automation

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/

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

What Changes When AI Becomes Part of How the Business Runs?

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As AI adoption moves from pilot projects to integral enterprise operations, CIOs face challenges beyond technical capability, including trust, security, change management, and operational resilience. Successful scaling requires realistic evaluation of AI’s business value, capacity to support and maintain AI solutions, and risk management tailored to each use case, with governance that balances protection and flexibility. Organizations must prioritize where to move quickly and where to hold back, focusing on clear process improvements and readiness before depending on AI for critical decisions and operational workflows.

https://www.cio.com/article/4216543/what-changes-when-ai-becomes-part-of-how-the-business-runs.html

Before You Automate Anything, Learn to Measure It

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Automation delivers sustained value only when organizations measure its impact rigorously from the start by establishing a detailed baseline of current manual processes before implementation and then tracking a focused set of efficiency metrics over time. Without this discipline, initial gains erode as workflows, exceptions, and workarounds accumulate, underscoring the importance of standardizing improved processes and scheduling regular remeasurement to detect and correct value decay. For agentic automation, additional governance and metrics on autonomy, escalation, and reversals ensure that automation actions remain reliable and truly reduce human workload.

https://www.cio.com/article/4213758/before-you-automate-anything-learn-to-measure-it.html

Don’t Automate Bad Workflows: Why AI Should Begin with Redesign

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Organizations should prioritize redesigning workflows before applying AI automation, as many existing processes have become inefficient over time due to accumulated approvals, redundancies, and outdated steps. Effective AI deployment begins with simplifying, standardizing, and rethinking workflows to align with desired business outcomes, enabling automation to enhance value rather than merely accelerating outdated practices. This approach helps improve operational efficiency, employee experience, and measurable business results while avoiding the risk of embedding past inefficiencies into future technology.

https://www.cio.com/article/4207454/dont-automate-bad-workflows-why-ai-should-begin-with-redesign.html

How Will AI Automation Hit — Like a Crashing Wave or a Rising Tide?

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New research from MIT FutureTech shows that AI automation is progressing gradually across many text-based workplace tasks rather than arriving as sudden disruptive waves. Evaluations of over 6,000 tasks found AI can already perform 50–75% of them sufficiently without edits, with steady improvement allowing time for workers and organizations to adapt. The study suggests that AI-enabled task automation will vary across occupations, offering a window for strategic planning rather than immediate widespread disruption.

https://mitsloan.mit.edu/ideas-made-to-matter/how-will-ai-automation-hit-a-crashing-wave-or-a-rising-tide

The Production Assumptions AI Just Broke

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AI agents disrupt traditional production assumptions by acting autonomously, generating unpredictable workloads and traffic patterns that challenge existing operational models. CIOs must adapt production environments with enhanced observability, incident response playbooks, capacity planning, and change management tailored to AI’s distinct behaviors before scaling AI-driven workflows enterprise-wide. Preparing production for AI’s operational impact is critical to avoid instability, ensure traceability, and support sustainable AI adoption beyond pilot stages.

https://www.cio.com/article/4205139/the-production-assumptions-ai-just-broke.html

Should You Use AI for a Task? Here’s a Simple Way to Decide

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The article presents a practical framework for deciding when to use AI by distinguishing between “work” tasks, which require efficiency and completion regardless of process, and “gym” tasks, where the process itself fosters skill development and learning. AI is appropriate for work tasks that need to be done reliably and efficiently, but using AI for gym tasks—such as writing assignments meant to cultivate critical thinking—is counterproductive because it bypasses essential cognitive effort. This distinction also applies broadly to creative and professional activities, suggesting organizations should thoughtfully integrate AI while preserving tasks that maintain human skills and judgment.

https://www.schneier.com/blog/archives/2026/07/should-you-use-ai-for-a-task-heres-a-simple-way-to-decide.html

AI and Workplace Productivity: What Leaders Need to Know in 2026

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Gallup's data indicate that while AI tools have improved individual productivity for many employees, they have yet to produce significant organizational-level productivity gains, largely due to a lack of workflow redesign and insufficient manager support. Frequent AI use correlates with better integration into workflows and active managerial encouragement, which together drive deeper adoption and transformative impacts on work processes. Additionally, uneven AI adoption across industries and roles, combined with employee anxiety about job security, highlights the critical need for clear AI strategies and proactive change management led by managers to realize AI’s potential benefits and mitigate risks.

https://www.gallup.com/workplace/713063/ai-workplace-productivity.aspx

What Is RPA? A Revolution in Business Process Automation

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Robotic process automation (RPA) uses software bots governed by business logic to automate repetitive, rules-based tasks across enterprise workflows, enabling organizations to reduce costs, increase accuracy, and free up employees for higher-value work. Successful RPA implementation requires careful design, IT involvement, governance, and change management, and can be enhanced by integrating AI technologies for intelligent automation that handles more complex processes. Leading enterprises such as Siemens Mobility and the US Marine Corps demonstrate RPA's impact on operational efficiency, while the evolving market includes various RPA tools and certifications to support scalable deployments.

https://www.cio.com/article/227908/what-is-rpa-robotic-process-automation-explained.html

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