Navigating AI Transformation

𝗔𝗜 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝘀 𝗱𝗼𝗻'𝘁 𝗳𝗮𝗶𝗹 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. ⬇️ They die in the org chart. The pattern is always the same. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗳𝗼𝘂𝗿 𝗞𝗶𝗹𝗹𝗲𝗿𝘀 𝗼𝗳 𝗔𝗜 𝗥𝗢𝗜: ⬇️ ➜ 𝗙𝗿𝗮𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽: CAIO, CTO, CIO, COO - everyone has a stake, nobody has ACCOUNTABILITY. AI becomes a political football, not a business capability. ➜ 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗼𝗹𝗹𝗼𝘄𝘀 𝗦𝗽𝗲𝗻𝗱: Licenses get bought. Pilots get launched. Then, months later, someone asks: "Wait, what precise, measurable problem are we solving?" There is no clear strategy in place - neither for data nor for AI - and it remains unclear which problems are actually meant to be solved. ➜ 𝗗𝗮𝘁𝗮 𝗕𝗹𝗶𝗻𝗱𝗻𝗲𝘀𝘀: Every GenAI use case hits the same quality, access, and governance wall. The people who know how to fix the data are often the last ones invited to the strategy room. ➜ 𝗦𝗵𝗮𝗱𝗼𝘄 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻: The most valuable, successful AI work is often a solo side project in an Excel file. No sponsorship. No budget. No scale path. 𝗧𝗵𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘀𝘂𝗰𝗰𝗲𝗲𝗱𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗚𝗲𝗻𝗔𝗜 𝗮𝗿𝗲 𝗡𝗢𝗧 𝘂𝘀𝗶𝗻𝗴 "𝗯𝗲𝘁𝘁𝗲𝗿 𝗼𝗱𝗲𝗿 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀". 𝗧𝗵𝗲𝘆 𝗮𝗿𝗲 𝘀𝗶𝗺𝗽𝗹𝘆 𝗳𝗶𝘅𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹: ⬇️ ✅ 𝗖𝗹𝗲𝗮𝗿 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽: Single point of authority with budget and mandate. ✅ 𝗘𝗺𝗯𝗲𝗱𝗱𝗲𝗱 (𝗗𝗮𝘁𝗮)-𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: Not an afterthought, but the design principle from Day 1. ✅ 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝗙𝗶𝗿𝘀𝘁 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: AI initiatives tied directly to measurable revenue, cost, or risk metrics. ✅ 𝗣𝗿𝗼𝗽𝗲𝗿 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗶𝗻𝗴: Execution teams treated as mission-critical, not a side-hustle. AI maturity isn't about technology. It's about organizational readiness. If this org chart looks familiar, the problem isn't your AI strategy. It's probably your operating model. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗼𝗳 𝘄𝗵𝗮𝘁’𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜, 𝘆𝗼𝘂’𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

Cloud Native technologies have long been at the heart of scalable applications. But now, with AI and Agentic Systems, the game is changing!   Unlike traditional AI automation, Agentic AI can make decisions, execute workflows, and adapt dynamically to system changes—without constant human oversight. This means self-healing, self-optimizing, and autonomous cloud-native infrastructure!  Here’s how Agentic AI can transform each layer of Cloud Native skills:  1. Linux & AI-Optimized OS   - AI-powered package managers automatically resolve compatibility issues.   - Agentic AI monitors system logs, predicts failures, and patches vulnerabilities autonomously.  2. Networking & AI-Driven Observability   - AI-driven network forensics using self-learning algorithms to detect anomalies.   - Agent-based routing optimizations, ensuring seamless traffic flow even in congestion.  3. Cloud Services & AI-Augmented Workflows   - Agentic AI predicts cloud workload demand and pre-allocates resources in AWS, Azure, and GCP.   - Autonomous cost optimization adjusts instance types, storage, and compute in real time.  4. Security & AI Cyberdefense Agents   - Self-learning AI security agents actively detect and mitigate cyber threats before they happen.   - Generative AI-powered penetration testing agents simulate evolving attack patterns.  5. Containers & Agentic AI Orchestration   - Autonomous Kubernetes controllers scale clusters before demand spikes.   - Agentic AI continuously optimizes pod scheduling, reducing cold starts and resource waste.  6. Infrastructure as Code + AI Copilots   - AI-driven infrastructure agents automatically refactor Terraform, Ansible, and Puppet scripts.   - Self-adaptive IaC, where AI updates configurations based on usage patterns and compliance policies.  7. Observability & AI-Driven Incident Response   - AI-powered anomaly detection in Grafana & Prometheus—flagging issues before failures.   - Agentic AI handles incident response, running diagnostics and executing pre-approved fixes.  8. CI/CD & Autonomous Pipelines   - Agentic AI writes, tests, and deploys code autonomously, reducing developer toil.   - Self-optimizing pipelines that rerun failed tests, debug, and retry deployment automatically.  The Future: Fully Autonomous Cloud Native Systems!  𝗗𝗲𝘃𝗢𝗽𝘀 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 → 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗰𝗹𝗼𝘂𝗱 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. The result? Zero-touch, self-managing environments where AI agents handle failures, optimize costs, and secure systems in real time.  𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗲𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗰𝗹𝗼𝘂𝗱 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘆𝗼𝘂’𝘃𝗲 𝘀𝗲𝗲𝗻 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆?

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

Meta just hit Command + Zuck on its AI strategy - shredding the open-source playbook and replacing it with one that reads: Compute. Talent. Secrecy. The vibe is no longer “open source for all.” It’s “closed doors, infinite compute, elite team, existential stakes.” Let's break it down: (1) Compute: Zuck’s Manhattan Project Meta is building gigascale AI clusters. Prometheus comes online with 1 GW in 2026; Hyperion scales to 5 GW soon after. For context, Iceland’s total electricity consumption is ~2.4 GW, Cambodia is at ~4 GW. Meta’s Hyperion cluster alone could out-consume entire nations. These clusters are for training frontier models - GPT-4-class and beyond. In this new regime, FLOPS per researcher is the KPI, and Meta is going from GPU-starved to GPU-dripping. Each researcher now has more compute to play with than entire labs elsewhere. That’s not just good for performance, it's a hell of a recruiting pitch. (2) Secrecy: From Open Arms to Closed Labs Meta won developer love by open-sourcing its LLaMA models. But it also accidentally became the free R&D department for its own competitors. DeepSeek AI, for example, built on Meta's models and vaulted ahead. Now Meta is reportedly shelving its most powerful open model, Behemoth, due to both internal underperformance and external regret and shifting toward a closed frontier model, aligning more with OpenAI and Google. This is a massive philosophical reversal from “open wins” (as Yann LeCun would say) to “closed dominates.” (3) Talent: Just Buy Everyone Comp packages reportedly range from $200 million to $1 billion for AI leads. All AI efforts are now housed under a new unit, Superintelligence Labs, run by Alexandr Wang (ex-Scale AI). This elite team is small, only ~12 engineers, working in a separate, high-security building next to Zuckerberg himself. Forget beanbags and 10xers. This is a DARPA-style moonshot with a trillion-dollar company behind it. Zuckerberg has said, basically, “Look, we make a lot of money. We don’t need to ask anyone’s permission to spend it.” He’s not wrong. While OpenAI, Anthropic, and xAI rely on outside capital to fund their ambitions, Meta runs on a $165B/year ad engine. And unlike Google and Microsoft - who have boards, activist investors, and share classes that allow for dissent - Zuckerberg controls Meta, structurally and operationally. Meta’s unique dual-class share structure gives Zuckerberg over 50% of the voting power, even though he owns less than 15% of the company. He doesn’t need anyone’s approval, he can build whatever he wants. This makes Meta less like a public company and more like a founder-led sovereign AI lab - with Big Tech cash and startup flexibility. That governance structure is a strategic weapon, letting them place bold, long-term bets at breathtaking speed. Meta’s open-source era is over. This is the closed, compute-soaked, capital-fueled empire play. Less GitHub, more Los Alamos.

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?

One thing is clear in Accenture’s latest report on building an AI‑ready cloud foundation; organizations aren’t just modernizing their tech stacks; they’re redefining how they create value.     What we’re seeing now is a shift from cloud as an efficiency play to cloud as the backbone of continuous reinvention. AI is accelerating that shift, but AI can only deliver its full potential when the underlying architecture is ready for it.      The companies pulling ahead are the ones treating cloud, data, and AI as one integrated system, not separate investments. They’re simplifying core operations, creating flexible digital foundations, and empowering their people with the skills and tools to move with speed and confidence.     This isn’t about chasing every new technology. It’s about building the resilience and adaptability to keep reinventing, again and again as the environment changes. At its core, an AI‑ready cloud foundation is about preparing the enterprise for what’s next, not just optimizing for today. The leaders who understand this will set the pace for their industries.    https://lnkd.in/gvrX4rqp    Andy Tay, Lan Guan, Jason Dess Jefferson Wang, Shalabh Kumar Singh 

You automate one task… then suddenly realize your entire workflow could run without you. That’s the real shift happening right now. Businesses don’t scale because they hire more people. They scale because they move from manual steps → multi-step workflows → resilient automations → agentic operations. Here’s a clear breakdown of the 4 levels of AI automation maturity, and what changes at each stage: 𝗟𝗲𝘃𝗲𝗹 𝟭 - 𝗦𝗶𝗻𝗴𝗹𝗲 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 Where automation starts: one repetitive task replaced. You automate a basic flow like sending an email after a form submission. You still monitor errors manually because breakages aren’t handled automatically. 𝗟𝗲𝘃𝗲𝗹 𝟮 - 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗲𝗽 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 Your workflows start connecting across tools. One trigger runs multiple connected steps - routing, syncing, enrichment, CRM updates. This is where onboarding flows, reporting loops, and follow-ups become structured and predictable. 𝗟𝗲𝘃𝗲𝗹 𝟯 - 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 & 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 Automation gets smarter, and reliable. Your workflows handle edge cases automatically using conditions, validation rules, retries, and alerting. Human approvals appear only when needed, not for every step. 𝗟𝗲𝘃𝗲𝗹 𝟰 - 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻𝘀 Automation becomes its own operating system. Agents make decisions, choose tools dynamically, adapt workflows, remember past outcomes, and continuously improve results. They self-heal, optimize, and explain their reasoning - turning automation into autonomous operations. Companies think they want “AI agents.” What they actually need first is a path that moves them through Levels 1 → 4 sustainably. Automation isn’t one tool. It’s a maturity curve. #AI

Here’s the line no one wants to say out loud, especially in consulting and analytics circles: AI is already better at collecting and summarising data than most analysts. Not opinion, just fact. AI scrapes reports, processes datasets, and outputs coherent summaries in seconds. Which means the future won’t belong to analysts who produce information, it will belong to analysts who produce insight. 𝗗𝗮𝘁𝗮 𝗚𝗮𝘁𝗵𝗲𝗿𝗲𝗿𝘀 𝘃𝘀. 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝗧𝗵𝗶𝗻𝗸𝗲𝗿𝘀 Data gatherers: Pull facts, Organise tables, Summarise trends AI does that faster. Pattern thinkers: Spot discontinuities, Connect dots others miss, Predict what happens next AI can generate outputs, humans must interpret implications. 𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝘀𝗲𝗿𝘀 𝘃𝘀. 𝗦𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘇𝗲𝗿𝘀 AI summarises beautifully. It compresses, It rephrases, It repackages. But synthesis? That’s different. Good synthesis answers: 👉 “So what does this mean for our business?” 👉 “Where will this break first?” 👉 “What decision does this enable?” Summaries inform. Synthesis influences. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝘃𝘀. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗙𝗿𝗮𝗺𝗲𝗿𝘀 Everyone’s learning to write prompts. Few are learning to define problems. This matters because: AI answers the question you ask, not the question you should have asked. Problem framers don’t just seek answers, they formulate the right questions. That’s the rare skill companies will pay for. 📍𝐇𝐞𝐫𝐞’𝐬 𝐭𝐡𝐞 𝐁𝐢𝐠 𝐒𝐡𝐢𝐟𝐭 𝐘𝐨𝐮’𝐥𝐥 𝐒𝐞𝐞 𝐓𝐡𝐢𝐬 𝐘𝐞𝐚𝐫 The analysts who thrive post-AI will be the ones who: ✔ ask better questions ✔ think multiple moves ahead ✔ integrate context, human behaviour, politics, incentives ✔ challenge assumptions before reporting data AI won’t replace analysts who think. It will replace analysts who don’t. Here’s what I want to know: In your experience, what’s one thinking skill that AI can’t replicate, but makes all the difference in analysis and strategy? 👇 Drop it below. #AI #Analytics #Consulting #FutureOfWork #Strategy #DataScience #Leadership #DecisionMaking

The recent MIT report that 95% of generative AI pilots are failing to deliver ROI set off alarm bells across the tech industry. At Upwork, we were not surprised to see this data. We hear from our customers every day how they struggle to capture value from AI, and increasingly rely on skilled talent on our platform to solve this. We’ve seen what separates the companies making AI work from those that stall out. The winners? They stop treating AI like a one-time experiment and embrace it as a new way of working, requiring new approaches and new talent models to support it. Two tips I’d offer any company that has a stalled-out AI implementation: 1) Don’t design a new system just to accommodate AI. It has to work for humans too. AI technology is incredible, but too often, it isn’t enough on its own, and doesn’t fill a “worker-sized” shape in an organization. Often it can do both more and much less than your typical colleague. Our clients—from the Fortune 100 to fast-moving startups—are finding the most success when they rethink both processes and talent to incorporate the right combo of humans (who bring vision, problem-solving, curiosity, judgement and taste) working together with AI tools. That’s where the ROI lives. 2) Refresh your talent strategy to meet the AI moment. The talent you need to get an AI experiment or prototype off the ground might not be the same talent you need to scale, maintain or tune the approach over time. We see successful companies rethinking the workforce they need to capture AI value. Deploying contingent workers and teams, including freelancers, gives them faster learnings and makes an immediate impact on workflows. They can train and upskill FTEs as part of implementation, before rotating to the next project or the next company. Approximately half of businesses (49%) are turning to freelancers on platforms like Upwork to address critical skill gaps, because this talent is more highly skilled in emergent technologies like AI. This MIT report is a necessary reminder that AI implementations don’t just happen overnight—after all, this is science, not magic. The companies that invest in adapting their workflows and talent models to meet this moment are already leading the next era of innovation. I’d love to hear your thoughts on the study and what techniques are enabling you to capture AI ROI in the comments. #AI #FutureOfWork #DigitalTransformation

I've been thinking a lot about talent and AI. The recent headlines have been dominated by conversations on 'unicorn technical hiring' — $100 million+ pay packages for the world’s top AI thinkers. It's fun to watch, but let’s be honest: it’s not the practical reality for most companies. What most organizations actually need is far harder — building bilingual teams. Teams that combine deep technology capabilities with sharp business acumen. Because AI tools, on their own, don’t create value. They only do so when they’re applied to fundamentally change a business. We're approaching a reality most leaders aren't prepared for: managing companies where humans, AI agents, and automated systems work as integrated teams. The universities training today’s analysts aren’t teaching them how to collaborate with AI. The executive development programs aren’t covering how to lead hybrid human-AI operations. Industry is being forced to build these capabilities from scratch. This goes beyond retraining employees on new tools. It requires rethinking talent strategy for a world where value creation happens through human-AI collaboration. The most progressive CEOs I’m working with are asking the right questions: How do we recruit for AI-augmented roles that don’t exist yet? How do we develop managers who can optimize both human and artificial intelligence? How do we create career paths in a world where many traditional roles are being redefined? And these questions can’t be delegated to HR. They require the same level of C-suite attention as any major business transformation. This is the new leadership challenge: assembling organizations where technologists and business leaders work side by side, not in silos. The magic is in how you get them to collaborate — fluently. It's what we are doing every day inside of Bain. It’s also the premise behind our recent partnership with Andrew Ng and AI Aspire. He brings world-class technical expertise. We bring business transformation at scale. That’s the kind of thinking companies need to build into their own talent models. https://lnkd.in/ggadFYEx