For the past several decades, enterprises have been integrating various processes and workflows through tools that reach out to every corner of the enterprise. Some called this “business intelligence,” “business process management,” or “robotic process automation,” but no matter the buzzword, the solution was the same: a centralized system to hoover up data, chew it, and spit out reports, linking together as many of the various systems throughout the organization as possible.
AI has always been a big part of this equation. Many early RPA tools used rudimentary optical character recognition to assist document ingestion. Even this basic conversion of documents was sold as AI. Other tools included basic decision-making driven by data collected along the way. This was also called AI by the marketing folks.
Bolting machine intelligence onto such systems is now getting dialed up to 11. Dozens of companies are combining traditional API-driven orchestration with large language models (LLMs) and machine learning tools, superseding the basic tools with all the smarts available from the top AI companies.
As a result, it’s now relatively easy to inject top-grade LLMs into your dataflow. Workflow tools can now be used to grab data from various parts of your company and ask an LLM to make decisions based on it. If someone wants details, it takes a few seconds to write up a nice report or build a flashy dashboard.
Some of today’s AI-enabled workflow tools don’t stop there, adding LLMs as meta-supervisors to enable anyone to take no-code processing to a new extreme. Instead of just dragging and dropping icons around the screen, you can ask the LLM to create the workflow itself and then reach in to tweak it after you get back with your coffee.
Of course, there are deep questions about how well this works, but the answer likely depends on just how well you write your prompt and how much the LLM can absorb about your larger workflow. If your task is the kind that LLMs excel at, you should see great success. If your assignment is tricky and the data is noisy, it’s anyone’s guess what will happen.
The good news is that the experimentation will be fast and full of opportunities. You won’t be spinning your wheels trying to link up databases or reformat tables. Moving data is relatively easy, allowing you to focus on fiddling with the prompts and swapping out models.
All this promises to unlock deeper and more thorough collections of data from across the enterprise giving leadership better grounding for better decisions. To assist you in creating a more data-driven enterprise, here is an alphabetical overview of the more popular tools companies are using to add LLM smarts into their workflows.
Activepieces
The open-source platform from Activepieces connects with hundreds of MCP servers, APIs, services, and AI tools to push data through a workflow pipeline. All of the major components, many MIT licensed, are available on npmjs.org and GitHub making it much easier to embed them with Node projects.
Apache Airflow
This open-source tool dates from the age when it was a challenge to link together data sources and sinks. Now many IT teams are leveraging the dataflow frameworks built around Apache Airflow and adding AI tools so that the dataflows can add a bit of artificial intelligence to the processing pipeline. You don’t need to use AI, but you can. A large and diverse ecosystem of users and providers offers one of the deepest sources of tools for building a successful data processing pipeline. Well, technically it’s a data-processing directed acyclic graph (DAG).
CrewAI
Projects that depend on a crew of agents, working independently but in harmony, are the bread and butter for crew.ai. Each agent gets instructions in a natural language and are then deployed to ingest data, make decisions, and coordinate responses. Many developers think of each agent as having a different role (e.g., “Researcher,” “Writer”), a rubric that makes developing complex teams a bit simpler. There’s a nice “no code” interface for those who want to work at a high level, but there’s also an “all code” panel for those who celebrate getting into the guts of the machine.
Dagster
Anyone who needs to juggle large collections of data that form a foundation for an AI model or app can use Dagster to organize the dataflows that move along the directed acyclic graphs (DAGs) that inspired the name. Data is “orchestrated,” “cataloged,” “integrated,” and tested for quality. Once you find the data you need, you can feed it into a foundation model to customize the answer. Available as low-priced starter plans for projects with simple pipelines or larger versions needed for the full enterprise tasks. An open-source version released under the Apache 2.0 license is also available.
Dify.ai
Dify.ai is less of a product and more of a marketplace for AI agents, services, and backend tools like RAG databases. Users can work together to link models and backend services into finished products. When they’re done, they can publish the workflow as a web app. Students and those just kicking the tires get free access to the cloud version. Professional developers can get full access to the API for a monthly fee.
Flowise AI
Not everyone trusts the new AIs. That’s why Flowise makes it easy to design workflows that keep a Human in the Loop (HITL). Their workflow tool stitches together a collection of multiple agents and then lets developers make design decisions about how to open this work to humans and the rest of the enterprise stack. A strong API opens up options for Python and Typescript developers.
Gumloop
Teams that need a data-focused, no-code visual designer can use Gumloop to integrate hundreds of apps and APIs. A full set of templates are designed to simplify data gathering and screen scraping for enterprise tasks such as sales, document processing, or HR. AI enhanced decision-making can automate the workflows to update dashboards and reports so the humans can focus on important details.
LangChain / LangGraph
This is less of a “platform” and more of a foundational open-source framework for developers. Many of the tools on your list (like Flowise AI) are built using it. LangChain provides the core building blocks for chaining LLM calls together, while LangGraph allows for creating more complex, cyclical agentic behaviors.
Make (formerly Integromat)
Another popular tool for creating elaborate workflows has also embraced the power of adding various machine learning algorithms to the mix. Make (formerly Integromat) can tap into LLM providers such as OpenAI and use their answers as first-class parts of any workflow. Its visual editor and dashboard help design and track the flow of data through the network of nodes and thousands of connected apps. The reporting and tracking work can now be enhanced with agentic intelligence.
Microsoft Power Automate
Any company that’s heavily invested in Microsoft tools such as Office 365 can now use the Power Automate collection to add even more intelligence to their workflows. The AI builder can take the power of AI to speed form processing and prediction. While the tool may be known first for supporting apps from the Microsoft ecosystem, there are hundreds of outside integrations as well, including many major services, such as SAP or Salesforce.
N8n
Developers and non-developers alike turn to the n8n platform because it promises the “flexibility of code and the speed of no-code.” The drag-and-drop interface handles the standard use cases for AI integration of hundreds of apps and if the dev team needs more they can drop down to the code level for low-level access to the dataflows. A “sustainable use” license opens up the Github code for anyone to see and, depending on their needs, to use.
Node-RED / FlowFuse
The browser-based flow editor for Node-RED was built in Node and so, naturally, it delivers Node applications. While the tool has been around for years, lately a company called FlowFuse has emerged to support development and integrate the tool with AI. The low-code platform connects to thousands of pre-integrated sources, gathers data, displays it, and makes informed decisions with or without you. The Node-RED platform is also available open source with an Apache 2.0 license for those who want the freedom that comes with that option.
Pipedream
Developers in need of a platform that processes event-driven Python and Node code in a serverless environment can turn to Pipedream and use its 10,000-plus tools to link more than 2,800 apps. The option to use Python or JavaScript can make this ideal for teams that need to inject custom code into the workflow to make things run smoothly. Secure connections with some of the major foundational platforms such as Slack, Salesforce, or Stripe ensure that users can tackle pretty much any standard enterprise use case.
Prefect
This Python-based open-source tool is often seen as an alternative to simpler systems that rely on directed acyclic graphs of nodes to model dataflows. It’s attractive for those who like to write complex and dynamic workflows in Python. There’s also a service called Prefect cloud that offers easy deployment, monitoring, and security for those who want more of a turnkey system.
Retool
One of the best ways to create internal applications for back-office chores is to fire up Retool, spec out some agents, and let them act on your behalf. If they need extra code, you can write custom functions in Python or JavaScript. The visual process, though, will be very accessible to any user. Hourly pricing can help CFOs keep the budget in check.
StackAI
One of the key approaches for embedding knowledge is to combine a vector database filled with documents with a foundation model. StackAI specializes in delivering these kinds of retrieval-driven solutions so an enterprise can build a knowledge base with their document collections. Full citations build trust in the answers that come from the web apps or AI copilot.
Tray.io
The Merlin Agent Builder is a low-code tool that lies at the center of Tray.io’s low-code tool for taking control over your enterprise stack. The agents that can be built and deployed in the system can access data from various pre-integrated endpoints and then act, while staying constrained by the governance rules that you put in place. The simplest use cases involve deep process integration and chat-driven interfaces.
Vellum AI
The no-code workflow builder for Vellum AI is where any user can describe crucial tasks to be assembled from a collection of tools and agents. Vellum’s design focuses on establishing a solid debugging cycle so the user can teach the budding agent how to get the right answer. A solid knowledge base engine incorporates document chunking with optical character recognition to turn any collection of documents into a foundation for decision-making.
Workato
Some call them agents or AIs. Workato uses the word “genies” to describe its models trained on many of the standard enterprise tasks such as marketing or sales. Under the hood is an MCP-savvy switching house that juggles major LLMs such as OpenAI to set up a strategic plan that is then executed with any of the hundreds of APIs connected to the system. The Agent Studio is the center of the action where users can use no-code prompts to design the workflows.
Zapier
IT managers looking for ways to automate workflows have been turning to Zapier for a long time. Now they can add AI tasks to the flowcharts and use the new AI smarts to unlock the power of all the previous generation of app integrations. By Zapier’s estimate, that means you can connect any number of models to more than 8,000 apps. It’s one of the quickest ways to add a bit of event-triggered intelligence to an existing stack.
Read More from This Article: 20 AI workflow tools for adding intelligence to business processes
Source: News