Tips from the Experts: Evaluating AI in iPaaS

By Maneeza Malik, Director Product Marketing
Originally published August 6, 2025

The use of Artificial Intelligence — including machine and deep learning, NLP (Natural Language Processing) and GenAI (Generative AI) — is becoming ubiquitous. In the next generation of AI-powered iPaaS (Integration-Platform-as-a-Service), AI is increasingly being used to automate a wide range of repetitive tasks, orchestrate complex workflows, make recommendations, generate code and documentation, and enable intelligent automation at scale.

But while AI has tremendous capacity to simplify and streamline operations, it can also create new complications if it isn’t approached with the right mindset. In a recent study by Gartner®, How to Combine AI and Integration to Deliver Business Value, Gartner explains why AI should be seen as both a tool to aid in delivering integration and an endpoint that requires integrating.

Why Integration is Key to AI Success

Gartner’s experts start by outlining the dangers of deploying the latest “shiny new object” that doesn’t fully address your data integration, automation and orchestration challenges or needs. Like any technology, particularly one evolving this quickly, AI comes with its share of benefits and risks. This includes both the level of AI maturity and readiness within your organization, as well as the complexity of the IT environment in which your organization operates.

An iPaaS solution that provides a unified, flexible integration layer helps AI initiatives succeed by strengthening governance, improving data accuracy, and enabling seamless scalability.

Stronger Governance and Security

It’s imperative that you map any new AI solution against your business and IT needs, and do your proper due diligence when evaluating and selecting AI tools and emerging protocols to ensure that they’re both widely adopted and enterprise-hardened. This evaluation should then be coupled with a thorough assessment of the AI guardrails you’ll need from the onset to meet your data privacy, governance, security and regulatory compliance requirements.

Improved Data Accuracy

Regardless of how advanced the AI models are, they’re only as good as the underlying data they are trained on. Poor quality or erroneous data can lead to flawed outcomes. Without clear rules and comprehensive guardrails in place, even the most advanced AI can operate blindly and can negatively impact your business.

More Seamless Scalability

However, it’s equally critical to ensure that AI-powered iPaaS passes the scalability, flexibility and seamless integration litmus test. For instance, it should include integration with Large Language Models (LLMs) as well as the plethora of applications, systems, data sources and APIs in an enterprise’s environment, both on-premises or in the cloud.

How AI Assistants and Agents Are Transforming Modern iPaaS

GenAI in particular has been a game changer for businesses, and has given rise to a new generation of modern, AI-powered iPaaS. These powerful platforms increasingly leverage AI assistants and agents, unlocking the ability to deploy Agentic AI at scale. Understanding the depth and scope of what these capabilities can deliver and how they work in tandem will further help you assess and prioritize how best to leverage them in your own environment.

For instance:

AI Integration Challenges

AI agents and assistants deliver value only when they can operate across a unified integration layer. Without solid integration, AI risks creating silos, security gaps, or inefficiencies.

The autonomous nature of AI agents introduces additional challenges that enterprises must be ready for. As reported in the 2025 Jitterbit Automation Benchmark Report, 31% of enterprises are already planning for agentic AI. This is corroborated by findings from Capgemini¹ which reported that 82% of organizations plan to leverage AI agents by 2027.

However, the global consulting firm does caution that every enterprise, regardless of their size, should establish robust safeguards from the onset to ensure full transparency and accountability for any agent-led decisions. It also notes that AI agents require rigorous governance to prevent agent sprawl and related security risks, including securely managing the tight correlation between agents and APIs.

As we’ve seen with previous AI advancements, customer service is among the use cases at the forefront of implementing these powerful new technologies. Gartner predicts that agentic AI will autonomously perform 80% of customer service tasks and resolve issues without human intervention by 2029, resulting in significant productivity gains and a 30% reduction in operational costs².

How Jitterbit is Securing and Democratizing AI Integration

We recognize that each enterprise will have its own unique needs based on its AI readiness and requirements, and that some may want to take a phased approach to developing with AI. The Jitterbit Harmony platform is designed to ensure customers retain control over their AI journey by allowing enterprises of all types and sizes to build and manage integrations, workflows, applications and APIs using their choice of AI low-code or both.

This flexibility extends to empowering both IT and line-of-business users. With Jitterbit’s AI-infused integration platform, users can leverage a suite of Jitterbit AI assistants, agents and features as needed to create and modify integrations, automations and more — with no coding required. And while enterprises can build their own custom AI agents in Jitterbit AI Studio if they wish, those who lack available technical resources to do so can leverage Jitterbit Agentic AI Professional Services to design and build custom, purpose-built agents.

But while the Jitterbit Harmony Platform is designed to maximize flexibility, this never comes at the expense of security. Comprehensive AI accountability, governance, security and regulatory compliance is at the very core. This commitment is reflected in our overall layered security approach, which helps safeguard you at the physical, logical and organizational levels, but also in leveraging enterprise-hardened AI approaches such as Retrieval-Augmented Generation (RAG), which enhances AI models by limiting responses to verified data sources.

For more on how Gartner recommends approaching integration requirements resulting from AI initiatives — including valuable insights for evaluating AI capabilities in iPaaS — get your complimentary copy of the Gartner report, How to Combine AI and Integration to Deliver Business Value.

Or, if you’re ready to see Jitterbit AI in action, check out the Jitterbit App Builder AI Assistant demo or learn how to simplify employee onboarding with Jitterbit AI Agents.