Artificial Intelligence in Business

AI’s ability to make tasks not just cheaper, but also faster, is underrated in its importance in creating business value. For the task of writing code, AI is a game-changer. It takes so much less effort — and is so much cheaper — to write software with AI assistance than without. But beyond reducing the cost of writing software, AI is shortening the time from idea to working prototype, and the ability to test ideas faster is changing how teams explore and invent. When you can test 20 ideas per month, it dramatically changes what you can do compared to testing 1 idea per month. This is a benefit that comes from AI-enabled speed rather than AI-enabled cost reduction. That AI-enabled automation can reduce costs is well understood. For example, providing automated customer service is cheaper than operating human-staffed call centers. Many businesses are more willing to invest in growth than just in cost savings; and, when a task becomes cheaper, some businesses will do a lot more of it, thus creating growth. But another recipe for growth is underrated: Making certain tasks much faster (whether or not they also become cheaper) can create significant new value. I see this pattern across more and more businesses. Consider the following scenarios: - If a lender can approve loans in minutes using AI, rather than days waiting for a human to review them, this creates more borrowing opportunities (and also lets the lender deploy its capital faster). Even if human-in-the-loop review is needed, using AI to get the most important information to the reviewer might speed things up. - If an academic institution gives homework feedback to students in minutes (via autograding) rather than days (via human grading), the rapid feedback facilitates better learning. - If an online seller can approve purchases faster, this can lead to more sales. For example, many platforms that accept online ad purchases have an approval process that can take hours or days; if approvals can be done faster, they can earn revenue faster. This also enables customers to test ideas faster. - If a company’s sales department can prioritize leads and respond to prospective customers in minutes or hours rather than days — closer to when the customers’ buying intent first led them to contact the company — sales representatives might close more deals. Likewise, a business that can respond more quickly to requests for proposals may win more deals. I’ve written previously about looking at the tasks a company does to explore where AI can help. Many teams already do this with an eye toward making tasks cheaper, either to save costs or to do those tasks many more times. If you’re doing this exercise, consider also whether AI can significantly speed up certain tasks. One place to examine is the sequence of tasks on the path to earning revenue. If some of the steps can be sped up, perhaps this can help revenue growth. [Edited for length; full text: https://lnkd.in/gBCc2FTn ]

Everyone is talking about #AI in logistics. Some still believe logistics is simply about moving goods from A to B. And now headlines around the world are asking: Can logistics be replaced by AI-driven software? The answer is both simple and incomplete. ▶️ AI enables us to process billions of data points in real time. ▶️ It anticipates risk before it materialises. ▶️ It increases transparency across global networks. ▶️ It reduces manual errors while accelerating throughput. In short: AI drives efficiency. And further: There is no future for logistics without AI. But here is the real question: Will AI make supply chains more efficient or more human? Yes, you read correctly: human. Because efficiency alone is not the benchmark. #CustomerExperience is. Let me explain this by looking into the status quo. Already today, we use AI to: Predict more reliable ETAs by real-time recalculation. Detect disruptions earlier allowing for proactive route and capacity planning. Automate end-to-end workflows, reducing manual work, errors, and processing time across core operations. This is not theory, it’s no longer experimental, it’s daily practice. And there is a lot more to come. Yet, what matters most is this: The more powerful AI becomes, the more decisive the #HumanExpertise becomes. In an AI-driven world, customers will not differentiate us by who has access to technology. Technology will become mainstream. Customers will differentiate us by: ▶️ Who explains complexity clearly. ▶️ Who takes ownership when disruption hits. ▶️ Who anticipates consequences, not just data patterns. ▶️ Who acts as a strategic partner, not just a service provider. AI allows us to be faster. Customer experience requires us to be better. The real opportunity for our industry is not to automate relationships but to elevate them. AI can process billions of data points. But trust is built through clarity, reliability, and accountability. Kuehne+Nagel’s ambition is simple: Lead in AI. Lead in customer experience. Because the future of logistics will not be defined by algorithms alone but by how intelligently and responsibly we use them to serve our customers. We’ll share further insights into our AI strategy during the Kuehne+Nagel Conference Call on March 3, 2026.

Nvidia & the story of intentionally stumbling on innovation Sometimes, the most transformative innovations don’t come from your product plan, they come from actively listening to how customers use your product in unexpected ways. Nvidia’s rise to becoming one of the most valuable companies on earth is exactly that story. As a kid, I remember saving money so I can buy an Nvidia graphics card so I can play the games I loved. Back then, Nvidia was gaming to me. Fast forward to today, most people probably associate Nvidia with AI, not video games. The story behind that shift is incredible. From the book “Chip War”:  “In the early 2010s, Nvidia—the designer of graphic chips—began hearing rumors of PhD students at Stanford using Nvidia’s graphics processing units (GPUs) for something other than graphics. GPUs were designed to work differently from standard Intel or AMD CPUs, which are infinitely flexible but run all their calculations one after the other. GPUs, by contrast, are designed to run multiple iterations of the same calculation at once. This type of “parallel processing,” it soon became clear, had uses beyond controlling pixels of images in computer games. It could also train AI systems efficiently. Where a CPU would feed an algorithm many pieces of data, one after the other, a GPU could process multiple pieces of data simultaneously. To learn to recognize images of cats, a CPU would process pixel after pixel, while a GPU could “look” at many pixels at once. So the time needed to train a computer to recognize cats decreased dramatically. Nvidia has since bet its future on artificial intelligence.” This wasn’t just luck. Actively listening to your customers requires intentionality…spending time with them…asking questions…leading with curiosity. What massive opportunities could you unlock if you listened more closely to how people use your product today?

Everyone talks about Salesforce as a CRM giant. But if you zoom out, they’ve quietly become The most dominant DISTRIBUTION LAYER for enterprise AI. Not by building models or launching flashy labs. They did what AI-native companies currently can’t: → Deep inside Fortune 500 workflows → Embedded in procurement-approved vendor lists → Installed on the desktops of 150,000+ enterprise reps While everyone is chasing or waiting for AGI, Salesforce focused on ADJACENCY. ✔ Embedding generative AI into Sales Cloud, Service Cloud, and Tableau ✔ Building industry-specific copilots tailored to how real teams sell and support ✔ Acquiring vertical intelligence via Data Cloud, Slack, and Mulesoft integrations The result? A defensible wedge that sits on top of 𝗺𝗮𝘀𝘀𝗶𝘃𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗱𝗮𝘁𝗮. And a flywheel where AI adoption directly increases platform stickiness. They’re not trying to win the model war. They’re winning the 𝘥𝘦𝘱𝘭𝘰𝘺𝘮𝘦𝘯𝘵 𝘸𝘢𝘳. And if you’re building in B2B SaaS, that’s a lesson worth stealing. 💡 #artificialintelligence #growth #strategy #startups Image Credit: Eric Flaningam

15 weeks left before the first rules of the AI Act come into effect. Struggling with where to start on AI implementation and compliance? Start with a multidisciplinary team; conduct an AI inventory; carry out AI Impact Assessments; draft AI policies; amend contracts, policies, and data protection documents to reflect AI’s role in your organisation. Ensure your team is trained in AI literacy, as required under the AI Act. To navigate AI implementation and compliance under the EU AI Act, companies must begin by understanding its scope and risk-based approach. The Act categorises AI systems into prohibited, high-risk, or general-purpose. Prohibited AI systems (the first rules coming in) include those exploiting vulnerabilities or engaging in certain AI emotional recognition. High-risk systems, such as those used in management of critical infrastructure, require strict oversight, including documentation, risk assessments, and ongoing monitoring. General-purpose AI systems, widely used across industries, may also face regulatory scrutiny due to their broad impact. The first step for companies is conducting a comprehensive AI inventory. This involves cataloguing all AI systems in use or under development to determine their classification under the AI Act. Through this inventory, companies can assess their compliance obligations and identify any systems that may need modification or discontinuation to meet the Act’s standards. Data protection is a cornerstone of AI compliance. The AI Act mandates that data used in AI systems be high quality, representative, and free from bias. This is especially crucial for high-risk systems, which must undergo continuous risk assessments to protect fundamental rights. GDPR compliance is also essential for any AI system that processes personal data, and companies must ensure their data governance strategies focus on transparency, accountability, and safeguarding individual rights. Contracts are a critical component of AI implementation. Organisations must revisit and amend contracts to address how AI impacts their legal and operational frameworks. These amendments should explicitly cover liability for AI-generated decisions, intellectual property ownership of AI-generated outputs, and data protection compliance. Contracts must minimise legal exposure. Additionally, intellectual property issues around AI, such as ownership of outputs or the use of third-party data, should be clearly defined in these agreements. Following the AI inventory, companies must conduct an AI impact assessment. This assessment includes both a Data Protection Impact Assessment (DPIA) and a Fundamental Rights Impact Assessment (FRIA). The extraterritorial scope of the AI Act means that even non-EU companies must comply if their AI systems impact the EU market. Non-compliance can result in significant fines, making early compliance essential. 15 weeks left to comply.

In the next phase, AI agents will be autonomous economic participants. The economy will evolve dramatically as agents operate continuously, share perfect information, and rapidly adapt. However our existing human-centric economy is not designed for agents. A very interesting paper “Unlocking AI Agents Potential Through Market Forces” (link in comments) explores in detail the barriers to the economic potential, and the enablers to move past those. 🚧 Human-centric infrastructure as a barrier. The current digital ecosystem was built for human users, with interfaces, identity verification, and payment systems designed around human behavior. These constraints prevent AI agents from seamlessly integrating into digital economies, limiting their ability to create and exchange value autonomously. 🔍 Challenges in service discovery. AI agents struggle to find and evaluate services because discovery mechanisms—such as industry events, peer recommendations, and human-oriented documentation—are not machine-readable. Future solutions must include structured registries, machine-friendly descriptions, and automated indexing for real-time service discovery. 🔑 Identity and authorization limitations. AI agents lack traditional identity markers like physical documents, email addresses, and human-verifiable credentials. Current authentication methods are slow and require human intervention, making them unsuitable for machine-speed operations. Cryptographic identity systems, decentralized reputation models, and dynamic access control could solve these challenges. 🌐 Software interfaces designed for humans. Digital services currently separate human-friendly visual interfaces from APIs meant for machine interactions, creating inefficiencies for AI agents. Future systems should support adaptive, machine-readable interfaces that dynamically adjust based on the consumer, whether human or AI. 💰 Payment systems block AI participation. Online transactions rely on human verification, anti-bot measures, and rigid business models like subscriptions and credit card payments. AI-friendly payment solutions should incorporate cryptographic attestation, machine-scale wallets, and real-time micropayments to enable seamless economic activity. 🚀 Future infrastructure for AI-driven markets. To fully integrate AI agents into digital markets, the ecosystem needs machine-readable service discovery, scalable identity and authorization systems, flexible payment mechanisms, and new market protocols. These advancements will unlock economic efficiency, innovation, and autonomous value creation at an unprecedented scale. This is a central theme in my work on AI-driven business model innovation, I will be sharing a lot more related insights on this.

The conversation around AI is shifting. It's no longer about if the technology works, but if we can operationalize it for genuine, enterprise-wide impact. Too many organizations are stuck in "pilot purgatory"- impressive demos that never translate into production value. The gap isn't in the technology; it's in the operating model, and the leadership behind it. At ServiceNow, we built a foundational pact between the offices of CIO and COO. Kellie and I agreed, we need to treat AI not as a standalone tool, but as an integrated business system with shared ownership and clear, measurable outcomes. This disciplined approach is how we generate significant value from our AI investments. Moving from potential to performance requires a clear blueprint. Here’s the framework we use: 1️⃣ Start with the Work, Not the Model: Begin by identifying high-impact business problems, not by experimenting with the latest model. Focus on use cases that directly move the needle for your employees and customers. 2️⃣ Fix Data Chaos with Platform Power: A resilient, integrated platform is essential. It’s the only way to turn siloed data into actionable workflows and drive adoption across the entire enterprise. 3️⃣ Govern AI Like a Business System: Effective governance isn't a one-time check. It's an ongoing discipline - a central function that ensures every AI agent is secure, observable, and aligned with business goals. 4️⃣ Redesign Work for Human + Agent Teams: Our goal is to amplify human potential, not replace it. By using AI to handle routine tasks, we free our teams to focus on strategic priorities, innovation, and relationship-building. 5️⃣ Make the CIO-COO Pact Real: This is the cornerstone. It means co-owning a unified backlog, tracking outcomes on a shared dashboard, and creating a culture where responsible innovation can thrive. The future belongs to organizations that can make AI a seamless part of their operational fabric. It’s about building the discipline to scale and the partnerships to lead. The time for experiments is over. The time for execution is now. https://lnkd.in/g7Ycw29u #OperationalExcellence #FutureOfWork

AI in Customer Support isn’t new. I’ve been rethinking how we actually use it. Customer Support is moving past basic "faster replies" and learning to implement Claude as a core part of our workflow. The goal? Shifting from reactive firefighting to structured, scalable systems. It’s a work in progress, but here is the blueprint we’re using to turn Claude into a true CX reasoning engine: 1️⃣ It’s not about speed. It’s about structure. Yes, you can draft replies faster. But the real value comes from setting it up properly: → align it with your tone and guidelines → connect it to your knowledge base → define clear boundaries (what it can and can’t say) → train it to understand context, not just keywords That’s how you get consistent, reliable output across the team. 2️⃣ It helps move Support from reactive → proactive Used well, it’s not just answering tickets. It’s helping you: → detect sentiment and urgency → identify recurring friction points → surface gaps in self-service → spot early churn signals That’s where Support starts influencing the whole customer experience. 3️⃣ It fits into your existing workflows (not replaces them) The most effective setups I’ve seen are simple: → Claude + Zendesk → ticket analysis → Claude + Zapier → automate workflows → Claude + Gong→ review calls → Claude + Intercom → inbox support → Claude + n8n → workflow automation → Claude + Notion → knowledge management No complex rebuilds. Just better use of what you already have. 4️⃣ The quality of output = quality of input Small things make a big difference: → assign a role (support agent, CX lead, analyst) → provide context (customer, goal, constraints) → iterate with examples (good vs bad responses) Without this, you get generic answers. With it, you get something your team can actually use. From a leadership perspective, this isn’t about “adding AI.” It’s about designing how your Support team operates at scale. Because the goal isn’t to answer more tickets. It’s to build a system where fewer things break, and when they do, the experience still feels consistent. If you’re already using AI in Support, what’s actually working for you? 👇

"The rapid evolution and swift adoption of generative AI have prompted governments to keep pace and prepare for future developments and impacts. Policy-makers are considering how generative artificial intelligence (AI) can be used in the public interest, balancing economic and social opportunities while mitigating risks. To achieve this purpose, this paper provides a comprehensive 360° governance framework: 1 Harness past: Use existing regulations and address gaps introduced by generative AI. The effectiveness of national strategies for promoting AI innovation and responsible practices depends on the timely assessment of the regulatory levers at hand to tackle the unique challenges and opportunities presented by the technology. Prior to developing new AI regulations or authorities, governments should: – Assess existing regulations for tensions and gaps caused by generative AI, coordinating across the policy objectives of multiple regulatory instruments – Clarify responsibility allocation through legal and regulatory precedents and supplement efforts where gaps are found – Evaluate existing regulatory authorities for capacity to tackle generative AI challenges and consider the trade-offs for centralizing authority within a dedicated agency 2 Build present: Cultivate whole-of-society generative AI governance and cross-sector knowledge sharing. Government policy-makers and regulators cannot independently ensure the resilient governance of generative AI – additional stakeholder groups from across industry, civil society and academia are also needed. Governments must use a broader set of governance tools, beyond regulations, to: – Address challenges unique to each stakeholder group in contributing to whole-of-society generative AI governance – Cultivate multistakeholder knowledge-sharing and encourage interdisciplinary thinking – Lead by example by adopting responsible AI practices 3 Plan future: Incorporate preparedness and agility into generative AI governance and cultivate international cooperation. Generative AI’s capabilities are evolving alongside other technologies. Governments need to develop national strategies that consider limited resources and global uncertainties, and that feature foresight mechanisms to adapt policies and regulations to technological advancements and emerging risks. This necessitates the following key actions: – Targeted investments for AI upskilling and recruitment in government – Horizon scanning of generative AI innovation and foreseeable risks associated with emerging capabilities, convergence with other technologies and interactions with humans – Foresight exercises to prepare for multiple possible futures – Impact assessment and agile regulations to prepare for the downstream effects of existing regulation and for future AI developments – International cooperation to align standards and risk taxonomies and facilitate the sharing of knowledge and infrastructure"

AI is already impacting 93% of U.S. jobs, and its effects are outpacing expectations by a factor of 4.5x. The broader question for leadership today is no longer if, but how, we channel this transformative power to benefit all levels of society and the economy. Two perspectives stand out. In our TIME article, "AI Should Belong to Workers," my coauthors and I explored how AI disrupts traditional hierarchies by democratizing intelligence. Unlike previous technology waves, AI doesn't demand specialized technical expertise for adoption. Frontline employees — from HVAC technicians to nurses — are using AI-driven diagnostics to expand their leverage and decision-making. When workers shape AI applications tailored to their tasks, value creation accelerates closer to the work itself, enabling better wage leverage and upward mobility. (read here: https://lnkd.in/eSwqbDpT) Our "New Work, New World" research, spanning nearly 1,000 professions, found AI exposure has accelerated well beyond predictions — average exposure scores jumped 30% in just three years, when models forecasted a decade. The yearly rate of jobs impacted by AI has skyrocketed from 2% to 9% annually, underscoring why leaders must design pathways that empower workers to harness this opportunity at every level. (read here: https://lnkd.in/ekWPUxQE) In the Newsweek article, "When Capital Can Think, Who Pays?", we examined AI's fiscal misalignment: digital agents that augment or replace workers contribute nothing toward payroll taxes sustaining Social Security, Medicare, and unemployment insurance, while human labor bears these costs. Temporarily rebalancing this burden — lowering it on labor, raising it on automation — creates an AI-driven economy that rewards augmentation over displacement, much like prior revolutions that generated the 60% of jobs that didn't exist 80 years ago. (read here: https://lnkd.in/eUvMvZMK) AI's potential to create value for the U.S. economy already exceeds $4.5 trillion. But unless enterprise adoption and wider societal architecture move in tandem, these gains could concentrate in narrow economic bands. Leaders today have a choice: manage AI passively, reinforcing the inequities new technologies could correct — or reimagine how intelligence, tasks, and rewards flow through our organizations. The most important innovation of the coming decade may not come from AI itself. It will come from the deliberate systems we create to amplify every worker's potential and ensure technological progress fuels enduring human value.