Business strategies for a successful gen AI solution

A large insurance company has deployed a gen AI assistant to help claims adjusters draft case summaries. Six months later, leadership finds that only 15% of adjusters use the tool regularly. Surveys reveal that many employees fear the tool will replace their jobs and are unsure how it fits into their existing workflow. Which action should the leadership team prioritize to improve adoption?

A financial services firm wants to fine-tune a gen AI assistant on its internal documents, which include a mix of public marketing material, confidential client records, and regulated PII. Before any data is used for training, the Chief Data Officer wants to ensure the firm avoids inadvertently exposing sensitive information through the model. Which governance step should be completed FIRST?

A retail company wants to build a gen AI assistant that answers employee questions using data from its HR system, finance platform, and internal wiki. During discovery, the project lead finds that the three systems use inconsistent formats, contain many duplicate and outdated records, and are managed by separate teams with no shared access policy. Leadership is eager to launch quickly. What should the project lead prioritize FIRST to set the initiative up for success?

A retail company ran a successful gen AI pilot for product description generation using a premium model. Finance approved a budget based on the pilot's monthly costs. Six months after full rollout, actual costs are three times the projected budget, even though usage grew only modestly. The leadership team wants to understand the most likely root cause of the cost overrun so they can plan future initiatives more accurately. What is the MOST likely explanation?

A European retail bank wants to deploy a gen AI assistant that processes customer transaction data. Regulators require that all customer data remains stored and processed within the EU, and the bank must be able to demonstrate this to auditors. As the AI leader evaluating deployment options, which consideration should most influence the platform and configuration you select?

A retail company launched six gen AI initiatives 18 months ago. During a portfolio review, an AI-powered product-description generator shows high monthly inference costs but a marginal, hard-to-attribute lift in conversion, while consuming significant engineering support time. Three other initiatives show strong, measurable ROI. As the gen AI program leader, what is the most appropriate governance action for the product-description generator?

A retail company rolled out a gen AI product-description generator across its e-commerce team six months ago. Leadership now claims the tool 'drove a 12% increase in online sales' and wants to expand the investment. The analytics lead is skeptical because online sales also benefited from a new seasonal ad campaign and a website redesign during the same period. What should the leader recommend before attributing the sales lift to the gen AI tool and approving expansion?

A retail company ran three successful gen AI pilots in isolated teams, each showing strong ROI. However, when leadership tried to scale these solutions enterprise-wide, adoption stalled: budgets were fragmented, teams duplicated effort on the same tooling, and no one owned enterprise-wide decisions on vendor selection or Responsible AI standards. As the gen AI program leader, what is the most effective organizational action to unblock scaling?

A large financial services company is scaling multiple gen AI projects across departments. Different teams are independently deploying models, choosing their own vendors, and setting their own acceptable-use rules. Leadership is concerned about inconsistent risk handling, duplicated spend, and potential regulatory exposure. What is the MOST effective organizational-level action to address these concerns?

A retail company ran a 3-month gen AI pilot for an internal customer-service assistant. Leadership now wants to decide whether to scale it enterprise-wide. The team has anecdotal praise from agents but no structured evidence. Before approving further investment, what should leadership require to make a defensible scaling decision?

A retail company's newly formed AI steering committee has approved four gen AI initiatives but has limited engineering capacity to launch them one at a time over the next year. The CFO wants to build organizational momentum and secure continued funding, while the CEO wants to ensure the program eventually delivers transformational value. Which sequencing approach best balances these leadership priorities when deciding what to build first?

A retail company launched a gen AI shopping assistant six months ago. In the quarterly review, the product team proudly reports that the assistant handled 2 million conversations and generated over 500,000 product recommendations. The CFO is unconvinced and asks the Gen AI Leader to demonstrate the solution's actual business value. Which metric should the leader prioritize to answer the CFO's question?

A large insurer is launching several gen AI initiatives at once: an internal tool that summarizes meeting notes, a customer-facing chatbot that answers policy coverage questions, and a marketing team's brainstorming assistant. The Responsible AI governance board has limited review capacity and must decide where to focus its most rigorous oversight first. Which approach best reflects a risk-based governance strategy?

A retail company deployed a gen AI tool that drafts product descriptions. Six months in, the executive team wants proof of business impact before expanding it company-wide. The project lead's report highlights that 4,000 descriptions were generated and that 85% of the marketing team logs in weekly. The CFO says these figures do not answer whether the investment is paying off. Which additional metric would best demonstrate the tool's ROI?

A retail company's leadership wants to launch its first gen AI initiative. Four proposals are on the table, each with different expected impact and feasibility. Leadership wants to pick the use case that best demonstrates measurable business value while managing risk for an initial pilot. Which proposal should they prioritize?

A retail company discovers that several teams have independently signed up for various consumer gen AI tools using corporate credit cards, pasting sales figures and customer records into them without any oversight. Leadership is alarmed by this 'shadow AI' but does not want to kill employee enthusiasm for the technology. As the organization's gen AI governance lead, what is the most effective response?

A retail company is rolling out gen AI tools across its marketing and operations teams. Leadership notices that adoption is stalling: many employees have access to the tools but rarely use them, and those who do produce inconsistent results. A skills assessment reveals most staff have never been trained on prompting or on when gen AI is appropriate. As the leader responsible for adoption, what is the most effective first step to close this gap?

A retail company's leadership approved a gen AI customer-service assistant based on a pilot that only accounted for model inference (per-token) charges. Six months after launch, the finance team reports that the initiative is significantly over budget. Which overlooked cost category is the MOST likely primary driver of the shortfall, and should be built into future gen AI total-cost-of-ownership estimates?

A retail company's leadership team has brainstormed four potential gen AI initiatives. With limited budget for a first project, they want to select the use case that will demonstrate the strongest business value while remaining feasible to deliver quickly. Which criterion should most guide their selection of a first high-value use case?

A retail company is choosing a gen AI platform to power several customer-facing and internal solutions over the next three years. During evaluation, an executive raises concern that committing deeply to one provider's proprietary APIs, prompt formats, and fine-tuned model artifacts could make it very costly to switch providers later if pricing or performance changes. From an organizational cost and risk governance perspective, which action best addresses this concern?