Thoroughly Modern: The Business Case For Success And ROI With Enterprise AI On IBM i

November 4, 2024 Monica Sanchez

AI is the new frontier for all organizations, and those running IBM i are no exception – but how will you move AI from buzzword to business case to stand up to the boardroom test?

To gain executive buy-in, a business case for AI must go beyond ideas. Executives need proof that AI will deliver business value and that the strategy includes a clear roadmap for AI adoption, scalability, and risk management.

With the right approach, AI can enhance IBM i’s capabilities, from cost savings to new revenue streams. A strong business case – rooted in solid ROI, strategic alignment, and a scalability plan that supports data quality and organizational growth – will earn the green light.

For an in-depth look at how to develop a robust AI strategy, download our whitepaper, Enterprise AI for IBM i: Craft an AI Transformation Strategy for Growth.

Learn how to develop a solid AI business case that leaves no room for hesitation and sets AI on a path to deliver tangible, lasting value:

1. Tie AI to Clear Business Goals

AI succeeds only when it solves a problem tied to business value. AI is not a solution looking for a problem, and for IT leaders, strategic alignment between AI projects and business objectives is critical.

In IBM i environments, your organization likely already relies on the system’s vast, historical data to manage key processes. The question is how to unlock AI’s potential to amplify these efforts. The business case should start with high-impact areas where AI can drive value. For IBM i systems, consider these areas:

Operational efficiency: Automate repetitive tasks, reduce manual errors, and optimize processes.
Customer experience: Use AI to analyze customer patterns, predict needs, and enhance experiences.
Risk management: Improve fraud detection and strengthen decision-making with AI insights from rich IBM i data.

A well-defined business case answers one question: How does AI drive measurable impact in the near term? Align each AI project with strategic goals to make sure its value is clear from the start.

For companies new to AI, initial experimentation through prototypes is both a practical and recommended approach. However, this experiment must follow a structured path and guide the organization from initial testing to effective application and scalability. Without this alignment and clear progression, AI initiatives become “tech experiments” that fail to secure executive buy-in.

Fresche’s upcoming webinar “From Guesswork to Enterprise AI: Navigate Your AI Journey with Precision” dives deep into AI strategy for IBM i. Sign up today to secure your roadmap to success.

2. Quantify AI’s Value in Terms of ROI

C-level executives prioritize investments with measurable returns, and AI is no exception —they want to see hard numbers and concrete outcomes. . . click here to dive deeper into AI business case insights.

Monica Sanchez is a senior executive and strategic advisor in information technology. Her expertise lies at the intersection of business and technology. As a former Fortune 500 CIO and now as a vice president of strategic transformation at Fresche, Monica guides organizations on their evolutionary journey by setting and delivering strategic and technical vision and direction, building successful teams, effective partnering, and utilizing technology-based solutions to drive business results. Monica has a bachelor’s degree in information technology, an MBA from UADE Business School, and an AI certification from London Business School.

This content is sponsored by Fresche Solutions.

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