Impact of Automation

Gartner just surveyed 350 large enterprises deploying AI. 80% cut jobs. Some by as much as 20%. The result? The companies that cut the most showed nearly identical financial returns to the ones that cut the least. In several cases, the ones that cut less performed better. No correlation between AI-driven layoffs and improved ROI. None. Gartner's Helen Poitevin was direct: "Workforce reductions may create budget room, but they do not create return." Cutting people frees up cash. It does not generate value. Most leadership teams are conflating the two. So what actually works? Upskilling staff to work alongside AI. Redesigning roles around what humans do well vs. what AI does well. Building operating models where people guide autonomous systems instead of getting replaced by them. There's a real difference between using AI to do the same work with fewer people and using AI to unlock work that was previously impossible. The first saves money on paper. The second compounds over time. We've already seen the pattern. Klarna cut 700 CS roles, watched quality decline, and started rehiring. IBM automated HR functions and reversed course. The Commonwealth Bank of Australia reversed 45 AI-driven layoffs after realizing those roles were never redundant. Gartner predicts half of companies that attributed headcount cuts to AI will rehire under new titles by 2027. If someone in your org is building an AI business case around headcount reduction, share this data. The assumption that fewer people equals better margins equals better returns is not supported by the evidence. AI is not leading to a jobs apocalypse. It's changing the shape of what people do. The companies that understand that difference will be the ones worth working for, and buying from, three years from now. Read the full piece on State of Brand here: https://lnkd.in/ggH-NXyM

Klarna bet big on AI. Now they're rehiring humans. After their valuation plunged from $45B to $7B in 2022, the company faced enormous pressure. One cost-saving measure was replacing 700 customer-service roles with AI. Then they learned a critical lesson: Some AI savings carry steep human costs. "It’s critical that customers know there will always be a human if you want." – Sebastian Siemiatkowski (CEO) The insight is strategic, not operational: → AI is transactional → Humans are relational → Automation optimizes predictable interactions → Humans manage unpredictable trust moments → AI builds efficiency → Humans build loyalty Firms that find balance will outperform those blindly bolting on technology. The new service blueprint: → Clearly map trust vs. transactional moments → Position humans strategically, not universally → Use AI to complement rather than to replace → Measure success beyond cost savings → Prioritize trust metrics (retention, advocacy, loyalty) Beyond fintech: → Consulting faces the same trust dilemma → Legal automation risks client trust → Finance must automate tasks, not judgment The winners won't automate fastest. They'll automate everything except trust itself. Because trust, judgment, and empathy never scale. And that's exactly why they're valuable.

🚨 BREAKING: Taiwan enacted its basic law on AI, which includes, among other innovative provisions, detailed AI governance principles and LABOR RIGHTS for humans who lose their jobs due to AI. Other countries should take note: According to the law's third article, the research and application of AI in Taiwan should adhere to the following principles (read them carefully!): 1. Sustainability: It should consider mental health, social equity, and environmental sustainability, reducing potential health risks or digital disparities, and enabling the public to adapt to the changes brought about by AI. 2. Human Autonomy: It should support human autonomy, respect fundamental human rights and cultural values such as the right to personality, allow for human oversight, and implement a people-centered approach that respects the rule of law, human rights, and democratic values. 3. Privacy Protection and Data Governance: It should respect the privacy and autonomy of personal data, adopt the principle of data minimization, and avoid the risk of data leakage. 4. Security: Cybersecurity measures should be established throughout the research and application of AI to prevent security threats and attacks, ensuring the robustness and security of the system. 5. Transparency and Explainability: AI outputs should be appropriately disclosed or labeled to facilitate risk assessment and understanding of their impact on relevant rights, thereby enhancing the trustworthiness of AI. 6. Fairness: AI research and application should avoid risks such as system bias and discrimination, and should not result in discrimination against specific groups. 7. Accountability: Traceability should be maintained, and different roles in AI research and application should bear corresponding responsibilities, including internal governance responsibilities and external social responsibilities. For those familiar with the EU AI Act, the way the principles above are framed is more direct and comprehensive than the European framework. As I wrote a few times before, the EU missed an opportunity to be more explicit and broad when protecting fundamental rights in the context of AI development and deployment (which could help set a stronger regulatory precedent). Another interesting provision is Article 12, focused on labor rights. It says that, in response to the development of AI, the government must address skill gaps and ensure workers' occupational safety, health, and labor rights, including providing employment assistance to those unemployed due to AI, based on their work abilities. To my knowledge, this is the first AI law that expressly foresees labor rights for those who lose their jobs due to AI. Well done, Taiwan! - 👉 To learn more about recent AI governance developments, join my newsletter's 90,000+ subscribers (below). 👉 To upskill and advance your career, join the 28th cohort of my AI Governance training in March (link below).

𝐅𝐨𝐫 𝐝𝐞𝐜𝐚𝐝𝐞𝐬, 𝐥𝐚𝐫𝐠𝐞 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐰𝐨𝐧 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐬𝐜𝐚𝐥𝐞 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲. 𝐍𝐨𝐰 𝐭𝐡𝐚𝐭 𝐚𝐬𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧 𝐦𝐚𝐲 𝐛𝐞 𝐛𝐫𝐞𝐚𝐤𝐢𝐧𝐠. Companies like Siemens, IBM or ABB built extraordinary advantages through global scale. Factories. Supply chains. Engineering networks. Regulatory structures. Smaller competitors often could not challenge them. Not because they lacked ideas. 𝐁𝐮𝐭 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐚𝐭 𝐭𝐡𝐚𝐭 𝐬𝐜𝐚𝐥𝐞 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝 𝐞𝐧𝐨𝐫𝐦𝐨𝐮𝐬 𝐜𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐢𝐨𝐧 𝐦𝐚𝐜𝐡𝐢𝐧𝐞𝐫𝐲. That machinery became part of the competitive advantage. But AI is starting to change the economics underneath it. 𝐍𝐨𝐭 𝐦𝐚𝐢𝐧𝐥𝐲 𝐛𝐲 𝐫𝐞𝐩𝐥𝐚𝐜𝐢𝐧𝐠 𝐡𝐮𝐦𝐚𝐧 𝐞𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞. 𝐁𝐮𝐭 𝐛𝐲 𝐫𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐭𝐡𝐞 𝐜𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐢𝐨𝐧 𝐛𝐮𝐫𝐝𝐞𝐧 𝐭𝐡𝐚𝐭 𝐡𝐢𝐬𝐭𝐨𝐫𝐢𝐜𝐚𝐥𝐥𝐲 𝐟𝐚𝐯𝐨𝐫𝐞𝐝 𝐢𝐧𝐜𝐮𝐦𝐛𝐞𝐧𝐭𝐬. And that changes something very important: 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐬𝐜𝐚𝐥𝐞 𝐚𝐧𝐝 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐬𝐜𝐚𝐥𝐞 𝐦𝐚𝐲 𝐧𝐨 𝐥𝐨𝐧𝐠𝐞𝐫 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐠𝐫𝐨𝐰 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫. For decades, companies assumed they did. The bigger the operation, the more layers, reporting lines, governance structures, synchronization mechanisms, and internal coordination were required to hold it together. But what happens if smaller firms can suddenly operate with the coordination power that once only existed inside global enterprises? Some companies will use AI to manage complexity more efficiently. 𝐎𝐭𝐡𝐞𝐫𝐬 𝐰𝐢𝐥𝐥 𝐫𝐞𝐝𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐞𝐦𝐬𝐞𝐥𝐯𝐞𝐬 𝐭𝐨 𝐧𝐞𝐞𝐝 𝐥𝐞𝐬𝐬 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 𝐢𝐧 𝐭𝐡𝐞 𝐟𝐢𝐫𝐬𝐭 𝐩𝐥𝐚𝐜𝐞. That is not the same strategy. And it may create very different winners. 𝐓𝐡𝐞 𝐮𝐧𝐜𝐨𝐦𝐟𝐨𝐫𝐭𝐚𝐛𝐥𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐥𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐭𝐞𝐚𝐦𝐬: If AI reduces the cost of coordination itself... 𝐡𝐨𝐰 𝐦𝐮𝐜𝐡 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐬𝐭𝐢𝐥𝐥 𝐞𝐱𝐢𝐬𝐭𝐬 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐢𝐭 𝐜𝐫𝐞𝐚𝐭𝐞𝐬 𝐯𝐚𝐥𝐮𝐞 - 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐦𝐮𝐜𝐡 𝐞𝐱𝐢𝐬𝐭𝐬 𝐛𝐞𝐜𝐚𝐮𝐬𝐞, 𝐡𝐢𝐬𝐭𝐨𝐫𝐢𝐜𝐚𝐥𝐥𝐲, 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 𝐰𝐚𝐬 𝐮𝐧𝐚𝐯𝐨𝐢𝐝𝐚𝐛𝐥𝐞? #Leadership #BusinessTransformation #FutureOfWork #Management #AI 𝘈𝘳𝘵 𝘤𝘳𝘦𝘥𝘪𝘵𝘴 𝘵𝘰 @𝘢𝘯𝘪𝘢_𝘢𝘳𝘵𝘦𝘨𝘰, 𝘧𝘰𝘶𝘯𝘥 𝘢𝘵 𝘢𝘳𝘵_𝘥𝘢𝘪𝘭𝘺𝘥𝘰𝘴𝘦

This headline captures a growing reality: China’s rapid automation drive is reshaping global industrial competition. The charts below the headline tell the real story — China now installs more industrial robots each year than the rest of the world combined, and its robot density (robots per 10,000 workers) has surged past advanced economies like Germany, the US, and Japan. This transformation isn’t just about scale. It reflects a deep structural shift — from labor-cost advantage to productivity and precision dominance. Chinese factories, powered by robotics and AI, are fast becoming the global benchmark for efficiency, threatening to erode the technological and manufacturing edge long held by Western economies. For multinational executives, the “fear” stems less from politics and more from competitiveness: China’s mix of automation, vertical integration, and government-backed industrial strategy is creating a self-reinforcing ecosystem — one that could define the next industrial era. Sources: on graph

AI is changing the economics and speed of cyberattacks. What once took threat actors days or weeks can now happen in minutes: automated reconnaissance, AI-assisted exploit development, credential targeting, lateral movement, and highly personalized phishing at scale. This is why Palo Alto Networks believes so strongly in the concept of autonomous resilience. The traditional model of security operations: fragmented tools, manual escalation paths, and human-speed response cycles - was not designed for machine-speed threats. Autonomous resilience means building security architectures that can continuously reduce exposure, validate trust, and contain threats in real time. What does that look like in practice? 🔸 Minimize attack surface Continuously identify and remediate exposed assets, misconfigurations, vulnerable APIs, and unmanaged cloud resources before attackers can weaponize them. For example, AI-driven exposure management can detect an internet-facing development environment created outside policy and trigger automated remediation immediately. 🔸 Secure every identity Trust must extend beyond employees to machine identities, workloads, APIs, and AI agents. This means enforcing least privilege, adaptive access controls, and continuous identity validation to stop credential misuse and token theft before attackers gain persistence. 🔸 Defend the software supply chain AI-assisted attacks increasingly target CI/CD pipelines, open-source dependencies, and code repositories. Organizations need runtime protections, code integrity validation, and automated policy enforcement to prevent manipulated code from reaching production environments. 🔸 Constrain blast radius Zero Trust architectures become even more critical in an AI-driven threat landscape. Microsegmentation, continuous inspection, and behavioral analytics help prevent attackers from moving laterally across environments once initial access is achieved. 🔸 Detect and respond in real time Security teams cannot rely on analysts manually correlating thousands of alerts. AI-driven SOC operations can automatically prioritize incidents, enrich telemetry, isolate compromised assets, and initiate containment workflows within minutes — dramatically reducing operational fatigue and response time. The outcome is not “fully autonomous security.” The outcome is resilient organizations that can adapt, contain, and recover faster in an increasingly automated threat environment. Cybersecurity is evolving from reactive defense into continuous operational resilience. The organizations preparing for that shift now will be far better positioned for what comes next.

As AI is replacing early-career jobs, the economy's productivity in the short-run increases, but productivity and welfare in the long run may decline. In a new paper, Enrique Ide argues that we may be witnessing "socially excessive automation of early-career work. Such automation may deliver immediate productivity gains, but it also erodes the skills of future cohorts and constrains long-run growth." Here's the abstract of his paper: "Recent advances in Artificial Intelligence (AI) have sparked expectations of unprecedented economic growth. Yet, by enabling senior workers to accomplish more tasks independently, AI may reduce entry-level opportunities, raising concerns about how future generations will acquire expertise. This paper develops a model to examine how automation and AI affect the intergenerational transmission of tacit knowledge—practical, hard-to-codify skills critical to workplace success. I show that the competitive equilibrium features socially excessive automation of early-career tasks, and that improvements in such automation generate an intergenerational trade-off: they raise short-run productivity but weaken the skills of future generations, slowing long-run growth—sometimes enough to reduce welfare. Back-of-the-envelope calculations suggest that AI-driven entry-level automation could reduce the long-run annual growth rate of U.S. per-capita output by 0.05 to 0.35 percentage points, depending on its scale. I further show that AI co-pilots can partially offset lost learning by assisting individuals who fail to acquire skills early in their careers. However, they may also weaken juniors’ incentives to develop such skills. These findings highlight the importance of preserving and expanding early-career learning opportunities to fully realize AI’s potential." What can policy do? In the concluding remarks, the paper offers several ideas: - government subsidies for "mentorship, apprenticeship, and other entry-level training arrangements" - "taxing entry-level automation" - reducing minimum wages for young workers - promoting AI systems that complement rather than replace entry-level jobs. Universities could also play a role by placing "greater emphasis on providing undergraduate students with opportunities to gain practical experience before they formally enter the labor market. Such initiatives would complement the traditional focus of undergraduate programs on codifiable knowledge and help foster the early development of tacit skills." Read the full paper here: https://lnkd.in/eMq3uktX (open access)

The Great Rewriting of Professional Services If there’s one shift that’s still flying under the radar, it’s this: AI is not just automating workflows—it’s reshaping the very structure of the services economy. Two sharp signals this week made that impossible to ignore: 🔹 Greg Isenberg’s “What’s Keeping Me Up At Night” 🔹 Ethan Batraski’s The Great Legacy Extinction They both point to a profound shift already underway. The next wave of consulting, healthcare, legal, and audit services will not look like firms. They will be built like software products—domain-specific, always-on, and radically scalable. 💡 What stands out: 🔹 Services are scaling like SaaS. AI-native firms are doing more with less—replacing labor scale with intelligent systems, delivering with speed and precision. 🔹 Trust and relationships are the new moat. In a world of commoditized tech, expertise and empathy still differentiate. Knowing your customer’s world matters more than ever. 🔹 Middle layers are where the action is. The real opportunity is not in building models—but in applying them with deep industry context. That’s where value and defensibility live. 🔹 The Comet Theory is real. Tech moves fast. Businesses adapt slowly. That gap is now the launchpad for AI-native service models. And in Healthcare? Hippocratic AI may be to healthcare what Palantir was to Defense—redefining how essential services are delivered. Their agent-based model brings empathy and efficiency to low-risk, high-volume interactions—from chronic care to post-discharge to emergency outreach. Rigorously tested by 7,000+ clinicians, it reflects what’s possible when safety, scale, and specificity come together. The bottom line? We are not just tweaking service delivery. We are rewriting the playbook. AI is not just changing how services are delivered. It is changing who gets to deliver them. Additional Reading 🧠 Greg Isenberg – What’s Keeping Me Up At Night ✍️ Ethan Batraski – The Great Legacy Extinction 🏥 Hippocratic AI – FierceHealthcare Coverage

When people talk about trustworthy AI, it's often left vague. In finance, it can't be. For me, AI you can trust comes down to three practical things. Confidence. The AI can show its work. You can see what it did, why it did it, and interrogate the result. Not a black box. A glass box. Control. Humans stay in charge. Consequential actions need approval. And when an agent isn't sure, it doesn't guess. It escalates. Accountability. Every action is traceable. What triggered it, what the agent did, who approved it, what changed. In finance, that isn't a nice-to-have. It's the whole game. That's the lens we're applying as we expand AI agents across finance, HR and operations. Automating real workflows. Keeping people firmly in charge.