Bridging the Gap with AI Integration
AI can enable organizations to make more informed decisions, optimize operational efficiency, and deliver more personalized, responsive customer experiences.
At the same time, the journey is complex and organizations often face challenges integrating AI with long-established systems and workflows. Legacy systems, messy data, integration complexity and organizational resistance to change can all create significant barriers.
Meanwhile, the landscape is rapidly evolving. Organizations are shifting from generic applications to tailored, workflow-centric AI that addresses specific business challenges. And as adoption grows, so does the need for responsible AI that prioritizes transparency, compliance, and trust.
Enterprise AI World, in partnership with DBTA, held a webinar, Bridging the Gap: Integrating AI into Existing Systems and Workflows, with experts in the field who discussed Key trends shaping the next wave of enterprise AI and more.
According to Matthew Groves, DevRel engineer, Couchbase, yesterday’s data architecture design is not ready for AI. AI requires flexibility with control. The new paradigm includes agentic autonomous workflows such as multi-agent systems with varying natural language inputs and artifacts, the structured and unstructured data layer, and AI models.
With Couchbase Capella, users can reduce the high cost of operations, Groves said. Developers code faster and write fewer complex applications. Other benefits include:
- Costs: Save on infrastructure and software costs
- Time: Save on administration and Integration labor costs
- Quality: Code better features, with less training cost, faster
- Effectiveness: Test and deploy efficiently in less time
AI ROI is not constrained by the models, explained Jerod Johnson, senior technology evangelist CData Software, it’s constrained by data infrastructure maturity.
According to the CData “State of AI Data Connectivity Report: 2026 Outlook,” three critical data infrastructure capabilities context, connectivity, and control—predict successful AI adoption and business impact, Johnson said.
Eliminate integration bottlenecks and accelerate AI time-to-value. CData Software delivers intelligent, connector-based access to more than 350 enterprise systems, reducing integration overhead and enabling AI systems to operate on your data, Johnson noted.
Ben Wiley, solutions engineer, Glean, recommended some best practices for using AI, including:
- Connect AI to the context it needs
- Empower AI-first employees
- Enable your builders with a platform
To deliver enterprise context with homegrown, legacy stacks, document the systems your organization relies upon and how it’s used across teams; connect systems and index enterprise data—permissions are critical to do this safely; and ID painful, repetitive processes where hours are wasted, repeatedly.
Know where your enterprise data resides and how it’s used, Wiley explained. AI can now search, retrieve, and summarize—without risk. And build agents to automate tasks across the enterprise.
For the full webinar, featuring a more in-depth discussion, Q&A, and more, you can view an archived version of the webinar here.