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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- 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. The lesson is not to stop agent testing. It is to govern any persistent, connected evaluation by its real blast radius, not 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
It is mid-2026 and the enterprise still cannot define the word agent. That is not a vocabulary problem. It is a budget problem. Custom instructions and document access do not create agency — they create a better-briefed chatbot — and the gap between those two things is where the entire adoption-versus-value story lives.
- 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 synthesis of the trust gap that AI-generated copy has opened in B2B. Kirk and Givi (JBR Vol. 186, 2025) measured it across seven preregistered experiments. Originality.ai catalogued the linguistic fingerprint across 3.3 million documents. The teams shipping AI-default content at scale are not winning a productivity war. They are accumulating a quiet, unattributable trust debt that is already showing up in 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 original-research executive analysis of how AI-powered B2B prospecting industrialised noise, halved reply rates, triggered platform-level discipline, and turned outbound volume into a balance-sheet liability. The fix is structural, not cosmetic.
- 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.
The strongest AI strategy does not start with AI use cases. It starts with business problems, value leakage, and intelligence allocation. The BASICS framework helps executives find where scarce, slow, inconsistent, or expensive intelligence is limiting performance, then decide whether AI, process redesign, analytics, rules automation, or governance change is the right intervention.
- AI Pilots Are a Form of Procrastination
Every Fortune 500 is running AI pilots. MIT Sloan put the failure rate at 95%. The story we tell ourselves is that pilots are how we learn. After 17 years inside transformations, I have come to a more uncomfortable conclusion. The pilot is not a learning mechanism. It is a decision-deferral mechanism dressed as one. Here is how to tell the difference, and the forcing-function alternative.
- The AI Transformation Office Is the Last Job You Should Create
Every Fortune 500 is currently standing up an AI Transformation Office. The big-four advisors are selling the blueprint. After 17 years watching Digital Transformation Offices, Cloud Transformation Offices, and Agile Transformation Offices become the bottlenecks they were created to remove, I have a clearer position. The AI Transformation Office is the single clearest signal that a company has already lost the transformation. It is a containment structure dressed as a leadership structure. Here is the dissolution model that actually works.
- The CAIO Mandate Fails When It Owns Activity, Not Decisions
The CAIO title exploded in 2024 and 2025. Most of the people in the seat have a 60-slide strategy, no P&L, no infrastructure budget, and no mandate to retire a single role. They are not leaders. They are shields. Here is what a real CAIO mandate looks like, and the seven questions any candidate should ask before accepting the role.
- Human-in-the-Loop Is the New Technical Debt
Every consulting deck currently sells human-in-the-loop as the safe default for AI. After 17 years inside operating models that try to scale, I have watched HITL quietly turn into the single biggest tax on AI value. It caps ROI, erodes trust in the system, trains the org to never let go, and accumulates in exactly the same way technical debt accumulates. Here is the four-step move to retire it without losing trust.
- AI Programs Fail Before the Model Arrives
MIT 2025 found that 95% of enterprise GenAI pilots return zero P&L impact. The cause is rarely the model. It is the question being asked. Most companies are calling chatbots agentic, supervising AI like a junior intern, and asking where AI fits instead of which problems would change shape if intelligence were unlimited and autonomous. Here is the reframe that fixes it.
- I Watched an AI Build an Entire Ad Agency in 45 Minutes. Here Is What That Actually Means.
An AI agent created brand guidelines, researched competitors, built a storefront, generated compliant ads from real inventory, deployed them on Meta, prospected 20 dealerships, and sent outreach DMs. All in 45 minutes. No API integrations. Just browser control. This is not a demo. It is a structural shift in how value gets created.
- Why AI Is Not a Bubble: The Scaling Evidence Every Executive Needs to See
AI task complexity is doubling every 3 months, six times faster than Moore's Law. AI scores 90% on PhD-level benchmarks where human experts average 65%. AGI timelines have collapsed from 50 years to possibly 5. The bubble narrative is not just wrong. It is dangerously complacent.
- The Orchestration Era: 6 Surprising Truths About AI's Real Impact in 2026
The starry-eyed experimentation phase is over. We have entered the Orchestration Era, where the ability to design human-agent workflows commands a 56% wage premium, 75% of CEOs have taken the AI seat, and 40% of projects will fail because organizations keep automating broken processes instead of redesigning them.
- The Operating Model Behind AI at Scale
Only 1% of organizations have achieved full AI maturity while 80% of AI projects fail. The gap between AI leaders and laggards is widening dramatically. The root cause is not technology. It is treating AI like a conventional IT project when it fundamentally operates by different rules.
- The Vibe Coding Revolution: What It Actually Means for Enterprise Software Development
Vibe coding is exponentially delivering more value, with fewer errors, and faster results than any development paradigm I've witnessed in 17 years. This is code democratization at its maximum expression, and organizations that dismiss it today are making the same mistake as those who dismissed the internet in 1995.
- The Autonomous Enterprise: Navigating the Strategic Shift to Agentic AI (2026-2030)
The emergence of autonomous agentic AI represents the most significant transformation in enterprise technology since the internet. By 2030, this technology will reshape how B2B organizations compete, operate, and create value. Early adopters are already achieving 20 to 60% productivity gains.
- Agentic AI Changes the Workflow Before It Changes the Brand
The death of the SaaS Feature War has arrived. In 2026, platforms are evaluated on their capacity to host autonomous agent ecosystems. If your infrastructure isn't agent-friendly, you're invisible to the systems driving B2B discovery.
- What Actually Works: AI Applications That Deliver ROI
After 17 years of AI implementations, I've seen what delivers ROI and what doesn't. Here's what the vendors won't tell you.
- How to Find AI Use Cases That Actually Make Money
I watched a $3M AI initiative die because nobody could answer one question. Here's the framework that prevents that.
- The People Problem Nobody Wants to Talk About
The question I hear most isn't about algorithms. It's about people. After guiding dozens of AI transformations, here's what I've learned about the human side.
- AI Productivity Is Real, But Not How You Think: 3 Years of Daily Use
From Grammarly to GPT-5, here's what three years of AI writing assistance taught me: the productivity gains are real, but the non-native speaker perspective changes everything.
- Sales Strategy Development: Enhancing Customer Satisfaction
Insights from my CRM consulting experience on building sales strategies that drive both revenue and lasting customer relationships, including the convergence of Sales and Customer Success into unified Revenue Teams.
- Interview Your Data: Google NotebookLM and the End of Surface-Level Research
After testing nearly every AI research tool on the market, I can state definitively that Google NotebookLM is the most useful tool for understanding complex material that I have ever encountered. This is the assessment of someone who has spent 17 years evaluating enterprise technology.