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IBM

Deploying, Governing, and Scaling AI-Powered Automation

IBM via Coursera

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Overview

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A working assistant in a trial environment is not the same as a governed, measurable automation that drives enterprise value. Most automation programs struggle to move from successful pilots to sustained, scaled results because they lack the testing discipline, governance structures, and measurement practices that keep automation accountable over time. This course, Deploying, Governing, and Scaling AI-Powered Automation, addresses every dimension of that challenge. You will design test packs, plan controlled rollouts, set up monitoring that catches drift, and establish feedback loops. You will also apply the IBM AI Ethics and Trust Framework to build responsible-AI assessments, governance controls, and audit trails; define automation metrics; design dashboards for operational and executive audiences; and close the loop with the original business case. From there, you will plan how a single successful assistant becomes an enterprise automation program. By course completion, you will have assembled a portfolio-ready End-to-End Automation Solution integrating opportunity assessment, assistant build, governance, measurement, and a scaling plan.

Syllabus

  • Testing, Deploying, and Monitoring Assistants
    • In this module, you will build the discipline of moving an AI assistant from a pre-production build to a deployment you can stand behind. You will design a test pack that exercises happy paths, edge cases, and exception flows, plan a controlled rollout sized to the risk, and set up monitoring that catches drift, errors, and unexpected behavior. You will also establish a feedback loop that turns production signal into improvements. The labs build a test pack, a phased rollout plan, and a monitoring signal set for the assistant you carried over from Course 2.
  • Responsible AI and Automation Governance
    • In this module, you will bring the IBM AI Ethics and Trust Framework into operational focus. You will apply principles such as fairness, accountability, transparency, privacy, and explainability to the assistants you have built, identify the governance controls that higher-risk automation requires, and design audit trails that support accountability and explainability. You will also learn to recognize the regulatory considerations that shape automation design. The lab produces a responsible-AI assessment for the assistant you carried over from Course 2.
  • Measuring Automation ROI, Operational Performance, and Scaling
    • In this module, you will close the measurement loop and open the path to scale. You will choose value, quality, and adoption metrics that actually drive behavior, design dashboards and a reporting cadence for operational and executive audiences, and tie post-deployment performance back to the business case that funded the work. You will then plan how a single successful automation becomes an enterprise program, drawing on IBM Consulting automation-at-scale playbooks. The lab produces a metric set, a dashboard mockup, a reporting cadence, and a one-page scaling proposal for the assistant you built in Course 2.
  • Capstone: End-to-End Automation Solution
    • In this module, you will assemble work from all three courses into a single, portfolio-ready automation solution covering opportunity assessment, design, build, governance, measurement, and a scaling plan. You will learn to demonstrate the alignment between those choices, tell a coherent end-to-end story, and defend the solution against the challenges a stakeholder reviewer is likely to raise. The lab has you build out and submit your capstone package against the supplied rubric and complete a peer-review pass, preparing you for the Specialization capstone submission.
  • Specialization Capstone Project and Course Wrap-Up
    • In this final module, you will assemble work from all three courses into a single, portfolio-ready Specialization capstone: an End-to-End Automation Solution. The deliverable spans opportunity assessment and prioritization, a future-state design, a configured and tested assistant with integration specifications, and a responsible-AI assessment, monitoring plan, ROI and performance dashboard, and one-page scaling proposal. The module closes with a wrap-up featuring an SME interview, a day-in-the-life view of an Automation Program Lead, and guidance on presenting the capstone artifact in your portfolio

Taught by

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