For years, IBM i leaders have heard the same thing: legacy systems hold them back, that they can’t compete with newer, cloud-based platforms. But here’s the truth: IBM i holds untapped power that, with the right tools, outshines many modern systems.
AI adoption has enabled IBM i businesses to shift from predictable to proactive. Companies now forecast customer needs, streamline workflows, and pull valuable intelligence from raw data, all through the power of AI.
This kind of transformation, however, requires a strategic approach. This article explores how industry leaders use AI in IBM i environments to drive success and maintain a competitive edge.
Discover the specific enterprise AI strategies and real-world applications that separate the top players from the rest and learn how your organization can adopt these principles to achieve success with AI adoption.
The Top 4 AI Applications That Take IBM i to the Next Level
As per IBM’s AI in Action 2024 report, successful AI adopters follow a clear roadmap that balances vision with practical applications. They strategically invest in four essential pillars: AI strategy, advanced toolkits, robust data management, and impactful applications.
These companies focus their efforts on specific use cases that deliver the most business value. Here are four major AI use cases that deliver tangible benefits for IBM i environments.
i. Predictive Analytics for Proactive Decisions
Predictive analytics is the use of historical data and statistical algorithms to forecast future outcomes. It enables businesses to make proactive, data-driven decisions. In IBM i environments, AI-driven predictive analytics uncovers patterns in historical data, helps organizations anticipate issues and act before they impact operations.
For example, in manufacturing, IBM i systems analyze production logs and performance metrics to forecast equipment failures, guide maintenance schedules and reduce unexpected downtimes. This proactive maintenance reduces costs and increases productivity.
ii. Process Automation on Autopilot
Automation replaces manual, repetitive tasks with AI-driven workflows, and allows IBM i systems to streamline operations and reduce costs. Through automation, industries like retail and finance transform processes that previously demanded extensive time and manual effort.
For instance, in finance, IBM i systems use AI to automate complex tasks such as billing and reconciliation, eliminate errors and speed up transaction times. In retail, similarly, IBM i systems, combined with AI, automate inventory tracking to allow businesses to respond faster to demand changes. Hence, with automated critical workflows, AI helps IBM i systems directly support business goals and positively impact the bottom line.
iii. Enhance Customer Experience
Customer experience is a key area where AI adoption adds value, especially in sectors like retail and banking. IBM i systems, equipped with AI, process large volumes of customer data to uncover insights that enable more personalized interactions and predict customer needs.
Retailers use IBM i data to analyze purchase behaviors, which helps them recommend products tailored to individual preferences. Financial institutions apply similar AI-driven insights to analyze transaction data and offer personalized financial advice. AI leaders prioritize virtual assistants in customer-facing roles to ensure their systems handle client inquiries 24/7, reduce wait times and enhance satisfaction.
iv. Strengthen Cybersecurity
Cybersecurity remains a critical priority for AI leaders, especially as data privacy concerns grow. IBM i systems, known for their robust security, gain even greater resilience with AI-driven threat detection.
AI enhances IBM i’s ability to detect anomalies, flag unusual behaviors, and prevent security breaches before they occur. Data security, AI model hacking, and regulatory compliance represent top challenges in AI adoption. AI integration with IBM i addresses these challenges directly and ensures data privacy and compliance standards are met. Through AI, IBM i systems evolve into highly secure environments that proactively protect both business and client data.
If a well-defined AI strategy is the next step for your business, Fresche’s AI-Celerate program provides a structured 12-week solution. We help identify the right AI use cases, define a clear roadmap, and create a solid business case to secure leadership buy-in for AI adoption. Find out more about AI-Celerate’s approach to AI success
The Four Essentials for AI Adoption on IBM i
AI integration with IBM i systems needs a phased approach that aligns with these strategic pillars. Here’s a straightforward path to achieve impactful AI integration:
1. The First Step: Identify High-Impact AI Adoption Use Case
As Cathy Reese, Senior Partner at IBM Consulting, says:
“To get AI to scale, you have to present a really tight value case on how AI is going to achieve your organization’s mission.”
The key is to focus on applications that align with IBM i’s strengths and deliver clear business value. Choose use cases that can showcase ROI quickly, like customer experience enhancements or IT automation.
To learn how to create a strong business case for AI, here’s a detailed guide: Enterprise AI for IBM i: The Business Case Blueprint for Success and ROI.
2. The Secret Ingredient: Data Preparation and Governance
For leaders, the road to successful AI adoption starts with one critical step: data preparation. They know AI’s potential is only as strong as the data it processes.
AI leaders understand that raw data need‘s structure. They map legacy data formats to ensure that IBM i data flows seamlessly into AI models, and create a unified system where insights are consistent and actionable. This data alignment avoids costly errors and redundant processes and empowers AI to drive high-quality insights without disruption.
They also ensure IBM i data is accessible to those who need it while enforcing strict governance policies to protect privacy and security. Strong data governance supports transparency and ensures that AI applications meet regulatory requirements, protecting both the organization and its customers.
3. The Smart Start: Begin with Pilot Projects and Scale
Leaders understand that a small start with pilot projects minimizes risk and builds a foundation for long-term success. Pilot projects test AI’s impact on IBM i systems in targeted areas, which allows organizations to measure success before a wider rollout. These projects provide actionable insights, prove AI’s value, and guide further investment.
Once initial results validate AI’s impact, scaling becomes a calculated move. Leaders expand AI applications systematically, assess outcomes at each stage, and refine strategies as they scale. This phased approach enables organizations to control the pace of AI integration while ensuring each phase delivers measurable value. Scaling with success reduces risks, conserves resources, and aligns growth with business priorities.
4. The Competitive Edge: Leverage Expert Partnerships
AI Leaders leverage expert support to overcome integration challenges and tailor AI solutions to their specific business needs.
Experienced AI specialists offer technical insights that smooth the integration process, which helps companies address challenges unique to IBM i and tailor AI solutions to their needs.
Expert support empowers companies to focus on impactful use cases, make informed decisions, and drive meaningful results.
For a deeper dive into these strategies and more, explore our whitepaper: Enterprise AI for IBM i: Craft an AI Transformation Strategy for Growth.
Ready to make AI work for your business?
With AI on board, IBM i does more than process data—it predicts, automates, personalizes, and protects. But here’s the catch: AI success hinges on more than tech alone. It requires a clear path, an AI strategy that turns potential into proven results.
Ready to take the next step? Join our exclusive webinar, From Guesswork to Enterprise AI, where Fresche’s experts will walk you through the AI-Celerate framework. Learn how to turn AI from a buzzword into a powerful tool that drives bujsiness results. Gain insights on how to measure AI’s impact, choose the right AI use case, and prioritize initiatives that move the needle for your business.
Sign up today and take the first step toward a smarter, more innovative future for your IBM i system.
Frequently Asked Questions:
How does AI enhance IBM i system security?
With AI adoption, your IBM i system can detect potential threats in real-time, flag unusual behaviors, and prevent data breaches before they happen. This advanced level of security helps IBM i users comply with data privacy regulations and enhance overall data protection.
What are the common AI integration challenges?
Common challenges include data compatibility issues, AI model bias, and data security and compliance. To address these issues, you need a clear strategy, effective data governance, and a phased AI roadmap.
How can IBM i users create a roadmap for AI adoption?
An AI adoption roadmap for IBM i systems should focus on clearly defined goals, high-impact use cases, data preparation, and leadership buy-in. AI leaders recommend starting with small, manageable projects and scaling based on measurable results.
How do AI leaders measure the impact of AI in IBM i systems?
AI leaders measure impact through improvements in efficiency, customer satisfaction, cost savings, and compliance. They prioritize high-impact use cases and establish metrics to evaluate AI’s ROI and ensure that AI projects align with organizational goals.
