Why AI adoption has no impact on earnings for most companies

As organizations embrace Gen AI tools, it's crucial to understand the journey ahead. While these innovations promise immense value, many firms encounter a "productivity J-curve," facing initial challenges before realizing long-term gains. Emphasizing a systematic approach to experimentation can help navigate this transition smoothly. https://okt.to/nqHM3L

Explore a structured strategy for experimenting with generative AI through this insightful article on Harvard Business Review: https://lnkd.in/gwwunX8V. This piece offers valuable guidance on systematically integrating AI innovation into your projects. #GenAI #InnovationStrategy

Recently, I read an insightful article from Harvard Business Review titled “A Systematic Approach to Experimenting with Gen AI.” It highlights how organizations can move beyond hype and adopt generative AI through structured, evidence-based experimentation starting small, measuring clearly, and scaling what works. My main takeaway: successful AI adoption isn’t about massive projects, it’s about building a culture of experimentation testing, learning, and iterating. Interestingly, I believe many of these principles can already be applied using tools like n8n. Even without large AI budgets, we can automate workflows, test ideas rapidly, and create tangible value exactly the kind of experimentation the article talks about. full article: https://lnkd.in/dD4jnkUX

Experimenting with Gen AI doesn’t have to be chaotic. The HBR article points out that successful organisations adopt a systematic approach to generative AI – starting with clear use-cases, controlled pilots, data readiness, and strong governance. Set up a process: Choose meaningful use-cases → Pilot with governance → Measure impact → Scale responsibly. At dataGridz, we help embed this discipline into your Data & Analytics programmes—so Gen AI becomes a strategic capability, not a random side project. #GenAI #ArtificialIntelligence #DataStrategy #dataGridz #DigitalTransformation https://lnkd.in/gxjZ6_FJ

The Hype Is Easy; The ROI Is Hard. The latest HBR research offers an interesting antidote: Systematic Organizational Experimentation. It urges to stop running random pilots. Smart pilots are about using a disciplined, scientific approach to bridge the gap between #GenAI adoption and measurable #impact. The formula for success is three-fold: Targeted: Focus on authentic, high-impact customer needs. Disciplined: Use scientific methods to test and refine solutions. Scalable: Accelerate learning to turn potential into real performance gains. #Experimentation is the engine of #competitive advantage in the AI era. #GenAI #ArtificialIntelligence #Innovation #DigitalTransformation #HBR

Gen AI offers huge potential—but value doesn’t come overnight. Smart firms are closing the gap through organizational experimentation—testing, refining, and scaling solutions to accelerate learning and reduce risk. Read more in HBR: A Systematic Approach to Experimenting with Gen AI #GenerativeAI #Innovation #Leadership

Some good thoughts on different areas where AI can be helpful, on how experimentation makes a big difference to understanding what is actually causal, and how AI won’t replace your most senior employees any day soon but is an immense help to those newer.

"At the heart of any successful gen-AI experiment lies a deep understanding of customer needs. Organizations must focus on solving specific, high-impact problems." It struck me that deep into a Harvard Business Review article on AI, we are back to the fundamentals. Technology isn't the barrier anymore. The first step is a commitment to evolve and clarity that this is evolution is perpetual & accelerating faster than we've ever seen. The second step is definition & prioritization. Then go! #AI #LLM #agenticAI #transformation #CX #revenue #productivity #competition

AI in AEC and MFG only works with clean data and strong processes. Learn how to reduce costs, curb knowledge loss, and build a practical AI strategy. Hear from Mike Hagedorn #AEC #Manufacturing #ConstructionTech #ProcessImprovement #DigitalTransformation #AIStrategy

🚨 Manufacturing just got a massive AI upgrade that changes everything. While most AI vision systems need THOUSANDS of reference images to work properly, Mindtrace.ai just launched NeuroForge™ - and it only needs 20. That's not a typo. Twenty images. 98%+ detection accuracy. Here's what makes this breakthrough different: → Automatically adapts when production changes (no system resets) → Integrates with legacy factory systems seamlessly → Delivers ROI faster than traditional ML solutions → Paired with Inspectra™ for predictive analytics across multiple production lines The timing couldn't be better. Rockwell Automation's 2025 report shows 50% of manufacturers plan to use AI for quality control in the next 12 months. But here's the reality check: Most "one-size-fits-all" ML solutions get mothballed after painful deployments due to inaccuracies. Mindtrace's Brain-Sense™ platform is solving this with physical AI that actually works in real manufacturing environments. The result? Virtually eliminated defect escapes, reduced costs, and improved productivity for precision manufacturers. What's your experience with AI implementation in manufacturing? Are you seeing similar breakthroughs in your industry? #ManufacturingAI #QualityControl #IndustrialAI #SmartManufacturing #AIInnovation #PrecisionManufacturing #Manufacturing #ArtificialIntelligence