The core problem described is a classic "technology in search of a problem" scenario. The pilots were initiated based on the appeal of AI technology itself, rather than being strategically aligned with specific, well-defined business challenges. The failure to define business outcomes and corresponding success metrics upfront makes it impossible to measure value, demonstrate ROI, or build a compelling business case for moving from a pilot to a production system. This lack of strategic alignment and measurement is a primary barrier to scaling AI initiatives.

A is incorrect because the bank was able to run ten pilots, indicating they had sufficient access to technology to experiment. The problem was not the availability of models but the lack of a strategic purpose for using them.

C is incorrect as the scenario provides no information about change management or communication efforts, so one cannot conclude there was an overinvestment. The problem is rooted in strategy, not implementation support.

D is incorrect because the scenario does not mention regulatory compliance. The struggle to justify investment stems from a failure to show business value, not from being hindered by compliance requirements.

1. Microsoft AI Business School. (2023). Strategy. This courseware consistently emphasizes starting with the business problem, not the technology. A key lesson is, "Successful AI strategies are not about AI. They’re about your business... Identify the business scenarios where AI can create the most value." The Contoso scenario illustrates a direct violation of this principle. (Module: "Defining an AI Strategy").

2. MIT Sloan Management Review. (2020). Building the AI-Powered Organization. The article states, "A common mistake is to become enamored with the technology and then search for problems it might solve... Instead, leaders must start with a clear understanding of the business’s strategic objectives and then identify how AI can help achieve them." This directly diagnoses the issue at Contoso Finance. (Section: "Start with Business Problems, Not with AI").

3. Microsoft. (2024). Microsoft Cloud Adoption Framework for Azure: AI/ML strategy. The framework advises, "Define your business outcomes. What are you trying to achieve with AI/ML? ... Without clear business outcomes, it's difficult to measure the success of your AI/ML initiatives and justify the investment." (Section: "Strategy," Subsection: "Define business outcomes").