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Scooped by
JC Gaillard
onto Digital Transformation Leadership March 7, 1:46 AM
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The more leaders learn about AI, and the more thoughtful they are about implementing it, the better they can safeguard their companies.
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From
digitalcxo
As the digital landscape continues to shift, the strongest organizations will be those where finance and technology work hand in hand.
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From
www
Forget simple IT bridges; today's architects command the complex nexus where business strategy, tech innovation and ecosystem survival converge.
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Experts weigh in on key AI challenges for business leaders, including risk management, ethics, and data management.
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From
www
Competitive advantage today depends not just on innovation, but on rethinking how organizations are structured and led. Innovative businesses are transforming decision-making, leadership and governance to build resilience in an era of rapid change.
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From
www
Because deploying technology is a means to an end rather than an end in itself, here’s a recap of the keys to achieving great outcomes by deploying a winning genAI infrastructure and architecture.
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Regenerative leadership can be an effective antidote to AI doomerism in the office, says this management expert.
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From
www
For CIOs, CAIOs and CTOs, the message is clear: AI wastes resources when treated as just a technology investment. It demands transformation at both leadership and operational levels.
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From
www
Some leaders perceive it as an all-or-nothing gamble.Taking a strategic, incremental approach can mitigate the risks that hinder leaders from making the right choice.
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From
hbr
Organizations that view AI as just another technology project will increasingly find themselves irrelevant. Success will go to those who adopt a balanced approach—being radically optimistic about AI’s potential while remaining cautious about its risks. By integrating structured frameworks like OPEN and CARE, organizations can navigate this challenge, harnessing AI’s transformative power while building the resilience necessary to thrive in an uncertain future.
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From
www
Implementing AI into DevOps requires developing a work environment that supports experimental approaches combined with data-driven insights.
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From
www
Leaders must instead embrace a structured, empirical approach to problem-solving—one rooted in objectivity and continuous learning.
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From
www
In the rush to implement AI, mistakes have been made. But there is a lot we can learn from these rollout errors.
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The more leaders learn about AI, and the more thoughtful they are about implementing it, the better they can safeguard their companies.
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From
www
Companies that combine innovation and trust have a competitive edge. Discover the best practices that ensure ethical, sustainable deployment.
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From
www
The rush to invest in AI has reconfigured IT’s priority list, exposing some organizations to future risks as legacy tech lingers, architecture updates get backburnered, and work with other emerging technologies peters out.
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From
www
Disruptive forces dictate the need for speed when it comes to key digital initiatives. Here’s how IT leaders are overhauling strategies and processes to streamline IT for quicker success.
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From
www
The shift to AI must be focused on how AI enhances decision making, efficiency and customer experiences, not just implementing AI for the sake of trend compliance.
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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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Are you experimenting with artificial intelligence, or are you “AI future-ready”? A new model maps four stages of enterprise AI maturity.
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From
www
Many enterprises are struggling with data silos, regulatory complexities and the rapid evolution of AI and analytics.
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In this latest version of the annual McKinsey Global Survey on AI, we look at the current trends that are driving real value from artificial intelligence.
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From
hbr
To drive continuous AI innovation, businesses should consider three key strategies: rigorous technical reviews, cross-functional AI integration, and structured conflict resolution. Regular, transparent progress assessments ensure AI investments align with customer needs, while embedding AI experts within mission-based teams enhances adaptability and upskills the workforce. Additionally, fostering a culture that embraces constructive conflict through clear escalation frameworks accelerates decision-making and innovation velocity. These principles have enabled Intuit to significantly boost efficiency, improve customer experiences, and scale AI-driven solutions.
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How can organizations equip their C-suite with essential skills for gen AI adoption and AI strategy to drive transformation and navigate regulatory challenges?
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Leaders need a low-cost, practical AI approach that quickly demonstrates ROI, avoids AI's security risks and solves specific business problems.
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From
www
AI’s impact on businesses has evolved dramatically over the years. Once seen as a tool for automation, it’s now become central to shaping business strategy. In the mid-2020s, AI is no longer just optimizing workflows—it’s redefining how industries like healthcare, finance and manufacturing operate.
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