How to avoid the AI experimentation trap and manage technical debt

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.

Great snippet: “The real opportunity—the one that will actually generate returns—is to look carefully at your internal operations and the external customer journey and start with how you can create real value, in the near term, using AI tools.”

“…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

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?

MIT’s latest study shows: 95% of AI projects fail to deliver measurable business impact. The reason is not weak models – but the missing depth in research. Most companies simply lack the time, expertise, and resources to properly fine-tune, test, and adapt AI for their real business context. This is where ImplementAI MH comes in. We provide flexible, on-demand AI research support – from fine-tuning, pre-training, and evaluation to custom architectures across domains like Computer Vision, GenAI, and beyond. Our strength: turning complex research into robust, precise, and trustworthy AI solutions that companies can actually use – and doing so fast and flexibly, because we know that companies not only lack personnel but also the time to wait for results.

"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

🚨 95% of generative AI projects are failing. An MIT study highlights a tough reality: while investment in generative AI is skyrocketing, most initiatives don’t make it beyond pilot stages. The main reason? A gap between hype and real, sustainable value. Organizations often underestimate the complexity of integrating AI into workflows, aligning it with business processes, and addressing trust, governance, and data quality. The lesson is clear: success in AI isn’t about chasing the latest model—it’s about building reliable systems, defining clear outcomes, and ensuring trustworthy data foundations. Generative AI holds transformative potential, but only when deployed with a focus on practicality, transparency, and trust.