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Scooped by
JC Gaillard
onto Digital Transformation Leadership June 2, 3:33 AM
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AI governance does not fail because accountability is absent. It fails because accountability is scattered across roles that were not designed for AI's cross-functional reach.
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For many years, enterprise transformation was largely framed around digitization, cloud migration, automation, and data-driven decision-making. Those priorities remain important, but they no longer define the frontier.
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From
www
Most IT leaders believe their organizations need major operating model and business processes changes to realize the value of AI.
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www
CIOs are turning agentic explorations into production services. From getting past the fear to embracing rapid change, here’s how digital leaders turn agents into powerful work colleagues.
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www
Companies Winning With AI Measure It Differently and Manage the Human Side
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www
Enterprise leaders already sense a constraint on their AI efforts, but many aren’t looking in the right direction for solutions.
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www
Leading enterprises are redesigning workflows, decision-making and business models around intelligence instead of layering AI onto existing processes.
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www
How can companies lean into the innovation agenda when there are so many urgent demands on a company’s resources and leaders’ time? And how can they address innovation holistically when AI, understandably, dominates the agenda?
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www
How do you create real ROI from autonomous AI? Three digital leaders share lessons they've learned in the field.
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www
With data quality and governance key to AI success, IT leaders — and their CEOs — can no longer overlook data debt. Experts offer tips for remediation.
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www
Pressured to bring AI benefits to the bottom line quickly, CIOs are cutting corners in ways that will result in some of IT’s most debilitating tech debt yet.
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www
Buying AI tools is easy, but getting your team to actually use them requires real trust and leadership, not just a launch email.
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While AI offers transformative opportunities for growth and productivity, it also introduces new and rapidly evolving risks, from security and bias to operational and reputational exposure.
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The real risk is not that AI replaces the CEO. It is that AI is already replacing the leadership competency that matters most—the judgment calls about what the organization is, what it stands for, and what it treats as true.
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As the agentic era reshapes the AI economics of enterprise technology, organizations have an opportunity to govern run-rate exposure as a growth investment, protecting operating budgets and margin while compounding enterprise value.
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From
thenewstack
Developers are building AI apps at a breakneck pace, but most organizations don’t have the infra or operations capacity to move them into production.
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From
hbr
There’s a consistent pattern in failed or underperforming AI initiatives. Business leaders tend to frame AI through the lens of what they see as the most urgent problems—bottlenecks in productivity, rising costs, slow decision-making, or inefficiencies in workflows. This focus on urgent and immediate challenges may be why AI initiatives start fast and generate excitement, but ultimately fail to transform organizations in meaningful or lasting ways.
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Since everyone has access to the same AI tools, winning is no longer about the tech itself, but how smartly you build your strategy around it.
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Why AI bills are exploding, and what CEOs and CIOs can do about it. The good, bad, and ugly of AI overuse, and steps to tie AI use to ROI and business outcome metrics.
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www
Almost every company has AI tools, but few really know how to use them. Leaders at 15 AI-savvy companies say the difference comes down to seven operating truths—that most organizations still get wrong.
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From
hbr
Generative AI’s gifts come with a hidden danger: decay in the accuracy and quality of organizational knowledge. This decay is the organization-level version of the “workslop” problem. When workslop occurs in sequence across a business’s processes, those processes themselves—and their outputs—start to deteriorate, errors compound and pile up, trust erodes, and the productivity gains of AI disappear.
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From
dataconomy
For years, large companies have treated transformation as a program: a centralized office, a single roadmap, multiple parallel streams, and regular reporting to the top. This model creates structure, but it also introduces limits of its own.
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From
www
You cannot buy technology and assume it will create an advantage. Competitive advantage tends to come from who is most effectively using their technology.
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As AI supercharges what business technology can do, CIOs and CFOs must work together to demonstrate the return on their companies’ IT investments.
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www
The economics of autonomous agents depend less on the model and more on how much thinking, looping, and tool use you permit.
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Embedding trust, transparency, and governance into engineering processes
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