Creating A Roadmap For Technology Adoption

We hear all about the amazing progress of AI BUT, enterprises are still struggling with AI deployments - latest stats say 78% of AI deployments get stall or canceled - sounds like we’re still buying tools and expect transformation. But those that have succeeded? They don’t just license AI, they redesign work around them. Because adoption isn’t about the tool. It’s about the people who use it. Let’s break this down: 😖 Buying AI tools just adds to your tech stack. Nothing more, nothing less! Stat you can’t ignore: 81% of enterprise AI tools go unused after purchase. (Source: IBM, 2024) 🙌🏼 But adoption, adoption requires new workflows, new roles, and new routines - this means redesigning org charts, updating SOPs, and rethinking “a day in the life.” Why? Because AI should empower decisions—not just automate tasks. It should amplify human strengths—not quietly sideline them. That’s where the 65/35 Rule comes in! 65% of a successful AI deployment is redesigning business processes and preparing the workforce. Only 35% is tools and infrastructure. But most companies still do the reverse. They invest 90% in tech and 10% in training… and wonder why they’re stuck in “perpetual POC purgatory” (my term for things that never make production. It’s like buying a Formula 1 car and expecting your team to win races—without ever learning to drive. Here’s the better way: Step 1: Start with the “day in the life” Map how work actually gets done today. Not hypothetically. Not aspirationally. Just reality. Step 2: Identify friction points Where do delays, errors, or bad decisions happen? Step 3: Redesign with intent Now—and only now—do you introduce AI. Not to replace the human. But to support and strengthen them. Recommendation #1: Design AI solutions with your workforce, not just for them. Co-create roles, rituals, and reviews. Recommendation #2: Adopt the 65/35 Rule as your north star. If your AI strategy doesn’t spend more time on people and process than tools and tech… it’s not ready. ⸻ AI doesn’t fail because it’s flawed. It fails because the org using it is unprepared. #AI #FutureOfWork #DigitalTransformation #Leadership #OrgDesign #HumanInTheLoop #AIAdoption #DataDrivenDecisions #Innovation >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Sol Rashidi was the 1st “Chief AI Officer” for Enterprise (appointed back in 2016). 10 patents. Best-Selling Author of “Your AI Survival Guide”. FORBES “AI Maverick & Visionary of the 21st Century”. 3x TEDx Speaker

In the past few months, we've worked with partners who've run into the same challenge with AI adoption. They rolled out policies or guidelines without bringing people into the conversation first—no workshop, no consensus building, just documents that needed signatures or implementation. Unsurprisingly, the result was frustrated staff expected to enforce or follow rules they had no part in creating, and leaders facing resistance instead of adoption. Both AI policies and guidelines are critical for responsible AI adoption, but they have to be built intentionally, with stakeholders driving consensus, or they most likely won't work. After working with hundreds of districts, we've created the resource below. Here are the best practices we recommend. Policies are your compliance layer and are designed to protect your district. We suggest adaptations to existing: ✔️ Acceptable use policies ✔️ Data privacy/FERPA protections ✔️ Academic integrity standards ✔️ Cyberbullying policies (to add deepfakes) Guidelines are your change management layer. They are the "why" that brings people along. We recommend including the following in your AI guidelines: 💡 Vision for GenAI adoption across your district 💡 GenAI misuse/academic integrity response protocols 💡 GenAI chatbot and EdTech tool vetting processes 💡 Digital wellbeing, data privacy, and student safety practices 💡 Implementation tips and instructional supports 💡 AI Literacy training opportunities and expectations What matters most is that both policies and guidelines should be built with stakeholders, not handed down to them. They should evolve with feedback, evidence of impact, and technical advancements. In all of our guideline and policy development work, we always start with AI literacy. It's important to build foundational understanding across stakeholders so that when policies and guidelines are developed, people can contribute meaningfully to the process and understand the "why" behind what they're being asked to implement. Intentional stakeholder engagement isn't a nice-to-have. It's what we've seen drive adoption. #AIforEducation #GenAI #ChangeManagement #AI

Everyone’s talking about AI adoption. But not enough are asking: “Are people actually ready to use it—well?” 72% of employees now say they’re using AI regularly. But when you zoom in on the frontline? Just 51%. That's what BCG calls the "Silicon Ceiling"— Interest is high, tools exist… But value? Still stuck at the surface. Here’s why AI often flops after launch: 🧱 36% feel untrained 🧰 37% don’t have the right tools 🧭 Only 25% feel supported by leadership What happens next? Over half turn to unauthorized AI tools (54%) to get their work done. That’s a compliance nightmare waiting to happen. What actually works? ✅ Coaching. ✅ Real training. ✅ Leadership that leads. Just 5 hours of focused support drives real ROI: 📈 +19pp in AI adoption 📈 +14pp with access to a coach 📈 +12pp from in-person learning And the biggest unlock? When leaders actively back AI: Confidence jumps from 15% → 55% If you're a leader, this is your roadmap: 1. Measure what matters – Track AI impact on productivity, quality, satisfaction. 2. Don’t DIY your training – Build formal pathways, not one-off workshops. 3. Support your people – Tools are useless without workflows and skills. 4. Experiment fast, fail safe – A/B test AI agents to learn quicker, with less risk. Here’s the real truth: AI won’t transform your business. Your people will. But only if they’re trained, guided, and empowered. If you’re done chasing AI hype and ready to build real capability— Let’s talk. 👉 This post is inspired by recent insights from BCG, June 2025. ♻️ Share this with your network if it resonates. ☝️ And follow Stuart Andrews for more insights like this.

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

Adopting the latest technology alone won’t build an effective AI roadmap. Leaders need a thoughtful approach—one that empowers their teams and stays true to their values. Over the past few years, we’ve seen AI’s incredible potential, but also its complexity. Crafting effective AI strategies can challenge even the most seasoned tech leaders. To truly unlock AI’s value, we need to put people at the core of our roadmap. At RingCentral, we’ve made it a priority to envision AI in ways that benefit our teams, partners, and customers. Here are a few strategies my team has found essential for building human-centered AI: 1. Emphasize the “why” behind AI adoption: Start by identifying the specific needs AI will address. Help your team see the value of AI as a tool to enhance their work—not replace it. 2. Start with small, targeted wins: Choose use cases that tackle real challenges and show early success. These wins build trust in AI’s potential and create momentum for further adoption. 3. Prioritize transparency and ethics: Set clear guidelines around data privacy and responsible AI use, ensuring that team members feel they’re part of an ethical and trusted process. Guiding AI adoption with a clear, people-first approach enables us to create a workplace where innovation truly serves the people behind it, paving the way for meaningful growth. 💡 How are you approaching AI within your teams?

18.🚨XR Professionals – some key lessons from past waves of transformational technology to learn in 2026!🚨 👉 Adoption of breakthrough tech is never linear. Electricity, PCs, mobile and the web all endured hype → stagnation → quiet refinement → then sudden “of course we use this” normalisation. XR is on the same path. Here are 8 lessons from history to accelerate adoption now: 💡Play the long game Transformational tech matures over decades, not quarters. Build multi‑year roadmaps, not one‑off pilots. 💡Ditch the hype, prove the value Overpromising killed early waves of many technologies. Lead with evidence, not slogans. Make benefits measurable and repeatable. 💡Relentlessly remove friction Mass adoption follows when hardware is comfortable, onboarding is simple, and setup is boring (in a good way). Reduce cost, complexity and cognitive load. 💡Go vertical, not vague Computers won with spreadsheets, CAD and email—not “general purpose.” XR wins with targeted, high‑value use cases per industry and role. 💡Build the ecosystem, not just the device Every breakout tech rode an ecosystem: standards, content pipelines, integration, governance, skills. Invest there or stall. 💡Win hearts through familiarity People resist until they experience small, safe wins. Use micro‑scenarios, champions, and great training to build confidence. 💡Standardise to de‑risk Common frameworks, safety and interoperability reduce buyer anxiety and shorten procurement cycles. Make XR the safe choice. 💡Ride convergence Inflection points happen when complementary tech aligns. Pair XR with AI, better optics/sensors, edge/5G and cloud streaming to cross the chasm. _____The Bottom line: XR isn’t “behind schedule”—it’s following the same social adoption physics as every major wave of technology before it. 👉 Which lesson will you prioritise first this year—and why? #XR #AR #VR #SpatialComputing #ProductManagement #ChangeManagement #LearningAndDevelopment #HumanPerformance #DigitalTransformation #redlightxr

In our recent work with organisations, I keep seeing the same patterns emerge when it comes to adopting AI. Yes, there are technical considerations like security and privacy, but at the heart of it these are people issues. Nobody wants to use a technology if they feel it puts them or the business at risk. Trust matters, and without it, adoption stalls. Change management and training are also critical. Helping people develop an AI mindset allows them to use these tools in increasingly creative ways, producing higher-quality outcomes rather than just faster ones. Another big one is executive-level commitment. This cannot sit only with the CIO. Every leader, from the CEO to the CFO and beyond, needs to be able to explain why AI matters for the organisation. When leaders can clearly articulate that story, it signals to the whole business that this is a strategic priority, not just an IT project. Equitable access is just as important. Too often I see organisations give AI tools to a select group to control costs. While that makes sense in the short term, the result can be a cultural divide between the haves and the have-nots. People left out either disengage or start using unapproved tools, both of which create risk. Providing broad access, with the right guardrails and support, helps avoid that divide and encourages responsible experimentation across the organisation. These human, cultural, and leadership factors are what really drive successful AI adoption. The technology is only part of the equation.

If your AI enablement plan starts and ends with Copilot training… you won’t scale. Yes, people need to learn the tools. But tooling alone doesn’t change organisations. Real adoption happens when you engage people’s heads, hearts, and confidence, not just their keyboards. Before you even think about training on the tools, get these five things in place: 1. Start with the “why.” Be clear about why you’re adopting AI and what problem you’re solving for your sector, organisation, and people. 2. Communicate the roadmap. Share how AI will be introduced over time, including expectations around continuous learning and change. 3. Show leadership commitment. Leaders need to model how they’re using AI and create psychological safety for others to experiment. 4. Make space for the human reaction. Give people time to talk about how they feel about AI; curiosity, concern, scepticism and all. 5. Build a real learning strategy. Teach practical AI skills alongside critical thinking, human judgement, and management capability. Because here’s the tension: You’re asking people to adopt a technology they constantly hear might replace them. Your job isn’t just to deliver AI. Your job is to deliver AI that works for people and performance. Productivity and efficiency matter. But so do motivation, confidence, and job satisfaction. And it’s achievable. We’re collaborating with our clients to make it happen right now. Get in touch to learn more.

𝗦𝘄𝗶𝘁𝗰𝗵𝗶𝗻𝗴 𝗙𝗿𝗼𝗺 𝗔𝗻𝗮𝗹𝗼𝗴 𝘁𝗼 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗗𝗼𝗲𝘀 𝗡𝗼𝘁 𝗛𝗮𝗽𝗽𝗲𝗻 𝗢𝘃𝗲𝗿𝗻𝗶𝗴𝗵𝘁 𝗪𝗵𝘆 𝗽𝘂𝗿𝗰𝗵𝗮𝘀𝗶𝗻𝗴 𝘁𝗲𝗰𝗵 𝗹𝗶𝗰𝗲𝗻𝘀𝗲𝘀 𝗶𝘀 𝗻𝗼𝘁 “𝗰𝗵𝗮𝗻𝗴𝗲” Many believe giving professionals access to state-of-the-art AI software solutions transforms an organization into an AI-ready powerhouse. Senior leaders say it all the time: “𝗪𝗲 𝗺𝘂𝘀𝘁 𝗯𝗲 𝗔𝗜-𝗿𝗲𝗮𝗱𝘆 𝗮𝘀 𝗳𝗮𝘀𝘁 𝗮𝘀 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲.” 𝗦𝘁𝗿𝗲𝘁𝗰𝗵 𝗴𝗼𝗮𝗹𝘀 𝗮𝗿𝗲 𝗴𝗿𝗲𝗮𝘁 - 𝗯𝘂𝘁 𝗼𝗻𝗹𝘆 𝗶𝗳 𝘁𝗵𝗲𝘆’𝗿𝗲 𝗿𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰. And in most organizations… they aren’t. Many organizations still lack 𝘁𝗵𝗲 𝗰𝘂𝗹𝘁𝘂𝗿𝗲, 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗼𝗿 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝗻𝗲𝗲𝗱𝗲𝗱 𝗳𝗼𝗿 𝘀𝘆𝘀𝘁𝗲𝗺-𝗹𝗲𝘃𝗲𝗹 𝗔𝗜 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁. And in those companies and law firms that celebrate broad-scale tech “adoption”, meaningful KPIs to measure productivity gains, cost-efficiencies and faster service delivery are largely missing: Putting an AI tool in front of a lawyer and measuring “log-ins per week” as a KPI for success is the equivalent of: 🛞 Buying someone a brand-new car 🚪 Complimenting them for how often they open the door 🎛️ and celebrating how much they touch the dashboard… ...𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗰𝗵𝗲𝗰𝗸𝗶𝗻𝗴 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝘁𝗵𝗲 𝘃𝗲𝗵𝗶𝗰𝗹𝗲 𝗶𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗿𝗶𝘃𝗲𝗻 𝘁𝗼 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗳𝗮𝘀𝘁𝗲𝗿 𝗳𝗿𝗼𝗺 𝗔-𝗭. Digitalization and AI readiness take time - not months… years. Organizations as well as technology vendors consistently 𝘂𝗻𝗱𝗲𝗿𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗲 𝗰𝗵𝗮𝗻𝗴𝗲 𝗮𝘃𝗲𝗿𝘀𝗶𝗼𝗻 and consistently 𝗼𝘃𝗲𝗿𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗲 𝗰𝗵𝗮𝗻𝗴𝗲 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀. Here is my 10-Step Journey to real, systemic AI Transformation ⭐ 1️⃣ Map your current task portfolio (segment into meaningful categories) 2️⃣ Define your Target Operating Model 3️⃣ Align on roadmap, budget & capacity 4️⃣ Communicate strategy & collect feedback 5️⃣ Identify pilot scope and priority use case(s) 6️⃣ Assess your current tech stack 7️⃣ Evaluate new tech options 8️⃣ Bring roadmap + selected tech vendors together 9️⃣ Execute the roadmap 🔟 Communicate progress and failures - and secure top-level transformation sponsorship   𝗔𝗜 𝘁��𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗽𝘂𝗿𝗰𝗵𝗮𝘀𝗲. 𝗔𝗜 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘁 𝗮𝗻 𝗜𝗧 𝗽𝗿𝗼𝗷𝗲𝗰𝘁. 𝗔𝗜 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮𝗻 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻. It requires patience, foresight, and determination. If you get the sequence right, the rewards will compound forever. If you rush it… you’ll end up with unused licenses, ROI challenges at renewal - and endless tech frustration. 𝗧𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝘁𝗶𝗺𝗲. 𝗕𝘂𝗶𝗹𝗱 𝗶𝘁 𝗿𝗶𝗴𝗵𝘁. 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝘄𝗶𝘁𝗵 𝗶𝗻𝘁𝗲𝗻𝘁. ♻️ Repost to help your network grow   🔔 Follow Tom Pfennig for innovation  📩 DM me for Transformation support 🎥 Video credit: DM for credit. #Leadership #Transformation #Mindset #Execution #Growth #AI 

A few days ago, I participated in a heated discussion together with two friends (a CHRO and a CIO). The point of friction: 𝗪𝗵𝗼 𝘀𝗵𝗼𝘂𝗹𝗱 𝗹𝗲𝗮𝗱 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝘆’𝘀 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆?. My answer was clear:  𝗙𝗼𝗹𝗹𝗼𝘄 𝗠𝗼𝗱𝗲𝗿𝗻𝗮’𝘀 𝗲𝘅𝗮𝗺𝗽𝗹𝗲. Most companies treat AI as a "tech tool" to be installed by the CIO. Moderna just proved AI is part of the "people strategy" to be led by the CHRO. 📉 𝗧𝗵𝗲 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲: They recently created a new role: 𝗖𝗵𝗶𝗲𝗳 𝗣𝗲𝗼𝗽𝗹𝗲 𝗮𝗻𝗱 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗢𝗳𝗳𝗶𝗰𝗲𝗿. They didn’t hire a Silicon Valley tech guru. They promoted their CHRO, Tracey Franklin. 💡 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: They realized that in 2025, you cannot separate the 𝘸𝘰𝘳𝘬𝘧𝘰𝘳𝘤𝘦 (people) from the 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸 (AI agents). 🛠️ 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗱𝗶𝗱 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆: Instead of just deploying software, they restructured how they work: • 𝗙𝗿𝗼𝗺 "𝗛𝗲𝗮𝗱𝗰𝗼𝘂𝗻𝘁" 𝘁𝗼 "𝗧𝗮𝘀𝗸-𝗰𝗼𝘂𝗻𝘁": They stopped asking "How many people do we need?" and started asking "Which tasks can be autonomous?" • 𝗧𝗵𝗲 𝗔𝗜 𝗔𝗰𝗮𝗱𝗲𝗺𝘆: They built an internal university to train employees not just on 𝘱𝘳𝘰𝘮𝘱𝘵𝘪𝘯𝘨, but on 𝘥𝘦𝘭𝘦𝘨𝘢𝘵𝘪𝘯𝘨 to AI. They invested in training and coaching to ensure 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 AI change management. • 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: They deployed 3,000+ custom GPTs, effectively giving every human employee a team of digital interns. 🚧 𝗧𝗵𝗲 𝗚𝗮𝗽 𝗶𝗻 𝘁𝗵𝗲 𝗠𝗮𝗿𝗸𝗲𝘁: Most leaders know what Moderna did, but they don't know how to replicate it. They lack the roadmap to bridge the gap between "𝗯𝘂𝘆𝗶𝗻𝗴 𝘁𝗵𝗲 𝘁𝗼𝗼𝗹" and "𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝗰𝘂𝗹𝘁𝘂𝗿𝗲." 🚀 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 𝗔𝗜 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗙𝗮𝗰𝗶𝗹𝗶𝘁𝗮𝘁𝗼𝗿. Many companies today need that role. They need to integrate AI as an additional worker in the company through a three-step approach: 𝟭. 𝗧𝗵𝗲 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝗱𝗶𝘁: Analyze your current processes to identify the "pain points" and separate human judgment from robot work. 𝟮. 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗔𝗜 𝗨𝗽𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 & 𝗖𝗼𝗮𝗰𝗵𝗶𝗻𝗴: Train & coach your managers to stop viewing AI as a tool and start managing it as a workforce. Move from "𝘤𝘰𝘯𝘵𝘳𝘰𝘭" to "𝘤𝘶𝘳𝘪𝘰𝘴𝘪𝘵𝘺". 𝟯. 𝗧𝗵𝗲 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗥𝗼𝗮𝗱𝗺𝗮𝗽: Design the governance and cultural changes needed to ensure the technology is actually used. ⚡ 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲: AI creates speed. Leadership sets the direction. 𝗥𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗶𝘁𝗵 𝗔𝗜 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻, 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗰𝗼𝗮𝗰𝗵𝗶𝗻𝗴 𝗶𝘀 𝗮 𝗿𝗲𝗰𝗶𝗽𝗲 𝗳𝗼𝗿 𝗳𝗮𝗶𝗹𝘂𝗿𝗲. ❓ 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: 𝗜𝗻 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝘄𝗵𝗼 𝗶𝘀 𝗱𝗿𝗶𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗰𝗵𝗮𝗻𝗴𝗲? The one who buys the technology or the one who leads the people? #HR #AITransformation #Moderna #FutureOfWork #Leadership #OrganizationalDesign #ChangeManagement