Insights zur KI-Transformation im industriellen B2B
Executive-Analysen zur KI-Transformation im industriellen B2B, zu agentischen Systemen, Operating Models, CRM, Daten, Adoption und messbarer Wirkung.
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- The Verification Ceiling: When AI Produces Faster Than You Can Check
Suppose an AI-assisted quote is ready in minutes while engineering, finance and operations still need to decide who can sign it. The Verification Ladder turns that review gap into an operating decision.
- Your First AI Profit May Be in the Order Book
AI demand is reaching power, transformers, controls, automation, and semiconductors. Test the opportunity with live customer evidence before funding it.
- Your AI Sandbox Is a Production System Now
A highly isolated OpenAI evaluation still reached Hugging Face production. Keep testing agents, but govern any persistent, connected evaluation by its real blast radius rather than by the word sandbox.
- EU AI Act Article 50: The Label Is the Easy Part
Article 50 starts applying on 2 August 2026. Most companies will react by adding labels. Industrial B2B leaders need something harder and more valuable: a defensible chain from source evidence to AI use, human judgment, release, and monitoring.
- It's Not an Agent If You Still Press the Button
Enterprises still use the word agent for configured chatbots. Custom instructions and document access create a better-briefed assistant, and the gap between the two explains much of the distance between AI adoption and AI value.
- Nach der Knappheit: Was aus uns wird, wenn das ökonomische Problem gelöst ist
Eine unabhängige zivilisatorische Prüfung, die ich in persönlicher Eigenschaft verfasst habe. Fast vier Jahre nach Beginn der GenAI-Welle ist die interessantere Frage nicht mehr die der Produktivität. Sondern was die Zivilisation tut, sobald das ökonomische Problem von Keynes wirklich in greifbare Nähe rückt. Die empirischen Erkenntnisse von Keynes bis Hirsch, von Brickman bis Killingsworth, von OpenResearch bis GiveDirectly, von Sahlins bis Veblen, von Aristoteles bis Bostrom laufen auf eine einzige Schlussfolgerung hinaus: Post-Knappheit löst nicht das menschliche Problem. Sie kehrt es um.
- Auswirkungen des KI-Fiebers auf Unternehmen: Was fast vier Jahre GenAI tatsächlich gebracht haben
Fast vier Jahre nach der Veröffentlichung von ChatGPT melden 88 % der Unternehmen eine regelmässige KI-Nutzung, aber nur ein Drittel hat mit der Skalierung begonnen und nur 6 % qualifizieren sich als KI-High-Performer in Bezug auf das EBIT. Die Absichten der CEOs haben sich schneller entwickelt als die Neugestaltung von Workflows, die Erfolgsmessung und die Anpassung von Betriebsmodellen. Eine Synthese vom Mai 2026 aus McKinseys „State of AI 2025“, MIT NANDAs „GenAI Divide“, IBMs „2026 CEO Study“, den Preislisten von Anbietern und öffentlichen Unternehmensberichten.
- The Word 'Delve' Is Costing You Deals: Why B2B Buyers Already Know Your Marketing Is AI Slop
A field guide to the trust gap AI-generated copy has opened in B2B. Kirk and Givi measured the loss across seven preregistered experiments, and buyer surveys show the heaviest AI users check the most. Most dashboards cannot see the damage, which builds up as slow erosion in reply rates and shortlists.
- Der generative KI-Reset: Warum die meisten Marketingorganisationen die nächsten 36 Monate nicht überleben werden
Eine Synthese als Arbeitspapier zur Diskontinuität, die Suche, Marketing und die Architektur des Internets neu definiert. ChatGPT erreichte in 18 Monaten 900 Millionen wöchentlich aktive Nutzer. Der Publisher-Traffic aus der Suche fiel in einem einzigen Jahr um ein Drittel. Holdinggesellschaften haben Zehntausende von Stellen abgebaut. Die Marketingfunktion, wie sie heute strukturiert ist, wird die kommenden Entwicklungen nicht überleben.
- The Cold Outreach Reckoning: Why AI Broke B2B Prospecting in 18 Months
An executive analysis of how AI-powered prospecting industrialised noise, cut reply rates, triggered platform penalties and turned outbound volume into a reputational liability, and what to do instead.
- KI-Inhalte in grossem Massstab: Das Markenrisiko, das niemand beziffern will
Ein forschungsgestützter Essay für Führungskräfte darüber, wo die Produktion von KI-Inhalten echten Mehrwert schafft, wo sie das Markenvertrauen beschädigt und wie Führungskräfte Übersetzung, Copywriting, Bilder, Video, Software, Websites, SEO und Offenlegung in grossem Massstab steuern sollten.
- Der Tod von SaaS: Warum die Lizenzwirtschaft durch KI neu bewertet wird
Ein forschungsgestützter Essay für Führungskräfte, der argumentiert, dass SaaS nicht verschwindet, sondern seine Gatekeeper-Ökonomie zerbricht. KI-gestützte Entwicklung, agentische Ausführung und ergebnisorientierte Preismodelle verlagern den Wert von Unternehmenssoftware von Nutzerlizenzen (Seats) auf Ergebnisse und vom reinen Anbieterzugang auf das Urteilsvermögen der Anwender.
- Der Stand der KI, April 2026: Was die Zahlen für Führungskräfte wirklich bedeuten
Eine Synthese für Führungskräfte der strategischen KI-Bewertung vom April 2026, aktualisiert für die Veröffentlichungen von Ende April (GPT-5.5 und DeepSeek V4 Pro). Ein vierfaches Unentschieden an der Spitze, eine Verdopplung der agentischen Aufgabenhorizonte alle vier Monate, eine Adaptionsrate von 88 % bei einer EBIT-Erfassung von nur 6 % und ein Capex-Superzyklus der Hyperscaler von 660 Mrd. USD. Die Zahlen zeigen deutlich, welche Entscheidungen im Jahr 2026 die Wettbewerbsposition für den Rest des Jahrzehnts bestimmen werden.
- Das intelligente Industrieunternehmen
Ein szenariobasiertes Arbeitspapier darüber, wie künstliche Intelligenz die B2B-Schwerindustrie, industrielle Betriebsabläufe, Energiesysteme, Robotik, Modelle für die Arbeitswelt und Wertschöpfungsketten zwischen 2026 und 2036 transformieren könnte.
- Skalierung der Rechenleistung jenseits des Mooreschen Gesetzes
Ein Arbeitspapier über das Rechenregime nach Moore. Es untersucht das Wachstum der Trainingsrechenleistung, algorithmische Effizienz, wissenschaftliche Entdeckungen und die Beschränkungen, die Frontier-KI bis 2030 prägen werden.
- Stop Looking for AI Use Cases. Start Allocating Intelligence.
A strong AI strategy starts with business problems, value leakage and the allocation of intelligence, with use cases coming later. The BASICS framework helps executives find where scarce, slow, inconsistent or expensive intelligence limits performance, and decide whether AI, process redesign, analytics, rules automation or governance is the right intervention.
- AI Pilots Are a Form of Procrastination
Many enterprises run AI pilots that never reach a decision. MIT NANDA found that about 95% of generative AI pilots return no measurable P&L impact. This essay explains why many pilots defer decisions instead of informing them, and the forcing functions that turn experiments into deployments.
- The AI Transformation Office Is the Last Job You Should Create
Many large companies are standing up AI Transformation Offices. Earlier Digital, Cloud and Agile offices often became the bottlenecks they were created to remove. This essay argues for a disposable 90-day catalyst team, P&L-owned AI leads in every unit and a board scorecard tied to changed unit economics.
- The CAIO Mandate Fails When It Owns Activity, Not Decisions
The Chief AI Officer title spread quickly in 2024 and 2025, often without a P&L, an infrastructure budget or authority to reshape a single role. This essay describes what a real CAIO mandate looks like, four archetypes that work, and seven questions any candidate should ask before accepting.
- Human-in-the-Loop Is the New Technical Debt
Human-in-the-loop is often sold as the safe default for AI. Applied to every output, it caps the return, wears out reviewers and compounds like technical debt. This essay explains when human review genuinely belongs, and a four-step way to replace blanket review without losing trust.
- AI Programs Fail Before the Model Arrives
MIT NANDA found that about 95% of generative AI pilots return no measurable P&L impact. The cause is usually the question asked at the start. Companies call chatbots agentic, supervise AI like a junior intern and ask where AI fits instead of which hard problems would change shape if intelligent attention were abundant.
- I Watched an AI Build an Entire Ad Agency in 45 Minutes. Here Is What That Actually Means.
A video shows an AI agent researching competitors, building a brand and storefront, generating ads from real inventory, deploying them on Meta and prospecting 20 dealerships in 45 minutes. What that means for service businesses, where the governance gap lies, and what executives should map first.
- Why AI Is Not a Bubble: The Scaling Evidence Every Executive Needs to See
AI capability is compounding quickly: METR's measurements show the length of tasks frontier models can complete doubling within months, and frontier models now exceed expert scores on hard science benchmarks. Valuations may still correct, but the technology is not going away, and planning should reflect that.
- The Orchestration Era: Six Shifts in AI's Real Impact in 2026
Most companies now have more AI tools than impact. This essay describes six shifts in 2026, from orchestration skills and process debt to machine identities and synthetic people, and why value now comes from designing how people and agents work together.
- The Operating Model Behind AI at Scale
Many AI initiatives fail because they are managed like conventional IT projects, with fixed requirements and a go-live date. This essay describes the operating model AI needs: data ownership, continuous monitoring, named outcome owners, the right structure for each maturity stage and governance built in from the start.
- The Vibe Coding Revolution: What It Actually Means for Enterprise Software Development
Vibe coding lets domain experts build working software in hours, and the evidence on speed and security is messier than its advocates admit. This essay sets out where it works, where it breaks, and the risk tiers and guardrails that let an enterprise use it without shipping the next CVE-2025-48757.
- The Autonomous Enterprise: Navigating the Strategic Shift to Agentic AI (2026-2030)
Analysts forecast a $47 billion to $93 billion agentic AI market by 2030 and also expect more than 40% of agentic projects to be cancelled by 2027. This essay looks at the evidence behind both, with a focus on industry, and at what separates the few companies turning agents into measured results.
- Agentic AI Changes the Workflow Before It Changes the Brand
AI content is being produced on an industrial scale, and that is making human trust scarcer and more valuable. This essay argues that B2B marketing now has to serve two audiences at once, the buyer and the buyer's AI assistant, and sets out the platforms, metrics, governance and plan that requires.
- What Actually Works: AI Applications That Deliver ROI
Two companies with the same budget, data and vendors can get opposite results from AI. This essay covers four applications that have delivered measurable returns in my work, with the messy numbers, what vendors leave out, the red flags I watch for and the four questions I ask before any AI project goes ahead.
- How to Find AI Use Cases That Actually Make Money
I watched a $3 million AI initiative die because nobody could say how it would make money. This essay covers how I now choose AI use cases: start from the costliest problems, answer three hard questions, budget for the hidden costs, and know when not to use AI at all.
- The People Problem Nobody Wants to Talk About
The question executives ask me most is how to prepare their people for AI. This essay covers what history says about automation and jobs, why middle managers resist, why fear is often rational, and how a Zurich company went from 23 volunteers to 180 active users without a mandate.
- AI Productivity Is Real, But Not How You Think: 3 Years of Daily Use
Three years of daily AI use, starting with GPT-3.5 Turbo in March 2023, have saved me 10 to 12 hours a week, but only after months of practice. This essay covers where the time comes from, why AI matters more for people working in a second language, the prompting habits that work, and the email I almost sent.
- Sales Strategy Development: Enhancing Customer Satisfaction
Executives talk about customer-centricity while their compensation plans reward something else. Drawing on more than 50 CRM implementations, this essay argues that satisfaction and revenue grow together when compensation, handoffs, metrics and data are designed around the customer's lifetime rather than the quarter.
- Interview Your Data: Google NotebookLM and the End of Surface-Level Research
Google NotebookLM is the most useful tool I have found for understanding a large body of material, because it answers from your own sources and shows where each answer came from. This essay covers how I use it, from Audio Overviews to Deep Research, how it compares with other assistants, and where it still needs checking.