Foundations of Trust: Navigating AI’s Reliability (a four-part series)

Part 4: Use case fit and cost

This being the last part of the series, let’s start by recapping what we covered.

In the first article, we introduced you to the idea that you should look at your AI solution the same way as you look at any advisor.  We identified the criteria to evaluate and implement AI responsibly, and delved deeper into each of these criteria, grouping some together for comprehensive analysis, describing the pros and cons of Public vs. Private LLMs: 

In Article 2, we examined how the scope and customization of AI-generated content affect trust and utility, while Article 3 focused on the importance of data privacy and security in AI solutions. Now, we turn to Use Case Fit and Cost, highlighting the need to assess how well an AI solution aligns with specific use cases and its cost-effectiveness, which together determine its value as a trusted advisor or conflicted agent.

Use Case Fit

For knowledge workers, public LLMs are most effective for general tasks like content creation, email drafting, translation, coding, data analysis, Q&A, and summarization. They serve as powerful assistants, but users must interpret outputs and take further action. As AI use deepens, integrating AI seamlessly into business processes becomes crucial. This drives the need for Process Prompt Engineering, which requires precise, intentional prompts aligned with business logic, compliance, and operational goals.

Rocket Software supports this through Rocket^® API, which automates processes across IBM i, IBM z Systems, and MultiValue environments. By integrating AI-driven logic into workflows, Rocket API turns prompt engineering into a structured, repeatable part of enterprise automation—ensuring AI outputs are accurate, actionable, and compliant while maintaining control and security.

In a previous post “AI is not always a surefire cure”, I covered why AI does not replace the knowledge worker’s understanding of a topic but rather enhances it when the AI is leveraged and can be counted on to be a trusted advisor.  The value of a solid Generative AI Process Prompt Engineering Framework for any enterprise requires focus on the following needs:

• Alignment with the business objectives
• Quantifiable value
• Competitive advantage
• Support for regulatory constraints and risk management
• Easily deployable, refined and adaptable to maximize benefit

Cost

Since the initial setup cost of using a private LLM is more than what is required to get started with a public LLM, one might assume this gives the public option a clear advantage. Yet, for prolonged use across various enterprise use cases, a formal integration of a private LLM can deliver long-term cost savings over reliance on public solutions. This is especially true when factoring in recurring expenses such as API usage fees, data egress charges, and the operational risks associated with external data handling. Rocket Software’s solutions, such as Rocket^® Data Intelligence and Rocket API, help mitigate these costs by enabling secure, on-premises AI deployment and seamless integration with existing infrastructure—reducing the need for third-party services and ensuring that AI investments scale efficiently across the enterprise.

Evaluating whether an AI solution serves as a trusted advisor requires more than choosing between Private and Public LLMs. Enterprises need a comprehensive Prompt Engineering Framework that extends beyond crafting effective prompts to encompass scalability, governance, and secure data management. As Generative AI adoption grows, optimizing prompts—especially when handling sensitive or proprietary data—is critical for long-term success and adaptability within a broader AI strategy.

Rocket Software advances this vision by integrating Generative AI into its products with a focus on enterprise control and data privacy. Rather than relying on public cloud models, Rocket enables organizations to deploy AI securely on-premises or in hybrid environments, ensuring data protection and customizable, governable prompt engineering tailored to unique business needs. Through its DataEdge platform, Rocket unlocks the value of unstructured data like legal documents, emails, and handwritten notes by providing GenAI-powered insights and natural language interfaces that democratize data access for non-technical users. This secure, real-time intelligence delivery empowers enterprises to confidently embed AI into their workflows while maintaining compliance. Discover how Rocket Software’s solutions can help you harness AI responsibly—contact me today to learn more.