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Scooped by
JC Gaillard
onto Digital Transformation Leadership March 29, 1:48 AM
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The emergence of agentic AI is putting fresh pressure on the infrastructure layer. If Nvidia CEO Jensen Huang is correct in his assumptions, demand for accelerated compute will increase by 100x as enterprises deploy AI agents based on reasoning models. Where will customers get the necessary GPUs and servers to run these inference workloads?
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From
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Enterprises don’t fail at transformation — they fail at learning. Only those who engineer continuous, semantic adaptability will survive the next wave.
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We are experiencing one of the most transformative moments in human history. We must prepare strategically, integrating skills, flexibility, and security awareness.
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From
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The cloud is moving rapidly in new directions. Here’s a rundown of the latest top strategy trends.
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Never forget that people are key to your digital transformation project.
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The future of digital transformations will belong to leaders who can masterfully integrate engineering talent with organizational readiness.
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From
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Many enterprises will delay a quarter of their planned AI spending as they hunt for more positive impact on their bottom lines, Forrester predicts. Others contend spending will hold — but get smarter.
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From
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Why AI initiatives stall under accumulated technical debt — and how CIOs can overcome it.
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From
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Digital transformation “fatigue” is becoming a real problem as enterprises look to modernize at rapid pace
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If you are running faster in the wrong direction you will only get to the wrong place faster.
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Agentic AI is not just for tech companies and, no, it won't replace all jobs. As you move forward, ignore the rumor mill.
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AI’s next leap isn’t about thinking faster—it’s about lasting longer. The future of intelligence depends on engineering trust and endurance.
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Most AI projects never scale. MIT's Paul McDonagh-Smith explains why culture, governance and leadership, not technology, decide who succeeds.
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We’re on the cusp of a new software-enabled business model that will determine winners and losers in the coming decades. We call this service-as-software. Specifically, we believe enterprises will begin to organize knowledge work in new ways that harmonize islands of automation into a build-to-order assembly line for knowledge work.
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Resilient leaders don’t aim to restore what was; they reinvent what’s next.
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From
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Actionable guidance from leading CIOs and experts to balance innovation and data protection in the age of AI.
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From
technative
Aligning cloud infrastructure, data management and organisational skill sets will be the difference between firms that talk about AI value and those that actually realise it.
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From
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IMD’s AI Maturity Index assesses how the top 300 companies in the Forbes Global 2000 are advancing their AI strategies. One key pattern is that AI maturity requires strong ethical governance.
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Interestingly, it’s not usually the quality of the technology but misaligned goals, poor planning or unrealistic expectations that caused failure.
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As AI adoption accelerates, trust and security must become part of the foundation, not an afterthought.
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From
www
After a flood of AI experimentation, IT leaders are going back to the basics of governance, change management, and metrics to ensure initiatives deliver key value for the business at scale.
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As organizations race to achieve outsized benefits from AI, CFOs must address a frequently overlooked driver of optimal AI returns: internal control structures.
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Most generative AI projects fail to show measurable ROI despite billions in investment. Experts point to weak data infrastructure as the underlying cause preventing enterprise AI from reaching profitable scale.
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From
technative
Too many organisations are still operating as they go. Measuring the quality of agent behaviour is often ad hoc, based on gut feel rather than consistent benchmarks, which undermines trust and makes it incredibly difficult to prove value.
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From
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While a large percentage of IT and business leaders believe their AI efforts will meet or exceed expectations, only a small number have successfully deployed projects thus far.
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From
hbr
Companies are under pressure to pause digital innovation as costs rise and uncertainty grows—but holding back risks falling further behind in the age of AI and data-driven change. Research shows the most effective organizations avoid waste by funding projects incrementally based on evidence, pairing autonomous teams with shared resources, and continually reallocating talent and capital to initiatives with the greatest strategic impact. By adopting these habits, firms can double down on digital innovation while ensuring that every investment advances long-term business value.
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