People
Burak Kiral
Partner & Managing Director
Los Angeles
"AI is the future of enterprise software" is a two-article miniseries jointly created by the AlixPartners private equity and technology teams. It is part of a larger campaign on the future of enterprise software called "The End of a Software Era." Read all articles in the series here
As we stated in our previous article “Farewell, SaaS: AI is the future of enterprise software,” the challenge for enterprise software companies goes far beyond simply launching artificial intelligence (AI) agents or generative AI (GenAI) solutions. These companies must also transform the ways they work across internal teams, processes, and customer touchpoints.
For leaders of enterprise software companies, the question is clear: How do you evolve the software-as-a-service (SaaS) model so it not only uses AI but is centered around it? Those that succeed will adapt their business models and ensure that every function—from product development to sales, to finance, to operations—is equipped and ready for the transformation.
Through our hands-on experience with more than a dozen SaaS-to-AI transformations, we have seen what accelerates scalable growth and what stalls progress. Companies that build new and better capabilities across four areas will position themselves to execute a speedy and sustainable AI transition in today’s new era of enterprise software.
Product and pricing
Create a comprehensive product roadmap by defining the AI features that meet evolving customer needs. A clear plan for legacy offerings, including end-of-life timelines as needed, is critical
Define your AI architecture—such as retrieval-augmented-generation (RAG) models, agentic models, and small-language models—and fine-tune methods by leveraging a mix of proprietary and third-party training frameworks to support product features. Ensure that the supporting infrastructure is designed to dynamically scale for usage spikes.
Tailor your value proposition to different customer segments by highlighting the benefits of AI integration into your products and services. Aligning features with customer pain points aids in adoption and retention.
Develop a pricing strategy that reflects the added value of both GenAI and AI agent capabilities. The strategy should include tiered pricing structures that account for higher computing costs. Additionally, companies must benchmark against emerging competitors to determine whether outcome-based pricing is required and then execute accordingly.
Go-to-market strategy
Segment customers based on their propensity to adopt AI.
Develop a targeted sales motion and coverage model, and hire AI specialists to drive AI sales.
Defining the role of partners in the AI sales motion and structuring partner agreements accordingly are also critical.
Develop a clear migration plan and timeline by customer segment, and establish sales incentives to transition customers to new, AI-driven platforms and solutions.
Launch targeted marketing campaigns to communicate the value of AI solutions to key
customer segments, and optimize customer acquisition costs by applying data-driven insights. Additionally, ensuring alignment between marketing and sales teams so as to achieve a unified AI growth strategy is important.
Service delivery
Implement a robust customer success model to support ongoing customer engagement and retention—especially for outcome-based pricing models. Additionally, customers will require tailored onboarding to integrate AI agents into their workflows and tie adoption to measurable results.
Optimize your service delivery model to handle the migration from SaaS to new AI solutions, including tools for customer support, by establishing clear processes for AI-related escalations. It is usually helpful to create a dedicated, AI-focused center of excellence (CoE) to swiftly correct technical issues and ensure their rapid resolution.
General and administrative expenses (G&A) readiness
Ensure that your company's infrastructure, including IT systems and processes, is ready to support AI capabilities. Readiness includes capturing and metering usage data for accurate pricing.
Align financial planning to manage volatility in token costs and outcome-based revenue. Companies have to track key performance indicators (KPIs) like token efficiency and cost per outcome in the AI business model.
Define contractual terms to limit liability from adverse AI outcomes. Companies must establish data ownership rights up front by specifying the ways client data can be used for training while maintaining confidentiality.
Within each pillar, companies must ensure cross-functional collaboration across product, pricing, marketing, sales, and IT systems to drive the necessary changes. Establishing a transformation project management office (PMO) to oversee execution of the full transition, to manage stakeholders, and to measure progress is a critical step in achieving success.
Enterprise software companies are at various stages of their transformation journeys: some are transitioning from on-premises to SaaS, and others are accelerating their SaaS offerings or managing mature SaaS products. But regardless of stage, every enterprise software company must consider where and how AI will play a pivotal role in the company’s strategy.
Companies moving from on-premises to SaaS will have to integrate GenAI into their initial SaaS products, whereas those accelerating their SaaS journeys must focus on launching new products with embedded AI capabilities. Meanwhile, mature SaaS companies might explore adding agentic AI features to enhance their offerings. No company can escape the disruption AI brings, and embracing the innovations is essential to staying competitive in the market.
Our approach in action
We worked with a leading software-driven infrastructure services provider that had to launch a GenAI-embedded SaaS solution to jump ahead of its market competition.
To do so, we assessed the client’s readiness across all the aforementioned dimensions. Beyond identifying major gaps, we embedded ourselves within client teams to drive execution, thereby ensuring a seamless launch of the new GenAI-powered SaaS solutions. Our work consisted of deriving:
- A new value proposition: We validated product benefits and identified high-value use cases for the GenAI-embedded SaaS platform by evaluating more than 50 potential scenarios for the existing customer base. The effort enabled us to prioritize use cases and develop clear customer journeys for faster adoption.
- A GenAI pricing and monetization strategy: Our team introduced GenAI credit management and monetization strategies to maintain healthy cloud cost structures. We designed a tiered subscription model to maximize revenue and profit while maintaining the company’s competitive positioning. Additionally, we implemented a rigorous deal desk process to prevent excessive discounting, thereby improving margins for applicable products by 20–30%.
- A sales motion: We developed multiple customer segments—based on size
of the customer, current install base, existing contracts, and several other parameters—to plan migration of customers from legacy licenses to the AI-powered SaaS platform. We collaborated with the client’s frontline sales team to refine the approach by segment. - An accelerated AI migration: We built a detailed migration timeline based on renewal cycles. Our execution-focused approach targeted a 90% annual contract value (ACV) migration within two years to sustain the business while generating additional upsell opportunities to higher tiers of products.
As a result of our collaboration, the client experienced double-digit growth in annual recurring revenue (ARR) in its first year of the AI-enabled SaaS implementation.
AI is no longer a capability; it's the core
The transition from SaaS to AI marks the dawn of a new era for enterprise software companies. Succeeding in making the shift requires more than just layering AI into existing products; it necessitates a rethink of the ways organizations create and deliver value. The transition represents an opportunity to design adaptive systems that don’t just serve customers but also learn from and evolve with them.
The key lies in executing on the right goals to drive continuous value for your customers. We think about execution through what Extreme Networks’ Nabil Bukhari calls an ARC framework that emphasizes acceleration, replacement, and creation. Acceleration focuses on the bottom line—meaning, implementing AI faster, more cheaply, or with fewer people than the competition is using. Replacement asks what AI can take the place of in your organization by streamlining processes and reducing required resources. Creation—meaning, using AI to generate more revenue and more value—is the piece that keeps the framework sustainable.
Companies that take a structured, hands-on approach to proactively develop clear value-driven products, dynamic pricing models, robust operations, committed service delivery methods, and focused go-to-market strategies will position themselves as the leaders of an AI-powered future.
