How to create value with AI tools in operations and customer journey

The real AI challenges are more "people & process" than technology. IME AI experiments "fail" because they're designed around existing hierarchical structures, and executive sponsors are (understandably) generally unwilling to rock the corporate boat; once you've got a "10x process improvement quick win," the logical next step is to zoom out and reengineer the organization's fundamental CX/organizational structure/workers' economic incentives—legacy implementations, which include entire departments with specialized bureaucratic functions, are rarely fit for purpose in this new context. Until there's enough pain to drive fundamental organizational change, we'll keep seeing expensive AI theater instead of transformation. The technology demands flatter, more agile structures between executive decision-making and customer interaction; most (but not all!) enterprises are not quite ready to confront the particulars that get you from here to there. (Above is my $0.02; Harvard Business Review article, linked, resonated with my own firsthand experience—and with what I've heard from other credible applied AI practitioners—and is worth reading in its entirety.) https://lnkd.in/eke28jMC

This would measure the maturity of IT governance, organizational culture, innovation appetite and if IT is a strategic partner or just service delivery.

“…The technology demands flatter, more agile structures between executive decision-making and customer interaction; most (but not all!) enterprises are not quite ready to confront the particulars that get you from here to there…” Christian Ulstrup #ai #management #digitaltransformation

“Think more AI experiments equals more value? Not if they're scattered and aimless.” A new MIT Media Lab report shows 95% of generative-AI investments deliver zero returns. Avoid repeating digital-transformation mistakes: focus AI pilots on core customer needs, keep them low-cost and scalable, and empower agile “ninja” teams. How can your next AI initiative drive real value? #AIExperimentation #DigitalTransformation #SmartInnovation Read more: https://lnkd.in/epKfqcjH

The Harvard Business Review article "Beware the AI Experimentation Trap" serves as a practical illustration of how "uncontrolled control" leads to organizational technical debt. The article argues that many companies are failing in their AI initiatives by repeating the mistakes of past digital transformations, namely, by funding a scattershot of pilots and experiments that are not connected to a cohesive strategy or measurable business value. This chaotic, reactive approach—where short-term hype drives investment decisions rather than long-term strategic goals—is the essence of uncontrolled control. It prioritizes the appearance of progress over genuine, sustainable innovation. This lack of discipline and strategic foresight results in a form of technical debt far more damaging than just bad code. When 95% of investments yield no measurable returns, a company accrues significant organizational and financial debt in the form of wasted resources, eroded trust, and lost opportunities. The article's core message is a call to pay down this debt by replacing uncontrolled “control” with disciplined management. The proposed solution—to focus experiments on solving core customer problems and to design them with a clear path to scalability—is the equivalent of a strategic "refactoring" effort aimed at ensuring future AI investments build real value instead of simply adding to a growing pile of failed projects. https://lnkd.in/gjJpwiFq

"US researchers that analysed hundreds of enterprise level generative AI tools found only one in 20 actually delivered significant value, despite big businesses pouring more than $60 billion into the technology." With much of the recent "productivity" conversation framing AI governance and regulation efforts as merely as a handbrake on progress and other "guaranteed" upsides, this piece by Joseph Brookes highlights that blind faith AI adoption is equally counterproductive and inefficient. Taking a more responsible approach to AI is largely about paving the way to more success and positive impact with AI implementations (and less re-work and backtracking) - happily, some of the clients we're working with on AI risk and governance see it exactly this way. Please reach out if you'd like to discuss more. https://lnkd.in/g3eFrNnT

AI’s Clock Speed is Accelerating This chart from METR tells a profound story: The length of tasks AI can complete at a 50% success rate is doubling every 7 months. What started with GPT-2 answering questions in seconds has scaled to today’s frontier models (GPT-4, GPT-4o, Sonnet 3.7) handling tasks like training classifiers or building robust image models—things that once took humans hours. The implications are staggering: Productivity Compression → Work that took months to execute may soon take minutes. Capability Compounding → Each model builds on the last, accelerating discovery and application. Strategic Urgency → Enterprises and governments don’t just need AI roadmaps—they need adaptive AI operating systems that evolve with this pace. But speed also sharpens risks. As models race forward, governance, safety, and resilience must scale at the same rate—or faster. This is the defining paradox of our age: AI is compounding like Moore’s Law on steroids, but our institutions move at human speed. The question isn’t whether AI can keep doubling—it's whether leadership, governance, and society can keep up. 👉 What do you think? Are we ready for this pace, or is the gap widening too fast?

AI’s Clock Speed is Accelerating This chart from METR tells a profound story: The length of tasks AI can complete at a 50% success rate is doubling every 7 months. What started with GPT-2 answering questions in seconds has scaled to today’s frontier models (GPT-4, GPT-4o, Sonnet 3.7) handling tasks like training classifiers or building robust image models—things that once took humans hours. The implications are staggering: Productivity Compression → Work that took months to execute may soon take minutes. Capability Compounding → Each model builds on the last, accelerating discovery and application. Strategic Urgency → Enterprises and governments don’t just need AI roadmaps—they need adaptive AI operating systems that evolve with this pace. But speed also sharpens risks. As models race forward, governance, safety, and resilience must scale at the same rate—or faster. This is the defining paradox of our age: AI is compounding like Moore’s Law on steroids, but our institutions move at human speed. The question isn’t whether AI can keep doubling—it's whether leadership, governance, and society can keep up. 👉 What do you think? Are we ready for this pace, or is the gap widening too fast?

AI’s Clock Speed is Accelerating This chart from METR tells a profound story: The length of tasks AI can complete at a 50% success rate is doubling every 7 months. What started with GPT-2 answering questions in seconds has scaled to today’s frontier models (GPT-4, GPT-4o, Sonnet 3.7) handling tasks like training classifiers or building robust image models—things that once took humans hours. The implications are staggering: Productivity Compression → Work that took months to execute may soon take minutes. Capability Compounding → Each model builds on the last, accelerating discovery and application. Strategic Urgency → Enterprises and governments don’t just need AI roadmaps—they need adaptive AI operating systems that evolve with this pace. But speed also sharpens risks. As models race forward, governance, safety, and resilience must scale at the same rate—or faster. This is the defining paradox of our age: AI is compounding like Moore’s Law on steroids, but our institutions move at human speed. The question isn’t whether AI can keep doubling—it's whether leadership, governance, and society can keep up. 👉 What do you think? Are we ready for this pace, or is the gap widening too fast?