AI in SaaS: 5 deep shifts no one saw coming

Artificial intelligence is at the heart of every conversation in the tech sector, but what is its real impact on long-established business models like B2B SaaS? Beyond surface-level automation, AI is reshaping the way software is designed, sold, and used. This article highlights five of the most surprising and fundamental transformations currently underway.

1. Beyond Efficiency: The Value Proposition Is Being Completely Reinvented

The most fundamental shift brought by generative AI isn’t just about improving efficiency; it’s about profoundly transforming the software value proposition.

AI enables hyper-contextualized services for every user and makes access to knowledge far easier. This evolution goes far beyond traditional automation adapting the service to each user’s specific needs and context, making tools more relevant and intuitive.

Thanks to pre-trained models, the time-to-value is drastically reduced. SaaS solutions can now deliver immediate, relevant value without the long configuration phases that used to take weeks. For instance, complex testing services can now be operational in just a few days.

This shift changes what customers actually buy. They no longer purchase a tool to configure but an almost instant outcome. This is made possible through advanced RAG (Retrieval-Augmented Generation) systems built on mostly pre-trained models, replacing old, time-consuming data qualification and training processes.

“I believe AI especially generative AI is deeply changing the value proposition of SaaS solutions, far beyond simple efficiency gains,” explains Aurélien Couloumy, CEO of Dylogy.

2. The End of the One-Size-Fits-All Subscription: Toward Hybrid and Complex Pricing

Integrating AI is disrupting the traditional SaaS business model historically based on fixed subscriptions per user or feature.

While the classic subscription model still applies to the core solution, it’s now blending with new usage-based pricing models combining base subscriptions for core features with usage credits or per-request billing for AI-powered capabilities.

This evolution poses a real challenge for ROI measurement, since AI’s financial impact can take a year or more to fully materialize. To address this, defining intermediate KPIs (like adoption rates, error reduction, or processing time savings) is becoming essential to demonstrate tangible gains long before financial ROI is achieved.

This new pricing logic is both necessary and delicate aiming to align price with value while introducing greater complexity for both providers and clients. It now requires defining usage thresholds, relevant performance metrics, and billing models that, while fairer, are also harder to predict.

3. The User Becomes the Creator: Software Is No Longer a List of Features

AI is turning users from passive operators into active creators an idea that initially seems counterintuitive.

We’re witnessing a profound shift: users move from 'doing' to 'being able to do.' They are no longer limited by pre-set software features. Instead, they can harness AI often via integrated conversational agents to create custom workflows and solutions on the fly, perfectly adapted to their current needs.

This transformation gives rise to more minimalist and natural interfaces. Instead of navigating complex menus, users interact through plain language. For example, a client no longer asks for a 'compare documents' button but tells an agent: 'Generate a markdown comparison report with tables, annotations, and PDF export.'

This redefines the future of software design itself. A product becomes less about a fixed set of features and more about the capacity to achieve goals an intelligent platform serving user intent.

4. The Glass Box Demand: Transparency and Traceability Are Now Non-Negotiable

AI integration brings an unavoidable demand from B2B clients: turning opaque 'black boxes' into fully transparent 'glass boxes.'

First, clients want to understand the 'why' behind every AI-generated output. In industries where decisions must be justifiable, it’s essential to explain which data and reasoning the model relied on.

This demand translates into stricter traceability. Companies now expect detailed logs and audit trails documenting every AI action — like Dylogy’s audit trail service — ensuring accountability and compliance.

Next, clients expect greater support and education. Providing the tool isn’t enough users must be trained, AI demystified, and guidance offered to correctly interpret outputs and get the best from them.

Finally, data governance is under sharper scrutiny. Clients expect strong guarantees on confidentiality, GDPR compliance, and data ethics — pushing software providers to lead by example.

“Our B2B clients have very clear expectations,” says Aurélien Couloumy. “First, they want transparency and explainability: to understand exactly which data and reasoning the AI relies on — especially in industries where every decision must be justified.”

5. The SaaS of Tomorrow: A Hybrid Service Between AI, Software, and Human Expertise

The future of B2B SaaS isn’t one of total automation it’s an integrated service combining intelligent technology with human expertise.

Tomorrow’s SaaS models will not be 100% autonomous. Instead, they’ll embed more human services alongside AI features. Human experts remain in the loop validating, refining, and applying the most complex insights, where judgment and experience are irreplaceable.

A concrete example is 'support as a service,' where domain experts assist users directly within the platform helping them interpret legal knowledge bases or leverage AI-generated claims graphs.

The true competitive edge will no longer lie in pure technology, but in the ability to orchestrate close collaboration between software, AI, and human expertise. This convergence will enable truly customized, high-value results.

Conclusion

Far from being a simple technical enhancement, AI acts as a powerful catalyst reshaping the entire B2B SaaS model from value proposition to pricing, user experience, and service design.

As AI turns tools into partners, the real question is no longer what our software can do, but what we can achieve with it. Are you ready for this new kind of collaboration?