How to move AI from pilot to production with CloudZA

The results of this MIT report come at little surprise. Only a small number of young startups got real value by generative AI while 95% of generative AI pilots at companies are failing. Yes, there are some unicorns in our mythology, but magics don't help business. Value only comes when smart people identify the right need and satisfy it with sustainable solutions. https://lnkd.in/eEiXCteC

Most companies don't get value from AI pilots. As Dr. Robert Konrad Maciejewski points in https://lnkd.in/e5UJ4ujt this gives employees a new perspective. Perhaps.

IFS has released the results of its global study on the accelerating scale of Industrial AI adoption across asset-intensive industries. The study found that while organizations, particularly in the service industry, are adopting AI today, they are not fully prepared for its full implementation.

For all the hype around AI, a recent MIT study reported that 95% of GenAI pilots failed to deliver a meaningful return on investment. That’s a staggering number (I was certainly surprised by it)- especially given how much time AND money organizations are pouring into these projects. Does that sound right to you? Why do you think so many AI initiatives struggle to deliver real value? https://lnkd.in/en8ScFWc

The New Era of AI: Why Efficiency and Openness Will Define the Next Wave Modern AI models are revealing something profound about where the industry is headed. It is about a structural shift in how intelligence is built, deployed, and scaled. Let me explain why. 1️⃣ Efficiency Is the Core Game For years, progress meant “bigger models, better results.” But modern AI models have exposed the real frontier: how much performance you can extract per dollar, per joule, per bit. The new question isn’t “How big can we go?” but “How lean can we scale?” Models that deliver frontier-level reasoning at a fraction of the cost will reshape accessibility — enabling deployment across edge devices, smaller labs, and emerging markets. This isn’t downsizing — it’s right-sizing. The future winners won’t be those who build the biggest models, but those who run intelligence efficiently. 2️⃣ Large Yet Lean: Sparsity Comes of Age Dense architectures once ruled AI because they were simple to train and parallelize. Now, sparsity and mixture-of-experts approaches are changing the game. By activating only the “experts” needed for each task, modern models are both massive in potential and nimble in execution. You get scale without waste — intelligence that routes itself like a living system. The shift is clear: efficiency isn’t the trade-off to performance; it’s becoming the multiplier. 3️⃣ Openness as Strategy The conversation around open models is evolving. Openness isn’t just ethical — it’s strategic. When the underlying systems are accessible, innovation compounds. Communities build faster, competition gets healthier, and trust grows. Closed ecosystems may hold power now, but open frameworks will win longevity. In AI, transparency fuels velocity. 4️⃣ Infrastructure Becomes the Differentiator Underneath the algorithms lies the quiet revolution — infrastructure. Performance gains increasingly come from optimized clusters, memory bandwidth, interconnects, and software stacks tuned for AI efficiency. Success now requires being as much an infrastructure company as an AI company. The next wave of competitive advantage won’t come from another transformer variant — it’ll come from who best fuses hardware, data, and model design into one seamless system. 5️⃣ The Shift From FOMO to Focus For organizations, this new reality means: • Stop chasing “bigger” — pursue smarter scaling. • Value openness and interoperability over exclusivity. • Build infrastructure competence as a core capability. • Expect falling costs — and rising responsibility — as AI becomes ubiquitous. We’re entering an era where the winners won’t just be model architects or data scientists, but efficiency engineers and infrastructure visionaries. Modern AI models mark the turning point from brute-force intelligence to sustainable, open, and efficient intelligence. The real question now isn’t how big can we build? — it’s how intelligently can we scale?

"AI will save us money." I’ve heard that line so many times, I’ve lost count. But after reading a recent Bain & Company report on scaling AI while controlling tech costs, I realized something, AI isn’t just a cost saver. It’s a cost amplifier and a cost detective. Here’s what I mean. AI gives companies incredible visibility. It can tag expenses in real time, spot underused software, and even uncover “shadow IT” spending that slips through the cracks. One company used it to find tens of millions in hidden costs. Another used AI to right-size its cloud usage, cutting unnecessary spending and simplifying infrastructure. But here’s the twist: AI also brings new costs. Shorter tech lifecycles. Heavier data needs. Constant retraining. It’s like cleaning out your closet, you free up space, but you also realize how much more you own than you thought. The leaders getting this right aren’t chasing shiny tools. They’re simplifying architectures. Embedding AI where it matters. And using the savings from today to fund tomorrow’s transformation. So maybe the real question isn’t “Can AI cut costs?” It’s “Can we scale AI without letting complexity eat the savings?” https://lnkd.in/d7DEGG5K

Many companies are facing challenges with generative AI, particularly in industries like insurance, financial services, private equity, and HR. A recent report highlights that a significant number of internal generative AI projects are failing, with 95% of pilots not achieving desired outcomes. To mitigate these risks, it's crucial to partner with specialists who understand the complexities of enterprise AI. Engaging firms like CR Labs.ai can lead to exponential improvements in results without increasing overall build costs. The report indicates that collaborations with specialized vendors succeed about 67% of the time, compared to only one-third for internal builds. As organizations navigate the landscape of generative AI, leveraging decades of experience from trusted partners is essential to ensure successful implementations and minimize risks. The stakes are too high to go it alone. For more insights, check out the full report here: https://lnkd.in/eNyMB3DT

Generative AI – and now Agentic AI – is transforming the way we live and work on a scale that hasn’t been seen since the dotcom boom. Organizations everywhere are looking for ways to use GenAI to gain efficiencies and boost their competitive advantage. However, while advances in these technologies are enabling innovation, they are also increasing infrastructure complexity. https://lnkd.in/dhVpSV_5 #DigitalFriction #KeepAIInnovation #OnTrack

At first it was all about how AI can take out cost, or at least improve productivity. But the conversation is increasingly also about how to embrace AI at scale without ballooning tech costs.

Reinventing IT Services 🧑💻👩💻 in the Age of AI 🤖 Insightful piece from Bessemer Venture Partners on how #AI isn’t just transforming IT services, it’s redefining where and how value gets created. This means moving from building technology-for-the-business to building technology-as-the-business. The next frontier belongs to those who can turn AI from a buzzword into a business multiplier, that’s where the next wave of differentiation and margin growth will come from. https://lnkd.in/ew8gfwQ8