strategy

A ‘Safe-to-Fail’ Culture Can Help IT Teams Achieve Great Things

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Building a safe-to-fail culture in IT teams involves removing fear of failure by providing employees with time, resources, and clear boundaries to experiment with new technologies in secure environments. Organizations should design controlled experiments focused on specific business problems, incorporate safeguards to limit potential damage, and recognize learning outcomes to drive practical business results. This approach promotes innovation while managing risks through disciplined testing, transparency, and predefined operational and security rules.

https://www.computerworld.com/article/4223580/how-to-build-a-safe-to-fail-culture-for-it-teams-and-why-you-should.html

The CIO’s New Mandate: Rearchitecting Enterprise Work

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The article argues that CIOs must lead a shift from simply managing enterprise applications to redesigning enterprise work itself, leveraging AI agents to orchestrate work outcomes rather than just automating tasks within discrete systems. This new discipline, called Enterprise Work Architecture (EWA), involves mapping business outcomes to work activities, defining authority and execution across humans and AI, and measuring economic impact, enabling more efficient, secure, and transparent operations across integrated applications. The transformation requires CIOs to collaborate across business functions to reimagine how work flows through technology and human judgment, focusing on outcomes rather than individual applications.

https://www.cio.com/article/4230816/the-cios-new-mandate-rearchitecting-enterprise-work.html

If AI Makes the Decision, Who Owns the Consequence?

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As AI increasingly shapes operational decisions rather than merely assisting tasks, organizations face a governance challenge in clearly defining who holds authority and accountability for AI-driven outcomes. Boards should focus on identifying material AI-supported decisions, mapping who can authorize, oversee, and accept consequences for these decisions, and demanding evidence that authority boundaries are respected and exceptions managed. Effective AI governance requires moving beyond inventories toward accountability frameworks that trace decision authority and ensure humans remain responsible for consequences.

https://www.cio.com/article/4226780/if-ai-makes-the-decision-who-owns-the-consequence.html

Stop Automating Old Processes. Design New Ones Instead.

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Companies often fail to realize enterprise-wide value from AI investments by automating individual tasks rather than redesigning entire workflows that produce business outcomes. The authors recommend a framework focused on redesigning workflows, explicitly assigning human authority, and preparing for exception handling to ensure AI improves end-to-end processes, preserves outcome quality, and manages risks effectively. Successful AI transformations require deliberate decisions about what to redesign, how humans and AI collaborate, and how to handle uncertainties, rather than mechanizing legacy work.

https://hbr.org/2026/09/stop-automating-old-processes-design-new-ones-instead

Why the Most Obvious AI Use Case Is Often the Wrong One

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Midsize law firms should focus AI adoption on work where efficiency gains are measurable and verification burdens are manageable, rather than simply targeting the highest-volume tasks. Key factors influencing ROI include the ease of verifying AI outputs and the firm’s pricing model, with fixed-fee work providing the clearest demonstration of commercial value. Firms already using AI informally must formalize their approach by selecting AI-suitable tasks that balance verification complexity and billing structure to build a strong internal business case and guide strategic investment.

https://www.thomsonreuters.com/en/institute/spotlight/midsize-firms-obvious-ai-use-cases

Employees Use AI, but Many Say They Don’t Know Why

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A Culture Amp study found that while 85% of employees are encouraged to use AI at work and 71% feel more productive with it, only 58% understand why their companies are implementing AI, and just 60% have received examples from managers on how AI supports their work. The report also noted similar workload levels between AI users and nonusers, suggesting a gap between perceived productivity gains and actual output, and emphasized the need for leadership to provide clearer communication and strategic frameworks around AI adoption to realize true organizational ROI.

https://www.hrdive.com/news/employees-use-ai-but-many-say-they-dont-know-why/831073/

7 Ways to Restructure IT for Maximum Productivity

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CIOs can boost IT productivity by restructuring around business value, consolidating key teams like security and engineering, and creating cross-functional groups with end-to-end ownership of business capabilities. Emphasizing workflow optimization, domain expertise with mentoring, disciplined tool management, and collaborative problem-solving reduces friction, breaks down silos, and aligns IT efforts with strategic outcomes. Integrating AI thoughtfully into platforms and operations further enhances agility and impact across technology functions.

https://www.cio.com/article/4224624/7-ways-to-restructure-it-for-maximum-productivity.html

Enterprise AI Desperately Needs a Lifecycle for Context

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Enterprise AI applications require disciplined management of business context—such as definitions, policies, and rules—that goes beyond data accuracy and security. Adopting a Context Development Lifecycle, akin to software development practices like version control and testing, can help enterprises define, encode, review, test, publish, and update context consistently, ensuring AI systems remain aligned with evolving business knowledge and reducing maintenance complexity.

https://www.cio.com/article/4223499/enterprise-ai-desperately-needs-a-lifecycle-for-context.html

AI Failures Are Inevitable. So Is the CIO Getting Blamed.

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CIOs are increasingly held accountable for AI failures within their organizations, despite often lacking control over AI vendor selection or visibility into AI agent actions. This accountability gap highlights the urgent need for stronger AI governance frameworks, shared organizational responsibility, and automated controls to manage AI risks effectively. Experts emphasize that while CIOs should lead AI governance efforts, responsibility must be clearly distributed to avoid unsustainable blame on IT leaders alone.

https://www.cio.com/article/4222997/ai-failures-are-inevitable-so-is-the-cio-getting-blamed.html

3 Best Practices to Bridge the AI Strategy and Governance Divide

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To bridge the divide between AI strategy and governance, organizations should first move risk tiering upstream by integrating risk assessment into early portfolio and funding decisions to avoid costly oversight later. Second, CIOs should focus on owning observability by building transparent AI inventories and telemetry systems that assign clear accountability to business units rather than IT alone. Third, making technical governance artifacts part of the AI platform—rather than standalone policies—ensures governance is embedded in operational systems, promoting scalable oversight aligned with business objectives.

https://www.cio.com/article/4214897/3-best-practices-to-bridge-the-ai-strategy-and-governance-divide.html

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