10 AI Workflow Platforms to Consider in 2025

By the end of 2025, AI‑enabled workflows are expected to grow from just 3% to 25% of all enterprise processes, representing an eightfold surge in adoption. That dramatic shift reflects two truths:

• AI only delivers when embedded in real business workflows. Models and insights must translate into automated actions, approvals, or notifications to drive meaningful impact.
• Automation is becoming smarter and more accessible. Today’s tools blend low-code builders, real-time data, and agentic AI logic, enabling workflows that think, decide, and act.

But while interest is soaring, many organizations face the same friction points. Here’s the problem in a nutshell:

• You may have AI models and bots, but they’re disconnected from your operational tools or decision engines.
• Multiple apps, teams, and data sets are stitched together manually, slowing execution and scaling.
• Your workflows live in silos, trapping productivity in reactive mode instead of proactive orchestration.

Enter AI workflow platforms: unified environments that combine data integration, intelligent routing, and automation logic. These tools go beyond simple business process automation to use advanced intelligence to let you build flows that trigger actions based on predictions, surface alerts in dashboards, and adapt as business rules evolve.

This article will break down what AI workflow platforms offer, why they matter, and what to evaluate when choosing one. Then we’ll dive into 10 platforms worth considering in 2025, including Domo, ServiceNow, UiPath, Automation Anywhere, and more.

Let’s dive in.

​​Benefits of using an AI workflow platform

An AI model is only as powerful as the system it lives in. Without a way to operationalize intelligence, it remains shelfware. AI workflow platforms close that gap by embedding intelligence directly into the rhythms of your business.

Here’s what that unlocks:

1. End-to-end automation, not just isolated tasks

Legacy automation tools often focus on simple task replacement: sending emails, routing forms, or copying data from one tool to another. AI workflow platforms go further to chain logic, context, and prediction across systems. That means fewer handoffs, fewer clicks, and entire processes that run autonomously.

2. Smarter decisions, delivered in real time

With built-in access to AI models and real-time data feeds, these platforms allow workflows to make decisions dynamically. A support ticket can be routed based on predicted urgency. An invoice can be flagged based on anomaly detection. A supply chain delay can trigger alerts and alternate vendor sourcing, without waiting for human eyes.

3. Consistency and scale across teams

By centralizing automation logic, AI workflow platforms reduce the risk of one-off scripts or tribal knowledge living inside spreadsheets. They make workflows repeatable, auditable, and adjustable, so you can scale automation across departments while maintaining visibility and control.

4. Low-code agility with enterprise-grade power

Many platforms now offer visual builders and prebuilt connectors that make it easier for non-technical users to participate. That means operations leads, analysts, or business managers can orchestrate flows without waiting on dev teams while IT retains governance over data access and infrastructure.

5. Reduced lag between insight and action

In traditional BI or AI environments, insights often live in reports or dashboards—requiring human review before anything changes. AI workflow platforms bridge that last mile. They can take action the moment a threshold is crossed, a forecast is made, or a condition is met—shrinking the gap between knowing and doing.

6. Futureproofing your AI investments

As AI capabilities evolve, so will the workflows that depend on them. A good AI workflow platform makes it easy to plug in new models, swap out APIs, or retrain logic without rebuilding the entire flow from scratch. That modularity helps organizations stay agile in the face of rapid AI innovation.

What to look for in an AI workflow platform

Not all workflow platforms are built for the era of AI. Some were designed for basic task automation or data syncs. And while those use cases still matter, modern businesses need more than triggers and templates. They need platforms that can think, adapt, and evolve alongside their strategies.

Whether you're evaluating your first AI workflow tool or reassessing your current stack, here are the capabilities that matter most:

1. Native AI capabilities, not just integrations

Look for platforms that offer AI as a first-class citizen, not a bolt-on. That means native support for embedding machine learning models, applying natural language processing, using generative AI, or making predictions as part of a workflow. Bonus points if it includes a model management layer or lets you bring your own models easily.

2. Real-time data connectivity

Intelligent workflows rely on timely data. The platform should be able to ingest, process, and act on real-time signals from across your ecosystem, whether that’s a CRM update, a change in inventory, or a drop in sentiment. Static, batch-only data pipelines limit the speed and value of your automations.

3. Low-code or no-code builder

To scale automation across teams, the platform must be approachable. Look for drag-and-drop builders, prebuilt logic blocks, and simple UI elements that empower non-developers to build and modify workflows without sacrificing depth or control for technical teams.

4. Flexible integrations and extensibility

Your AI workflow platform should connect with the tools you already use—CRM, ERP, help desk, cloud storage, databases, and more. But just as important is extensibility: Can you connect to custom APIs? Can you run scripts or trigger webhooks when needed? Versatility matters, especially as your tech stack evolves.

5. Automation orchestration and conditional logic

The best platforms don’t just automate individual tasks—they orchestrate processes. That means supporting conditional logic, branching paths, exception handling, and sequential triggers across multiple tools and systems. Look for visual logic editors or rule builders to manage complexity with clarity.

6. Scalability and performance

Will the platform hold up under real-world load? Can it support thousands of concurrent workflows or transactions? Can it handle spikes in data volume without degrading performance? Be sure to assess how well it scales with usage, especially across teams or regions.

7. Governance, security, and visibility

As automation expands, governance becomes critical. Look for platforms that offer permission controls, audit logs, role-based access, and usage analytics. You should be able to see who built what, what’s running where, and how changes are tracked—all while meeting your compliance needs.

8. Model lifecycle and feedback loops

If you plan to embed AI models in your workflows, pay attention to how the platform supports them. Can it retrain models based on new data? Can you monitor performance or send outputs back into training pipelines? Feedback loops are key to making your AI smarter over time.

10 best AI workflow platforms in 2025

There’s no shortage of automation tools on the market, but not all of them are built for the era of intelligent, adaptive workflows.

Some focus on task automation. Others emphasize integrations. A few are now layering in AI. But the platforms leading in 2025 share a common thread: they help teams go beyond static flows and build systems that respond in real time, leverage predictions, and connect insight to action—all while staying flexible enough to evolve with business needs.

Choosing the right platform depends on more than just features. It’s about how easily your team can build, deploy, and scale workflows that actually deliver value. It's about whether the platform fits your tech stack, supports your users’ skill levels, and aligns with your data and governance requirements.

In this section, we break down 10 leading AI workflow platforms—what makes each unique, how they’re typically used, and which roles and use cases they’re best suited for. Whether you’re a startup building AI into your onboarding flows or an enterprise standardizing service operations across thousands of users, there’s a tool here worth considering.

1. Domo

How it’s used:
Users build automated workflows inside Domo’s platform that weave together live data, visual dashboards, AI services, and code-enabled steps. For example, you can ingest customer feedback, classify sentiment via AI, and trigger alerts or actions while visualizing the results in real time

Standout features:

• AI Service Layer: Seamlessly integrates models (OpenAI, Bard, custom ML) across workflows, dashboards, and notebooks
• Code-engine service tasks: Enables external integrations or custom logic within workflow steps
• Infinite scalability in workflow execution for high-throughput environments

Ideal roles & use cases:

• Data teams or analysts building predictive operational dashboards
• Customer experience/workforce teams monitoring sentiment and taking action
• Business intelligence teams operationalizing data insights

2. ServiceNow

How it’s used:
Built for enterprise service workflows—HR, IT, customer support—ServiceNow connects AI agents, business logic, and real-time data using its new AI Platform. Users can have intelligent agents resolve incidents or run cross-functional approval flows.

Standout features:

• AI Engagement Layer and Knowledge Graph for conversational interfaces and cross-system context
• Workflow Data Fabric connecting silos to power AI agents across systems, vendors, and applications
• AI Control Tower for centralized governance, compliance, audit, and multi-model orchestration

Ideal roles & use cases:

• IT, HR, and support teams managing service operations at scale
• Enterprise architects or process owners orchestrating model-driven workflows
• Compliance officers seeking unified control over AI agents and policies

3. UiPath

How it’s used:
UiPath stitches together RPA bots, AI models, and human-in-the-loop interactions via its Orchestrator. Workflows can automatically process documents, route tasks, and repair broken automations with minimal friction.

Standout features:

• Agentic Automation / AI Fabric enables bots and AI agents to make context-informed decisions aligned with business rules
• Healing Agent detects and fixes pipeline breakages automatically
• Document Understanding supports NLP, handwriting recognition, and long document comprehension—used by organizations like Omega Healthcare to save thousands of work hours monthly with high accuracy

Ideal roles & use cases:

• Operations teams automating document-heavy processes (claims, invoices)
• Finance and HR workflows needing accuracy and exception handling
• UiPath developers or Centre of Excellence building enterprise-wide orchestration

4. Automation Anywhere

How it’s used:
With its APA (Agentic Process Automation) system, Automation Anywhere allows teams to build workflows driven by reasoning AI agents that dynamically plan and adapt work across humans, bots, and systems.

Standout features:

• Process Reasoning Engine: AI-powered decision matching and routing within workflows
• Prebuilt agentic solutions and natural‑language workspace optimized for common domains like accounts payable or support
• Responsible AI Layer: built-in governance, privacy, and compliance controls for secure automation

Ideal roles & use cases:

• Business process owners automating shared processes (AP/AR, customer requests)
• Healthcare, finance, or HR teams integrating conversational bots and workflows
• Compliance leaders requiring reputable AI governance embedded in automation

5. Microsoft Power Automat

How it’s used:
Drag‑and‑drop builder for cross‑app automation within the Microsoft ecosystem and beyond.

Standout features: 

• AI Builder (image/text analysis, sentiment)
• Deep M365/Dynamics integrations

Ideal for: 

• Office teams
• Power analysts
• Citizen developers automating internal workflows.

6. Make (formerly Integromat)

How it’s used:
Make offers a highly visual interface for building multi-step workflows, or “scenarios,” using drag-and-drop logic, branching paths, custom variables, and real-time data streams. It doesn’t have native AI modules, but users can embed AI calls—like OpenAI or Google Cloud APIs—directly into flows. Developers can use HTTP modules or custom webhooks to extend beyond the UI.

Standout features:

• Visual editor with advanced conditional logic and looping
• Built-in API calling and webhook support, great for custom AI calls
• Scenario versioning and scheduling for robust deployment
• Error handling tools with execution history and logging
  
  

Ideal roles & use cases:

• Ops managers or solopreneurs building lightweight automations across marketing, CRM, and support tools
• Technical users at startups or creative agencies orchestrating content generation or data enrichment with external AI APIs
• SaaS teams prototyping AI-driven internal tools without heavy engineering lift

7. Zapier

How it’s used:
Zapier uses “Zaps”—simple event-based automations that connect thousands of apps. In 2025, Zapier has expanded its AI capabilities through Zapier AI, a set of generative AI tools and GPT-powered actions that let users build smarter, context-aware flows. You can now summarize emails, classify inputs, or generate text dynamically based on triggers.

Standout features:

• Zapier AI Actions: Add GPT-based steps like summarization, translation, or data enrichment
• AI-powered interfaces (e.g., Zapier Canvas) for building logic collaboratively
• Extensive app ecosystem (5,000+ apps) with prebuilt triggers and actions
• Autoreplay, conditional paths, and multi-step logic for more complex workflows

Ideal roles & use cases:

• Marketing and growth teams automating content production, form routing, or campaign follow-ups
• RevOps and sales teams enriching leads or routing high-intent prospects based on AI scores
• Small teams using AI to triage support tickets, summarize feedback, or auto-update CRM records

8. Workato

How it’s used:
Workato is a high-powered integration and automation platform built for enterprise use. It supports complex logic, layered workflows, and embedded AI capabilities, such as predictive analytics, document classification, and human-in-the-loop review. With “Recipes,” teams can chain business rules, AI decisions, and cross-app actions in a scalable, secure way.

Standout features:

• Prebuilt “Recipe” templates for common workflows across apps like Salesforce, NetSuite, and Slack
• AI-driven data mapping, document processing, and sentiment analysis
• Human-in-the-loop integration to blend automation with review steps
• Robust governance, access controls, and auditability for large teams

Ideal roles & use cases:

• Integration architects or automation leads creating secure, cross-functional workflows
• IT and ops teams managing approvals, finance processes, and employee onboarding
• Enterprise teams embedding AI into customer service, compliance, or procurement flows
  
  

9. n8n

How it’s used:
n8n is an open-source workflow automation platform that can be self-hosted or run in the cloud. Unlike Zapier or Make, n8n prioritizes flexibility and developer control. It supports hundreds of integrations and allows for custom JavaScript functions, external AI service calls, and granular control over workflow logic and triggers.

Standout features:

• Open-source and self-hostable for full data privacy and customization
• JavaScript function nodes to build logic that goes beyond drag-and-drop
• Custom HTTP and webhook nodes for connecting to AI models or APIs
• Community-driven plug-ins and active GitHub ecosystem

Ideal roles & use cases:

• Developer teams building highly customized workflows with AI/ML logic
• Organizations with strict data privacy needs (e.g., finance, healthcare, or legal)
• Engineers automating internal tools, agent-driven workflows, or research pipelines

10. ProcessMaker

How it’s used:
ProcessMaker is a business process management (BPM) platform with strong support for structured approvals, case management, and AI-driven decisioning. It offers a low-code designer for process modeling, and now includes AI tools for form processing, routing logic, and document classification. Think of it as BPM for the AI-native era.

Standout features:

• Low-code BPMN modeler for process design and automation
• AI enhancements like document parsing, entity recognition, and email classification
• Case management tools for structured workflows with rules and approvals
• Audit-ready reporting and compliance features
  
  

Ideal roles & use cases:

• Process and compliance teams in healthcare, government, legal, or banking
• Managers orchestrating case-based workflows like loans, claims, or hiring
• Organizations needing structured, multi-step approvals and documentation tracking

Building smarter workflows starts here

AI isn’t just changing what businesses can do—it’s changing how they get things done. As workflows become more intelligent, real-time, and distributed, the platforms you choose matter more than ever. The right AI workflow platform doesn’t just automate tasks—it empowers your teams to act faster, adapt quickly, and unlock value from every data point and decision.

Whether you're optimizing internal approvals, orchestrating AI-powered service operations, or connecting real-time insights to frontline action, these ten platforms offer powerful starting points for building scalable, future-ready automation.

If you’re looking for a solution that brings AI, data, and automation together in one seamless environment, Domo stands out. Its ability to integrate real-time data, activate AI models, and automate across your business—without forcing you to toggle between tools—makes it a uniquely powerful choice.

Ready to bring intelligence to your workflows?
Explore how Domo powers AI workflows →

‍