Building A Culture That Embraces AI

𝗬𝗼𝘂𝗿 𝗔𝗜 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲𝘀 𝗮𝗿𝗲 𝗳𝗮𝗶𝗹𝗶𝗻𝗴. 𝗔𝗻𝗱 𝗶𝘁'𝘀 𝗻𝗼𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝘆𝗼𝘂𝗿 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. 70-85% of AI projects fail to deliver value. But here's the thing: → Your algorithms work fine → Your data is clean   → Your APIs connect perfectly So why are you still stuck? 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝘆𝗼𝘂'𝗿𝗲 𝘁𝗿𝘆𝗶𝗻𝗴 𝘁𝗼 𝘀𝗼𝗹𝘃𝗲 𝗮 𝗽𝗲𝗼𝗽𝗹𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝘄𝗶𝘁𝗵 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. The real blocker isn't your tech stack. It's your culture. 𝗧𝗵𝗲 3 𝘀𝗶𝗹𝗲𝗻𝘁 𝗸𝗶𝗹𝗹𝗲𝗿𝘀 𝗼𝗳 𝗔𝗜 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻: 𝗧𝗵𝗲 𝗘𝘅𝗶𝘀𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗧𝗵𝗿𝗲𝗮𝘁 "If AI can do my job, what happens to me?" (Employees resist what they can't control) 𝗧𝗵𝗲 𝗠𝗶𝗱𝗱𝗹𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗦𝗾𝘂𝗲𝗲𝘇𝗲 You're asking them to implement tech that threatens their role (While still judging them by old metrics) 𝗧𝗵𝗲 𝗜𝗻𝗰𝗲𝗻𝘁𝗶𝘃𝗲 𝗠𝗶𝘀𝗺𝗮𝘁𝗰𝗵 Your AI recommends preventative shutdowns Your managers get rewarded for uptime (Guess which one wins?) 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀: • Elevate people, don't eliminate them • Create safe-to-fail zones for experimentation   • Put domain experts in control of AI implementation • Align incentives with AI-enhanced productivity • Address career anxieties with concrete transition plans 𝗧𝗵𝗲 𝗯𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: - Technical advantages last weeks. - Cultural advantages last years. Your competitors can copy your algorithms. They can't copy your culture. 𝗪𝗵𝗮𝘁'𝘀 𝗵𝗮𝗿𝗱𝗲𝗿 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Building a chatbot or getting people to actually use it? Your answer says it all. I just published a deep dive on this in The AI Journal: "The Hidden Barrier to AI Success: Organizational Culture" It breaks down exactly how to build a culture that makes AI adoption inevitable (not just possible). 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗮𝗿𝘁𝗶𝗰𝗹𝗲→ 𝗵𝘁𝘁𝗽𝘀://𝗮𝗶𝗷𝗼𝘂𝗿𝗻.𝗰𝗼𝗺/𝘁𝗵𝗲-𝗵𝗶𝗱𝗱𝗲𝗻-𝗯𝗮𝗿𝗿𝗶𝗲𝗿-𝘁𝗼-𝗮𝗶-𝘀𝘂𝗰𝗰𝗲𝘀𝘀-𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝗮𝗹-𝗰𝘂𝗹𝘁𝘂𝗿𝗲/ Want more insights on the human side of AI transformation? 🔔 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 for weekly posts on AI + organizational psychology 📧 Join other informed leaders getting my "AI + Human Edge" newsletter for frameworks like this 𝘞𝘩𝘢𝘵'𝘴 𝘣𝘦𝘦𝘯 𝘺𝘰𝘶𝘳 𝘣𝘪𝘨𝘨𝘦𝘴𝘵 𝘣𝘢𝘳𝘳𝘪𝘦𝘳 𝘵𝘰 𝘈𝘐 𝘢𝘥𝘰𝘱𝘵𝘪𝘰𝘯? 𝘛𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺 𝘰𝘳 𝘱𝘦𝘰𝘱𝘭𝘦? 𝘋𝘳𝘰𝘱 𝘢 𝘤𝘰𝘮𝘮𝘦𝘯𝘵 𝘣𝘦𝘭𝘰𝘸 👇

AI field note: Reducing the 'mean time to ah-ha' (MTtAh) is critical for driving AI adoption—and unlocking the value. When it comes to AI adoption, there's a crucial milestone: the "ah-ha moment." It's that instant of realization when someone stops seeing AI as just a smarter search tool and starts recognizing it as a reasoning and integration engine—a fundamentally new way of solving problems, driving innovation, and collaborating with technology. For me, that moment came when I saw an AI system not just write code but also deploy it, identify errors, and fix them automatically. In that instant, I realized AI wasn’t just about automation or insights—it was about partnership. A dynamic, reasoning collaborator capable of understanding, iterating, and executing alongside us. But these "ah-ha moments" don’t happen by accident. Systems like ChatGPT or Claude excel at enabling breakthroughs, but it really requires us to ask the right questions. That creates a chicken-and-egg problem: until users see what’s possible, they struggle to imagine what else is possible. So how do we help people get hands-on with AI, especially in enterprise organizations, without relying on traditional training? Here are some approaches we have tried at PwC: 🤖 AI "Hackathons" or Challenges: Host short, low-stakes events where employees can experiment with AI on real problems. For example, marketing teams could test AI for campaign ideas, while operations teams explore process automation. ⚙️ Sandbox Environments: Provide low-friction, risk-aware access to AI tools within a dedicated environment. Let users explore capabilities like text generation, workflow automation, or analytics without worrying about “messing something up.” 🚀 Pre-built Use Cases: Offer ready-to-use templates for specific challenges, such as drafting a client email, summarizing documents, or automating routine reports. Seeing results in action builds confidence and sparks creativity. At PwC we have a community prompt library available to everyone, making it easier to get started. 🧩 Embedded AI Mentors: Assign "AI champions" who can guide teams on applying AI in their work. This informal mentorship encourages experimentation without formal, structured training. We do this at PwC and it's been huge. ⚡️ Integrate AI into Existing Tools: Embed AI into everyday platforms (like email, collaboration tools, or CRM systems) so users can naturally interact with it during routine workflows. Familiarity leads to discovery. Reducing the mean time to ah-ha—the time it takes someone to have that transformative realization—is critical. While starting with familiar use cases lowers the barrier to entry, the real shift happens when users experience AI’s deeper capabilities firsthand.

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?

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

Most technology leaders at larger companies will tell you that implementing AI and generative AI at scale is no small task. Many will also tell you that strong change management is one of several components of a successful implementation plan but the most challenging to get right. As widespread use of generative AI has taken shape, there are a handful of themes I’ve heard consistently about change management as it relates to the technology: ✋🏽 Preparing for resistance: Introducing generative AI may be met with apprehension or fear. It's crucial to address these concerns through transparent communication and consistent implementation approaches. In nearly every case we are finding that the technology amplifies people skills allowing us to move faster versus replacing them. 🎭 Making AI part of company culture and a valued skill: Implementing AI means a shift in mindset and evolution of work processes. Fostering a culture of curiosity and adaptability is essential while encouraging colleagues to develop new skills through training and upskilling opportunities. Failure to do this results in only minimal or iterative change. ⏰ Change takes time: It’s natural to want to see immediate success, but culture change at scale is a journey. Adoption timelines will vary greatly depending on organizational complexity, opportunities for training and—most importantly—clearly defined benefits for colleagues. A few successful change management guiding principles I have seen in action: 🥅 Define goals: Establishing clear objectives—even presented with flexibility as this technology evolves—will guide the process and keep people committed to their role in the change. 🛩 Pilot with purpose: Begin small projects to test the waters, gain insights and start learning how to measure success. Scale entirely based on what’s working and don’t be afraid to shut down things quickly that are not working 📚 Foster a culture of learning: Encourage continuous experimentation and knowledge sharing. Provide communities and spaces for people to talk openly about what they’re testing out. 🏅 Leaders must be champions: Leaders must be able to clearly articulate the vision and value; lead by example and be ready to celebrate successes as they come. As we continue along the generative AI path, I highly suggest spending time with change management resources in your organization—both in the form of experienced change management colleagues and reading material—learning what you can about change implementation models, dependencies and the best ways to prioritize successes.

I've spent a lot of time recently thinking about how to ensure Zebra is truly integrating AI into our company, not just faking the use of AI. It has been a significant wake-up call for me and a feeling of immense pressure to get this right. The biggest thing I've learned is the the crucial role of change management. Building an AI-ready culture isn’t just an IT task (I believe IT should take the lead); it requires the synergy of EVERY SINGLE PERSON in the company. It crosses people, processes, technology, and a supportive corporate culture as part of a broader digital transformation. An article by Joe McKendrick in Forbes prompted me to reflect on our AI strategy. Have I effectively outlined it? Have we fostered a truly innovative and collaborative culture around AI? Are we providing adequate education and training? How fast do we want to push? Top down or bottoms up adoption? And last, but definitely not least, how are we measuring its impact? My key takeaways from this article: ➡️ Set clear AI goals tied to strategy. Are you planning to do more with the same, the same with less, or something in the middle? Then define work items like automating tasks or enhancing the efficiency of frontline work, to better realize ROI. ➡️ Communicate AI’s role clearly, to reduce resistance and build trust. Make sure what you communicate aligns to your goals. A mismatch of goals and communication is a recipe for disaster. ➡️ Ensure AI is accessible to everyone, fostering a culture of experimentation and teamwork. Remember that AI is not perfect, and neither are people. Accept it for what it can do well, and provide feedback on what it doesn't. ➡️ Provide ongoing training so employees understand AI’s impact on their roles. With the pace of change right now, AI training is not a one-and-done or even annual exercise. It needs to filter into team members learning rhythm weekly or monthly. As Zebra continues on our journey, I'm focused on these areas to help guide our progress. I invite you to share your thoughts and experiences on building an AI-ready culture. How are you approaching this challenge within your own organization?

These days I’m sure grateful for the Change Management work I did as a student at Harvard. The data is sobering. 👉 MIT’s NANDA study: 95% of generative AI pilots fail to move into production. 👉 McKinsey: 70% of initiatives remain stuck in development or expansion after a year. 👉 Abandonment: 17% of projects in 2024 → 42% in 2025. 👉 Scaling success: only 5–10% of companies ever get there. The technology is not the problem. The people, processes, and organizational structures are. That’s where John Kotter’s 8 Steps for Leading Change still feel urgent today. AI isn’t just a tool you stack on top of existing workflows. It requires rewiring how companies operate. Yet most organizations continue to treat AI adoption like a software upgrade rather than a deep transformation. ↳ Create Urgency → Leaders assume urgency is obvious. It’s not. AI must be framed with data and stories that make stakes clear: competitors will use efficiency to outscale you. ↳ Build a Guiding Coalition → Pilots run by IT alone fail. Cross-functional coalitions with visible champions succeed. ↳ Form a Strategic Vision → Saying “we’re investing in AI” is not a vision. Linking it to growth, efficiency, and innovation is. ↳ Remove Barriers → Resistance is natural. Job fears are real. Change management has to dismantle these barriers directly. ↳ Generate Short-Term Wins → Early ROI in back-office functions builds trust and momentum. Without visible wins, resistance hardens. ↳ Institute Change → AI sticks when embedded in hiring, training, incentives, and culture. Startups don’t wrestle with this. They scale with AI by avoiding new hires and redesigning work as they go. Large companies face the harder task: unlearning, rewiring, and rebuilding. The lesson from Kotter and from the data is the same: Transformation is not about the technology. It’s about change leadership. If we want AI to succeed inside large companies, we have to stop asking: ❌ “How do we scale the model?” ✅ “How do we scale trust, adoption, and organizational learning?” Three actions to drive forward now: ✅ Use data and stories to prove urgency at every level. ✅ Create early ROI wins and broadcast them widely. ✅ Embed AI into culture, not just IT, through hiring, training, and incentives. Do. Fail. Learn. Grow. Win. Repeat. Forever. ♻️Repost & follow John Brewton for content that helps. 📬 Subscribe to Operating by John Brewton for deep dives on the history and future of operating companies (🔗 in profile).

The foundation for using AI strategically isn’t what you think. It’s not technology, trending AI agents, or top vendors…it’s culture. My co-author Katia Walsh and I have seen this play out across industries, with companies investing in tools, hiring consultants, and building AI task forces. But the initiative stalls. Not because the tech didn’t work, but because the culture wasn’t ready. Many leaders forget: ❎ You can’t transform with AI if your culture punishes experimentation. ❎ You can’t innovate if asking “What if?” is seen as a waste of time. ❎ You can’t scale new tools when people are afraid of getting it wrong. The organizations seeing the biggest returns from AI right now are the ones that made room for curiosity, iteration, and trust. Successful leaders say:  ✅ “Let’s test it, even if we fail.”  ✅ “Your insights matter, even if you’re not in IT.”  ✅ “We’re learning this together, even if it’s uncomfortable.” If you want AI to work, start with how your people work together. #LeadingDisruption #AILeadership #AIAdoption

Most companies will fail at AI. Here's why. 𝗦𝘁𝗼𝗽 𝗰𝗮𝗹𝗹𝗶𝗻𝗴 𝗶𝘁 𝗔𝗜 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻. It's organizational change. (And most companies are missing this completely.) When you treat AI as just a tech upgrade, you ignore the real drivers of success: → Employee engagement. → Strategic alignment. → Cultural change. Here's the hard truth: ✅ IT can't own AI alone. ↳ Successful AI integration requires HR, Communications, Operations, and senior leadership—not just the tech team. ✅ Tools don't guarantee adoption. ↳ Without clearly articulated value and proactive training, even the best AI tools will gather digital dust. ✅ Culture drives AI success. ↳ Trust, clear communication, and alignment with organizational values make all the difference. AI adoption = organizational transformation. 𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗿𝘂𝘀𝘁. 𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝗺𝗶𝗻𝗱𝘀𝗲𝘁𝘀. 𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝗱𝗿𝗶𝘃𝗶𝗻𝗴 𝗿𝗲𝗮𝗹 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗰𝗵𝗮𝗻𝗴𝗲. So now the real question is: Is your company treating AI as a tech project... or as the strategic organizational change it truly is? Curious to hear your thoughts. Drop a comment below 👇🏾