People
Burak Kiral
Partner & Managing Director
Los Angeles
This is the second article of our enterprise software industry series, “The End of a Software Era.” You can read the other articles in the series here.
The enterprise software industry serves as a poster child for change and innovation. Just a few decades ago, enterprise software companies designed their products to be manually installed on servers. The jump to modern Software-as-a-Service (SaaS) offerings, managed remotely via the cloud, has revolutionized business models—but companies cannot afford to get comfortable. The next evolution of enterprise software is already here.
Generative artificial intelligence (GenAI) and AI agents are driving the industry’s next major revolution. These innovative technologies have arrived at a time when midsize enterprise software companies are caught in a big squeeze, pressured on one side by AI-native players driving innovation at lower costs and on the other by big tech companies pouring billions into the AI arms race. By undergoing their own AI transformation—from selling software to selling AI-powered services—enterprise software companies can boost both their revenues and their valuation multiples.
With that said, the rapid pace of AI adoption raises major hurdles. Like the shift from perpetual licenses to SaaS, the launching of new products is only part of the picture. A successful transition requires a brand-new business model and thoughtful implementation across product roadmap, pricing, sales, and operations. Without careful planning, transition to GenAI and AI agents could just as easily disrupt revenues as it could enhance them.
Enterprise software companies must ensure that integrating AI into their products creates a lasting competitive advantage rather than a short-lived gain—or even a decline in value.
Perpetual-to-SaaS is the past. SaaS-to-AI is the future
While industries with mature SaaS products such as productivity software, enterprise resource planning (ERP) systems, and customer relationship management (CRM) systems are quickly adopting GenAI and AI agents, early-stage companies in highly regulated industries such as healthcare, too, are starting to incorporate AI into their offerings.
We studied the journeys of several companies that transitioned from a perpetual to a SaaS model across multiple enterprise software industry subsectors. Our analysis found that the change drove a 4-6x increase in revenue multiples—and we believe that companies that successfully transition to GenAI and AI agents and make matching business model shifts will see additional jumps in their revenue multiples. This helps explain why, according to the 2025 AlixPartners Disruption Index, nearly 90% of software executives are optimistic about the impact of AI on their respective businesses.
AI agents: The new entrants in AI-driven transformation
When we mention AI agents, we are referring to AI systems that can independently set goals, make decisions, and take actions. AI agents fundamentally alter the traditional SaaS tech stack by replacing the logic and presentation layers that SaaS players rely on with an agentic AI layer, thereby transforming how businesses operate.
AI-native players are already starting to enact this model, and SaaS incumbents must follow suit by launching AI agents of their own to stay competitive. The shift can facilitate more autonomous, more personalized, and more scalable operations, significantly reducing costs while unlocking new revenue streams
Earlier this year, HubSpot introduced Breeze, a suite of agentic AI tools designed to enhance small-business operations. Breeze’s knowledge base agent, for instance, identifies and closes knowledge gaps in customer support data, thereby streamlining customer response and sales lead outreach. ServiceNow is growing its AI agent capabilities through its recent acquisition of Moveworks, a company specializing in AI assistants that integrate into enterprise systems, including HR, IT, and finance.
Activating impact: How AI creates value
GenAI solutions, including AI agents, are transforming the ways enterprise software companies deliver value to customers and investors. Customers now expect AI-driven solutions that enhance the user experience and product functionality.
We recently worked with a software-based cloud infrastructure company to launch a SaaS platform with embedded GenAI capabilities. To drive customer adoption, we helped the client identify key use cases and created a tiered monetization system for the GenAI offering. We also established a transformation management office to facilitate the migration of existing customers to the new AI/SaaS platform. Those steps, combined with refined sales and marketing motions, led to a successful launch.
Enterprise software companies need to focus on three value-creation levers when integrating AI:
1. Thoughtful GenAI and AI agent product roadmap
AI products are emerging as key drivers of topline growth. By incorporating GenAI into their product roadmaps, businesses can develop innovative solutions that meet complex customer challenges. Such AI-driven products provide customers with personalized, data-driven insights, automated content generation capabilities, and advanced decisionmaking tools, among other solutions. As the products evolve, they will not only meet current customer needs but also open new revenue streams, positioning enterprise software companies at the forefront of AI-driven transformation.
Salesforce, for example, has closed 5,000 deals for its Agentforce AI platform since October 2024, including more than 3,000 paid customers.
2. Revenue models to monetize GenAI
AI integration can unlock new revenue models—such as usage-based and outcome-based pricing—that offer greater flexibility and align more closely with the value customers receive, enabling businesses to adapt pricing based on customers’ needs and usage patterns. Moreover, incorporating AI agents into product roadmaps can improve product adoption and present new opportunities to monetize.
ServiceNow is one company that has implemented both usage-based and outcome-based pricing to alter its revenue models. It lets customers pay per automated incident resolution or per AI-driven workflow while also tying pricing into reduced ticket resolution times and lower labor costs. Sierra, too, offers outcome-based pricing for its AI agents, stating “we’re only paid when we drive real results.”
3. Streamlined business operations
Integrating AI within a software company’s core operations can also drive value. AI capabilities can expedite product development, boost customer engagement, and streamline workflows. For example, AI agents can handle and process data more efficiently, which leads to better decision-making and customer insights. By automating repetitive and time-consuming business processes, AI frees team members to focus on more strategic tasks
According to Klarna, the company’s AI assistant, powered by OpenAI, is already doing the job of 700 workers. The assistant managed 2.3 million conversations within a month of its launch, matching human agents in customer satisfaction. Klarna says the result contributed to an estimated $40 million profit improvement for the company in 2024.
Four key challenges to GenAI and AI agent implementation
Companies undertaking the SaaS-to-AI journey face several challenges that must be met if the companies are to deliver value for customers and investors.
The challenges include:
1. Competition from AI-native players
AI-native competitors, operating with leaner business models, can offer superior solutions at lower prices, making it difficult for traditional SaaS companies to maintain their margins. The pace of AI advancement enables the AI-native competitors to quickly replicate and enhance AI features, which can lead to intense price competition, further squeeze margins, and increase the pressure on incumbents to innovate and adapt.
2. Increased costs
SaaS companies pivoting to GenAI or AI agent offerings face elevated compute costs because GenAI incurs higher expenses by adding token fees on top of standard hosting and support costs. The move to agentic AI also increases support costs because the need for higher-level engineers for complex AI debugging replaces less-expensive service agents. To maintain their current operating incomes, companies will have to reduce costs and increase efficiency elsewhere in their businesses (e.g., back-office operations, organizational redesign, and third-party vendor spend) to offset AI-related cost increases and fund the transition journey.
3. Revenue unpredictability
The shift from seat-based pricing, with fixed revenue streams, to outcome-based pricing can increase unpredictability. Because revenue is now tied to customer outcomes, any fluctuation in those outcomes could lead to variations in financial performance. In addition, revenue now depends on multiple factors outside the company’s control, such as customer data accuracy and the effectiveness of a given AI solution.
4. Operational disruptions
Companies often have to overhaul their processes and redesign workflows to ensure AI tools get embedded seamlessly into day-to-day activities. Such an overhaul generally requires extensive employee training. Additionally, companies must upgrade their infrastructures so as to construct the right technological foundation that will support AI capabilities. This can cause operational disruptions alongside cost increases if the transformation isn’t executed correctly
The enterprise software industry’s new frontier
AI is neither just a new product in the SaaS portfolio nor an add-on to existing services; it is the industry’s next evolution, and it requires fundamental business model changes. If implemented strategically, GenAI products and AI agents will propel the next significant leap in valuation multiples. Enterprise software companies that embrace that evolution by taking thoughtful approaches will position themselves to leap ahead of the competition
In an upcoming article, we will dive into the business model changes required to successfully transform a SaaS company into an AI-first company.
