Generative AI (GenAI) has emerged as the most disruptive force in enterprise software since the dawn of software-as-a-service (SaaS). This is according to a recent report by consulting firm McKinsey & Company, which reveals that software companies must drastically overhaul their traditional subscription models to thrive in the new “AI+SaaS” era.
According to McKinsey, AI is transforming software from a tool that enables work into a platform that actively performs and orchestrates work.
While GenAI is expected to generate up to $4.4 trillion in incremental economic potential, and 46 percent of companies will already capture financial impact from AI by 2025, the growth in AI software monetization is slower than expected.
Global enterprise spending on AI applications has increased eightfold over the last year to close to $5 billion, it still only represents less than 1 percent of total software application spending.
McKinsey identifies three key challenges underlying the slower growth in AI software monetization:
Value communication and realization: Many companies highlight potential use cases for AI, but only 30 percent have published quantifiable ROI in dollar terms from real customer deployments. Companies that do this well, such as Salesforce’s Agentforce ROI Calculator—which demonstrates how AI-agent-led customer service inquiry handling has a quantifiable cost savings versus human agents—leave less to the imagination to close a deal or need to be proved as part of a pilot. At the same time, many companies are experiencing rises in IT costs without yet being able to make corresponding decreases in labor costs. For instance, enabling AI across the full customer service tech stack of a typical organization could result in a 60 to 80 percent increase in list prices.
Price predictability: Customers want to understand how AI costs will scale with usage, but many current pricing models are complex and opaque. One CFO of a Fortune 500 company described the problem: “It is frustrating that I have no idea what we’re going to spend on AI this quarter. My business units have no forecast of what they are going to use, and it is spread across tens of software vendors. By comparison, my spend on cloud computing is also usage-based, but it’s predictable because of the structure of our negotiated buys.”
Sustained adoption (postpilot): Even when pilots go live, many fail to scale due to underinvestment in change management. Our experience from successfully scaled pilots suggests that a good rule of thumb is that for every $1 organizations spend on model development, they should expect to have to spend $3 on change management (such as forward-deployed engineering, employee user training and reinforcement, and standing up performance monitoring).
As AI+SaaS products increasingly perform instead of merely support work, the new era calls for a business model that aligns customer value with units of work completed. Consumption-based models are a natural fit, a flexible and seemingly fair way to monetize the wide range of customer value that AI generates. This is especially important as the growth in human users (and their associated “subscription user seats”) may slow and AI takes on a greater share of the work.
Many software companies are moving towards hybrid models, where the traditional per-user subscription remains, but additional AI usage above a certain capacity is charged via ‘buckets’ (like credits, for example with HubSpot) or ‘metered throughput’ (where processing capacity, such as tokens, is limited if exceeded, similar to ChatGPT Enterprise).
Choosing to switch to a consumption pricing model, in whole or in part, is just the start of the process for a software provider embracing AI+SaaS. Picking the right pricing metric—the one most aligned to customer value—is just as critical, and complicated, a decision
Activity-based pricing (e.g., per workflow completed) is currently the most common. While outcome-based pricing (e.g., per qualified lead) sounds ideal, it’s challenging to implement due to the need to automatically confirm success and the varying definition of “success” from customer to customer. Companies like ServiceNow and Salesforce (with their Flex Credits or Assists) are examples of vendors already transitioning to consumption-based pricing for their AI products.
Based on an analysis of 150 global vendors, the McKinsey report underscores that the traditional per-user subscription model won’t disappear completely, but that incorporating a consumption element is crucial to unlocking the full growth potential of the AI era.
