The SaaS industry, known primarily for boosting workplace productivity with automation and collaboration tools, is now embracing large language models (LLMs), ushering in a new era of innovation and productivity worldwide. As leaders in generative AI (GenAI) adoption, SaaS companies are optimistic, forecasting a 10% revenue increase over the next two years by integrating GenAI into their platforms. McKinsey predicts that the market could even triple to an astounding $10 trillion by 2030.

As competition intensifies, SaaS leaders are moving beyond simple efficiencies to develop smarter, autonomous decision-making features that can navigate complex workflows—known as agentic capabilities. These advancements are poised to significantly increase workforce productivity and could potentially free employees from up to 70% of hours spent on mundane tasks, allowing them to focus on more complex and higher value activities.

In this post, we look at how SaaS providers have approached the first wave of adoption, and then offer examples of agentic solutions that may soon follow.

The First Wave of Adoption

Applications built on LLMs represent the most attractive investments within the generative AI landscape, so it's unsurprising to see SaaS providers racing to launch new products using the technology. Over half of last year’s generative AI announcements came from traditional SaaS companies. Products launched by SaaS businesses and startups over the last 12 months have centered around personal AI assistants, knowledge management and research assistants, and auto-generation of content. The following are real-world examples of each.

Personal AI Assistants

These solutions can help users perform everyday work tasks more quickly and easily, even generating the first draft of documents when needed. Tech leader Oracle, for instance, partnered with Cohere to add new AI capabilities to its enterprise resource planning application suite, Fusion. The new AI-enhanced, SaaS-based offering dramatically boosts HR productivity by saving talent managers and other HR experts hours spent filling out paperwork, searching for information, and other common tasks. For instance, the software’s AI assistant enables HR professionals to quickly generate an initial job description draft by inputting the job title and a few key details. Once resumes start coming in, they can use the software to summarize key accomplishments and qualifications outlined in each applicant’s resume to rapidly prioritize candidates. As the company onboards a new employee, an HR team can use Fusion’s AI capabilities to quickly locate relevant information to answer questions and create the first draft of an email based on their search results.

Similarly, Borderless AI, a legal tech startup, set out to make it easier for lawyers and HR teams to analyze and create employment contracts, and find answers to questions about local labor laws and regulations. Using Cohere Command R with retrieval-augmented generation (RAG), the company launched a first-in-kind AI agent, Alberni, that can automatically understand documents, deliver answers, and draft contracts in local languages, enabling Borderless to support companies worldwide.

Knowledge Management Assistants

At AtomicWorks, a global provider of IT service management and service desk solutions, a new digital workspace experience solution enables enterprise employees to more quickly get answers to IT support questions around software updates, app provisioning, and IT issues. The company estimates that these activities can take up to 50% of IT’s time. To achieve the scalability, accuracy, and performance needed in responding to IT requests in real-world situations, AtomicWorks uses Cohere Command R+ with Cohere Rerank, which can significantly improve enterprise search and retrieval with only a few lines of code.

Content Generators

Such systems can be designed to generate code, document summaries with citations, marketing materials, and call summaries, among other outputs. One global provider of video conferencing services uses Cohere Command R to provide high-quality meeting summaries and lists of action items for call participants to help them better collaborate and be more productive.

Example use cases for generative AI in the SaaS industry.

What’s Next for SaaS Solutions

The introduction of LLMs tailored for enterprise-scale retrieval systems combined with advancements in RAG and Tool Use (which gives models the ability to use external tools or APIs) marks a significant leap forward in the development of more sophisticated solutions. And SaaS businesses are primed to test these innovations. New AI agents can integrate insights from an entire tech stack — including CRMs, supply chain management tools, and ERP systems — to deliver more comprehensive and autonomous responses and solutions.

Cohere Command R+, for instance, includes tool-use capabilities that can initiate a series of calls through APIs or LangChain to multiple systems to evaluate a situation, consider various courses of action, and initiate one or more steps that align with programmed goals or objectives.

Some potential agentic use cases for SaaS include:

A payment application that enables a retailer to offer targeted customer discounts in real time based on customer satisfaction and loyalty and inventory levels, and automatically notify the necessary suppliers of changes in standard order quantities
A procurement management solution that automatically interacts with CRM, inventory, and order management systems to help project managers ensure that the necessary materials are available for project execution or alert teams to potential delays
A content management system that can interact with BI tools, CRM, social media accounts, and email marketing systems to seamlessly personalize and distribute approved marketing content across channels

As GenAI in the SaaS market gears up, software providers need to move quickly and effectively. Fast iteration and build cycles using plug-and-play components like the Cohere Toolkit, an open-source repository for quick experimentation and deployment, come in handy when speed and security is of the essence.

If the past is any indication of the future, those who lead may experience the highest rate of growth. Moreover, SaaS providers must gain experience with all the levers they can pull to achieve the necessary levels of performance and accuracy that set new standards for productivity. With investors eyeing this space, now is the critical time to dive in and get ahead of the competition.

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SaaS Is Readying for an Agentic Future