Thoroughly Modern: AI Success for IBM i Organizations – Small Beginnings, Big Benefits

December 2, 2024 John Clark

AI is transforming how businesses operate, but for many IBM i users, the path forward feels unclear. Questions pile up: Where does one begin? How do you choose a project that delivers real results? What if AI causes more disruption than benefit?

The challenge is real: large-scale AI projects demand significant time, resources, and expertise. There is new terminology, new skills, and a blizzard of possible tools. For most organizations, this feels out of reach. But the solution lies in starting small.

Small AI projects solve specific problems, provide value, and build confidence without overhauls or unreasonable risk. Learn how IBM i organizations can use focused AI projects to solve critical challenges, build momentum, and create confidence for long-term success.

Small AI Projects Deliver Quick Wins

The smartest way to start is to focus on one specific problem and apply AI to solve it. Traditionally, IBM i databases hold a wealth of data—transaction records, inventory patterns, and customer behavior—that businesses can leverage without system overhaul. Small projects make use of this data and reveal value in incremental steps. A clear and manageable scope helps businesses minimize complexity and deliver measurable outcomes.

Small projects succeed because they answer immediate questions. For example, manufacturers often face challenges with demand forecasting, which leads to overproduction or stockouts. AI models built with historical data can help forecast production needs with accuracy.

For another example, think about companies that manage large internal document repositories. AI can enable these companies to structure data into searchable, efficient systems that save time and boosts productivity. These solutions address immediate challenges and demonstrate tangible value.

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Early results build trust in AI’s potential. As businesses see improvements – fewer stockouts, faster document access, or better decisions – they gain confidence to explore additional use cases. These wins create buy-in from leadership and teams. Successes show the organization how AI augments the existing systems and increases the ability to recognize future use of AI to bring new efficiency.

IBM i environments already have an edge. Their systems are stable, their data is reliable, and their operations are proven. Targeted AI projects built on this foundation create an opportunity to explore innovation with low risk and high potential. It gives businesses a chance to test AI, reduce risks, and create value. The aim is not to disrupt what already works but to enhance it.

Fresche’s on-demand 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.

Every AI Project Needs A Data Scientist

AI projects succeed or fail based on one core factor: the quality and usability of data. Raw data on IBM i systems does not translate to value on its own. A data scientist bridges this gap. They help refine data into an asset that serves clear business objectives, and ensure AI delivers practical, measurable outcomes…. click here to learn more on how businesses can start small with AI projects on IBM i

John Clark, CTO of Fresche Solutions, drives technological innovation, process optimization, and security implementation. With extensive expertise in commercial software development and modernization, he has led software, IT, and professional services teams at Fresche and The Quadrant Group. Previously, as director of application architecture at Orchard Brands, he streamlined enterprise systems for major brands.

This content is sponsored by Fresche Solutions.

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