
loanDepot, one of the largest mortgage lenders in the U.S., encountered a major hurdle in February 2022. The shift to remote work caused a surge in phone support requests, with thousands of calls pouring in daily. Their manual ticketing system couldn’t keep up, leading to IT issue wait times exceeding an hour. New hires were particularly affected, struggling to get the access they needed to start working efficiently. Donald Small, Director of IT Service Management, knew it was time for a fresh approach.
The answer was Elle-Dee, an AI assistant powered by Moveworks and integrated with Microsoft Teams. Instead of navigating complicated portals or filling out forms, employees could simply describe their needs in everyday language. Whether it was requesting software, unlocking accounts, or updating distribution lists, Elle-Dee streamlined these processes directly through Teams. This setup tackled the IT bottlenecks that often slowed down a new hire’s first week. It’s a great example of how AI can address onboarding challenges while driving operational improvements.
loanDepot ensured high engagement with Elle-Dee by prioritizing clear employee communications. Over half of the workforce began using the AI assistant, with managers approving or denying requests directly in Teams. New hires received real-time guidance, and in just its first month, Elle-Dee handled 1,000 employee queries.
The rollout was impressively fast - Elle-Dee was fully operational and delivering results in less than 100 days. This allowed the 30-person Level 1 support team to shift away from routine tickets and focus on more strategic tasks. The operational shift was transformative, as Donald Small highlighted:
"At loanDepot, we're laser-focused on the future. The only way forward is to build an amazing employee experience, where all our people get the support they need. Moveworks' AI is critical to our strategy."
Results
The impact was immediate. Onboarding sped up significantly, with IT approvals dropping from days to under 5 minutes. New hires could hit the ground running, and 90% of employees reported that Elle-Dee was helpful during their first week.
Beyond onboarding, Elle-Dee now automates around 2,000 IT issue resolutions every month. This has freed up the IT service desk to focus on more impactful work, turning what was once a major bottleneck into a competitive edge.
McKinsey – Workflow Redesign with Lilli Platform
McKinsey & Company faced a familiar challenge: its 45,000 professionals were drowning in a sea of proprietary knowledge - ranging from documents and frameworks to past client work. In August 2023, the firm introduced Lilli, a generative AI platform aimed at simplifying consultants' access to this vast internal repository.
The journey started with thorough research to pinpoint workflow bottlenecks where AI could make a difference. McKinsey zeroed in on four key areas: empowering high-performing teams, enhancing client strategies with AI-driven insights, delivering exceptional client service, and ensuring quality communication post-engagement. Lilli was initially rolled out to a select group to gather feedback and refine the platform.
To encourage adoption, McKinsey created superuser communities across ten offices and launched a beta program. Leadership played a key role by consistently asking, "Have you asked Lilli?" during meetings, helping embed the platform into daily routines. These efforts normalized Lilli's use as part of everyday consulting workflows.
The platform brought a major shift in how consultants worked. Junior team members moved away from spending hours on background research and manual document searches, allowing them to focus on building narratives and activating insights. Meanwhile, senior team members could redirect their attention from oversight tasks to strategic planning and mentorship. McKinsey supported this transition with micro-learning initiatives like 15-minute demos, leaderboards for tracking "scheduling wins", and opt-in communities to ease what some users called "prompt anxiety".
One technical challenge emerged when Lilli initially struggled to process PowerPoint documents, only managing to parse 15% of them. McKinsey's team tackled this by developing a custom tool that boosted the success rate to over 85%, unlocking access to thousands of client presentations and internal frameworks. By early 2025, the firm introduced a "one-click deliverables" feature, enabling consultants to generate client-ready slide decks from a simple prompt. This innovation reduced the time required for a first draft from 6–10 hours to just minutes. As Kate Smaje, McKinsey's Global Tech & AI Leader, put it:
"The shift eliminates the need for excessive manual PowerPoint creation, freeing those hours for hypothesis testing and client dialogues instead of layout work."
Lilli's development highlights how targeted AI tools can transform information workflows.
Results
Today, 72% of McKinsey's workforce regularly uses Lilli, generating over 500,000 prompts each month and interacting with the platform an average of 17 times per week. This has cut search-and-synthesis time by approximately 30%.
The platform saves an estimated 50,000 consultant hours every month, translating to about $12 million in labor savings. Additionally, Lilli grants users access to a proprietary library of over 100,000 documents and frameworks, turning McKinsey's institutional knowledge into a highly accessible resource.
To ensure these time savings were directed toward meaningful work, McKinsey incorporated AI usage metrics into quarterly HR development sessions. Nick Talwar, McKinsey's CTO and AI Engineer, summed up the platform's impact:
"Lilli has become the foundation for how McKinsey works... Time that used to be spent searching through documents is now dedicated to high-value tasks like strategy development and client engagement."
Fortune 100 Company – Coaching Scale-Up with Nadia
Prudential Financial faced a daunting challenge: only 1% of its 40,000 employees - around 400 leaders - were receiving coaching. Traditional in-person coaching methods couldn’t keep up with the scale required, and the leadership software in place lacked the personalised touch needed for meaningful development. On top of that, managers were struggling with a new leadership model that required balancing their own business unit goals with an overarching "Enterprise First" mindset.
In April 2024, Matt Dreyer, Head of Talent, and Robert Gulliver, Chief Talent & Diversity Officer, introduced Nadia, an AI-powered coaching platform tailored to Prudential's "2+2" performance framework. Instead of mandating its use, they launched a pilot program with 1,500 managers, including participants from Business Resource Groups and high-potential programs. This approach gave employees the freedom to explore AI coaching in a judgment-free environment. The initiative highlighted how AI can make valuable resources more accessible while driving company-wide transformation.
Nadia addressed two major obstacles: limited availability and lack of contextual relevance. It provided real-time coaching without the delays of scheduling traditional sessions. As Robert Gulliver explained:
"The really good coaches we've worked with at Prudential have taken steps to learn our business, but that takes time. With Nadia, it was instantaneous."
After seeing the demo, Matt Dreyer enthusiastically shared:
"I literally walked away from the demo texting my boss and my CHRO saying, I've solved our coaching problem".
By July 2025, Nadia had conducted over 18,000 coaching sessions, including 2,300 in just one month. The platform’s reach expanded from 400 leaders to more than 4,800 employees - a ninefold increase.
Results
The six-week pilot program was a resounding success, achieving a 90% engagement rate and a Net Promoter Score of 91, showcasing strong employee buy-in. By August 2025, Prudential extended Nadia’s reach to an additional 14,000 employees across APAC and Brazil. On a daily basis, 40% of employees used the platform to prepare for feedback, while 45% created skill-building plans after evaluations. As Matt Dreyer put it:
"Nadia solved the scale issue for us, and she solved how we help our people when they need it, not when a coach is available".
Prudential’s journey with Nadia shows how AI can democratise professional development across an entire organisation, empowering employees with real-time guidance while respecting their autonomy. It’s a clear example of how AI can drive meaningful change in workforce management.
Lessons Learned and Rebel Force Enablement Approaches
Before and After AI Adoption: Impact Metrics Across 6 Organizations
The experiences outlined above highlight the importance of a well-thought-out approach when integrating AI into an organisation. Three key practices have emerged as drivers of success: staged pilots, leadership modeling, and early governance. Companies that excelled in AI adoption started by testing the waters with staged rollouts and pilot programs, focusing on high-volume, low-risk processes before widening the scope. They also leaned on leadership modeling, with executives openly demonstrating their use of AI to foster trust and encourage adoption. Lastly, they implemented clear AI use policies early on, defining boundaries and conducting ethics reviews to balance innovation with risk management.
The human factor remains at the heart of this transformation. As Trine Danielsen from Arla Foods aptly remarked:
"When we first set out on the AI journey, we thought the challenge was about tools. We quickly realised it was about people".
This underscores a vital truth: technology alone cannot drive success - it requires a focus on empowering and enabling people.
Rebel Force has developed a structured approach to AI enablement through 12-week Enablement Sprints for quick wins and 12-month Programs for more foundational changes. Both follow a similar methodology. As Rebel Force explains:
"Every engagement starts with diagnosis, not design. We identify the core constraint - the point where flow breaks - before touching tools, teams, or strategy".
This method has proven effective, with their cross-functional Rebel Flow Units improving over 220 processes and delivering an average ROI of 70%. Nik Korstanje, former CFO at Blijkgroep, praised their approach:
"Rebel Force, through their fractional leadership, achieved this by creating a unified approach - from strategy to reporting, all within one integrated system".
These carefully designed strategies have led to measurable improvements in performance and efficiency.
Before and After AI Adoption Metrics
The numbers speak for themselves, showcasing the impact of effective change management in AI adoption. For instance, IBM's AskHR system automated 94% of HR inquiries, saving a staggering 3.9 million hours in 2024. Virtual Outcomes slashed response times from over 24 hours to just 30 seconds while reducing administrative workloads by 60%. At Arla Foods, daily AI usage by employees increased by 204%, while Inspired Pet Nutrition (IPN) achieved a ninefold speed boost in order processing, along with 60–80% cost savings in just six weeks.
These results highlight how proper planning and execution can lead to faster processes, cost savings, and improved employee engagement, proving that AI adoption is not just about technology - it's about enabling people to thrive.
Conclusion
The evidence is clear: successful AI adoption depends more on people and processes than on the technology itself. Just look at the results. IBM reported $4.5 billion in productivity gains, Help Scout achieved full team adoption in just six weeks, and Inspired Pet Nutrition saw a ninefold boost in order processing speeds. What’s the common thread? Each of these organisations prioritised strong leadership, structured enablement programs, and a strategic focus on manageable, high-impact applications.
On the flip side, companies treating AI as just another tech upgrade often faced challenges. Those that invested in change management, thorough training, and solid governance saw tangible results - often within months. Structured enablement is the key to scaling beyond one-off pilots. For example, a pharmaceutical company using Microsoft Copilot saved an average of 3.7 hours per week per employee. But this wasn’t just about deploying the tool. Their success stemmed from leadership support, comprehensive training, and ongoing assistance.
The takeaway? A structured enablement strategy is essential for lasting transformation. Organisations ready to move past experimental phases can work with experts like Rebel Force. Their approach - starting with a constraint diagnosis, leveraging cross-functional teams, and validating results through measurable ROI - turns AI potential into operational success. Whether through their 12-week Enablement Sprints or 12-month Programs, they provide the framework to make AI adoption both impactful and sustainable.
FAQs
How do I choose the best AI pilot to start with?
To pick the right AI pilot, start by identifying a specific area where AI can make an immediate difference - think resource allocation or automating a repetitive process. Look for a project that's small in scope but has clear, measurable goals, like improving efficiency in one team or streamlining a single workflow.
Strong leadership backing is essential to keep things on track. Equally important is involving employees who will use or be affected by the AI solution - it ensures smoother implementation and better adoption. Bringing in experts can also help you create a solution that fits your needs and validate its performance, making it easier to expand if it works well.
What governance policies should be in place before launch?
Governance policies need to cover several key areas to ensure responsible AI use. These include centralized, role-specific controls that define who can access and manage AI systems, as well as auditable usage to track and review how these systems are being utilized. Clear data handling guidelines are also crucial to protect sensitive information and maintain compliance with regulations. Finally, employee training plays a vital role in helping teams understand how to use AI responsibly, reducing risks like data leaks or inconsistent results.
How do we measure AI ROI beyond time savings?
AI's return on investment (ROI) isn't just about saving time - it can also be measured through various metrics like boosted capacity, higher productivity, better customer satisfaction, and smoother operations. Real-world examples illustrate this impact, with some businesses reclaiming 50–83% of their capacity and achieving impressive cost savings, clearly demonstrating how AI can drive tangible improvements in business performance.