Change Management Strategies For Technology Rollouts

Why do so many legal technology implementations fail to deliver their promised value? Too often, legal teams rush to adopt the latest tools without first understanding their actual pain points. Here are the critical steps that separate successful implementations from costly failures: 📊 Start with Discovery, Not Solutions Map your current workflows meticulously. Track how long tasks take, where errors occur, and what frustrates your team most. 🎯 Set Measurable Goals Replace vague aspirations like "improve efficiency" with concrete targets: -Reduce contract turnaround by 30% -Eliminate 50% of manual compliance errors -Increase client intake capacity by 25% These specific metrics give you clear success criteria and help demonstrate ROI to stakeholders. 👥 Embrace Change Management Technology fails when people resist it. Appoint enthusiastic "technology champions" who can provide peer support and bridge the gap between IT and daily users. Their grassroots advocacy often proves more effective than top-down mandates. 🔄 Pilot, Learn, Iterate Test solutions with a small group for 6-8 weeks before full rollout. That same legal department reduced their NDA processing time to 1.5 hours and cut errors by 80% during their pilot. These wins built momentum for broader adoption. Remember: legal technology adoption is about solving real problems, not chasing innovation for its own sake. #legaltech #innovation #law #business #learning

My AI lesson of the week: The tech isn't the hard part…it's the people! During my prior work at the Institute for Healthcare Improvement (IHI), we talked a lot about how any technology, whether a new drug or a new vaccine or a new information tool, would face challenges with how to integrate into the complex human systems that alway at play in healthcare. As I get deeper and deeper into AI, I am not surprised to see that those same challenges exist with this cadre of technology as well. It’s not the tech that limits us; the real complexity lies in driving adoption across diverse teams, workflows, and mindsets. And it’s not just implementation alone that will get to real ROI from AI—it’s the changes that will occur to our workflows that will generate the value. That’s why we are thinking differently about how to approach change management. We’re approaching the workflow integration with the same discipline and structure as any core system build. Our framework is designed to reduce friction, build momentum, and align people with outcomes from day one. Here’s the 5-point plan for how we're making that happen with health systems today: 🔹 AI Champion Program: We designate and train department-level champions who lead adoption efforts within their teams. These individuals become trusted internal experts, reducing dependency on central support and accelerating change. 🔹 An AI Academy: We produce concise, role-specific, training modules to deliver just-in-time knowledge to help all users get the most out of the gen AI tools that their systems are provisioning. 5-10 min modules ensures relevance and reduces training fatigue.  🔹 Staged Rollout: We don’t go live everywhere at once. Instead, we're beginning with an initial few locations/teams, refine based on feedback, and expand with proof points in hand. This staged approach minimizes risk and maximizes learning. 🔹 Feedback Loops: Change is not a one-way push. Host regular forums to capture insights from frontline users, close gaps, and refine processes continuously. Listening and modifying is part of the deployment strategy. 🔹 Visible Metrics: Transparent team or dept-based dashboards track progress and highlight wins. When staff can see measurable improvement—and their role in driving it—engagement improves dramatically. This isn’t workflow mapping. This is operational transformation—designed for scale, grounded in human behavior, and built to last. Technology will continue to evolve. But real leverage comes from aligning your people behind the change. We think that’s where competitive advantage is created—and sustained. #ExecutiveLeadership #ChangeManagement #DigitalTransformation #StrategyExecution #HealthTech #OperationalExcellence #ScalableChange

I used to make software to help machine manufacturers manage their machines remotely. Twelve plus years ago I had a client that would roll out software updates to their technology kiosks. Even though they only had single digit thousands of devices, they did not push them out all at once. They pushed updates to their zip code, then their town, then their state, then their timezone, and then the whole US. Why did they follow this procedure even though they thoroughly tested the updates? Because if there was a software failure they wanted to limit the potential damage that their update would cause. They would "roll a truck" to fix the problem. They knew that selecting machines closer to headquarters would mean that they would have a lot smaller headache. Additionally, even if they bricked all of the local machines, the number of machines with problems would be measured with two or three digits and not four digits spread across the country. That is why the most shocking thing to me about the recent Crowdstrike issue is that they deployed to millions of devices all at once! From Crowdstrike on how they intend to prevent this from happening again: Refined Deployment Strategy ● Adopt a staggered deployment strategy, starting with a canary deployment to a small subset of systems before a further staged rollout. ● Enhance monitoring of sensor and system performance during the staggered content deployment to identify and mitigate issues promptly. ● Provide customers with greater control over the delivery of Rapid Response Content updates by allowing granular selection of when and where these updates are deployed. ● Provide notifications of content updates and timing. I am glad that they are taking this issue seriously but it seems crazy to me that an event like this had to happen for these type of changes. Message to everyone solution provider that makes an agent or every customer that uses an agent. A staggered rollout strategy should be absolutely required. Even if your company does not use Crowdstrike/Windows, you should be looking at all of your vendors that have an agent. What do you think? Are you going to take a look at agents as part of your vendor reviews? #fciso #crowdstrike

Lesson from 15+ years of change work: Tech projects don’t fail because the system breaks they fail because adoption breaks. 👉 I’ve watched it happen again and again: 💸 Millions invested. ⚙️ The system goes live. 😬 And then… resistance sets in. People log in, but they hesitate. Old habits creep back. Frustration bubbles under the surface. Not because the technology didn’t work. But because the people weren’t ready to work with it. And here’s what many forget -> AI isn’t just innovation, it’s a technology rollout. And like any rollout, it needs to follow the same change management protocols that ensure people adapt, adopt, and thrive with it. That’s why Change Management isn’t optional. It’s the missing piece. And the ADKAR model shows us exactly where adoption breaks: A- Awareness: ☑️ Do people truly know why AI is being introduced? ↳ Awareness means leaders explain how AI connects to strategy, purpose, and people. Not just efficiency metrics. D- Desire: ☑️ Do they actually want to use it, or do they fear it? ↳ Desire grows when organizations emphasize partnership. AI as a tool to amplify human work, not erase it. K- Knowledge: ☑️ Do they know how to use AI in their day-to-day? ↳Training isn’t a one-time webinar. It’s real-world, role-specific, hands-on guidance. Without this, AI sits idle. Knowledge is about building confidence, not just checking a compliance box. A- Ability: ☑️ Can they actually apply what they’ve learned? ↳It’s one thing to sit through training. It’s another to open your laptop Monday morning and use AI in your workflow. Ability grows when people get coaching, safe spaces to experiment, and reinforcement to move from theory to practice. R- Reinforcement: ☑️ Will the change stick when it gets hard? ↳The real test comes months later, when stress rises and habits try to return. Reinforcement means leaders recognize wins, reward adoption, and keep the momentum alive so AI becomes part of culture and not just a short-term experiment. Ignore change management, and AI becomes another tool people resist. Embrace it, and AI becomes the spark that transforms how we work, lead, and grow. The difference isn’t the tech—> it’s the people. ♻️ Repost if you’re investing in people, not just tech. For More on AI + Future of Work→ Janet Perez

This was our advice on deploying Microsoft 365 Copilot a year ago (September 2023), two months BEFORE the M365 Copilot GA date of Nov 1. If you're a Gartner client, we hope you followed some of these recommendations to get ahead (and it's still not too late): ✅ Establish new generative AI skills and policies by evaluating Microsoft Copilot as a new technology stack rather than merely a productivity tool. ✅ Establish a M365 product team with direct oversight of governance of generative AI services that interact with the Copilot stack. ✅ Review and communicate to stakeholders key Microsoft online service terms and data protection and privacy commitments, all of which apply to M365 Copilot. ✅ Reinforce the need for information governance and access controls in M365 with stakeholders to ensure users don’t overshare information that could be exposed through the Copilot stack. ✅ Maximize adoption and reduce features overlap by coordinating with business unit leaders on use of Copilots and other generative AI tools from enterprise applications. ✅ Lead a coalition with your stakeholders to make your initial Copilot investments immediately valuable, and pave the way for an impactful and successful long-term integration of multiple generative AI technologies. ✅ Plan for a multivendor generative AI portfolio that includes Microsoft alongside other vendors, each likely with different approaches. ✅ Prioritize the rollouts to employees in a controlled way. A “big bang” rollout will cause confusion among employees, leading to a surge of support issues. ✅ To ensure ROI is achieved, plan and execute a meticulous rollout strategy that includes a series of communications, multichannel training and support, and a holistic change management strategy with buy-in from executives and business leaders. From: "Assessing the Impact of Microsoft’s Generative AI Copilots on Enterprise Application Strategy" https://lnkd.in/ewXescAp