Generative AI programs often fail at prioritization before they fail at technology. A company may collect dozens of ideas\u2014customer chatbots, proposal generation, coding assistants, document review, knowledge search, marketing content, forecasting explanations\u2014but have no disciplined way to decide which should receive engineering effort first.<\/p>\n
For the Generative AI Leader<\/a>, a good portfolio starts with measurable business outcomes and works backward to the technology. The goal is not to maximize the number of AI pilots. It is to identify use cases with meaningful value, feasible implementation, acceptable risk, and a credible path to adoption.<\/p>\n \u201cUse generative AI in customer service\u201d is not a use case. \u201cReduce average time to resolve tier-one support questions by 20% while maintaining customer satisfaction and escalation quality\u201d is much closer because it identifies a process, an outcome, and a way to measure success.<\/p>\n Define the baseline before estimating benefit. If the current process takes eight minutes, costs a known amount per case, and has a measurable error rate, the team can estimate what an improvement would be worth. Without a baseline, ROI discussions become a contest of optimistic assumptions.<\/p>\n Success metrics should include quality as well as speed. A system that generates twice as many responses but creates more corrections, complaints, or compliance reviews may destroy value even though productivity appears higher.<\/p>\n High-value ideas are not always easy to implement, and easy ideas are not always worth doing. Use separate dimensions so a technically simple pilot does not automatically outrank a harder project with much greater business impact.<\/p>\n Value can include revenue growth, cost reduction, cycle-time improvement, customer experience, risk reduction, employee capacity, or strategic differentiation. Feasibility can include data readiness, integration complexity, model capability, process clarity, skills, security constraints, and the amount of organizational change required.<\/p>\n A useful portfolio contains both quick wins and strategic bets. Quick wins prove the operating model and build confidence. Strategic projects may take longer but can justify deeper platform investments if the upside is material.<\/p>\n Not every automation problem needs a generative model. Traditional software is often better when rules are deterministic, outputs must be exact, or a structured workflow already captures the decision logic. Predictive machine learning may be better for forecasting, ranking, anomaly detection, or classification when generation adds no value.<\/p>\n Generative AI is compelling when the work involves unstructured language, multimodal content, synthesis, conversational interaction, flexible reasoning, or creation of new content. It can also be useful as an interface over tools and knowledge systems.<\/p>\n Choosing a simpler technology can improve ROI because deterministic systems may be cheaper, easier to test, and easier to govern. Good AI strategy is not measured by how often the answer is AI.<\/p>\n A knowledge assistant sounds straightforward until the team discovers that the organization’s documents are duplicated, obsolete, inconsistently permissioned, or spread across repositories with no owner. A model cannot compensate for a broken source of truth.<\/p>\n Ask whether the required data exists, is accessible, is legally usable, and is good enough to support the outcome. For retrieval-based systems, consider metadata, document freshness, permissions, and whether users trust the underlying content.<\/p>\n Data cleanup can be part of the business case, but it should be visible. If six months of governance work is required before a prototype can access reliable information, that use case belongs in a different portfolio category from one that can use well-managed data today.<\/p>\n Use cases differ in consequence. Drafting an internal brainstorming note is lower risk than recommending a loan decision, generating medical guidance, or executing privileged infrastructure changes. The higher the consequence, the stronger the evidence and controls required before launch.<\/p>\n Risk dimensions include sensitive data, regulatory exposure, safety, discrimination, financial loss, reputational impact, autonomy, reversibility, and the ability of users to detect errors. Some high-risk use cases may still be worthwhile, but they need human oversight, stronger evaluation, narrower scopes, and slower rollout.<\/p>\nStart with the business problem and success metric<\/h2>\n
Score value separately from feasibility<\/h2>\n
Evaluate whether generative AI is actually the right tool<\/h2>\n
Data readiness can move a use case up or down the list<\/h2>\n
Risk should change the required evidence, not automatically kill the idea<\/h2>\n