The Four Core Approaches to AI Connectors

It's the right question. Because here's the truth: your AI is only as powerful as the data and systems it can access. An AI assistant that can't pull customer data from your CRM, update your ERP, or trigger workflows in your business applications isn't transformative—it's just expensive autocomplete.

The connector strategy you choose today will determine whether your AI becomes a core competitive advantage or another abandoned pilot project. But which approach is right for your organization? That depends on questions most vendors won't help you answer: What's your real appetite for technical complexity? How will costs scale with success? Where does control matter more than convenience?

I've spent the last year helping enterprises navigate these decisions, and I've seen the same patterns emerge. Let me walk you through the four fundamental approaches to AI connectors—not to sell you on one solution, but to help you ask the right questions about your own situation.

1. API-Based Connectors (Direct Integration)

The Approach: Your engineering team builds custom connections directly between your AI system and business applications using their native APIs.

Think of companies like Stripe powering payment processing for AI agents, Salesforce APIs enabling AI to read and update CRM records, or SAP's OData services allowing AI systems to trigger supply chain workflows. Major enterprises often build direct integrations when connecting AI to core systems like Workday for HR operations or ServiceNow for IT service management.

How it actually works in practice:

When a financial services firm wants their AI assistant to check customer account balances, their developers write code that authenticates with their banking core system's API, formats the request with proper customer identifiers, handles the response data, manages rate limits, and gracefully handles errors when the system is unavailable. Every edge case—from expired authentication tokens to malformed data—needs explicit handling.

Real-world example: A healthcare provider building an AI triage system that needs to access patient records from Epic's FHIR API, check insurance eligibility through Clearinghouse APIs, and schedule appointments via their proprietary scheduling system—all requiring custom integration code.

Advantages

• Complete control over integration logic and data transformations
• Optimize performance through caching, batching, and parallel calls
• Customize for unique business logic and proprietary systems
• Cost-effective at scale—no per-transaction fees
• Implement security exactly to your enterprise standards
• Own your integration destiny with no vendor dependency

Challenges

• Requires 2-6 months development time per major integration
• Ongoing maintenance as APIs evolve and deprecate features
• Need experienced developers familiar with both AI and target APIs
• Must build comprehensive error handling and retry logic
• Documentation gaps in vendor APIs create integration roadblocks
• Each new integration starts from scratch

Do you have the engineering bandwidth to build these integrations AND maintain them as your AI capabilities expand? Let's discuss this at length.