Top Priorities for Technology Leaders

🚀 The Evolving Role of Tech Leaders: From Protectors of Technology to Guardians of Business Resiliency 🚀 Cybersecurity alone isn’t enough. Today’s tech leaders must protect the entire enterprise—from revenue and continuity to digital trust—to counter today’s rising risks. With AI, interconnected systems, and legacy tech in play, securing just the IT infrastructure won’t cut it. The stakes are high: $10.5 trillion in potential global cybercrime costs by 2025, and $400 billion in annual downtime losses for top companies. A lack of holistic protection leaves companies exposed to fines, reputational damage, and lost customer trust. Protecting the whole business isn’t just smart—it’s essential. Strategies for Building Business Resilience 🔍 Prioritize Critical Assets Not all assets are created equal. Focus on the 30% of assets that drive 70% of business impact. By securing the core, tech leaders can dramatically reduce risk across the enterprise. 🛠️ Shift Security Left Embed cybersecurity early in the development process to reduce risks down the line. Adopt “policy-as-code” practices to ensure security is a foundational part of every product or service, resulting in fewer vulnerabilities and a more resilient product lifecycle. 🔐 Build Digital Trust Digital trust goes beyond compliance. Be transparent with customers and address third-party risks proactively. Today, only 30% of companies follow best practices for cybersecurity and digital trust. Companies that prioritize this build both customer confidence and regulatory resilience. 🌐 Take an End-to-End View of Resilience Don’t just look at technology—analyze the entire business function. Partnering with other business units can help tech teams identify weak points across processes, people, and systems, rather than focusing solely on the technology stack. ⚙️ Address Technical Debt Tech debt is the “silent killer” of modernization. Right now, 20-40% of IT budgets go toward servicing tech debt instead of innovation. Proactively tackling this debt enables modernization without paying the hidden tax of past issues. 🧩 Test and Scenario Plan for Continuity Regularly simulate incidents with key stakeholders and vendors. This ensures that 50-60% of downtime, which is often due to process issues rather than technical failures, can be mitigated before it impacts the business. Planning isn’t just preventative—it’s protective. In a world of growing digital complexity, evolving from tech protector to business guardian is essential. Is your team ready to embrace resilience beyond cybersecurity? #CyberSecurity #BusinessResilience #DigitalTrust #EnterpriseTech #TechLeadership #AI #RiskManagement #DigitalTransformation

Last month, I watched an AI agent debug a production issue, write a fix, create tests, and deploy the solution in twelve minutes. Two years ago, this would have taken days of engineering effort. This isn't about AI replacing developers. It's about what I call "The Great Inversion of Coding"—the shift from humans writing code that machines execute to humans defining intent that machines implement. After leading technology at The New York Times, Wall Street Journal, Conde Nast, and Reddit, I've seen how transformative moments reshape entire industries. We're living through one now. The CTO role is evolving from chief builder to chief orchestrator. Instead of managing coders, you're curating capabilities—orchestrating AI agents alongside human judgment and creativity. Traditional technical debt meant code that was hard to change. Now you're managing model drift and AI-generated code that no humans fully understand. The CPO transformation is even more dramatic. When AI can generate features faster than users can adopt them, sustainable differentiation comes from holistic experiences that blend functionality with emotion and purpose. The constraint shifts from building to choosing. Five things technology leaders must do now: 1. Build AI literacy throughout your organization, not just in engineering 2. Redesign hiring for learning agility over current skills 3. Experiment with radical organizational models today 4. Develop clear AI ethics frameworks before you need them 5. Cultivate strategic patience with tactical urgency The leaders who thrive won't resist change or blindly embrace it, but thoughtfully navigate this transformation. We're not choosing between humans or AI—we're orchestrating their collaboration to create something neither could achieve alone. I've written a complete playbook for technology leadership in the AI age, including frameworks for human-AI work delegation and architectural principles for AI-first organizations: https://lnkd.in/eyNyNPA5 What changes are you seeing in your organization? How are you preparing your teams for this shift?

Over the past 10 weeks, I’ve interviewed 35 talent and learning leaders at Fortune 1000 companies for a report I’ll be releasing this fall. One of my favorite questions has been the very first one: 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐲𝐨𝐮𝐫 𝐭𝐨𝐩 𝐭𝐡𝐫𝐞𝐞 𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐞𝐬 𝐫𝐢𝐠𝐡𝐭 𝐧𝐨𝐰?” With 105 priorities and counting, the responses vary widely given differences in industry, scope, and role (VP of Learning, talent, talent management, leadership development) but here is a slice of what has been shared so far: ➡️ AI and work transformation: Clarify what AI means for the workforce, its implications for roles, and how teams can adopt it to accelerate development and efficiency. ➡️ AI Coaching Pilot: Launch an AI-powered coaching pilot program across the organization to scale leadership development support. ➡️ Generative AI Upskilling: Upskill employees and leaders to effectively use generative AI in day-to-day work ➡️ Future of Work & Workforce Planning: Prepare for disruptions to job architecture by integrating human and digital workforces. Rethink responsibilities, structures, and collaboration models. ➡️ Change management: Embed change management capabilities at all levels, particularly around AI adoption. ➡️ New leadership Behaviors: Equip leaders with new capabilities to thrive in a changing environment, including adaptability, resilience, and the ability to lead in an AI-augmented workplace. ➡️ Skills and Career Paths - Creating paths by prioritized skills in our organization ➡️ Rethinking the Function: Redesign the talent and learning function to reflect disruption caused by AI ➡️ Change Leadership: Navigate a period of executive turnover and transition by stabilizing the leadership team, clarifying roles, and building confidence with functional business leaders. ➡️ Facilitating Connection: Partnering with our employee experience and workplace teams to use in-office team days for learning and connection ➡️ Linking Performance and Development: Redesign performance processes to connect directly to development, helping employees understand what growth means in practical and tangible terms. ➡️ Manager Development: Continue to strengthen manager capability and resources, ensuring managers are equipped to drive performance and support employee development ➡️ VP and SVP Development: Support and accelerate the growth of new vice presidents and senior vice presidents as they step into expanded leadership roles. ➡️ Building a Leadership Bench : Develop and execute a strategy for strengthening the leadership bench, with a focus on preparing our Top 200 leaders ➡️ AI/Learning : Using AI internally within the learning function and focusing on key skills in AI for client-facing practitioners ➡️ Academies For AI/Data Roles: Developing and rolling out an academy for our AI & Data Product Employees I’d love to hear your perspective: What stands out most to you about this list, or what themes are you seeing in this list?

Your tech team isn’t watching what you say. (they’re watching what you do) 👇 Even the best tech leaders struggle with this. Because leadership isn’t about big decisions. It’s about small moments that define trust. Here’s what real leadership looks like: 1.  Stay in the Trenches ↳ When deadlines loom, don’t disappear. ↳ Be there, even if it’s just to check in. 💡When a release is tight, join stand-ups. 💡Ask: “What’s blocking you? How can I help?” 2.  Own Your Mistakes Publicly ↳ If you want learning, start with yourself. ↳ Show failure as a lesson, not a liability. 💡Share when a decision went wrong. 💡Say: “I pushed X, but Y was the better call.” 3.  Learn What You Preach ↳ Need to upskill in AI? Don’t just assign it. ↳ Learning should be a team sport. 💡Enroll in a similar course as your team. 💡Post your takeaways and struggles in chat. 4.  Make Action Louder Than Words ↳ Innovation isn’t a slogan. ↳ Build, test, and show...not just talk. 💡Instead of meetings, build a prototype. 💡Let results speak louder than strategy decks. 5.  Respect Your Team’s Time ↳ If you expect focus, model it. ↳ Block deep work time and protect it. 💡 Set “no-meeting Tuesdays” and stick to it. 💡 Decline non-urgent requests to show it matters. In tech, leadership is built in the trenches. Own setbacks, share lessons, and show up every day. Your actions set the standard for the team. ♻️ Share to help others lead with action.   🔔 Follow me (Nadeem) for more like this.

As a CTO who has successfully scaled AI and tech products, I’ve refined productivity strategies that can transform your leadership workflow and enhance your team’s output. If you’re leading in the tech industry, and grappling with overwhelming demands, the 3 targeted tactics I’m about to share are tailored for the unique challenges you face. My guiding principle each week is the 'Rule of Three': identifying three top priorities that serve as my North Star. These aren't just scribbled in a planner but physically placed on my office wall, a constant visual reminder of my core focus. This practice not only keeps me centered amidst the whirlwind of daily tasks but also ensures that every action is a step toward our most critical goals. Sharing these priorities with my direct reports does more than foster transparency — it aligns our efforts, synchronizes our strides, and forms the bedrock of our collective pursuit. It's a simple yet profoundly effective strategy that has continually steered us toward meaningful progress and impactful results. Next, time blocking has been a critical strategy. Carving out dedicated blocks for deep work, meetings, and even unexpected tasks allows me to create a rhythm amidst the chaos. This isn't just about sticking to a schedule; it's about allocating mental space and ensuring that high-priority projects get the uninterrupted attention they deserve. I always check each Friday that my time blocked schedule appropriately reflects the work I need to accomplish for my top three priorities. Lastly, I leverage automation and delegation. By automating routine tasks and delegating effectively, I maintain focus on what truly requires my expertise. It's not just about offloading work; it's about empowering my team by entrusting them with responsibilities that aid their growth while freeing me to lead more effectively. A framework I really like using is the Eisenhower matrix around categorizing work based on its urgency and importance. I try and focus as much of my work as I can on the important and urgent tasks. Implementing these strategies hasn’t just boosted my personal productivity; it sets a precedent for the whole team. When leaders manage their time effectively, it cascades down, fostering a culture of efficiency and clarity. Remember, in the world of tech and AI, where the ground shifts daily, these strategies aren't just nice-to-have—they're essential for survival and success. If you're leading in this space and looking to refine your approach to productivity, let's connect and share insights that propel us forward! #techleadership #productivitytools #teamleader

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.

Here's what your CEO actually cares about when evaluating technology leaders. Not your technical roadmap. Not your team's velocity. Not even your budget management. They care about one thing: Can you translate technology into business value? Time and again, I see technically excellent directors wondering why they're not advancing. Their metrics are perfect, their teams perform well, yet when the C-suite is discussing market expansion they aren’t in the room. “Just keep doing what you’re doing” - they are told. The problem isn’t technical capability. The problem is business fluency. While one leader is perfecting deployment pipelines, their peer is learning customer acquisition costs. While one leader is optimizing databases, another is studying competitor analysis. Guess who got the VP role? The harsh truth: Your technical excellence is assumed at this level. What separates good directors from great VPs is the ability to connect technology decisions to business outcomes. Start thinking like an investor in your own company. Every technical choice should answer: "How does this help us win in the market?" Your next promotion depends less on what you build and more on why you build it. Challenge for this week: Before proposing any technical solution, write one paragraph explaining its business impact. #TechLeadership #TechnologyLeadership #Leadership #Motivation

AI isn't just changing work. It's redefining leadership. 5 skills every leader needs in the digital age: Yesterday's playbook is obsolete. Tomorrow's leaders need a new toolkit. Here's how to stay ahead: ▶️ 1. Quick Thinking, Faster Acting The 5-year plan is dead. Think on your feet. · Use AI to spot trends before they hit · Turn monthly meetings into weekly sprints · Mix teams up to spark fresh ideas Try this: Play "What if?" once a month. Imagine wild scenarios for your industry. ▶️ 2. Tech-Savvy (No Coding Required) You don't need to build AI. Just know how to use it. · Test new apps like you're a curious teenager · Grab coffee with the IT crowd regularly · Jump into online communities about future tech Challenge: Explain ChatGPT to your mom this weekend. ▶️ 3. Digital Ethics Champion As tech gets smarter, we need to get wiser. · Form a diverse group to check new tech for fairness · Make it easy for employees to flag AI concerns · Always ask: "Could this hurt someone?" Key Question: "What's the worst way someone could use this?" ▶️ 4. Human + AI Teamwork It's not us vs. robots. It's us + robots. · List tasks where AI helps vs. where humans shine · Train your team to work alongside AI tools · Create spaces where people and tech mix naturally Experiment: Solve a problem with and without AI. What's different? ▶️ 5. Master of Unlearning Forget "always learning." Start "always questioning." · Have a "spring cleaning" for old ideas quarterly · Surround yourself with people who challenge you · Turn letting go of outdated methods into a game Pro Move: Hold monthly "Idea Funerals." Bury old ways of thinking. Remember: Tech moves fast. People need time. Great leaders balance new tools with timeless people skills. Your job? Guide your team through the AI revolution with confidence. What leadership skill do you think is most crucial today? Share your thoughts in the comments ⬇️ Thanks for reading! If you found this valuable: • Repost for your network ♻️ • Follow me for more deep dives • Join our 300K+ community https://lnkd.in/eDYX4v_9 for more on the future of API, AI, and tech The future is connected. Become a part of it.

The Future We’re Not Ready For I cover these issues and more in my upcoming IEEE/Wiley book on technological leadership, offering practical tools and strategies leaders can use now. This isn’t theory — it’s a hands-on guide to navigating the biggest challenges in technology, security, and resilience. You won’t find this collection of insights and solutions anywhere else. The world is moving faster than we can keep up. Energy, cybersecurity, AI, and supply chains aren’t separate problems — they’re all connected. If we don’t adapt, the next crisis won’t be a surprise — it will be inevitable. 1. Energy and Cybersecurity: A Disaster Waiting to Happen: The U.S. power grid is outdated and vulnerable. The 2015 Ukraine blackout was a warning. The 2021 Colonial Pipeline hack showed how easily critical infrastructure can be disrupted. In 2024, cyberattacks on U.S. utilities surged by 70%, with vulnerabilities increasing by 60 daily. Utilities struggle to hire enough security experts. Energy security is now a digital battlefield. 2. AI, Quantum, and the Race for Control: Quantum computing threatens current encryption methods. The U.S., China, and Europe are racing for quantum supremacy, but few understand the implications. AI is transforming industries but also displacing jobs, reinforcing biases, and being used in autonomous weapons. Leadership that grasps both risks and potential is urgently needed. 3. The Supply Chain Crisis We Haven’t Fixed: The pandemic exposed weak supply chains, but little has changed. Semiconductor shortages, logistics bottlenecks, and resource dependencies still threaten industries. The U.S. relies on China for 72% of its rare earth imports, essential for batteries, solar panels, and defense systems. If trade restrictions or conflicts escalate, entire industries could halt. 4. A Workforce Unprepared for What’s Coming: Automation and AI are eliminating jobs faster than new ones are created. The skills gap is widening, and educational institutions aren’t keeping up. Remote work, hybrid roles, and AI-driven hiring reshape the job market. Trust in leadership and institutions is at an all-time low. Businesses that don’t adapt risk losing talent and collapsing. 5. Space: The Next Geopolitical Battleground: SpaceX, Blue Origin, and China’s lunar ambitions are turning space into the next economic and military frontier. Satellite networks control communication, navigation, and surveillance. But space lacks clear laws. Who controls asteroid mining? Who owns space-based solar power? The rules are being written now. Those who lead will shape the future economy. What Happens Next? These aren’t isolated problems. AI needs energy, and energy requires security. Supply chains impact national security. Leadership ties it all together. Those who prepare will shape the future. #AI #ASME #cybersecurity #economy #energy #IEEE #leadership #nationalsecurity #power #quantum #resilience #space #supplychain #technology #trust

Nam, With AI becoming a key player in every industry, what skills will actually matter in the next 10 years? My response: The future doesn’t belong to the most technical — it belongs to the most adaptable. The top skills for the future are: 1. Data Science: Crafting smart data pipelines, synthetic data strategies, data governance frameworks, data protection, and the ability to harness data will define success. We produce an ocean of data EVERY DAY. 2. Human-AI Interaction: Anyone who can interact, integrate, develop, and work toward better collaboration between humans and AI will be in high demand in the years to come — aka, the Human-AI Integrator. Note: AI literacy is NOT optional anymore. If you can’t guide your teams through AI integration, you’ll fall behind. 3. Emotional Intelligence (EI): Be human. AI can replace I, but not EI. We need to be more human and leverage our empathy, compassion, and emotional quotient. 4. Innovative and Critical Thinking: Since AI will EXECUTE, Humans will ELEVATE. We need to get better at decision-making with limited information and uncertainty. 5. Rapid Learning: As humans, we already have a lot of information to process. I often hear from Gen Z that they listen to my videos at 2x speed. Given this, we need to learn how to quickly pick and review relevant content. The skill of learning faster than the rate of change will be crucial. 6. Cultural Fluency: Global tech = global teams = cultural empathy. This skill is already essential and will continue to be as the world becomes more interconnected. 7. Ethical Tech Leadership: AI will bring as many risks as opportunities. Leaders who understand how to deploy tech responsibly — with transparency, fairness, and privacy in mind — will shape the next decade. 8. Cybersecurity: AI makes us faster, but it also makes threats smarter. As we integrate more AI tools and connect more systems, leaders need to prioritize cyber hygiene, digital risk management, and organizational resilience. Security won’t just be IT’s job — it will be everyone’s responsibility. We can’t predict every change AI will bring — but we can prepare with the right skills. What would you add to this list? #NamrataShah #ThoughtLeadership #SkillsOfTheFuture #DataScience #HumanAIIntegrator #EmotionalIntelligence #CulturalFluency #CriticalThinking #InnovativeThinking #CulturalAwareness #Cybersecurity #EthicalLeadership