AI In Professional Roles

I asked the smartest people I know about AI... I’ve been reading everything I can get my hands on. Talking to AI founders, skeptics, operators, and dreamers. And having some very real conversations with people who’ve looked me in the eye and said: “This isn’t just a tool shift. It’s a leadership reckoning.” Oh boy. Another one eh? Alright. I get it. My job isn’t just to understand disruption. It’s to humanize it. Translate it. And make sure my teams are ready to grow through it and not get left behind. So I asked one of my most fav CEOs, turned investor - a sharp, no-BS mentor what he would do if he were running a company today. He didn’t flinch. He gave me a crisp, practical, people-centered roadmap. “Here’s how I’d lead AI transformation. Not someday. Now.” I’ve taken his words, built on them, and I’m sharing my approach here, not as a finished product, but as a living, evolving plan I’m adopting and sharing openly to refine with others. This plan I believe builds capability, confidence, and real business value: 1A. Educate the Top. Relentlessly. Every senior leader must go through an intensive AI bootcamp. No one gets to opt out. We can’t lead what we don’t understand. 1B. Catalog the problems worth solving. While leaders are learning, our best thinkers start documenting real challenges across the business. No shiny object chasing, just a working list of problems we need better answers for. 2. Find the right use cases. Map AI tools to real problems. Look for ways to increase efficiency, unlock growth, or reduce cost. And most importantly: communicate with optimism. AI isn’t replacing people, it’s teammate technology. Say that. Show that. 3. Build an AI Helpdesk. Recruit internal power users and curious learners to be your “AI Coaches.” Not just IT support - change agents. Make it peer-led and momentum-driven. 4. Choose projects with intention. We need quick wins to build energy and belief. But you need bigger bets that push the org forward. Balance short-term sprints with long-term missions. 5. Vet your tools like strategic hires. The AI landscape is noisy. Don’t just chase features. Choose partners who will evolve with you. Look for flexibility, reliability, and strong values alignment. 6. Build the ethics framework early. AI must come with governance. Be transparent. Be intentional. Put people at the center of every decision. 7. Reward experimentation. This is the messy middle. People will break things. Celebrate the ones who try. Make failing forward part of your culture DNA. 8. Scale with purpose. Don’t just track usage. Track value. Where are you saving time? Where is productivity up? Where is human potential being unlocked? This is not another one-and-done checklist. Its my AI compass. Because AI transformation isn’t just about tech adoption. It’s about trust, learning, transparency, and bringing your people with you. Help me make this plan better? What else should I be thinking about?

McKinsey has 40,000 employees and 25,000 AI agents. Now it is adjusting remuneration to AI. An entire industry is being disrupted by AI. And it is not the only one. Less than 2 years ago McKinsey had just 3,000 AI agents. Its CEO originally expected to reach one AI agent per employee by 2030. Now it might be months away. 𝗕𝘂𝘁 𝘄𝗵𝗮𝘁 𝗱𝗼 𝗮𝗴𝗲𝗻𝘁𝘀 𝗱𝗼 𝗶𝗻 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗶𝗻𝗴? • Consulting is full of work that is structured, repeatable, research-heavy, and analysis-driven. Exactly the type AI can replace. • Agents can help consultants search internal knowledge, summarize documents, compare markets, draft first versions, structure analyses, test hypotheses, build models, prepare client materials, and accelerate the kind of linear problem-solving that used to consume large amounts of junior consultant time. This does not mean McKinsey no longer needs consultants. It means consulting is changing. If AI can produce the first draft, the benchmark, the synthesis, the model, or the analysis, humans have to become better at the parts AI cannot reliably do: • setting the right ambition • applying judgment • challenging answers • managing the client • connecting politics with strategy • turning analysis into decisions This is much bigger than automation. Consulting firms are now redesigning the economics of consulting around a new execution layer. 𝗟𝗲𝘁’𝘀 𝘁𝗮𝗸𝗲 𝗼𝗻𝗲 𝘀𝘁𝗲𝗽 𝗯𝗮𝗰𝗸. For decades, the consulting model was built around senior partners selling the work, large teams delivering it, and clients paying for expertise, time, and execution capacity. If now AI agents are doing an increasing part of this work, clients will ask why they should pay the same way for work that now takes less human effort. That means consulting firms need to adjust their business model: from selling hours and advice to selling outcomes. Savings, cost reduction, productivity improvement, revenue increase, real transformation. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗠𝗰𝗞𝗶𝗻𝘀𝗲𝘆 𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗻𝗼𝘄: Partners will receive a smaller share of profits in cash and a larger share in equity. In practice, part of the money that would have been paid out immediately stays inside the firm. 𝗪𝗵𝘆? • Because consulting cash flows may become more volatile. If more projects are tied to savings or performance improvements, the firm may only get fully paid once the client actually delivers the result. • McKinsey needs more capital inside the business: to absorb delayed payments, take more outcome risk, and invest in the technology needed to deliver work differently. Consulting companies are adopting 𝗼𝘂𝘁𝗰𝗼𝗺𝗲-𝗯𝗮𝘀𝗲𝗱 𝗽𝗿𝗶𝗰𝗶𝗻𝗴. Any industry built on expensive expert work, repeatable analysis, and billable hours will face the same pressure: to move from selling activity to selling outcomes. Opinions: my own, Graphic source: CB Insights Subscribe to my newsletter: https://lnkd.in/dkqhnxdg

It’s easy to think of AI as a time-saver that streamlines workflows and accelerates output. But the deeper opportunity lies in how it’s reshaping the nature of work itself. A new study from Harvard Business School’s Manuel Hoffmann followed more than 50,000 developers over two years, with half using GitHub Copilot. The results were striking: developers shifted away from project management and toward the core work of coding. Not because someone told them to, but because AI made it possible. With less need for coordination, people worked more autonomously. And with time saved, they reinvested in exploration—learning, experimenting, trying new things. What we’re seeing here isn’t just productivity. It’s a shift in how work gets done and who does what. Managers may spend less time supervising and more time contributing directly. Teams become flatter. Hierarchies adapt. This is just one signal of how generative AI is changing our org charts and challenging us to rethink how we structure, support, and lead our teams. The future of work isn’t just faster. It’s more fluid. And if we get this right, it’s a whole lot more human. https://lnkd.in/gaUgXnRY

Everyone is using AI for job search. That’s exactly why most people are not standing out. Over the last few months, I’ve seen the same pattern again and again. AI-generated resumes. AI-written cover letters. AI-crafted messages. Polished. Structured. Correct. And completely forgettable. Because when everyone uses AI the same way, it stops being an advantage. It becomes the baseline. The real question isn’t whether you use AI. 𝗜𝘁’𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝘁. Here’s what actually works: 📍𝗗𝗼𝗻’𝘁 𝗹𝗲𝘁 𝗔𝗜 𝘄𝗿𝗶𝘁𝗲 𝘆𝗼𝘂𝗿 𝘀𝘁𝗼𝗿𝘆, 𝘂𝘀𝗲 𝗶𝘁 𝘁𝗼 𝘀𝗵𝗮𝗿𝗽𝗲𝗻 𝗶𝘁 Most people paste their resume and say, “Make it better.” Instead, do this: Ask AI to challenge your bullets. “Is this outcome clear?” “What impact is missing here?” “How can this be made more specific?” Use AI as an editor, not a ghostwriter. 📍𝗧𝘂𝗿𝗻 𝗴𝗲𝗻𝗲𝗿𝗶𝗰 𝗿𝗲𝘀𝘂𝗺𝗲𝘀 𝗶𝗻𝘁𝗼 𝗿𝗼𝗹𝗲-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗻𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲𝘀 One resume won’t work anymore. Paste the job description and ask: “Map my experience to this role’s expectations.” “Where am I weak?” “What should I emphasize?” Then manually refine. AI gives direction. You add judgment. 📍𝗨𝘀𝗲 𝗔𝗜 𝘁𝗼 𝗽𝗿𝗲𝗽𝗮𝗿𝗲, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗽𝗽𝗹𝘆 Most candidates stop at applications. Use AI to simulate: • Interview questions based on the JD • Case-style prompts • “What would a hiring manager probe here?” This is where AI actually compounds. 📍𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝘆𝗼𝘂𝗿 𝗼𝘂𝘁𝗿𝗲𝗮𝗰𝗵 𝗺𝗲𝘀𝘀𝗮𝗴𝗲𝘀 Instead of: “Write a cold message for this role” Try: “Make this message sound more thoughtful and specific to this person’s background.” Then personalize it yourself. Because people don’t respond to perfect messages. They respond to relevant ones. 📍𝗔𝘀𝗸 𝗔𝗜 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂’𝗿𝗲 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 (𝘁𝗵𝗶𝘀 𝗶𝘀 𝘂𝗻𝗱𝗲𝗿𝗿𝗮𝘁𝗲𝗱) Most people ask AI to improve what they have. Few ask: “What am I not seeing?” Try: “Based on this profile, why would I get rejected?” “What concerns would a recruiter have?” This is where real insight comes from. The biggest mistake right now is using AI to look like everyone else. The smartest candidates are using AI to think better, prepare deeper, and communicate clearer. AI won’t replace effort in job search. But it will expose shallow effort faster than ever. Are you using AI to apply faster, or to get better? #JobSearch #ArtificialIntelligence #CareerGrowth #ResumeTips #InterviewPrep #LinkedInTips #ProfessionalDevelopment

Today's L&D is more than just content. Or at least it should be. When we think about AI in L&D, we often think about AI in learning design. Yet, to meet the needs of the business, L&D leaders need to orchestrate design, data, decisions and dialogue- incidentally, these are all things that AI can help with. In 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐝𝐞𝐬𝐢𝐠𝐧, we already extensively use AI not just for content production, but also for user research, as a sparring partner and a sounding board (that was one of the top write-in use cases in mine and Donald's AI in L&D survey last year). In 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲, AI can help make sense of business, people and skills data (featured use case: asking AI to find gaps in learning or performance support provision in your organisation), or work as a thought partner to help you bridge learning and business strategy. Crucially, it can also help you engage stakeholders by preparing you for conversations and tailoring your communications to different audiences. In terms of 𝐩𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐬𝐞𝐝 𝐬𝐮𝐩𝐩𝐨𝐫𝐭, AI interacts directly with employees to help them do their jobs: practise tricky conversations through role-plays and personalised feedback, prioritise and contextualise learning content to their needs, and, lately, retrieve exactly the information they need from almost anywhere in the company’s knowledge base. Finally, in 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬, AI can help do more than just draft emails and reports. Working together with humans, AI can help select the right vendors for the learning ecosystem, streamline employee help desk operations, analyse, make sense of and action on different kinds of data generated in L&D, and, of course, help L&D communicate with the rest of the business. Researcher, producer, thought partner, communicator — if your organisation only uses AI to write scripts, you’re leaving three quarters of the L&D value chain on the table. I like a good table, and I hope this one will help you think about how to get more value out of your AI use. --- P.S. I spent quite a lot of time arguing with myself about the dots on the table. Feel free to disagree and suggest AI roles or use cases that I have missed! Nodes #GenAI #Learning #Talent #FutureOfWork #AIAdoption

Over the last few years, we’ve seen the rise of distinct AI roles: Some focus on building models. Some specialize in prompting them. Some orchestrate entire multi-agent ecosystems. But here’s the challenge: Most people dive into AI without a clear path. They juggle multiple tutorials, frameworks, and buzzwords — without direction. And often feel stuck… despite all the learning. That’s why I created this visual roadmap to demystify what it actually takes to build a successful career in AI—whether you’re starting out, switching domains, or upskilling. 𝟰 𝗥𝗼𝗮𝗱𝗺𝗮𝗽𝘀. 𝟰 𝗖𝗮𝗿𝗲𝗲𝗿 𝗣𝗮𝘁𝗵𝘀. 𝟭 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗩𝗶𝘀𝗶𝗼𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 Master LangChain, LangGraph, AutoGen, CrewAI Design decision-making agents with memory, context, and orchestration Build truly autonomous multi-agent systems that reason, act, and collaborate 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 Learn the foundations of GenAI: transformers, LLMs, embeddings Build applications using OpenAI, Hugging Face, Cohere, and Anthropic Fine-tune models, use vector databases (RAG), and bring GenAI apps to life 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 Go deep into math, stats, algorithms, feature engineering, and modeling Master Python, Scikit-Learn, XGBoost, and model deployment Build solid ML portfolios that showcase real-world impact 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 (𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗔𝗜) Cover it all: computer vision, NLP, reinforcement learning, AI ethics, model governance Use TensorFlow, PyTorch, and integrate AI into products end-to-end Prepares you for both research-driven and production-focused roles What’s unique about this roadmap? Clear step-by-step milestones Specific tooling and frameworks to focus on Career-aligned structure based on real job roles End-to-end guidance from fundamentals to job search Who is this for? College students entering AI Professionals switching to ML or GenAI roles Engineers looking for clarity in a noisy landscape AI educators mentoring the next wave of practitioners Startups guiding their technical talent in AI-first environments This is the kind of map I wish I had when I started. If this helps you or someone in your network: Repost it to reach more learners

I’ve spent the last 3 months developing curriculum for an AI in finance course. Here’s what I’ve learned on this road of discovery. 1) We are in the very early stages of being able to utilize AI confidently and reliably for finance. 2) While I’m currently underwhelmed by the true integration of AI with financial applications, it’s getting better every month. The speed at which AI is improving is astounding. 3) I question anyone who believes they’re an expert in AI, especially for finance. There are just too many different applications of technology and not universal best practices to follow. There are also so many FP&A tools that incorporate AI and it’s impossible to master them all. 4) Copilot will change the game entirely since it’ll be on every PC running Windows or M365. Though still not a fully-mature add-in, it will eventually be the go-to AI tool within most Microsoft applications. 5) The integration of files, PDFs, and code with AI offers some of the most promising applications for finance professionals. Finance professionals will be able to upload their data, models, and reports and have AI augment them in seconds. 6) I speculate that chat bots will eliminate hundreds of thousands of clerical finance jobs. Basic and routine administrative duties should be replaceable with the technology. This is especially true for account reconciliations, accounts payable and receivable follow up. 7) My boldest prediction is that investment banks will be able to eliminate 80% of their analysts and associates within the next 5-10 years, as AI will be able to build robust financial models. Managing and interpreting them may still be left to humans but DCFs, valuation models, and pitch decks should largely be done by AI. 8) The best way to sharpen the saw is to experiment and push the limits of what the technology can do. Education isn’t finite and we can never reach the finish line. If we fail to proactively disrupt finance roles with AI, I guarantee that it will disrupt roles against our will.

🧯Moltbot and Codex are great but McKinsey just dropped a stat that should terrify every CEO 80% of companies now “use AI.” ~1% are doing a great job. That explains why every flashy AI announcement sounds big… and ships nothing. Most didn’t integrate AI. They outsourced curiosity: bought licenses, ran pilots, called it innovation. The few that did transform look different: --They rewired workflows around models (not slidedecks). --They retrained people to think in tasks, prompts, and guardrails. --They put AI governance next to the CFO, not buried in IT. Brutal signals: --Only 21% redesigned core workflows for gen-AI. --Just 28% have the CEO personally leading governance. --Those who did are already reporting higher EBIT from gen-AI. (Winners also build dedicated scaling teams, run rolebased training, and embed AI into processes, not side projects.) Most orgs are still “experimenting” = burning budget on POCs with no system design, no feedback loops, no KPIs. I was the opening keynote at the Take Off Istanbul summit talking about "What the 1% do": Own the work, not the model: Map the top 10 workflows by cost/time; redesign them end-to-end with AI. Make it measurable: Set first-try-right, handle time, and rework% as north-star KPIs. Stand up a PMO for AI: Product owners, ops, risk, finance, weekly reviews, promotion gates, real ROI. Train for behavior, not features: Prompt patterns, decision checklists, escalation rules. Govern where money lives: CFO + Legal + CISO signoff on data, evals, and rollbacks. AI ROI doesn’t come from the model. It comes from the org willing to break and rebuild itself to use the model. ---------- I was very impressed by the the vibrant startup community in Türkiye and Central Asia. Great job Türkiye Teknoloji Takımı Vakfı.

As investment professionals, we operate in a world defined by complexity, speed, and transformation. From artificial intelligence to the growth in private markets and the evolving expectations of clients, the pace of change is not slowing. That’s why the conversation around #SkillsOnTheRise is so important. If I had to highlight three skills that will define success in our profession going forward, they would be: 1️⃣ AI fluency paired with human intelligence, or, stated as an equation, AI+HI Artificial intelligence is already reshaping research, portfolio construction, risk management, and client engagement. But tools alone are not a differentiator. The real advantage lies in understanding how to apply AI responsibly, interpret its outputs critically, use its capabilities to complement the human aspect of what we do. Our profession is built on judgment, context, accountability, and trust. AI can enhance efficiency and surface insights. It cannot replace fiduciary responsibility, ethical reasoning, or the relationships we build with clients.   2️⃣ Soft skills that build trust and influence     In volatile environments, technical expertise is not enough. Investment professionals must communicate complex ideas clearly and guide clients through uncertainty. Clients are seeking more than numbers, they want perspective.  The ability to translate analysis into insights, navigate difficult conversations, and build lasting relationships is what distinguishes a trusted advisor from a transactional provider. As technology advances, the human capacity to influence, explain, and inspire becomes even more valuable. These soft skills are what will help establish an individuals’ career over the long term.    3️⃣ Adaptability and lifelong learning Careers in finance are no longer linear. Professionals will skill, reskill, and upskill multiple times throughout their working lives. The ability to continuously learn and embrace new perspectives will separate those who remain relevant from those who fall behind. Over the course of my career, and now leading CFA Institute, I have seen this repeatedly. The tools evolve. The markets evolve. Client expectations evolve. But disciplined analysis and a commitment to learning remain constant. The professionals who thrive will be those who pair new capabilities with enduring fundamentals. What skill, rising or foundational, has made the greatest difference in your career? #SkillsOnTheRise 

Will the Cloud Era Winners Win the AI Era as Well? Over the last decade, Western tech services firms dominated the enterprise cloud transformation wave. Firms like Accenture, Deloitte, and Capgemini had boardroom access and shaped cloud strategy, while Indian tech services companies largely won migration and managed services programs. The pattern was clear: Western firms led strategy; Indian firms led execution. This asymmetry was also visible in hyperscaler relationships—partner teams managing Western firms were significantly larger than those managing Indian providers, reflecting where influence truly sat. A key driver of this divergence was the Client Partner persona. #WesternTechServicesClientPartners were consultative, influential and domain-led. They were able to shape the narrative in board rooms. #IndianTechServicesClientPartners were personable, flexible, technology and delivery led, and highly effective at navigating ambiguity during execution. This distinction shaped the kinds of work each group won. AI changes the rules. Unlike cloud, AI transformation spans the entire technology stack—data, security, governance, and applications—and business value is often discovered during execution. As a result, the services multiple in AI (5–8x) is expected to exceed that of cloud (3–5x). Winning in the AI era will depend on the evolution of the Client Partner. This role now demands a blend of business credibility and technical leadership. Indian firms, in particular, must overcome internal resistance to lateral hires from clients and western peers and also aggressively retrain current Client Partners to bridge this gap. AI has created a window of opportunity for Indian tech services firms to gain not just wallet share, but real influence. Most than technology and IP, the winners will be decided based on the effectiveness of the client partners. Zinnov #Techservices #AITransformation