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Scooped by
JC Gaillard
onto Digital Transformation Leadership May 1, 2018 1:27 AM
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Innovation is perhaps the single most important area that companies and businesses need to strive towards.
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Among frontline employees, 42% who are regular AI users report saving eight hours a week—the equivalent of a full workday. But most organizations haven’t figured out how to convert the time into value.
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From Coherence to Change, leading CIOs are accelerating business impact by developing the mindsets and muscles necessary to succeed when disruption isn’t an event but the environment we operate in.
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Uber's AI budget crisis highlights 'tokenmaxxing,' where surging token consumption lacks ROI. Enterprises face challenges in optimizing AI usage and costs.
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Scaling AI takes more than ambition—it takes the right architecture. This article explains how enterprises can move from disconnected use cases to agentic workflows by redesigning systems for orchestration, governance, and specialized agents that drive repeatable, enterprise-wide impact.
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Forward-thinking enterprises are pursuing AI realignment over replacement -- reshaping teams, redistributing authority and redefining roles to work alongside AI, not against it.
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Early experience implementing agentic workflows highlights emerging trade-offs that frontline leaders need to understand.
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Deloitte research shows why organizations must reinvent processes and workforces to unlock the full potential of the agentic enterprise
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Nine in ten CEOs say they are starting to see initial value from AI. To build on that momentum and unlock value at scale, they can adapt the proven disciplines of transformation.
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A pilot tells you the model can think. Only the architecture tells you whether it can be trusted to run.
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The value of agentic AI is not in the technology but in redesign.
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AI-driven change succeeds when leaders put people at the heart of every decision, informed by a rich understanding of their emotions, thoughts, and behaviors. They create the conditions for new ways of working to take hold.
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Few AI pilots scale successfully. Asking key questions about goals, governance, and culture can help them thrive.
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There’s a coming governance shift that will define enterprise AI in the next decade.
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Shared context lowers the marginal cost of each new deployment because knowledge, permissions and business logic are built once and reused.
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New research shows that organizations undergoing AI transformation succeed not by adopting one “best” operating model, but by making intentional design choices and executing them effectively.
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Spurred on by an army of enthusiastic consultants with prepackaged solutions, companies forgot that technology should serve business needs, not the other way around.
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The companies that are truly innovating with AI aren’t winning just because of the tech they use—those tools are broadly available. Their advantage comes from how, and how fast, they apply technology to solving real business problems at scale. That requires new organizational capabilities that take time to build.
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The tools driving transformation keep changing. The truths about leading people through it mostly haven’t.
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Great tech won’t transform a business if people can’t turn it into action, value and lasting change.
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Data protection and cyber resiliency are far too often a checkbox for C-suite leaders, deprioritized in favor of flashy tools or AI solutions.
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Technology may power AI transformation, but people determine whether it succeeds. Leaders can build trust through transparency, clarity, and investment in employees to create lasting change.
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Enterprises looking to make the most of agentic AI will have to rethink how work gets done and how teams are organized, without forgetting the human workers who set their companies apart.
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CIOs can now track AI spending, but boards want proof of value. IT leaders offer advice on how to measure outcomes, not just token costs.
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Organizations should establish cross-functional AI governance committees that include executive leadership alongside technology specialists.
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IBM's recent earnings show a reallocation of investment toward AI infrastructure, yet CIOs must still find a way to fund IT as a whole.
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