Building AI workflows: what connects well with what?

The most effective AI workflows I have built are not single-tool solutions. They are chains of tools that handle different parts of a process. Example: we use Otter.ai for meeting transcription, then feed the transcript into Claude for summarization, then send the summary to Slack for distribution. The chain is 90% automated and saves about 2 hours per meeting. The key is designing handoffs: each tool should output something the next tool can consume. JSON is the most reliable format for structured handoffs between AI tools.

Zapier and Make.com have become the glue layer for AI workflows. They connect AI tools to the rest of your stack without writing code. I have built a workflow that listens to a shared email inbox, uses OpenAI to classify the request, routes it to the right team, drafts a first response, and sends it for approval. The whole thing took 3 hours to set up and now handles 70% of our inbound email. For non-technical teams, these no-code orchestration tools are the most important AI infrastructure layer.

The hardest part of building AI workflows is not the integration — it is the error handling. AI tools fail in ways that are not predictable. A model might hallucinate, an API might be down, or the output format might change. The workflows that survive in production are the ones that have fallback paths: if AI fails, the workflow routes to a human. If the output format is unexpected, the workflow flags it for review. Building these guardrails takes as much time as the happy path, and skipping them is the most common mistake I see.