AI Use Cases for SaaS: Benefits and Examples

Artificial Intelligence
Jul 24, 2023
AI Use Cases for SaaS: Benefits and Examples

The use of AI tools has skyrocketed in 2023. ChatGPT, for example, set a record for the fastest-growing user base, reaching 100 million users in barely 2 months' time.

For SaaS companies, starting new AI projects opens up a load of potential. In this article, we’ll explore how SaaS companies can surf along and capitalize on this new trend. Learn about the benefits and specific use cases of artificial intelligence for SaaS.

Importance of artificial intelligence in SaaS

Artificial intelligence is nothing new under the sun. It’s been around for decades in many forms and subsets.

  • Machine learning algorithms learn from past data to recognize and use patterns.
  • Natural language processing (NLP) specifically looks at understanding human language and text patterns.
  • Deep learning teaches computers to process data in a similar way to the human brain.

So why does artificial intelligence suddenly seem to be the only thing the tech industry is talking about? The answer is convenience.

Generative AI applications like ChatGPT, Bard, Dall-E and countless others have put AI within reach of anyone. Not only machine learning engineers or data scientists. Or the big guys like Amazon or Microsoft. Suddenly, marketers at early-stage startups are churning out blogposts with AI. Product managers use ChatGPT to analyze user feedback at scale. Even my mom is creating trivia rounds for her local women’s association.

For SaaS companies, this has two big implications.

  • Operations: internal teams can work more efficiently and save time
  • Innovation: product teams can drastically speed up research and product development

As a result, it’s becoming much easier for SaaS companies to add pioneering product capabilities to their product.

Benefits of artificial intelligence

First of all, saving time is one of the main advantages of AI. Marketing, sales or customer success no longer need engineers to automate business processes. Whether it’s analyzing customer feedback data at scale, or using chatbots to automate customer support questions.

And even for daily repetitive tasks, like writing blogposts, creating product release notes or drafting sales proposals, your team can now do them twice as fast. Or quicker. All that time saved can be used to solve more complex problems or tasks. And your SaaS growth will benefit.

Likewise, developers can solve complex coding problems faster by asking ChatGPT for help. It may not always hit correctly on the first try, but even the slightest hunch can set your dev team on the right track.

Example how developers can use ChatGPT to solve errors in their code

Besides more efficient workflows, AI algorithms can bring new innovations to your SaaS offering. Your engineers can plug in powerful AI technology into their own software apps to learn from historical data. The result is a more powerful application and a better user experience that attracts and retains customers.

So, what does that look like in practice? To illustrate, let’s look at some specific AI use cases to boost your SaaS business.

AI use cases for SaaS companies

1. Process automation

Administrative tasks can be a hassle. Just think of transcribing meeting notes, inputting data into a database, organizing emails or business documents, and so on. Now, you can automate most of these tedious, repetitive tasks with AI. With shorter, more efficient business processes, your team can get more important work done in less time. 

2. Predictive analytics

SaaS companies are sitting on a vast amount of big data, but they aren’t always making the most out of it. Many SaaS products offer some type of charts or reports to their customers, although it’s often limited to historical data.

It’s crucial to understand your company’s past performance and status quo to make data-driven decisions. But add the ability to make predictions for the future, and you’ve got a recipe for even more successful decision-making. You can use AI models to forecast many data points. Whether it’s predicting a warehouse’s inventory, product demand in different seasons, energy usage, or the likelihood a customer will churn.

3. Product development

Product managers and developers can use AI inside their apps to build more powerful products. Big SaaS providers have already started launching AI capabilities. For example, Notion launched an AI solution that summarizes meeting notes and adds action items. ClickUp lets you create project timelines and briefs, or design user testing studies with AI. At Luzmo, we’ve created an AI dashboard builder to create dashboards on top of your data on autopilot.

4. Customer experience

For customer success and support teams, chatbots were the first revolution in customer support. Customers got a much quicker reply to standard questions, instead of having to wait for a support person to answer.

But new AI tools bring new potential to improve the customer experience. Product managers can do sentiment analysis on social media posts or online reviews. These conversational analytics reveal exactly what’s on their customers’ minds. They can then use those learnings to improve their roadmap, and in turn, improve customer satisfaction.

HubSpot chatbot as an example of an AI use case

Industry-specific AI use cases

Every industry has different needs, so let’s look at some specific use cases depending on the industry your SaaS platform serves.


Manufacturing SaaS platforms can use AI for more efficient and productive process automation. 

  • Predictive maintenance. Predict and alert users when equipment might break down. It will reduce unexpected downtime.
  • Forecasting inventory. Alert users to adjust supplies so that they don’t run out of stock, or spend too much capital on overstocking.
  • Quality control. Analyze real-time data and alert users of anomalies or defects in your production lines.

Real estate

PropTech can use AI to avoid risks during construction projects or make the most revenue out of their real estate portfolio. All in real-time.

  • Property valuation. Analyze data on market trends and property pricing. With more accurate price predictions, you uncover investment opportunities for real estate investors.
  • Risk management. Alert users about construction risks and issues, based on past projects. It will lead to big savings and better safety.
  • Property viewings. Smoothen operations for property managers. Virtual AI assistants to schedule property viewings, or chatbots to answer questions in real-time.


The application of AI in marketing software extends to many different tasks and domains. What used to be time-consuming tasks for marketers can now be done in mere minutes.

  • Personalized marketing campaigns. Send more relevant marketing content, based on a user's preferences.
  • Predict customer behavior. Alert marketers of any interactions that show buyer intent, based on how they bought before.
  • Identifying new products. Learn from market data to spot opportunities for new products to add to their portfolio.

Supply chain

AI systems can help logistics and supply chain software further streamline their users’ workflows.

  • Inventory management. Adjust inventory based on past stock and seasonality. Cut supply chain costs drastically.
  • Supply chain management. Predict and manage risks like supplier reliability, geopolitical issues or weather events. Make the supply chain more efficient and cost-effective.
  • Route optimization. Suggest the most optimal route based on data about traffic conditions, road closures or weather conditions.
Example of using AI for supply chain - route optimization


AI in healthcare platforms can help improve patient care, diagnostics and improve the workflows of healthcare industry professionals.

  • Medical imaging. By using AI to analyze medical imaging, clinicians can make more accurate diagnoses. For example, identify patterns in radiology images that the human eye would have otherwise missed.
  • Admin tasks. Auto-refilling prescriptions, booking appointments and handling billing can all be automated through AI. This leaves more time and resources for patient care.


Fintech platforms can use AI to offer more accurate, personalized and secure financial services. Let’s have a look at a few applications.

  • Fraud detection. Use machine learning to spot suspicious patterns or anomalies to alert people of fraudulent transactions. 
  • Risk management. Banks can, for example, use AI to make better decisions about lending, based on someone’s credit history, transactions or other financial information.
  • Personal finance and investment. Make recommendations for savings or investments based on someone’s personal financial situation and behavior.


Finally, e-commerce platforms are adding AI to improve customer satisfaction of their online shoppers in different ways.

  • Shopping recommendations. Based on customer behavior and past purchases, you can make better recommendations on your e-commerce platform.
  • Customer support bots. With AI bots answering frequently asked questions 24/7, your team has more time for personal support with complex questions or orders.
  • Inventory management. Forecast demand of certain products in specific seasons, and maintain just the perfect amount of inventory to keep storage costs low, and product delivery fast.

Innovate your SaaS roadmap

Whichever your industry or use case, adding AI features to your SaaS platform is a great tactic for product innovation. With more tailored recommendations, more automation, and less effort for your customers, you’ll increase customer satisfaction and lifetime value. 

Of course, artificial intelligence is not the only path to innovation. To help your users make better decisions quickly, Luzmo helps SaaS companies add interactive analytics to their platform in days, not months.

Learn more about embedded analytics by talking to one of our product experts, or sign up for a free trial and build your first dashboard in minutes!

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