The software-as-a-service approach may be fading, but what's going to take its place? It could be a whole new level of SaaS. The so-called 'SaaS apocalypse' is over, venture capitalist Orlando Bravo proclaimed in a recent interview. Rather than being a software killer, 'AI is an enormous tailwind for software companies,' he opined. 'I can tell you in so many ways why AI is the biggest thing to happen to the software industry now. Software companies can move to a completely new level of business automation by automating some parts of human judgment.'

Understanding the Shift from SaaS to SaS

The narrative of a SaaS apocalypse gained traction as generative AI burst onto the scene, threatening to replace traditional software interfaces with conversational agents and automated workflows. However, as enterprises grapple with the reality of deploying AI at scale, it's becoming clear that the transition is not a sudden death but an evolution. Salesforce.com, the poster child of SaaS, doesn't appear to be feeling any apocalyptic pain. In May, the company announced revenue of $11.1 billion for its most recent quarter, up 13% year over year. Boosting its presence in the software space, the company recently acquired customer service software company Fin, formerly known as Intercom, for $3.6 billion.

The SaaS model may not be fading as we once thought, but it is evolving into a new form that was explored in an analysis by Saurabh Gupta and Phil Fersht of consulting firm HFS. Looking at the state of things to come, the current wisdom that SaaS and IT service providers will fall to the AI wave is misleading, they argue. The AI-replacing-software scenario 'only works if enterprises can actually deploy AI at scale, and today they simply cannot,' Gupta and Fersht observe. 'Until organizations resolve their technology, data, process, and talent debt, AI will remain trapped in pilots and proofs of concept rather than fundamentally changing how businesses operate.'

The Hybrid Model: Services-as-Software

What is coming is a hybrid approach to software delivery — which Gupta and Fersht define as 'services-as-software.' This is marked by a convergence of services and software firms. 'Services firms are increasingly becoming software businesses, software companies are moving deeper into implementation and business transformation, and both are converging on the same outcome-based economic model, even if investors have yet to recognize it,' they state. IBM is a good example of a converged software and services supplier. IBM has become 'a software and AI business that happened to own a consulting arm, rather than a consulting business trying to sell AI.'

This shift is not just theory; it's being driven by real-world market dynamics. Tech investors and their gurus have declared SaaS dead, but Main Street businesses are still figuring out how AI will fit into their operations and will likely stick with SaaS solutions for some time to come to keep things moving. The rise of AI-native software could give small to medium-sized businesses enterprise-level power, but only if it comes with the service wrappers that ensure successful deployment.

Five Ways to Adapt to the Services-as-Software Model

Gupta and Fersht offer five pieces of advice for adapting to this new services-as-software model. Let's delve into each one with additional context and actionable steps.

1. Treat Enterprise Debt as an Up-Front Business Issue

Measure, prioritize, and fund technology, data, process, and talent debt 'with the same discipline you apply to capital investments.' Enterprise debt — the accumulated technical and organizational debt that hinders innovation — is often overlooked in AI initiatives. Many companies rush to adopt AI without addressing legacy systems, siloed data, outdated processes, or skill gaps. By treating debt as a capital investment, organizations can create a solid foundation for AI. For example, a financial services firm might invest in data cleanup and integration before deploying an AI-driven customer service bot. This upfront investment pays off by reducing errors and improving AI accuracy.

2. Focus on Business Outcomes, Not Pilots

'If an AI initiative cannot demonstrate meaningful commercial impact within 90 days, question whether it deserves further investment. Every failed pilot delays the transformation you're actually trying to achieve.' The temptation to run endless proof-of-concept projects is strong, especially with new technologies. However, successful firms set clear KPIs from day one and ruthlessly evaluate progress. A logistics company that implements AI for route optimization should measure savings in fuel costs and delivery times within the first quarter. If results are lacking, they pivot or cut losses rather than sinking more resources into a doomed experiment.

3. Buy Outcomes Instead of Effort

Think business value — not licenses, tokens, or full-time equivalents. 'If a supplier cannot explain how they improve your P&L, they are selling technology rather than transformation.' In the services-as-software model, pricing should align with value delivered. For instance, a software vendor specializing in predictive maintenance might charge based on the reduction in machine downtime, not per user. This shifts the risk to the supplier and ensures they have skin in the game. Enterprises should demand outcome-based contracts and avoid traditional per-seat or per-token models when possible.

4. Align Your AI and Services Partners

'If they are working independently, you will pay for the disconnect.' Many enterprises engage separate vendors for AI software and implementation services. This can lead to integration issues, misaligned incentives, and finger-pointing. The solution is to either choose a single hybrid partner that offers both software and services, or enforce tight coordination through a joint governance structure. A retail company using an AI chatbot from one vendor and a CRM system from another should ensure both teams share data and goals, perhaps through a common integration layer. The cost of failing to align can be significant delays and missed revenue opportunities.

5. If You're With an AI-Native Firm, Earn Trust Before Expecting Scale

'Every enterprise deployment that delivers measurable value strengthens your long-term valuation far more than another funding round.' For startups and scale-ups, focusing on a few successful, high-value deployments builds a reputation that attracts larger clients. Rather than chasing many small deals, an AI-native firm should prioritize deep engagement with a handful of enterprise clients. This approach not only generates case studies but also fine-tunes the product based on real-world feedback. Over time, trust and credibility lead to broader adoption without the need for aggressive marketing or large sales teams.

The Broader Implications for the Software Industry

The services-as-software model represents a maturation of the tech industry. Companies that were once purely product-focused are now embracing services, while traditional service firms are productizing their expertise. This convergence is reshaping valuations and investment strategies. As Gupta and Fersht note, 'Services firms are increasingly becoming software businesses, software companies are moving deeper into implementation and business transformation, and both are converging on the same outcome-based economic model, even if investors have yet to recognize it.' The market is already seeing examples beyond IBM: Accenture has acquired multiple software companies to bolster its AI capabilities, while Microsoft is expanding its consulting arm to help clients deploy Copilot. The lines between software and services will continue to blur.

For enterprises, the message is clear: The so-called SaaS apocalypse is not a death knell but a transformation. By adopting the five tips above, companies can navigate this new landscape and harness AI as a tailwind rather than a threat. The key is to treat the transition with strategic rigor, focusing on outcomes, alignment, and the reduction of debt. The future belongs to those who can blend software and services seamlessly, delivering tangible business value in an AI-driven world.

Source:ZDNET News