Approaches to Cultivating AI Innovation Mindsets

Over the last few years, I've seen two dominant approaches emerge. Neither is right.Neither is wrong. And each comes with a very different price tag. 💎 Approach #1: Intentful Innovation It’s like planting a vegetable garden - serves a specific purpose. Similar to: 💡Reduce customer churn 💡Improve forecast accuracy 💡Accelerate software development 💡Increase sales productivity Resources, funding, data, talent, and executive sponsorship are concentrated on specific initiatives. The upside? ✅ Faster ROI✅ Better governance and oversight✅ Easier change management✅ Stronger executive support The downside? 🔥You sacrifice discovery. 🔥You may never uncover the breakthrough use case hiding in a department nobody was paying attention to. 💎 Approach #2: Plant a Field of 1,000 Flower Seeds and See what Harvests It looks like: 💡Give employees access and encourage exploration. 💡Run hackathons. 💡Create AI champions. 💡Allow teams to build, test, fail, and learn. The upside? ✅You generate creativity at scale. ✅You uncover unexpected opportunities.✅You create organizational learning faster than any centralized team ever could. The downside? 🔥You create noise. 🔥Lots of noise. 🔥Some projects duplicate effort.🔥Some never produce value.🔥Some consume resources without producing outcomes. The choice isn't really between innovation and experimentation, the choice is between what you're willing to optimize for. Intentful Innovation optimizes for efficiency. 1,000 Flowers optimizes for discovery. One asks you to give up optionality. The other asks you to give up control. One burns less money. The other burns less opportunity. Organizations trying to do both simultaneously without acknowledging the tradeoffs. The result? Confusion. I’m the age of AI, the challenge isn't choosing a strategy. It's understanding what you're willing to sacrifice to pursue it.

If you’re only using AI for efficiency, you’re missing the real revolution. Everyone’s obsessed with AI as a productivity tool—automating tasks, writing emails, speeding up workflows. But that’s not the strategic unlock. That’s the warm-up act. They’ll say: ➡️ “AI helps us move faster.” ➡️ “It cuts costs.” ➡️ “It replaces admin work.” Sure. But they’re asking the wrong question. The real opportunity isn’t doing the same work faster. It’s doing entirely new work you could never do before. 🚨 Where today’s mindset is falling short: • We optimise tasks—but ignore transformation • We treat AI as a tool—not a collaborator • We measure output—but miss imagination • We chase savings—but leave opportunity on the table ✅ What a creative AI mindset unlocks: • Entirely new business models—built in days, not quarters • Unique brand voices—crafted in real-time for every customer • Product ideas—prototyped, tested, and improved overnight • Strategy that evolves live—with the market, not after it This isn’t about better workflows. It’s about building a new kind of company. Think Adobe Photoshop in 1990 vs. generative design tools today. Photoshop made creation easier. AI makes creativity infinite. 🔴 Old AI Mindset: 1. “How can we save time?” 2. “Where can we reduce headcount?” 3. “Can AI do what we already do?” 🟢 New AI Mindset: 1. “What could we create that was never possible before?” 2. “How do we lead teams of humans and machines?” 3. “What if strategy itself became a living system?” This isn’t just a tech shift—it’s a creative explosion. The orgs that thrive won’t be the most efficient. They’ll be the most imaginative. So here’s the question every CEO should be asking: Are we building an AI strategy— or building the creative capacity to imagine what strategy could become?

Becoming AI-native isn't about mastering prompts. It’s about upgrading how you lead. If your AI strategy is “train everyone to prompt better,” you’ll plateau fast. The leaders winning with AI aren’t the ones who write better instructions. They’re the ones who’ve redesigned how they think, decide, and manage teams. Human ingenuity + machine intelligence = exponential value. But only if you change the habits that got you here. Think of the shift as: Adopt → Design → Scale → Sustain. Here are 8 ways to make it real: 1/ 𝗔𝗱𝗼𝗽𝘁 𝗮𝗻 𝗔𝗜-𝗙𝗶𝗿𝘀𝘁 𝗠𝗶𝗻𝗱𝘀𝗲𝘁 Start asking: “If AI was foundational, how would we design this from scratch?” → Treat AI as a co-creator in strategy, not an afterthought → Ask AI daily to challenge assumptions and surface blind spots → Audit decisions weekly: Which ones were made without AI input? 2/ 𝗕𝘂𝗶𝗹𝗱 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗙𝗹𝘂𝗲𝗻𝗰𝘆 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗗𝗮𝗶𝗹𝘆 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 You can’t lead AI transformation if you’re not using AI yourself. → Use AI daily for decision briefs, planning, and risk analysis → Block short, consistent time for hands-on experimentation → Share personal wins and failures publicly with your team 3/ 𝗦𝗵𝗶𝗳𝘁 𝗳𝗿𝗼𝗺 𝗖𝗼𝗺𝗺𝗮𝗻𝗱𝗲𝗿 𝘁𝗼 𝗖𝗼𝗮𝗰𝗵 AI-native leadership isn’t top-down control. → Let AI prepare drafts and recommendations; humans decide → Focus leadership energy on judgment, priorities, and ethics → Coach hybrid human–AI teams instead of managing outputs 4/ 𝗖𝗿𝗲𝗮𝘁𝗲 𝗦𝗮𝗳𝗲𝘁𝘆 𝗳𝗼𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Fear quietly kills AI adoption. → Frame AI as augmentation, not replacement → Create low-risk sandboxes with clear boundaries → Reward learning and early failure, not just success 5/ 𝗥𝗲𝗯𝘂𝗶𝗹𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀, 𝗗𝗼𝗻'𝘁 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗢𝗹𝗱 𝗢𝗻𝗲𝘀 Automating a broken process scales dysfunction. → Redesign high-volume workflows from first principles → Build for autonomy and handoffs, not just assistance → Prototype one AI-native workflow before scaling 6/ 𝗕𝘂𝗶𝗹𝗱 𝗔𝗜-𝗡𝗮𝘁𝗶𝘃𝗲 𝗧𝗲𝗮𝗺𝘀 Talent strategy determines AI outcomes. → Hire for curiosity, systems thinking, and judgment → Tie advancement to measurable AI-driven impact → Form small, cross-functional squads around real problems 7/ 𝗢𝘄𝗻 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗹𝘆 AI governance is a leadership responsibility. → Set clear policies for data use and agentic systems → Require explainability and human oversight for key decisions → Review risks and controls on a regular cadence 8/ 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀, 𝗡𝗼𝘁 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 Usage doesn’t equal value. → Measure time saved, revenue impact, and speed to execution → Replace vanity metrics with business outcomes → Make AI impact part of performance conversations Becoming AI-native doesn’t require a bigger innovation budget. It requires a different operating system. Teams using AI as a tool get faster. Teams building with AI as infrastructure get ahead.

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.

Here’s a reality check for anyone riding the genAI wave: It’s not the technology itself that drives success—it’s the people. According to our research, 64% of CEOs agree that winning with AI depends more on employee adoption than on the tech alone. Yet, 61% admit they’re pushing their organizations to embrace AI faster than their teams may be ready for. So, how do you bridge the gap? By putting the human element first. 1. Eliminate Friction—Make AI Work for People - If employees resist AI, chances are it’s not the tech—it’s the experience. - Identify the pain points and fix what’s causing pushback. - Invest in tools that simplify daily tasks, not complicate them. - Use AI to streamline slow, inefficient processes that drain energy and creativity. 2. Invest in Inspiration—People Drive Innovation - Technology adoption isn’t just about deployment; it’s about belief. - Incentivize employees to reimagine their roles with AI as an ally. - Offer hands-on training so your team feels empowered, not overwhelmed. - Build a system where governance, skills, and tools align to maximize impact. 3. Stoke the Fire—Lead with Vision - AI isn’t just a tool—it’s a mindset shift. Inspire your team with a clear purpose. - Show how generative AI aligns with the company’s mission. - Let culture lead: Make the technology work for people, not the other way around. - Hand over the keys—trust your people to drive innovation forward. AI isn’t a magic solution—it’s a team sport. The future of AI isn’t just technical; it’s deeply personal. #IBM #IBMiX #AI #genAI

If you’re mid-career and feeling the ground shift under your feet, breathe. AI is compressing tasks, not your value. Your edge now is mental resilience, strategic psychology, and the habits that turn intent into impact. Let’s align your mindset first: 🔹️State management: Your emotional state determines your strategy. 🔹️Anxiety narrows options; curiosity expands them. Before decisions, regulate—slow exhale, label the emotion, reframe the situation as a skills challenge. 🔹️Identity before tactics: “I am a builder who learns fast” beats “I hope my role survives.” Identity drives behavior; behavior builds results. Mini-Sprint habit creation cycle: 1) Trigger: Anchor a clear trigger. eg: calendar block at 8:30am named “Deliver One Thing.” 2) Operation: A 45-minute (not longer, chunk it down) build sprint using AI as your co-pilot. Draft, test, or ship a micro-outcome (post, prototype, outreach). 3) Feedback: learn what is necessary and go back and correct it. 4) Reflection: 2min debrief. What worked? What will you improve tmr? This closes the loop and builds meta-cognition. Weekly O.S (for busy leaders): Mon: Map 1 customer problem you can solve in 7 days. Define the smallest shippable outcome. Tue–Thu: 3 focused sprints. Use AI to draft, iterate, and test with a real user or stakeholder. Fri: Publish the result (internal or external), collect feedback, and log a learning. Sat: 20-min review—update your playbook, stack your prompts, list next week’s experiments. Core AI skills yiu need: Prompt strategy and evaluation: Think like a coach. Clarify outcome, constraints, and evidence of success before you prompt. Customer psychology: Interview for emotions, not just features. What pains, fears, and gains show up in their language? Distribution habits: One insight post, one relationship nurture, one ask daily. Small, consistent motions build compounding visibility. Mental resilience principles: ✅️Control the controllables: energy, focus blocks, learning cadence. Let go of noise. ✅️Antifragile framing: When a junior task gets automated, ask, “What higher-order value does this free me to create?” ✅️Recovery rituals: Sleep, movement, and boundaries are performance multipliers. Protect them like meetings with your future self. ❗️The opportunity lens: A small, committed team can now build and validate solutions at lightning speed. Convert your expertise into assets like playbooks, prototypes, products. ❗️Aim for impact outcomes. Solve real, narrow problems for real buyers. Your next step today: ➡️Define 1 cue for a daily 45-minute “Deliver 1 Thing” sprint. ➡️Pick one customer problem and write a 5-line problem statement. ➡️Use AI to create version 0.1. Share it with one user before day’s end. Wanna join a community to unlock the AI mindset for your professional leadership or business growth? Let's connect!

Are your AI teams publishing papers but not shipping products? I’ve been there. The team is brilliant, the models are state-of-the-art, but nothing moves the needle with users. You might hear phrases like: “Our model is 10% better than last week on our test set” "We're not sure how to evaluate it with real users yet" "We’re thinking of submitting this to NeurIPS" While research excellence matters, these are often symptoms of teams disconnected from business impact. I’ve talked to leaders of AI teams who see the same. Here’s what I’ve seen work to shift the mindset: 📎 Introduce structured customer feedback loops: Require every AI initiative to tie back to a real user journey. Use product feedback, interviews or real data to guide what gets built and how it evolves. 🔎 Build cross-functional teams by default: Pair data scientists with product managers, designers and engineers. Make them accountable for shared outcomes, not isolated models. ✏️ Create roles that bridge AI and the business: Technical product managers or machine learning leads with product intuition can translate between model development and customer needs. They’re the ones who turn prototypes into production. 📈 Align metrics with business impact: Move beyond precision and recall. Track things like resolution time, customer success, or time saved by users. When the goal shifts, so does the mindset. AI can unlock tremendous value, but only if it makes it out of the lab. If you're navigating these challenges and want more insights, I share stories about AI leadership in my newsletter (link in comments).

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?

I'm truly honored to have contributed once again to #Focus magazine—an editorial institution that has inspired me since I was a teenager with its blend of scientific rigor, accessible storytelling, and forward-thinking topics. In the latest issue (No. 392), which features a striking cover dedicated to Artificial Intelligence, I was invited to share some practical reflections on how individuals can elevate themselves by embracing AI—not as a distant or abstract concept, but as a daily ally in professional and personal growth. Here’s a brief summary of the seven key principles I outlined—designed not only for AI experts, but for everyone looking to thrive in a world increasingly shaped by intelligent systems: 1. LIFELONG LEARNING. Keep your curiosity alive. From micro-courses to in-depth certifications, platforms like Coursera, Udemy, and LinkedIn Learning offer critical insights into AI’s fast-evolving landscape. Staying current is no longer optional—it’s strategic. 2. HANDS-ON EXPLORATION. Don’t just study AI—use it. Experiment with chatbots to enhance communication, leverage instant translators, or use generative tools to craft compelling presentations. Learning by doing is where transformation begins. 3. HUMAN-AI SYNERGY. Combine your traditional expertise with AI’s capabilities. Whether you're in operations, strategy, or design, the future belongs to those who know how to blend analytical intuition with algorithmic precision. 4. ECOSYSTEM THINKING. Engage with communities. Join forums, attend meetups, exchange best practices. Innovation doesn’t happen in isolation—shared learning amplifies both speed and impact. 5. ETHICS & TRUST. Adopt AI with integrity. Prioritize privacy, fairness, and transparency in every AI-powered process. Sustainable innovation is rooted in responsible adoption. 6. ADAPTIVE MINDSET. AI evolves fast—and so should you. Continuously revisit your assumptions, embrace emerging tools, and stay open to rethinking how you work, plan, and lead. 7. CREATIVE INTELLIGENCE. Unleash your imagination. Use AI not just to optimize tasks, but to dream bigger—writing stories, composing music, prototyping ideas. In the age of machines, human creativity is more valuable than ever. 📘 Focus remains, to me, a beacon of accessible intelligence—and I’m grateful for the chance to contribute to its ongoing mission. If any of these ideas resonate with you, I’d love to hear how you're using AI in your own journey. #ArtificialIntelligence #AIForEveryone #DigitalTransformation #FutureOfWork #Leadership #LearningCulture #HumanAndMachine #AIEthics #AIInnovation #AILeadership #ContinuousLearning

One of the biggest lessons I've learned working with Fortune 5 companies on AI transformation is that successful AI adoption isn't about having better models. It's about building better systems for innovation. Two methods we use to drive innovation at the speed of AI: Innovation Pods; Vibe to Vetted process 1. Innovation Pods Instead of treating innovation as everyone's side job, we create dedicated, cross-functional teams around each core business capability with the goal of driving and scaling AI innovation. Each pod typically includes: • A program manager who owns the roadmap and backlog. • Subject matter experts who understand the business and define user requirements. • FDE's who build and deploy solutions. These teams meet regularly, prioritize opportunities, validate ideas with users, and continuously improve how work gets done. More importantly, they become the owners of that capability. They don't just build tools—they create documentation, playbooks, and repeatable processes that allow innovation to scale across the business. 2. Vibe to Vetted The second lesson is that democratizing development only works when there's a shared foundation. The goal is simple: anyone in the business should be able to build and extend capabilities—not just software engineers. But freedom without standards creates chaos. We've created a common set of guardrails and process that includes shared skills, Git repositories, design systems, AI agents, design system, security guardrails, and engineering standards. Whether someone builds in Cursor, Claude, Antigravity, or another AI-native IDE, every project follows the same architecture, uses the same UI components, meets security requirements, and can be easily maintained by the next team because it has all of the correct documentation. Enterprise AI isn't a technology challenge. It's an operating model challenge. The organizations that build repeatable systems for innovation will outperform those that simply deploy more AI tools. Put these into practice for you and watch how fast innovation takes off.