Why the Per-Seat Business Model Faces Challenges in the Age of AI

The rise of AI is shaking up many industries, and one area where the impact is particularly significant is in SaaS companies that rely on the per-seat business model. Traditionally, these companies charge customers based on the number of users, or seats, accessing their software. But with AI’s ability to handle the work of multiple human employees, this model is facing serious challenges. AI can take on tasks at scale, reducing the need for multiple human users—and by extension, the number of seats needed.

AI Replacing Multiple Human Seats

One of AI's most disruptive effects is its ability to handle tasks that would typically require multiple people. In a per-seat model, each additional employee using the software equates to an additional charge. But when an AI system can perform the work of several employees, this dynamic changes. AI doesn’t occupy multiple seats—it’s often just one seat, if any, but it can scale its workload exponentially.

Take customer service, for example. Before AI, a company might need 20 customer service agents, and each of them would require a seat in a tool like Intercom or LiveChat. The SaaS company would charge based on those 20 agents. But if a single AI tool can handle the workload of those 20 agents, the company now only pays for one seat—or potentially none, if AI integrates outside the standard user framework. This scaling effect is fundamentally at odds with the per-seat model, reducing the total number of paying seats and, by extension, reducing the revenue of the SaaS provider.

Live Chat Tools: The Perfect Example

Live chat tools like Intercom, Help Scout, and LiveChat are some of the most impacted by AI’s rise. These platforms traditionally thrived on the need for customer support teams to communicate with users in real-time, charging businesses per seat or agent using their software.

With AI-driven chatbots and customer service tools, companies no longer need large teams of agents to manage support. AI can handle thousands of interactions simultaneously, and it learns from each interaction to improve responses over time. This efficiency dramatically reduces the need for human agents. Consequently, the number of seats required drops drastically, eroding the primary revenue stream for these SaaS providers.

Consider a mid-sized company that used to have 50 customer service agents. Each agent would need access to tools like Intercom, generating substantial revenue for the SaaS provider. Today, that same company might only need 5 agents for complex queries, while an AI chatbot manages 90% of the interactions. In this scenario, the SaaS provider’s revenue from that customer shrinks considerably. AI’s efficiency is a double-edged sword for these companies, providing value to their customers while threatening their business model.

The Dilemma of Changing the Business Model

Recognizing the challenges posed by AI, many SaaS companies may consider adapting their pricing models to stay competitive. Yet this shift presents a significant dilemma. Moving away from the per-seat model could help align their pricing with the new reality, but it would also hurt their current revenue structure.

A company could introduce usage-based pricing, charging based on the number of interactions handled by the AI, or the total volume of data processed. While this could potentially capture the value AI brings to businesses, it risks alienating long-time customers who are used to paying per seat. For customers accustomed to fixed costs tied to the number of users, usage-based pricing might seem unpredictable and more expensive. This can lead to pushback, cancellations, or even the adoption of competitor solutions that remain affordable.

Another possible approach is offering tiered subscription plans where AI-driven capabilities are part of a premium package. This may encourage businesses to pay more for enhanced services while preserving some revenue streams. However, the transition could still lead to a temporary or even permanent revenue drop as companies optimize their AI investments and reduce their reliance on human workers. The SaaS provider may be forced to face the reality of lower subscription numbers, even if AI allows their remaining customers to extract more value.

A Struggle to Maintain Original Revenue

For traditional SaaS companies that stick with the per-seat model, the challenge will be maintaining their original revenue in an AI-dominated world. As more companies adopt AI to automate processes, fewer seats will be needed across industries. The per-seat model, which once ensured predictable growth with the expansion of a customer’s workforce, will no longer be effective. Companies may find their existing customers shrinking their seat count year after year as AI continues to improve.

This situation creates a revenue gap that’s hard to fill. While AI offers incredible efficiency and productivity, it’s bad news for the companies relying on the headcount of a customer's team to drive their revenue. Even worse, competitors with more flexible pricing models could swoop in to attract customers looking for AI-friendly solutions.

SaaS companies face the choice of either evolving their pricing strategy or losing market share to more adaptive players. The pressure to innovate is immense, and the financial hit during the transition could be substantial.

The per-seat business model, which has served SaaS companies well for many years, is now under threat due to AI's ability to replace multiple human workers. Nowhere is this more apparent than in industries like customer service, where live chat tools such as Intercom, Help Scout, and LiveChat are seeing fewer seats required as AI takes over many tasks. Transitioning to new pricing models could be necessary, but it will likely hurt the company’s original revenue streams.

As AI continues to evolve, SaaS providers must rethink their approach to pricing or risk falling behind. Those that adapt may find new revenue opportunities, but the road ahead won’t be easy, especially for those tied to the traditional per-seat business model.

Live chatBusiness modelAI

Bring AI to your customer support

Get started now and launch your AI support agent in just 20 minutes

Try for free Get a demo

Featured posts

The Importance of Data Security for Every Business

Data security is crucial for all businesses, regardless of their size, industry, or location. Protecting business data is essential for safeguarding assets, maintaining customer trust, and ensuring long-term success.

How to Train a Deep Learning Model

Training a deep learning model is a fundamental task in machine learning, involving several steps from data preparation to model evaluation. This article provides a comprehensive guide on how to train a deep learning model, including practical code examples.

Open Source and Software Development Licenses

When starting as a developer, you'll quickly notice that software varies significantly in permissions. Numerous licenses exist, each with unique rules governing the use, modification, and distribution of software. Understanding software licenses can initially be confusing, but with basic knowledge, you can navigate through open-source and software development licenses effectively.

The Simplest Method to Deploy a Python Flask App on AWS

Deploying your Python Flask web application on Amazon Web Services (AWS) has never been easier with the use of AWS Elastic Beanstalk. AWS offers a comprehensive set of services, allowing you to launch your Flask app seamlessly to the web. This guide will walk you through the process step by step, ensuring a smooth deployment. For example, you can use this gude to deploy AskHandle widget as an independent web app on AWS.

Understanding Langchain in AI

Once upon a time in the world of technology, something exciting happened—a new concept called Langchain made its grand entrance into the scene of Artificial Intelligence (AI). But what is this Langchain, and why should you care? Let's jump right into the wonders of Langchain with a touch of simplicity and creativity!

The Classification Problems and Their Solutions in Machine Learning

Classification problems are vital in machine learning (ML) and artificial intelligence (AI) applications. They play significant roles across various industries, including healthcare and finance. Classification involves categorizing data into predefined classes or groups. The goal is to predict the class of an unlabeled instance based on input features. Addressing these problems accurately is essential for decision-making in different fields.

Top Fitness Equipment for Home Workouts

Working out at home has become increasingly popular, especially with the convenience it offers. You can exercise in your own space, on your own schedule, and without the need for a gym membership. The key to a successful home workout routine is having the right fitness equipment. In this article, we will discuss some of the best fitness tools that will help you stay fit and healthy without stepping outside.

The History of Artificial Intelligence: Evolution of Intelligent Machines

Artificial Intelligence has emerged as a transformative technology. It simulates human intelligence and performs complex tasks. AI has impacted various industries, such as healthcare, finance, and entertainment. The journey of AI is long and fascinating. This article explores its origins, significant milestones, and advancements.

Add this AI to your customer support

Add AI an agent to your customer support team today. Easy to set up, you can seamlessly add AI into your support process and start seeing results immediately

Try for free Get a demo

Latest posts

AskHandle Blog

Ideas, tips, guides, interviews, industry best practices, and news.

• August 13, 2024

Is it Free to Use Java?

Originally developed in the mid-1990s by James Gosling at Sun Microsystems, Java has grown and branched out into many areas of our digital lives. But there's a crucial question that often comes up for both aspiring developers and seasoned professionals: Is it free to use Java? The answer isn’t as straightforward as you might think, especially when it comes to using Oracle's version of Java in a commercial environment. Let’s dive into this important distinction!

JavaOracleJDKProgramming

• April 29, 2024

What is Perplexity AI and How to Get Started Using It

Perplexity AI is a cutting-edge platform that harnesses the power of AI to answer questions and generate content based on a vast database of information. It is designed to assist users in various fields by providing accurate, relevant, and timely answers. The name Perplexity might sound a bit puzzling; it actually refers to a measure in linguistics and information theory used to describe the complexity of a text. In the context of AI, it suggests the system’s ability to deal with complex queries and produce clear, understandable responses.

Perplexity AIOnline searchAI

• March 14, 2024

The Power of Hard Work: 10 Motivational Quotes to Inspire You

Hard work is the cornerstone of success. To help ignite your drive and maintain your momentum, here are 10 motivational quotes about hard work. Each quote embodies the spirit that effort and perseverance are the keys to unlocking potential. Let these nuggets of wisdom infuse your mindset and inspire you to push through, even when the going gets tough.

Hard workMotivational QuotesSuccess

View all posts