The state of workflow automation in 2025

Companies naturally strive to automate more operational tasks over time to gain productivity. Whether it’s routing leads, or summarizing customer messages, automation sits at the core of modern operations. The problem is that the ways to automate have multiplied, and the trade-offs are less obvious than they used to be.

This article outlines the main approaches to workflow automation in 2025, the trade-offs of each, and how they’re evolving. To keep things concrete, we’ll use one recurring example throughout: a team that handles customer support in Slack and wants to improve or automate parts of that workflow.

The main options

Teams today can automate workflows in several ways:

• Buy a SaaS purpose-built for the workflow
• Build an in-house solution, either from scratch or with internal tooling platforms
• Use an integrations platform like Zapier, Make, or n8n
• Use agents, either inside SaaS tools or deployed internally
• Rely on embedded integrations that come bundled with existing connectors

These approaches often coexist. It’s common to buy a SaaS for reliability, use an automation tool and agents to extend it.

1. Buying a SaaS

The most common approach is still to delegate the problem to a product that already solves it. You buy a specialized SaaS designed for a specific workflow—support, analytics, marketing, anything.

Using the Slack support example, that might mean adopting tools like Pylon or Plain, which convert Slack threads into tickets, manage SLAs, and integrate with CRMs or issue trackers.

Advantages

• Fast to implement
• Maintained by domain experts
• Reliable, secure, and supported

Disadvantages

• Opinionated workflows
• Limited flexibility for edge cases
• Harder to align with company-specific data models

Buying SaaS works best when the workflow is well understood and doesn’t differentiate your product or operations. You trade flexibility for speed and reliability.

2. Building in-house

When a workflow is core to your product or too unique for existing tools, teams often build in-house. This includes both:

• Fully custom builds, where engineers design the backend, UI, and integrations
• Internal tooling platforms like Retool, Appsmith, or Superblocks, which make in-house builds faster

Applied to Slack support, this could mean building a custom dashboard for triage, storing tickets in your database, and defining your own escalation logic.

Advantages

• Maximum flexibility and control
• Deep integration with internal systems
• Tailored UX and data ownership

Disadvantages

• Expensive and time-consuming
• Continuous maintenance cost
• Harder to evolve without dedicated ownership

Building in-house makes sense when automation is central to your business logic, or when off-the-shelf tools can’t represent your processes accurately.

3. Using an integrations platform

Integrations platforms like Zapier, Make, and n8n sit between tools. They’re built for deterministic, event-based workflows: things like “when X happens in app A, do Y in app B.”

For our Slack support example, this might mean posting a weekly summary of resolved tickets, alerting a manager when a thread stays unanswered, or syncing conversation data into a Google Sheet.

Advantages

• No engineers required
• Quick setup with prebuilt connectors
• Reliable for simple, repetitive workflows

Disadvantages

• Hard to maintain complex logic
• Better for one-off automations than solving a whole problem space (e.g., customer support)

Integrations platforms shine when you want non-technical ownership of simple targeted automations. They tend to break down when workflows get large and complex.

4. Using AI agents

Agents represent the newest layer of workflow automation. Instead of fixed triggers and actions, they interpret goals in natural language and plan the steps to achieve them.

You can use:

• Embedded agents inside SaaS tools (e.g., Notion AI or HubSpot AI)
• Agentic integration platforms like Activepieces, Lindy, Beam, CrewAI, and now OpenAI Agent Builder
• Internal agent deployments, where you control context and access (e.g., using OpenAI Agents or Vercel AI SDK)

In our running example, an agent might analyze the week’s Slack support conversations, detect recurring issues and sentiments, and suggest updates to internal documentation.

Advantages

• Works on unstructured or ambiguous inputs
• Easier to deploy and maintain (prompts instead of code)
• Adds flexibility around existing tools

Disadvantages

• Reliability and quality control are still limited
• Hard to test or guarantee consistency
• Not suited for critical, deterministic flows

Agents are especially effective in non-critical or human-in-the-loop workflows, where they synthesize or suggest, and humans review before action. They push the boundaries of what teams can automate themselves, even if they’re not ready for mission-critical tasks yet.

5. Embedded integrations

Many SaaS tools include built-in integrations that let them talk to each other. For support-related SaaS, that might mean syncing with issue trackers, CRMs, analytics platforms, or documentation systems.

These embedded integrations are easy to enable but opinionated in design. They work best when your workflow fits the vendor’s intended use cases. SaaS vendors themselves get to choose whether they want to build these embedded integrations from scratch or use dedicated infrastructure like Nango.

How these layers fit together

In practice, most companies combine these methods:

• SaaS layer: Handles the stable, complex & critical workflows (like customer support)
• Integrations layer: Adds deterministic, low-code automation for targeted tasks (e.g., weekly reports through Zapier)
• Agentic layer: Brings flexible reasoning for non-critical tasks (like summarizing or recommending doc updates)
• Embedded integrations: Enables complex workflows across different software tools, but opinionated

Each layer covers a different type of automation need. The SaaS focuses on reliability, the integrations platform on accessibility, the agent on flexibility, and the internal build on specificity.

Summary of trade-offs

Build vs. buy in the age of agents: is SaaS dying?

The old question build or buy? still applies, but the spectrum is wider now.

Agents and integrations platforms have made it easier to extend SaaS tools rather than replace them. Instead of choosing between rigid products or expensive in-house systems, teams can now fill specific gaps with smaller, flexible automations.

For example, if your support tool lacks a sentiment analysis feature, you don’t need to switch platforms or implement a support system from scratch. You can add an agent or small integration workflow that performs that one task.

It’s too early to say exactly how far this goes. Reliability, testing, and observability for agents are still catching up. But it’s clear that these tools make it easier to adapt around software, not just depend on it.

Some people are already claiming that agents will replace SaaS. But they’re meant for different use cases, as outlined in the Trade-Offs section above. SaaS isn’t just engineering, it’s deep product work grounded in vertical expertise and years of real-world scar tissue, which is hard to match for agents. Agents might actually provide more flexibility to adopt SaaS, knowing that you’ll be able to extend it easily with agentic workflows.

Where this is going

Workflow automation in 2025 is layered and modular:

• SaaS handles structured, high-reliability operations
• Integrations platforms automate deterministic, event-driven tasks
• Agents handle flexible reasoning and context-rich workflows
• In-house systems tie everything together when you need full control

These layers don’t always compete, they often complement each other. Companies aren’t buying less SaaS; they’re building more around it. The result is more specialized tools, more glue between them, and more automation that feels natural to how teams actually work.