AI

EY Global Vice Chair: AI’s Biggest Paradox Comes Down to What AI Can’t Do

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EY Global Vice Chair Julie Teigland emphasizes that while AI excels at repetitive tasks and data processing, it cannot replace essential human judgment required to set goals, interpret context, and make decisions. She highlights that companies miss out on significant AI productivity gains due to gaps in talent development, particularly in cultivating AI fluency as a leadership skill that integrates technical operation with critical evaluation. Teigland advocates for collaborative approaches across organizations and ecosystems to efficiently scale AI technology without reinventing solutions, citing EY’s work with partners that improved operational performance and speed.

https://fortune.com/2026/09/27/ai-fluency-human-judgment-leadership/

Why This CIO Won’t Let an AI Agent Take the Blame

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Brigitte Larivière, CIO at Bombardier, emphasizes human accountability in automation by requiring a named person responsible for each AI agent deployed, ensuring clear ownership and measurement of outcomes. She prioritizes automating IT tasks—aiming for 80% automation of outsourced infrastructure work by 2027—while maintaining on-site support to directly assist manufacturing workers and fostering controlled citizen development with approved tools. Larivière also highlights cybersecurity as a critical concern amid rapid technological changes, focusing on transparency and risk management as automation and AI adoption accelerate.

https://www.digitaljournal.com/article/why-this-cio-wont-let-an-ai-agent-take-the-blame/

Microsoft Copilot Now Lets Marketers Build Their Own Apps and Dashboards

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Microsoft has introduced a new Code workspace within Copilot that enables marketers to create internal apps, dashboards, trackers, and automations using natural language without traditional programming. This feature, along with Copilot Managed Runtime, allows organizations to build, host, and control custom tools directly within the Microsoft 365 environment. The update aims to empower non-developers to create and deploy small software solutions for marketing needs, potentially reducing reliance on separate third-party applications.

https://searchenginewatch.com/microsoft-copilot-now-lets-marketers-build-their-own-apps-and-dashboards/

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

How CIOs Use AI to Better Manage Data Lifecycles

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CIOs are leveraging AI to enhance data lifecycle management by improving data quality, curation, and accessibility across creation, storage, usage, archival, and destruction stages. While AI can automate tasks like data cleansing and anomaly detection, leaders emphasize that high-quality, trustworthy data and human oversight remain critical, especially in regulated industries. Despite current limitations and trust concerns, organizations are integrating AI-driven tools incrementally to boost efficiency in targeted processes such as metadata tagging, data governance, and fast information retrieval.

https://www.cio.com/article/4222776/how-cios-use-ai-to-better-manage-data-lifecycles.html

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/

The EU AI Act Is a CX Problem Now

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The EU AI Act, effective August 2026, extends beyond legal compliance to impact customer experience (CX) operations, as AI is deeply integrated into contact center tools, workflows, and analytics. CX leaders must actively govern AI use by understanding its deployment, risk levels, and ensuring appropriate human oversight to maintain customer trust and meet regulatory requirements. Technology vendors also play a critical role by providing transparency and controls to support AI governance, making compliance a fundamental part of CX strategy and technology decisions.

https://www.cxtoday.com/security-privacy-compliance/the-eu-ai-act-is-a-cx-problem-now-ttecdigital-cs-0062/

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

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