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Headline: Change Management Is Having Its Moment — And AI Is the Catalyst That's Making It Work The Change Management Moment Only half of companies have a change management strategy for AI. But that's changing fast. An AWS study found that while only 14% of companies had a change management strategy for AI adoption in 2024, that percentage jumped to 54% in 2025 and is expected to reach 76% by the end of this year . Change management, once seen as an administrative function, is having its moment in the spotlight. The reason is simple: AI is creating rapid, high-stakes shifts that are triggering ripple effects across businesses . Organizations that fail to adapt their change management strategies risk significantly undermining transformation efforts. AI as a Force Multiplier "AI is a force multiplier," said Giulia Sergi, Director of GTM and AI Fluency at Salesforce. "It's already shifting the change manager's role from being really detailed and task-oriented to one where they can think more strategically and proactively" . This is not theoretical. Change managers are using AI to: Analyze Data to Map Strategy: "You can upload tons of data into AI tools and say, 'I want you to create a stakeholder analysis based on this information. Tell me what the biggest gaps and highest impact areas are,'" explains Sergi. "In the past, this process could have taken weeks or months" . Redesign Roles: Stanford's Arvind Karunakaran suggests organizations use AI to map workflows, identify roles, and break roles down into tasks. "Instead of mandating that employees in nontechnical roles learn how to write prompts better, you should figure out the most complex parts of their jobs... and give them the skills they need for those tasks" . Provide 24/7 Support: Salesforce's change management team has trained AI agents on knowledge articles to answer employee questions autonomously. "Now, I don't have to manage or make sure there's a system in place for people to get answers because it's all autonomous and it's 24/7," says Sergi . Why AI Matters for Change Management Traditional change management approaches struggle to keep pace with the speed and complexity of constant evolution. AI helps organizations sense change early and respond in real time rather than relying on static plans . AI also changes how work is designed, not just how tools are used. Without rethinking roles, workflows, and decisions, organizations often limit AI's impact. Using it in change management pushes leaders to reconsider how value is created and how people and machines work together . The Future of Change Management The biggest benefit of AI for change management may be its role as a thinking partner. Sergi uses AI as a brainstorming partner: "I need to be the person that makes the final decisions, but I appreciate having this 24/7 brainstorming partner I can bounce ideas off of and that can share new ways of thinking I otherwise wouldn't have explored" . Organizations that embed AI into how they manage change are better positioned to adapt with purpose and resilience instead of reacting too late . Action Items for Your Organization Assess your current change management strategy for AI adoption Identify where AI can automate routine change management tasks Train change managers on AI capabilities Build AI-powered knowledge bases for employee support Use AI for stakeholder analysis and communication planning

Are You Doing Problem Management or Just Pretending? — The Problem Management Maturity Model The Maturity Model Problem management maturity describes how advanced your problem management practice is. Low maturity means problems are treated as incidents, root cause analysis is superficial, and recurring issues persist. High maturity means problems are proactively identified, root causes are systematically eliminated, and incidents are prevented before they occur. Maturity Levels Level 1: Initial/Reactive Characteristics: Problems are treated as incidents No formal problem management process Root cause analysis is ad-hoc Recurring incidents are common No known error database When you need to improve: If you can't tell the difference between incidents and problems Level 2: Repeatable Characteristics: Problem records are created Basic categorization and prioritization Some root cause analysis is performed Some workarounds are documented Some integration with incident management When you need to improve: If root cause analysis is superficial Level 3: Defined Characteristics: Standardized problem management process Formal root cause analysis techniques Known error database is used Process metrics are tracked Regular problem reviews are conducted When you need to improve: If problems still recur after being "resolved" Level 4: Managed Characteristics: Proactive problem management is practiced Trend analysis identifies emerging issues Predictive analytics are used Process performance is measured Continuous improvement is systematic When you need to improve: If you're still surprised by incidents Level 5: Optimizing Characteristics: Problems are prevented before they occur Continuous learning and improvement AI and automation are used extensively Integration with other processes is seamless Business value is clearly demonstrated When you need to improve: When you're ready to lead Maturity Assessment Questions Area Question Process Do you have a formal problem management process? Knowledge Do you have a known error database? Proactivity Do you proactively identify problems? Techniques Do you use formal RCA techniques? Metrics Do you measure problem management effectiveness? Integration Is problem management integrated with other processes? Building a Roadmap Level 1 → Level 2: Create problem record types Train on problem vs. incident distinction Link incidents to problems Level 2 → Level 3: Implement RCA techniques Create known error database Standardize workflows Level 3 → Level 4: Implement trend analysis Start proactive problem management Track process metrics Level 4 → Level 5: Implement predictive analytics Automate problem detection Continuous learning Conclusion Problem management maturity is a journey. Organizations that assess their maturity and build a roadmap for improvement will achieve fewer incidents, higher service quality, and better business outcomes. Action Items for Your Organization Assess your current problem management maturity Identify gaps in your process Build a roadmap to the next level Measure progress over time Celebrate improvements

You Can't Improve What You Don't Measure — Key VRM Metrics Why Metrics Matter Measuring vendor risk management effectiveness helps organizations identify areas for improvement, demonstrate value, make data-driven decisions, and track progress over time. Key VRM Metrics 1. Assessment Coverage Percentage of vendors assessed Percentage of high-risk vendors assessed Assessment completion rates 2. Risk Exposure Number of high-risk vendors Risk score distribution Number of unmitigated risks 3. Vendor Performance SLA compliance rates Incident frequency Resolution times 4. Program Efficiency Time to onboard new vendors Assessment completion time Manual effort reduction 5. Business Impact Breaches from vendor relationships Downtime caused by vendor failures Cost savings from risk mitigation The "Critical Vendor" Trap A common failure mode: teams pour energy into the obvious "critical" vendors while the broader ecosystem remains lightly assessed, inconsistently monitored, and operationally under-controlled . It's that long tail that will eat you much more quickly . The solution: Track coverage across all vendors, not just critical ones. Translating Risk to Business Outcomes Executives don't need more "orange/red/green." They need consequences, options, and tradeoffs expressed in business language . Key metrics for executives: Financial exposure from vendor relationships Expected loss from vendor incidents Mitigation cost and effectiveness ROI of VRM program Reporting Framework Through establishing ongoing monitoring and incident reporting within the TPRM framework, firms can easily outline a clear reporting framework for third party relationships . Reporting elements: Standard metrics that summarize the primary elements of vendor risk portfolios  Information that properly details risks  Reports easily understood by all stakeholders  Conclusion Measuring VRM effectiveness is essential for continuous improvement. Organizations that track the right metrics and use them to drive improvement will reduce vendor risk and demonstrate the value of VRM . Action Items for Your Organization Define key VRM metrics Set up measurement and reporting Establish baseline measurements Review metrics regularly Use data to drive improvement

Headline: Don't Use All Change Categories at Once — A Progressive Implementation Approach The Temptation of All Categories Most ITSM frameworks define three change categories: Standard, Normal, and Emergency. However, the right number of categories depends on your organization's volume and maturity, not simply on framework compliance. A Progressive Approach Start with Normal changes to calibrate the process. When beginning change management, start by classifying all changes as Normal. This allows you to: Define the process Establish roles Build change management muscle Understand your change landscape Graduate proven procedures to Standard status. Once a change type has been consistently successful, move it to Standard status: Criteria for Standard status Evidence Proven success No incidents from implementation Repeatable Same process each time Well documented Clear work instructions Trained personnel Certified staff Establish Emergency workflows for urgent situations. Identify what constitutes an emergency and establish expedited approval processes. Add categories only when volume justifies them. Resist the temptation to add categories for the sake of having them. Change Type Progress Stage Categories Description 1 Normal only All changes go through the same process 2 Normal + Standard Proven changes become Standard 3 Normal + Standard + Emergency Emergency processes added 4 Full ITIL categorization All three with mature processes The Normal Change Process Submit Request for Change Review for necessity Assess risks CAB approval (or Change Authority) Implement Test Review Document The Standard Change Process Pre-approved Requires no CAB Automated or documented runbook Scheduled and tracked Reviewed periodically The Emergency Change Process Expedited approval ECAB review Implemented immediately Reviewed post-implementation Conclusion Implementing change categories progressively ensures your process evolves with your organizational maturity. Start simple, learn what works, and add complexity only when needed. Action Items for Your Organization Start with Normal changes only Identify changes that could become Standard Document Standard change procedures Establish Emergency change workflows Add categories gradually

Headline: Employees Experience Change Differently — Why One-Size-Fits-No-One Is the Problem The Personalization Imperative Change isn't just organizational—it's personal. Just as airline passengers may land at the same destination but recall the journey differently, employees experience change in ways shaped by their roles, contexts, and engagement . The Expectation Gap In today's world, people have come to expect personalization everywhere—in the products they buy, the content they consume, and the experiences they choose. That same expectation now extends into the workplace. Employees want change that feels as intuitive and relevant as the technologies and services they use every day . Yet most change approaches still rely on one-size-fits-all tactics that overlook differing motivations, mindsets, and needs. The Data Gap While more than two-thirds (67%) of leaders believe it is important to customize the design and experience of work and workforce practices based on worker skills, behavioral patterns, motivations, passions, and work styles, only 7% of leaders are taking action . Hyper-Personalization The next evolution of change is hyper-personalization: change journeys that adapt dynamically to each person's role, readiness, and response . What hyper-personalization looks like: Personalized change journeys based on role, readiness, and previous experience Messaging adapted to leaders' voices Real-time feedback and behavior-based coaching Action prompting tailored to individual needs  Personalization in Practice Example: Field Representative Coaching A field representative preparing for a customer conversation might turn to a coaching chatbot—not for scripted answers, but for a space to experiment. The representative could roleplay different scenarios, refine their message, and receive personalized feedback based on their tone, approach, and confidence level . Instead of static learning, the experience becomes a personalized dialogue that builds skill, confidence, and ownership . Meeting People Where They Are Personalization in change isn't about giving everyone the same toolkit—it is about meeting people where they are. When an organization's learning and support initiatives adapt to an individual's context, employees gain the freedom to experiment, practice, and grow with confidence . Conclusion By using AI to offer hyper-personalized change journeys, organizations can open the door to personal change so people tune in instead of tuning out . The future of change is not about doing more of the same—it's about doing different things for different people. Action Items for Your Organization Map employee personas for change audiences Develop personalized change journeys for different roles Use AI to recommend content based on role and progress Measure engagement and adoption by persona Design change experiences that meet people where they are

SLA Compliance Isn't Enough — Why Problem Management Must Focus on Employee Experience   The Limitations of SLAs Traditional Service Level Agreements (SLAs) focus on: ? How quickly incidents are resolved ? Whether service availability targets are met ? Technical metrics of service performance But SLAs have significant limitations: ? They measure technical performance, not user experience ? They can be met while users are still frustrated ? They don't capture the quality of the resolution ? They don't reflect the impact of recurring problems The Shift to XLAs Experience Level Agreements (XLAs) focus on what users actually experience: ? User satisfaction: Were users happy with the resolution? ? Productivity impact: Did the issue affect user productivity? ? Effort: How much effort did the user expend? ? Friction: How smooth was the overall experience? The AI-driven approach to problem management shifts IT operations toward a ticketless future—moving past traditional SLAs to focus on XLAs . How XLAs Transform Problem Management Dimension SLA Focus XLA Focus Measurement Technical metrics User experience Success Meeting targets Positive outcomes Resolution Speed of fix Quality of experience Prevention Incident avoidance Friction elimination What XLAs Measure Experience Level Agreements typically measure: 1. User Satisfaction: CSAT scores and feedback 2. Productivity Impact: Time lost due to issues 3. Effort: How easy was it to get help? 4. Friction: How many interactions were required? 5. Repeat Contact: Did the issue recur? Why XLAs Matter for Problem Management Problem management has a direct impact on employee experience: Problem Management Activity Employee Experience Impact Eliminating recurring issues Reduces frustration and lost productivity Proactive prevention Prevents disruption entirely Known error documentation Faster resolution when issues occur Root cause elimination Permanent fixes, not temporary workarounds Benefits of Moving to XLAs Benefit Impact Better alignment with business Service metrics reflect business outcomes Higher user satisfaction Focus on what users actually experience More effective problem management Root causes of poor experience are addressed Clearer value demonstration Problem management ROI is more visible Improved decision-making Metrics drive the right priorities Conclusion The shift from SLAs to XLAs represents a fundamental change in how we measure service success. In the experience era, problem management is not just about preventing incidents—it's about creating a frictionless, satisfying experience for users.   Action Items for Your Organization ? Review your current problem management metrics—are they SLA-based or XLA-based? ? Define XLAs for key service experiences ? Start measuring employee satisfaction and productivity impact ? Use XLA data to prioritize problem management investments ? Demonstrate the connection between problem management and employee experience