Leveraging Technology In Leadership

After coaching and advising several CMOs all year, a few patterns keep showing up - no matter the company size, industry, or stage. 1️⃣ 2026 planning, 2023 budgets. Ambitions are high, resources aren’t. The best leaders are reworking orgs to leverage AI and operate with sharper focus and fewer layers. 2️⃣ Brand is quietly back. After years of performance obsession, the pendulum’s swinging. More investment in narrative, distinctiveness, community, and IRL experiences, less chasing short-term clicks (finally!). 3️⃣ Internal influence > external reach. The strongest CMOs don’t just move markets; they move companies. They invest in their first team and co-author initiatives across the org. 4️⃣ Clarity beats velocity. The leaders thriving right now know when to slow down, reset, and sharpen their “why.” Speed still matters, but not at the cost of direction. 5️⃣ Energy management is the new edge. The next wave of great marketing leadership isn’t about doing more; it’s about showing up clearer, steadier, and more intentional. These are some pretty big shifts, but I am here for it. If you're a CMO or Head of Marketing and feeling a lot of this as you head into 2026 and want some support, reach out - my DMs are open! #advisor #cmo #coaching

When I left my role as the Department of Homeland Security's first Chief AI Officer last year, our AI inventory had grown to more than 150 use cases - expediting travel screening, sharpening fraud detection, accelerating disaster recovery, and freeing agents and officers for the judgment work only they can do. None of that scale was possible without the governance program we built behind it. In government, you don't get to deploy AI at this kind of scope without a system that adapts to changing technology and threats, ensures protections for safety and human rights, and earns public trust. Done well, that system is what lets you say yes - to higher-impact uses, in higher-stakes domains, with the confidence that you can sustain them. That experience is the foundation of a paper I'm releasing today through the University of California, Berkeley Executive Fellowship in Applied Technology Policy: "Best Practices in Public-Sector AI Governance: A Practitioner's Playbook." It draws on a review of 66 U.S. public-sector AI governance policies, ten in-depth interviews with federal, state, local, and international leaders, and four years inside the third-largest federal agency. The core argument: AI governance is not a brake on innovation. Done right, it's what lets government move faster - and earn the public confidence to keep moving. The paper organizes the lessons into a five-stage playbook: policy development, leadership and resourcing, intake and inventory, risk assessment, and publication. The governments doing this well share a posture - ambitious about what AI can deliver, disciplined about the risks, and humble about the need to iterate. Thanks to Deirdre Mulligan, the UC Berkeley School of Information, and University of California, Berkeley, Goldman School of Public Policy for the Fellowship that made this possible, to my incredible research assistants Omar Morales, Isabelle Athena Qian, and Prakash Krishnan. And thank you to my fellow Executive Fellows for being an amazing group of collaborators over the past year: Judy Brewer, Charlotte Burrows, Alan Davidson, Marcela Escobar-Alava, Arati Prabhakar, Jenny Toomey, Denice Ross, Merici Vinton, Jenny Yang, and Vera Z.. Read the Playbook here: https://lnkd.in/eyw2rgca

It's funny that when I first started in marketing, I thought the hardest part would be the marketing itself. Boy, oh, boy, was I wrong! After working with several brands, leaders, and entrepreneurs, I've learned that the real challenge is getting everyone to agree. Whether for a content strategy, a demand generation campaign, or a new #ABM initiative, the challenge is always bridging the gap between what you know works and what leadership thinks is effective. Fundamentally, it's about building trust, and that's not always easy. Here's what I've learned about getting everyone on the table: ✔️Reframe the conversation. Don't just react to requests; guide the discussion. For example, instead of: 👉🏾"We need more leads," try. "Let's focus on what's driving the highest-value conversions." 👉🏾"We should be everywhere," try. "Let's invest in the channels where our target audience is most engaged." 👉🏾"We need to go viral," try, "Let's build a consistent strategy for creating content that resonates and generates long-term engagement." ✔️As a marketer, your role is not merely execution but also strategically shaping and defending the approach. ✔️Always acknowledge and address the tension. Leaders often want immediate results, while marketing strategies require a longer-term perspective. So don't just push back; explain the long-term vision while incorporating quick wins to demonstrate progress and build confidence. ✔️Find that balance between short-term goals and long-term strategy. ✔️Speak their language, avoid marketing jargon, and focus on business outcomes. For example; 👉🏾Instead of engagement metrics, focus on how marketing and ABM influence revenue. 👉🏾Clearly articulate your strategy and the rationale behind it. 👉🏾Invite input and questions to encourage collaboration. 👉🏾Save the detailed marketing metrics for internal team discussions. 👉🏾Prioritize your battles. Not every request is worth fighting over. 👉🏾Focus your energy on defending the core elements of your strategy, like positioning, messaging, and brand narrative. Don't let short-sighted requests derail your overall vision. It's about learning to operate like an executive, understanding their priorities, and communicating in a way that resonates with them. This approach will help you secure the resources and creative freedom you need to drive successful marketing campaigns. What are your go-to strategies for getting everyone from the leadership on board? #demandgeneration #b2bmarketing #marketingstrategy

Most marketers I meet today fall into one of these four boxes. But only one of them will grow into the next wave of leadership. Let’s break it down: 1. Legacy Executors Low AI adoption, low strategic depth. Stuck in channel silos. Still optimising ads the same way since 2019. Risk: The most automatable quadrant. 2. Tool Chasers High AI usage, low strategy. Running on prompt fatigue and jumping from app to app. No frameworks, no clarity only motion. 3. Strategic Integrators High AI fluency, high strategic focus. They design thinking systems.They don’t just use AI - they structure it to reduce decision chaos, codify insights, and enable faster outcomes. That’s where the leverage lives. Where am I right now? Somewhere between Tool Chaser and Strategic Integrator. I’ve spent the last 6 months: • Rewiring my thinking from execution to system design • Building repeatable frameworks for performance + brand + platform strategy • Using AI not just for speed but for clarity and scale However, it's a long and steep learning curve. How do you move to the top right? Few things that have been working. 1. Shift from Tasks → Systems Example: Don’t just ask AI to “write a copy.” Instead, build a prompt system that reflects your brand voice, audience segment, and product value. Then templatize it so your team can use it repeatedly across briefs. 2. Own a Point of View Example: Instead of asking “what works on LinkedIn?” define your own IP layer. Create your GTM belief system. Frame your CX model. That’s what lets AI amplify your expertise instead of regurgitating trends. 3. Codify Everything Example: Turn a successful campaign into a system: • A decision tree • A content brief template • A measurement checklist These become teachable assets and eventually, monetizable AI won’t make marketers irrelevant. But shallow thinking will. The next 3 years will define whether you stay valuable or become replaceable. Which quadrant are you in right now? What’s your move? #AI #MarketingLeadership #StrategicThinking #SystemsDesign #DigitalTransformation #TShapedMarketer #GrowthStrategy

Over the past decade, I’ve seen digital marketing evolve from basic banner ads and email blasts to a complex ecosystem of performance media, AI-powered personalization, and hyper-targeted storytelling. But regardless of the tools or platforms, one truth remains constant: 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝘀𝗲𝗹𝗹𝗶𝗻𝗴, 𝗶𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝗮𝗰𝗵𝗶𝗲𝘃𝗶𝗻𝗴 𝗴𝗼𝗮𝗹𝘀. Whether you're trying to: 👉 Launch a product 👉 Generate leads 👉 Build a personal brand 👉 Attract talent 👉 Drive thought leadership ...digital marketing can get you there. But only if done intentionally. Here’s what I’ve learned about making digital work for your goals: 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗲𝗻𝗱 𝗴𝗼𝗮𝗹. Don’t begin with, “We need a campaign.” Start with, “We need to increase demo bookings by 25%” or “We want to get 5,000 pre-orders.” Strategy begins with specificity. 𝗚𝗲𝘁 𝗼𝗯𝘀𝗲𝘀𝘀𝗲𝗱 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗮𝘂𝗱𝗶𝗲𝗻𝗰𝗲. Understand their needs, pain points, behavior, and how they consume content. You’re not marketing to them, you’re marketing for them. 𝗠𝗮𝗿𝗿𝘆 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗻𝘁𝗲𝘅𝘁. Great content in the wrong format or channel will fail. A 30-second vertical video might outperform a whitepaper in the right moment. Format matters. 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝘄𝗵𝗮𝘁 𝘁𝗿𝘂𝗹𝘆 𝗺𝗮𝘁𝘁𝗲𝗿𝘀. Vanity metrics (likes, impressions) can be distracting. Focus on metrics that drive action: cost per lead, conversion rate, and pipeline influence. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁𝗹𝘆. SEO for visibility, LinkedIn for B2B trust, Meta for scale, YouTube for storytelling, influencers for social proof. Choose based on your goal and not what’s trending. 𝗧𝗵𝗶𝗻𝗸 𝗳𝘂𝗻𝗻𝗲𝗹 𝗔𝗡𝗗 𝗳𝗹𝘆𝘄𝗵𝗲𝗲𝗹. Go beyond acquisition. Digital should also support retention, upselling, and advocacy. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 > 𝗧𝗼𝗼𝗹𝘀 Tools change. Strategy endures. Don’t chase platforms, chase outcomes. Digital marketing is not magic. But when aligned with clear goals, it becomes a powerful engine of momentum. Follow #socialJJ to read more of my posts. #personalbranding

My client built a successful AI pilot. It reduced analyst time by 18%. The demo impressed the board. The vendor featured us in a case study. Then we tried to scale it. The cloud bill more than doubled. Fourteen workflows had to be redesigned. Two senior managers quietly resisted adoption. That’s when we learned: AI doesn’t fail at the model level. It fails at the organizational level. If you're scaling AI right now, here are the hard truths. 1. Problem-Pull Beats Tech-Push “We need an AI strategy” is how innovation theater starts. Instead ask: -> What’s costing us the most? -> Where are cycle times killing margin? -> What work is high-volume but low-leverage? If you can’t tie the pilot to revenue, cost, or cycle time inside 90 days, it’s experimentation, not strategy. 2. Pilots Should Scale or Stop No 12-month purgatory. Time-box to 90 days. Define success in business terms. Graduate to production or shut it down. Momentum compounds. So does drift. 3. Foundations Before Features Everyone wants generative AI. No one wants to fix data quality. The companies scaling well spent their first year on: -> Pipelines -> Governance -> Integration architecture Unsexy work. Massive leverage. 4. Governance Is a Growth Strategy Most treat governance like compliance. Smart operators treat it like a moat. When regulators ask questions, you have answers. When clients ask about bias, you have documentation. When competitors scramble, you accelerate. 5. Stop Training Everyone on Everything Blanket AI certification programs waste time. Your marketers don’t need model architecture. Your data scientists don’t need prompt basics. Role-specific enablement > universal training. 6. Centralize Strategy. Decentralize Execution. Full centralization creates bottlenecks. Full decentralization creates chaos. Build AI guilds across business units: -> Shared standards -> Shared learnings -> Local ownership Coordinate without controlling. 7. Measure Organizational Readiness Model accuracy is not scaling success. Track: -> Adoption rates -> Time-to-value -> Cycle time reduction -> Employee confidence -> What should be automated but isn’t Most companies don’t have an AI problem. They have a prioritization and change management problem. 8. Budget for the Costs You Can’t See Visible costs: -> Licenses -> Compute -> Talent Hidden costs: -> Integration complexity -> Data cleanup -> Workflow redesign -> The productivity dip during adoption If you’re budgeting for AI, double your non-technology assumptions. The Bottom Line Scaling AI isn’t a technology challenge. It’s a capital allocation and organizational design challenge. The bank and the culture aren’t competing priorities. They’re the same priority. If you're scaling AI this year, audit one thing this week: Are you investing more in tools or in readiness? That answer predicts your outcome.

Most people are unaware of the magnitude of effort being invested in digitizing Pakistan’s public sector. Over the past few months, I’ve been actively meeting government officials and bureaucrats across different levels of administration — listening to their challenges and witnessing the intricate machinery of how our systems operate. But my perspective has always been… different. 🎮 As an avid PC gamer with countless hours in strategy and city-building games like Cities: Skylines and the Anno series, I’ve simulated urban planning, supply chains, public utilities, citizen satisfaction, trade networks, and policy decisions. And this left me with a question - what if real-world governance could learn from game mechanics and system design where processes are paperless and efficient. That curiosity led me to explore E-Governance — not as a buzzword, but as a transformational force (highly recommend watching Taha Ahad's podcast featuring Sir Aftab Haider on this - I'll drop the link in comment section, it's a great starting point to understand the impact of digitization). 💼 At Pakistan Single Window (PSW), I’ve been fortunate to witness real-world digitization in action (which was my motivation to join PSW by the way). I’ve seen how technology — when applied strategically — can radically streamline composite workflows like trade systems, bureaucratic workflows, and inter-agency collaboration. I see every institution as a system — and every system has inefficiencies that can be optimized. Sounds simple? I used to think so too. But the truth is: digitizing large-scale governance requires robust infrastructure (tremendous amount of compute resources for instance), uncompromised security, cross-ministerial coordination, and national-level scalability. It’s not easy. It’s not fast. But it is happening. Pak Identity, EZFILE by SECP are two of many examples in our context. 💡 Apart from that, I’ve been closely following the E-Government Development Index (EGDI) 2024, where countries like Denmark (Ranked #1 with 0.9847) and Estonia are global benchmarks. Their success is established on: - User-first digital services - Nationwide digital identity systems - Interoperable platforms - Long-term political commitment 🧠 E-Governance is not just for policymakers. It needs: - Technologists who can reimagine legacy systems - Product thinkers who design for real users - Developers who understand policy trade-offs - Curious minds who view bureaucracy not as a burden, but a puzzle worth solving So, if you’re someone who: 1. Wants to solve meaningful, large-scale problems 2. Is tired of inefficiencies in the system 3. Believes Pakistan can leapfrog through smart digital governance Then E-Governance is a field you should explore. It’s one of the most underappreciated, yet high-impact domains of our time. And if you’re already researching, writing, or building in this space — let’s connect and collaborate. #EGovernance #DigitalPakistan #SmartGovernance

Every so often, something lands in your hands that stops you in your tracks. A colleague shared with me the United States Marine Corps AI Adoption Plan, and it’s one of the best operational blueprints for technology transformation I’ve ever read. Forget the military context for a moment. What this document captures is how to deploy change at scale, across any domain; sustainability, finance, data, or AI. Three lessons stood out: Build the backbone before the brilliance. The Marines didn’t start with algorithms. They built a unified AI infrastructure, modular, secure, and flexible enough to evolve. Most businesses still treat data and AI as projects. The real power comes from a common, governed platform that lets innovation move fast without breaking everything else. Train for roles, not titles. Their workforce design splits into users, builders, and leaders, each with tailored pathways. It’s a model every organisation can copy: enable fluency for everyone, depth for specialists, and judgment for decision-makers. Capability scales only when understanding does. Win the culture, not the argument. They invested as much in change management as in code, trust, communication, and psychological safety. AI wasn’t framed as a threat to jobs but as a force multiplier. That’s how you turn adoption into alignment. The “so what” is bigger than AI. When you combine these three, you create a learning organisation, one that compounds decision advantage over time. That is business alpha: The ability to sense, adapt, and execute faster than the environment changes around you. It’s rare that a government document offers such transferable wisdom. This one does. If you work anywhere near transformation, technology, or sustainability, it’s worth your time.

⚠️ 𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗿𝗶𝘀𝗸 𝗶𝗻 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗮𝗱𝗼𝗽𝘁𝗶𝗻𝗴 𝗔𝗜 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝘄𝗵𝗲𝗿𝗲 𝗶𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀. The World Economic Forum's report makes one thing clear: #AgenticAI represents a move from digitizing processes to orchestrating decisions and outcomes end-to-end. 𝗪𝗵𝗲𝗻 𝗴𝗼𝘃𝗲𝗿𝗻𝗺𝗲𝗻𝘁𝘀 𝗴𝗲𝘁 𝗶𝘁 𝗿𝗶𝗴𝗵𝘁, they unlock end-to-end automation across workflows, improving speed, consistency, and citizen experience. 𝗕𝘂𝘁 𝘄𝗵𝗲𝗻 𝘁𝗵𝗲𝘆 𝗴𝗲𝘁 𝗶𝘁 𝘄𝗿𝗼𝗻𝗴, they burn budgets, fragment system and erode public trust. Here are my 𝟵 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁: ✅ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗵𝗲𝗿𝗲 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗶𝘀 𝗵𝗶𝗴𝗵. Functions like #cybersecurity monitoring, document processing, and queue management combine high impact with low complexity, making them ideal entry points. ✅ 𝗣𝘂𝗯𝗹𝗶𝗰 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗴𝗼𝗹𝗱𝗺𝗶𝗻𝗲. Two-thirds of citizen-facing functions sit in high-readiness zones, offering immediate improvements in experience, efficiency, and scale. ✅ 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻-𝗱𝗿𝗶𝘃𝗲𝗻 𝘄𝗼𝗿𝗸 𝗳𝗶𝗿𝘀𝘁. Monitoring, reporting, and validation tasks are easier to automate than judgement-heavy decisions like eligibility or policy design. ✅ 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻-𝗹𝗲𝘃𝗲𝗹 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗶𝘀 𝗮 𝗴𝗮𝗺𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝗿. The framework maps 70 government functions across potential vs complexity, shifting strategy from org charts to workflows. ⚠️ 𝗛𝗶𝗴𝗵 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗺𝗲𝗮𝗻 𝗮𝗰𝘁 𝗻𝗼𝘄. #Policy planning, crisis response, and regulatory decisions remain low-readiness due to complexity, #ethics, and data fragmentation. ⚠️ 𝗗𝗮𝘁𝗮 𝗶𝘀 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸. Without structured, accessible, and interoperable data, agentic AI cannot scale, no matter how advanced the models are. ⚠️ 𝗖𝗿𝗼𝘀𝘀-𝗮𝗴𝗲𝗻𝗰𝘆 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗵𝗶𝗱𝗱𝗲𝗻 𝘁𝗮𝘅. Multi-agency workflows slow adoption due to fragmented ownership and integration challenges but hold long-term compounding value. ⚠️ 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀𝗻’𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹. Explainability, auditability and human oversight are non-negotiable in public-sector #AI deployment. ⚠️ 𝗦𝗲𝗾𝘂𝗲𝗻𝗰𝗶𝗻𝗴 𝗯𝗲𝗮𝘁𝘀 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻. #Governments that start small, run pilots, and scale gradually outperform those chasing complex, high-stakes transformations too early. 𝗠𝘆 𝗳𝗶𝗻𝗮𝗹 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Agentic AI is not a #technology rollout. It’s a prioritization problem disguised as innovation. The winners won’t be those who deploy the most agents but those who choose the right functions, in the right order, with the right guardrails. 👉 If you had to choose just 3 government functions to #automate first, what would they be and why? 🔔 Follow for crisp perspectives on AI, governance and building systems. #AI #GovTech #Policy #PublicSector #Leadership #ResponsibleAI

Some of my earliest lessons in tech adoption came from my first engagement at the The World Bank.  Back then, “AI for development” wasn’t fashionable. Data science in public policy sounded futuristic. And yet, the challenge we were trying to solve feels just as relevant today.  Our team – the Science of Delivery – had one simple mission: support development practitioners from repeating the same mistakes again and again. The Bank had thousands of completed projects, but the wisdom from them kept getting lost.  We built DeCODE, a machine learning tool that reads thousands of project reports to predict where new projects might fail.  The hardest part? Not tech but adoption. Convincing people that an algorithm could extract insight from dense policy documents felt radical then.  What feels like common sense today still gets ignored in so many GovTech rollouts. Here are three lessons that stuck with me:  First, integration is non-negotiable. If your tool sits outside daily workflows, it won’t survive.  Second, build to augment, not replace. Tech that threatens expertise gets resisted; tech that enhances it gets embraced.  Third, adoption is emotional, not technical. People don’t use tools they don’t trust – no matter how “smart” the system is.  We built DeCODE to help projects learn from past mistakes. Somewhat poetically, it left us with lessons of what we should avoid in GovTech projects.   #GovTech #AIforDevelopment #DigitalTransformation #PublicPolicy