strategy

Want Employees to Embrace AI? Stop Selling It as a Productivity Tool

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Stanford researchers found that employees are more likely to embrace AI when it is introduced as a tool to enrich their jobs rather than merely boost productivity. Successful AI adoption depends on managers framing AI use to enhance meaningful work, providing time and training for exploration, and aligning performance metrics to reflect new job responsibilities. Without these supports, AI initiatives risk employee disengagement and reduced motivation to learn new skills.

https://hai.stanford.edu/news/want-employees-to-embrace-ai-stop-selling-it-as-a-productivity-tool

IT Modernization: Still a Make-or-Break Project for CIOs

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CIOs continue to view IT modernization and application portfolio rationalization as critical, complex initiatives essential for controlling costs and enabling innovation, especially in the evolving AI era. Leaders emphasize a business-driven approach that balances reducing complexity with meeting current and future organizational needs, often re-evaluating SaaS tools and considering custom AI-enabled solutions. Despite challenges like cost and data migration, ongoing modernization efforts remain indispensable for creating agile, future-ready IT environments that support better efficiency, risk reduction, and alignment with business goals.

https://www.cio.com/article/4225171/it-modernization-still-a-make-or-break-project-for-cios.html

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

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