AI and cloud are driving big disruptions in SaaS and are growing like wildfire.
The AI SaaS market alone is set to grow by 36% by the end of this year, and the global AI software revenue is estimated to reach $118.6 billion by 2025.
SaaS leaders who have integrated AI into their products are reaping the benefits of this growth.
For example, Canva has announced its $2.3 billion in ARR, a generous increase from the $1.7 billion recorded in October 2023.
AI is undisputedly the future of SaaS, and in this article, we explored the immense benefits AI holds for SaaS solutions and discussed crucial AI SaaS use cases in various industries.
First off, SaaS platforms allow users to benefit from software without the burden of owning it.
For instance, a business can use a project management tool without worrying about updating, maintaining, or managing the software space.
AI, on the other hand, is the science that involves designing machines that can mimic, match, or even exceed human reasoning.
Combining AI in SaaS gives rise to tools that not only respond to manual inputs but can act as intelligent partners, predicting outcomes and making adjustments that align with business needs.
Today, 92% of businesses are either replacing their traditional SaaS with AI-powered ones or considering doing so.
For example, traditional data management and sales tracking CRM tools are largely being replaced by AI-powered counterparts that automate data entry, forecast sales, and offer personalized customer interactions.
Recent studies show that staff reports an 80% improvement in overall productivity due to AI integration.
This is not surprising, as AI-driven platforms continuously learn from data, optimize operations in real-time, automate decision-making, and offer actionable insights, ultimately enhancing efficient business success.
AI comprises intelligent technologies that can analyze vast amounts of data, detect patterns, and better understand human language. Let’s examine three key technologies in AI SaaS.
A powerful feature of AI SaaS tools is that they get smarter as you keep using them.
Machine learning (ML) in AI enables SaaS tools to learn from data and improve through experience, allowing them to make better decisions over time.
A typical business use case of machine learning is customer support automation, where AI tools can quickly analyze previous interactions and provide suggestions tailored to a customer’s needs.
The ML market is estimated to grow by 36.08% (2024-2030), and 49% of organisations are already leveraging the technology to identify sales prospects and optimize sales, showing that machine learning AI will be crucial for competitive advantage in the future.
It is no news that big companies use big data to make strategic decisions that help them stay ahead of competitors.
However, the benefit your business can derive from big data is only as good as the analytic method you have in place.
In fact, recent studies reveal that 54% of organizations with advanced data and analytics recorded more revenue compared to those without it.
Predictive analytics is an evolutionary type of data analysis that tells you what could happen next in your business or company,
An AI SaaS use case for this is user interaction tracking, where a company derives insight into how people interact with their tool and uses this information as a guide for feature updates or product development.
Similarly, a business can use predictive analysis implemented in SaaS platforms to determine which products or services will be in demand, enabling proper inventory management.
Other notable AI tool use cases that use predictive analysis include:
Natural language processing (NLP) helps machines interpret and respond to human language in a useful way.
NLP is a critical technology because it allows the loading of data sets (such as chats, text, personal info, search history, and voice recordings) into SaaS tools in human language form.
To analyze the data and extract valuable insights from it, the tool has to understand this data properly.
Some NLP models go beyond understanding the meaning of the text to interpret the emotions in human interactions, thus powering user-friendly interactions.
Google’s search engine is an example of AI-based SaaS that heavily relies on NLP to respond to search queries.
In 2022, Google introduced Google Helpful Content, an update that can understand context and the relationship between words and phrases.
It enables Google to provide more relevant search results, improving user experience. Common AI use cases of NLP in SaaS AI tools include;
In the past, SaaS was just a way for businesses to cut costs in the IT department; today, the story is different.
Businesses are realizing that SaaS can transform their work processes, make intelligent decisions, and drive more sales, all thanks to AI integration.
SaaS AI has given rise to predictive analytics, advanced security, and smart workflow automation, all key trends in today’s SaaS landscape.
Currently, 35% of SaaS companies use AI to stay competitive, while another 47% plan to implement it soon.
Here are the top benefits of AI in SaaS companies:
SaaS companies perform repetitive and demanding tasks like data entry, email organization, and transaction processing.
These tasks are so time-consuming that most companies now leverage AI to automate them.
Automation improves employee efficiency and accuracy, allowing them to focus on higher-value work.
A crucial use of AI in business automation is in customer support, which is particularly helpful for user onboarding.
SaaS solutions can be complex for new users to navigate; however, AI assistants provide step-by-step guidance for users in real-time.
In addition to that, AI enables automated customer feedback analysis, which gives businesses insights into customer satisfaction so they can make improvements.
The benefits of automation in business are too numerous to ignore. It’s no surprise that 60% of customer service professionals in a survey voted automation as the next best action for customer success.
Generally, AI is trained using large language models (LLMs). These LLMs are quite adaptive and can scale up exponentially as more data is fed into them.
Hence, it’s easier for AI-powered SaaS products to handle work at scaled levels than their conventional counterparts.
More so, AI increases the number of clients SaaS companies can handle within a given time frame by automating workflows and reducing human workload.
Studies show that most companies using AI are large-scale, accounting for 42% of enterprise-level businesses. Here are some day-to-day AI SaaS use cases that improve scalability for SaaS solutions;
It is no news that AI saves SaaS companies money. According to a McKinsey report, 33% of businesses use Gen AI tools to cut costs, while 12% use this technology to make more money.
Three key ways AI helps SaaS companies to optimize costs include;
AI can revolutionize the reachability of SaaS tools to all, including people with disabilities.
Today, most SaaS use customer support chatbots that can offer voice-based interaction to accommodate people with impaired vision.
About 16% of the world’s population has at least one disability; therefore, enabling accessibility to your platform enhances its inclusivity and exposes it to a wider user base.
More ways SaaS Companies are integrating AI into their platforms to make them accessible include;
Between 2021 and 2023, data breaches on SaaS platforms surged by 72%, and these attacks are expected to grow in complexity.
Experts recommend adopting preventive measures instead of reactive ones to avoid these evolving threats.
With AI support, you can easily identify vulnerabilities in your platform before they escalate.
For instance, AI tools can help reduce the incidence of hacking by continuously monitoring network traffic and flagging unusual activity and data requests.
Large SaaS brands like Facebook use AI in their codebase to fix bugs.
The AI scans the code for potential threats and suggests fixes to developers, enhancing the overall quality of the platform.
Giving customers a personalized experience is key to boosting customer retention; by making recommendations based on past interactions.
AI helps SaaS companies personalize each user’s experience by analyzing large amounts of user data to understand individual preferences and needs.
A popular AI use case for personalization is Netflix. By analyzing viewing history and preferences, Netflix determines which movie users will prefer and offers recommendations.
This personalized experience keeps users glued to the platform.
Other Examples of AI-based products that offer personalization include;
Integrating AI into SaaS products enhances functionality, efficiency, and drives business growth for different industries.
For instance, integrating AI in patient management platforms can streamline processes like appointment scheduling and patient record management, which helps healthcare providers be more efficient at handling patient interactions and outcomes.
Let’s look at more use cases of AI technologies in SaaS industries:
A MailChimp study shows that 68% of marketers currently use AI in their day-to-day tasks while 88% of them believe they must implement AI to stay competitive, showing that AI in marketing is the new norm.
AI SaaS use cases in marketing include identifying prospects, customized marketing, and gaining insight into sales.
AI SaaS can automate and streamline routine roles like tracking user behavior, analyzing data, and optimizing strategies, allowing marketers time to focus on creative activities.
Some benefits of SaaS AI in marketing include;
SaaS AI is transforming how manufacturers stay ahead of the market. Up to 41% of manufacturers use AI-based applications to gather and manage supply chain data.
With SaaS tools, manufacturers can optimize operations, minimize downtime, and improve equipment effectiveness by simply flipping through tabs on their screens.
Let’s look at other use cases of AI in manufacturing;
AI has tremendous potential to revolutionize the healthcare sector by speeding up processes. In fact, recent data suggests that AI can potentially lower the cost of finding new drugs by 70%.
As such, healthcare institutions are using Machine learning SaaS solutions to accelerate medical research discoveries by analyzing data sets that would ordinarily take humans years to process in a few minutes.
This can translate to faster drug formulation, easy disease detection, and personalized treatment plans. It can also give physicians 20% more time to focus on patient care.
The real estate sector can greatly benefit from AI integration in its solutions.
From enhancing customer services with chatbots that ensure 24/7 customer support to optimizing property valuation using machine learning, the benefits are endless.
Other crucial use cases of AI SaaS tools in real estate companies include;
Since the adoption of AI, traditional e-commerce and its complexities have given way to a streamlined and efficient mode of operation.
It is little wonder that 65% of respondents in a McKinsey 2024 report use AI in their E-commerce.
A crucial AI use case in e-commerce is personalized product recommendation marketing.
Machine learning in AI provides suggestions tailored to a customer’s needs by analyzing data, behavior, and interactions.
It enhances the shopping experience, boosts customer engagement, and increases conversion rates.
A good example of this use case is Amazon, which utilizes AI to recommend products to its users based on purchase history and other user data, thereby impacting the e-commerce giant’s sales growth.
Emerging trends in AI SaaS are reshaping software solutions, making them more user-centric, intelligent, and responsive.
As we look towards the future, we expect to see a rise in the adoption rate of AI in SaaS.
76% of SaaS companies are using or exploring the adoption of AI to improve their operations. Other key trends we expect to see in the AI SaaS landscape are;
Natural language processing (NLP) is an effective way to leverage AI in SaaS.
It enables your SaaS tools to understand human language, respond effectively, and extract deep insights from human conversations.
You can start by integrating NLP-powered chatbots to automate customer support, which allows users to get instant answers without human intervention.
No, not all AI companies are SaaS companies. Some AI companies sell software, while others sell hardware or custom solutions.
SaaS companies deal only with software that you can use online through a subscription.
One of the roles of AI in SaaS is its ability to analyze data at scale, empower businesses with data-driven insights, helping them optimize operations and make better decisions.
Some of the best AI use cases in business include personalized customer experience, predictive maintenance, onboarding employees, user authentication, customer support, workflow optimization, and content creation.
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