Navigating the Productivity J-Curve with Gen AI Tools

80% of companies reported that AI had no significant impact on earnings in 2025, despite rapid adoption. This is because businesses lack a clear strategy for AI integration. According to this author, the answer lies in an experimentation approach. https://lnkd.in/gWQdKxW8

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

As I wade through all the noisy data about AI, the absurd claims, and ideological battles between believers and cynics, this new Harvard Business Review piece is among the most sensible and carefully researched articles I've have read. It is evidence-based, filled with concrete examples of AI experiments in places like Google and Procter & Gamble, and I especially loved this measured antidote to the quest for instant gratification that infects so many AI hucksters and victims of AI hurry-sickness out there: "For a historical parallel, think about electricity: Manufacturing plants took almost 40 years to adapt to the technology and optimize themselves around it, according to the late economic historian Paul David." I also liked the range of skills among the five authors on the paper--three academics (in different fields, it seems, two from Harvard Business School and one from Ludwig-Maximilians-Universität München) and two executives from Siemens.

Thank you to everyone who joined us for yesterday’s webinar on AI in PDM! In this clip, bananaz' Or Israel shares what led him to explore AI-powered design validation — a challenge every engineering leader will recognize. If you missed the live session (or want to revisit the demo), the recording is now available. You’ll see how teams are using embedded AI to accelerate reviews, improve visibility, and reduce rework, all without disrupting existing CAD or PDM workflows. 🎥 Watch the recording: https://bit.ly/4nQgvNf #PDM #AI #DesignEngineering