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AI Business Considerations: Use Cases, ROI, Vendors, and Hosting
This fifth episode of the ISACA Advanced in AI Audit (AAIA) exam prep series looks at the business side of adopting AI — how organizations justify the investment, weigh the trade-offs, and structure their relationships with vendors and hosting providers. It frames the financial, contractual, and governance lenses an auditor uses to decide whether a proposed AI tool is a sensible fit.
What this episode covers
- Change management and the AI business case — what every project documentation pack should justify before approval.
- Identifying the problem and assessing how mature the underlying technology actually is.
- Cost-benefit analysis and ROI — the categories of costs, the buckets of benefits, and why early returns can mislead.
- Hosting choices — the trade-offs between internal infrastructure and cloud deployment.
- Vendor vetting and service level agreements — what to test before signing and how to manage afterward.
- Off-the-shelf solutions and the shared responsibility model — how IaaS, PaaS, and SaaS divide accountability.
- AI governance and program management — why traditional IT governance frameworks usually need to be extended.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What should an AI business case include?
A documented AI business case must state the exact problem the AI is supposed to solve, explain how the technology will solve it, include a financial breakdown of expected costs versus anticipated returns, identify potential hazards and mitigation plans, and provide a step-by-step deployment plan. This ensures technology is chosen on purpose, not just because it is popular.
What are the trade-offs between internal hosting and cloud hosting for AI?
Internal hosting provides ultimate control, customization, and data security, but you are limited by your own hardware, face high electricity costs, and must hire technicians. Cloud hosting offers flexibility, scalability, and low initial costs, but brings loss of physical control, vendor lock-in, network latency, and data residency compliance concerns over where servers are located.
What is the shared responsibility model in AI?
The shared responsibility model defines which tasks belong to your organization, which belong to the vendor, and where they overlap. With Infrastructure as a Service you are responsible for most tasks, Platform as a Service shares responsibility, and Software as a Service shifts most work to the vendor. However, you always remain responsible for user training, access controls, and governing your own data.
What are the main risks of buying off-the-shelf AI solutions?
Off-the-shelf AI solutions introduce three major risks. Accountability becomes blurred if no one documents who maintains the system, ethical concerns arise over who is liable when a pre-built algorithm makes a biased decision, and data security weakens because sharing private data creates new attack vectors for hackers.
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Reference: This article is based on concepts discussed in AI Business Considerations: Use Cases, ROI, Vendors & Hosting.