In this article
Key Takeaways
- Yes. AI helps in three places: design time (suggested steps and flow templates), runtime (LLM steps that classify, draft, summarise), and observability (anomaly detection on failed runs).
- Gartner expects 40% of new integrations to use AI agents by end of 2026, up from a small fraction in 2024, with iPaaS as the main delivery layer.
- Albato exposes OpenAI as a step inside flows for classification, drafting, and summarisation. The pricing model is per successful action, which keeps AI-heavy flows predictable.
- The largest gains come from runtime use (AI inside a flow), not from AI building the flow itself. Start there.
The average company runs 342 SaaS apps in 2024 (Productiv, State of SaaS Usage 2024). McKinsey estimates 57% of US work hours are technically automatable (McKinsey, Agents, Robots, and Us 2025). AI is what closes the gap between deterministic if-this-then-that flows and the unstructured judgement calls that used to need a person. Gartner reports the iPaaS market grew 23.4% to $8.5 billion in 2024 (Gartner, iPaaS Market Share 2024) with AI agents projected to drive 40% of new integrations by end of 2026.
Three places AI changes workflow design
AI is not a single feature on top of automation. It changes work in three different layers.
| Layer | What AI does | Typical use |
|---|---|---|
| Design time | Suggests next steps, templates, flow patterns | New users picking the right trigger |
| Runtime | Classifies, drafts, summarises, decides | LLM step inside an existing flow |
| Observability | Detects anomalies, suggests fixes | Alert when a flow run looks off |
The table above gives the quick view. The sections below cover each point in more depth.
The largest gains in 2026 are at runtime, because that is where AI replaces a human judgement call inside an automated flow. Design-time help is real but secondary. Observability is the newest layer and the least mature.
AI at runtime: the real gains
Runtime AI means an LLM step lives inside a flow. The step receives data from a previous step (a lead, a ticket, a draft, a transcript) and outputs structured or unstructured text that the next step writes somewhere useful.
Four runtime patterns account for most of the value.
- Classification. Incoming ticket gets a category, priority, and routing tag before it lands on a queue.
- Drafting. Incoming email or deal note gets a suggested reply that the human can edit and send.
- Summarisation. Long transcript or thread gets a 3-bullet summary in Slack or CRM.
- Decision support. Lead with company size, industry, and stack gets a scoring note that helps sales prioritise.
For specific OpenAI patterns inside Albato flows, see what tools connect to OpenAI.
How Albato runs AI inside flows
Albato lists OpenAI at albato.com/apps/openai and integrates major LLM providers as steps. The step accepts a prompt built from prior step data, a model choice, and optional parameters. The output is plain text that any later step can consume.
The pricing model is per successful action. An AI step that returns a usable output counts once. Failed runs and filter steps do not. This matters because AI flows tend to fan out across many records (every lead, every ticket, every order), and per-task billing on other platforms can balloon at that scale.
Want to add an AI step to your real workflow? Start a free Albato account and try OpenAI inside any flow.
Try It Free
