Architecting for AI Success: Transforming Your Information Ecosystem for Gen AI

The generative AI revolution is transforming enterprise operations at unprecedented speed. In a recent AIIM webinar sponsored by Rocket Software, industry experts Doug Johnson and Greg Wilson shared critical insights on building AI architectures that deliver real business value while maintaining security and governance.

With analysts predicting that 30% of all knowledge work will be aided by generative AI technologies by 2027, organizations need strategic approaches that go beyond individual productivity tools to transform operational processes.

The Enterprise AI Challenge

While tools like Copilot excel at individual productivity, the real enterprise opportunity lies in operational processes. As Doug Johnson explained: "We're talking about operational processes, customer service areas, customer-facing applications, and effectively serving up information to get work done for employees, customers, and throughout your ecosystem of partners."

However, enterprise AI implementation faces significant challenges. Enterprise data exists across multiple systems rather than unified ecosystems, operational processes require sophisticated access controls, and organizations need cost-effective architectural strategies.

Greg Wilson captured a common client sentiment: "In a meeting a few weeks ago, a client said, 'We love Gen AI and what we're seeing with LLMs, but quite honestly, it feels like we're turning a tornado loose within our enterprise.'"

Real-World Success: 80% Efficiency Gains

A manufacturing distributor of cellular and broadband components demonstrates AI's transformational potential. Facing overwhelming customer service challenges with tens of thousands of different parts and technical specifications, they struggled with extended response times, high call center turnover, and lengthy onboarding periods.

The AI solution delivered remarkable results: 80% time savings in getting answers to customer queries, dramatically reduced onboarding time, and the ability to handle daily questions without manual document review.

Wilson reported: "They came away from this proof of concept saying they believe at least 80% of the questions they get asked day in and day out would be handled by Smart Chat and wouldn't require them to actually comb through documents."

The Three Essential Pillars for AI Success

1. Security and Governance First

Enterprise AI requires sophisticated security that goes beyond basic user permissions. Modern systems need metadata-based controls for draft versus final documents, location and time-based restrictions, section-level document access, and dynamic content redaction based on user roles.

The architecture must handle nuanced scenarios where different users receive different responses to identical questions based on their authorization levels, ensuring sensitive information remains protected while maximizing productivity for authorized users.

2. Integration Without Disruption

Successful AI implementations work with existing information infrastructure rather than requiring wholesale migration. Johnson emphasized: "We recognize that your information in operational contexts exists in many different places. Simply changing everything to get Gen AI value isn't realistic."

This means supporting cross-platform compatibility spanning mainframe, open systems, and cloud environments while preserving existing access controls and maintaining high-volume, high-performance data processing capabilities.

3. Future-Proof Flexibility

The AI landscape evolves rapidly, making architectural flexibility crucial. Organizations need pluggable LLM approaches for easy model switching, modular designs supporting emerging technologies like reasoning and agent-based architectures, and scalable foundations that grow with technological advancement.

Wilson noted: "We talk to customers all the time who say, 'I like my LLM of choice today, but what if the state of the art moves? Do I have to reconstitute everything, or am I stuck with one approach?'"

Addressing Stakeholder Concerns

When facing organizational resistance, the experts recommend focusing on outcomes rather than technology features. Johnson suggested starting with specific business questions: "What are some key questions you want to ask of key information in the business? Give me an example of 5-10 of those questions and the information where answers can be found."

Wilson emphasized relationship-building: "In my mind, this is more about relationship than rolling out a new solution. If we find organizations we can trust and that trust us, things just seem to go better."

Your Next Steps

The transformation is already underway. As Wilson reflected: "I don't even know where some of the Gen AI stuff in the job I do today will be different 12 or 24 months from now, but I know it will be dramatically different."

Start with specific use cases and desired outcomes rather than technology features. Design security and governance into your architecture from day one. Build systems that integrate with existing infrastructure, and choose approaches that provide flexibility for future evolution.

Johnson's advice resonates: "It's a combination of thinking big but also being practical...finding those practical wins, those first steps."

Organizations that invest in thoughtful, secure, and scalable AI architectures today will lead tomorrow's transformation. Watch the full webinar "Architecting for AI Success: Transforming Your Information Ecosystem for GenAI Value" to learn more.