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IBM

Identify and Prioritize AI Opportunities

IBM via Coursera

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Overview

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Turn the open question "where should we use AI?" into a defensible, prioritized AI opportunity portfolio. You learn to surface high-value use cases across functions and industries — including the fast-growing agentic and AI-assistant categories — by starting from how work actually gets done, qualify candidates honestly against value and feasibility, apply a value-effort lens to rank them, and group them into a balanced opportunity portfolio. These skills address the challenge research repeatedly names as decisive: choosing the right opportunities, not merely finding many.

Syllabus

  • Finding AI Opportunities
    • Find high-value AI opportunities across an enterprise by starting from how work actually gets done rather than from technology novelty. This module covers AI use case identification and discovery, workflow mapping, and process friction analysis, and it locates candidates by business function and industry, including the agentic AI and AI assistant categories reshaping enterprise work. Learners connect each candidate to a concrete source of business value, build an opportunity inventory from solicited input, observed use, and a standing audit, then estimate business value and implementation effort as two separate, arguable judgments. Learners also name the ledger: who verifies, corrects, escalates, or waits once a change is made, so relocated effort is recorded as a moved cost rather than a saving. Skills include AI opportunity assessment, business process analysis, stakeholder discovery, value and effort estimation, and AI strategy.
  • Qualifying Candidate Use Cases
    • Run a fast, structured screen that separates AI use cases worth deeper study from attractive-sounding ones that will stall. This module covers AI use case qualification, use-case triage, AI opportunity screening, and go/no-go criteria for AI projects. Learners normalize a vague proposal into a defined, comparable object, then judge it on separable dimensions — business value, workflow fit, data readiness, technical feasibility, risk, sponsorship, and measurement clarity — rather than as one blended impression. They learn why data readiness works as a gate rather than a weight, how to test whether the outcome a model would predict was ever recorded, and how to size AI risk by what a system is used for rather than by its technique. Skills include feasibility screening, data readiness assessment, AI risk screening, opportunity assessment, and AI pilot selection.
  • Prioritizing with a Value-Effort Lens
    • AI use case prioritization decides which initiatives an enterprise funds first, and this module teaches the value-effort lens that makes the decision defensible. Learners set prioritization criteria and weights before scoring candidate use cases, so impact and effort mean the same thing to every stakeholder. They estimate effort for AI initiatives by component — data readiness, integration, compliance burden, adoption resistance, and the operational capacity to absorb new AI workload — rather than as a single number. They price the direction of error into business value, plot candidates on an impact-effort matrix, and read the resulting groups into an AI opportunity shortlist with stated dispositions. The module builds skills in ranking AI opportunities, trade-off transparency, stakeholder alignment on AI priorities, and AI investment sequencing.
  • Building the Prioritized Opportunity Portfolio
    • Choosing among AI opportunities is where advisory work becomes accountable, and a list of good ideas is not yet a portfolio. This module teaches AI use case prioritization and portfolio prioritization for enterprise AI transformation. Learners assemble every candidate an organization can see through a tracked AI intake process, classify each candidate's regulatory and AI risk exposure against recognized risk management frameworks before weighing business value, and score the full set on two separate axes — expected value and expected effort — against one fixed set of scoring criteria. Learners then group the ranked shortlist into a balanced AI opportunity portfolio spread across value, risk, and effort, resolve duplicate automation through consolidation, and route the portfolio into an AI investment and resource allocation decision that can correctly return "not this cycle."

Taught by

LearnQuest Network

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