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You’ve probably worked with artificial intelligence (AI) in some form, and workflows aren’t new either. But what happens when the two come together?
That mix is what makes AI workflows different.
They take the structure of a workflow and layer in intelligence from AI models. What you get is something that can handle routine tasks, adapt to context, and move work along without constant oversight.
In the sections ahead, we’ll look at what AI workflows actually are, how they function, the key parts that make them work, and examples of how they’re already being used in legal settings.
AI workflows are sequences where inputs flow through various AI tools to produce clear outputs, such as insights, alerts, or completed actions.
They use AI-powered steps to move from input to output, connecting AI models with automation rules that keep business processes running smoothly.
In practice, this means the small but time-consuming jobs don’t have to fall on you or your team. AI automation tools can take over the routine tasks like scanning documents, flagging anything unusual, or even sending reminders before a deadline.
Let's dig a little bit deeper.
First things first, there isn’t one single recipe for an AI workflow. Different companies use different tech, techniques, and setups depending on what they need.
Still, most workflows follow the same basic idea: they bring data in, process it with AI algorithms, and then trigger the next step using workflow automation tools.
At its core, an AI workflow takes repetitive tasks off people’s hands and routes them through AI workflow automation tools that can handle both simple and complex processes.
Here’s a quick breakdown of the general flow:
For example, in contract management, an AI workflow might scan a new agreement, highlight key clauses, and automatically route it to finance for approval without anyone needing to push it along manually.
Aline does something like this. If you want to see how AI workflows function, start your trial today.
Now that you’ve got the big picture of how an AI workflow operates, it helps to look at the building blocks that make it work. Different setups may use different tools, but most workflows share a handful of core components that keep everything connected and running.
Natural language processing (NLP) is one of the key parts of modern AI solutions. Essentially, it gives machines the ability to read, interpret, and respond to text in ways that feel closer to human understanding.
Today, many AI automation workflows rely on large language models, which are advanced NLP systems trained on massive amounts of text. They make it possible for AI workflow tools to handle tasks like drafting content, summarizing long documents, or answering detailed questions.
The main benefit is that it reduces the need for constant human intervention. For instance, a legal team might set up a workflow where incoming contracts are scanned by NLP, important clauses are identified, and risky language is flagged before a lawyer even opens the file.
This same tech also powers AI assistants that can write replies, extract insights, or organize information, which makes NLP one of the most practical building blocks in any workflow.
Machine learning algorithms are the engines that give automated workflows their decision-making power.
Unlike traditional automation tools, which follow rigid rules, machine learning learns from data and adapts over time. This makes it better suited for complex tasks where outcomes aren’t always black and white.
Some key features of machine learning in AI workflow tools include:
On the technical aspects side, these algorithms use training data to build models, which then apply what they’ve learned to new information.
For example, in finance, a machine learning model could be part of a workflow that reviews expense reports, flags anything outside of normal spending patterns, and routes only suspicious items for human review.
APIs, or application programming interfaces, are the bridges that let different software talk to each other. Without them, AI applications would sit in isolation, which makes it hard to connect insights or actions across tools.
But with APIs in place, your existing systems, like CRM platforms, contract repositories, or email, can feed data into a workflow and receive updates back in real time.
You can think of an API like a translator between two people who speak different languages. It makes sure both sides understand what’s being asked and what’s being delivered.
When it comes to business process automation, that might mean pulling contract data from one platform and sending alerts through another without anyone needing to retype information.
For example, sales teams can rely on an API to connect their CRM with AI contract management software. So, when a deal is marked as closed, an AI workflow automatically kicks off the contract drafting and approval process.
You’ve probably already heard about generative AI. Basically, it’s the part of modern AI tools that can write text, create designs, or even generate video. In the context of AI workflows, it adds a layer of creativity and flexibility that goes beyond routine automation.
Generative AI can handle content creation tasks like drafting emails, writing contract clauses, or producing client-ready summaries. It can also support visual work by generating images or mockups for reports and presentations.
Beyond that, it’s powerful for productivity, since it can summarize meeting notes, draft quick follow-ups, and help teams understand context in long documents.
For example, in a legal workflow, generative AI could review a meeting transcript, highlight action items, and automatically draft the first version of a follow-up email. Done right, this can save hours that would normally be spent piecing everything together manually.
Optical character recognition, or OCR, is the tech that turns images or scanned files into editable, searchable text.
Within AI workflow automation tools, OCR removes the need for manual data entry by automatically pulling information from documents and feeding it into the system. This makes data collection faster and cuts down on mistakes that come with repetitive tasks.
Some examples of what OCR can scan include:
Automation triggers are the signals that set AI-powered workflows into motion. In many no-code platforms, triggers are easy to set up and can connect with other apps to keep an organization's workflows moving smoothly.
Combined with robotic process automation, they make automating tasks both simple and scalable.
Take a look at some examples of triggers in action:
Tip: Start small with one or two triggers that save your team the most time. Once those run smoothly, you can layer in more automation without overwhelming your workflows.
AI agents act as smart digital partners that go beyond simple bots. They’re designed for automating tasks, managing decisions, and even adjusting actions as new information comes in.
With AI-driven automation, these agents can handle more than just single steps. They can automate complex workflows that would normally require hours of manual effort.
They also provide immediate answers to common requests, which can make them feel more like active team members than passive tools.
Aline has its own agent, Aline Associate, built to support contract work from drafting through approvals. It helps legal, sales, and operations teams move faster without sacrificing accuracy.
Want to see how Aline’s AI can fit into your processes? Learn more about Aline AI today.
After looking at the core building blocks, it’s easier to see how they come together in real legal work. AI supports many different areas of practice, and here are some clear examples of how legal teams are putting AI workflows to use.
It might be difficult to believe that you can power almost the entire contract management workflow with AI, but with the right tools, it’s becoming common.
Modern platforms bring contract lifecycle management into a single system, helping teams cut down on manual work and improve oversight. One of the key benefits is that everything happens in one place, so nothing gets lost in the shuffle.
An AI-driven contract workflow often looks like this:
Of course, this approach doesn’t eliminate the role of people, but it keeps routine steps off their plate and reduces errors.
Aline makes this possible with AI contract review, dynamic templates, built-in signing, and automated tracking all in one platform. The result is that teams get a smoother contract process from start to finish.
Drafting documents can eat up a lot of time, but generative AI makes it easier to get a strong first draft in place. Using approved data sources and past work, legal software can pull together text that’s accurate and ready for you to review.
Plus, when linked with your other software applications, the whole process becomes quicker and more consistent.
With generative AI, you can draft:
Legal research often means digging through large volumes of information, which can take at least a few hours. With AI-powered data analysis, that process becomes a lot more manageable.
Modern AI features can scan case law, regulations, or company records and surface the most relevant pieces in minutes. The idea isn’t to replace your judgment, but to surface the most useful information so you can review it faster and with more context.
For example, say you’re preparing for a client meeting. Rather than spending the whole afternoon reading through dozens of rulings, an AI tool could summarize key precedents, highlight risk areas, and even provide customer insights tied to the industry.
You still decide how to use that information, but the upfront legwork is done for you.
AI workflows can reshape how you handle client interactions, from the first intake form to ongoing support.
Typically, they cut down on repetitive work and automate routine steps so you can respond faster and keep the focus on real conversations that build trust. That leads to stronger customer satisfaction and a smoother overall experience.
Here are some examples of how contract workflow automation fits into client services:
These workflows don’t replace personal service. They make space for it, which, in turn, gives you more time to focus on building relationships while routine tasks run in the background.
AI workflows aren’t limited to one part of legal work. As we've learned, they can support drafting, research, client services, and so more.
The real advantage comes when those pieces connect, creating a flow that reduces manual effort and keeps projects moving.
Aline pulls these elements together with AI review, dynamic templates, electronic signatures, automated tracking, and other essential contract tools in one platform.
The result is contract lifecycle management that feels less fragmented and far easier to manage. More importantly, you spend less time chasing details and more time focusing on decisions that matter.
So ask yourself this: How much could you get done if repetitive contract work handled itself?
Start your trial with Aline today and see what AI workflow automation tools can do for you.
An AI workflow is a structured process where AI models and automation rules work together to carry out tasks. It can handle anything from reading human language in documents to routing approvals or generating reports.
The common types are sequential workflows, state machine workflows, rules-driven workflows, and AI-powered workflows. Each serves a different need, from step-by-step approvals to automating repetitive tasks with advanced tools.
Many platforms now include workflow builders. Some use a drag-and-drop interface so even non-technical users can design flows that fit their team’s needs, like scheduling meetings or automating approvals.
Most follow a pattern: input data, process with AI models, decision-making, and output. The critical step is making sure the data stays up to date, since that directly affects accuracy.
They offer cost savings, stronger team collaboration, and better overall productivity. For example, sales professionals can use workflows for follow-ups, while legal teams can use them to review contracts faster.
Brent is an attorney, a full-stack JavaScript engineer and the CEO of Aline.
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