How AI is reshaping SaaS competition – companies need to adapt

As the industry evolves, leading companies are redesigning product and engineering teams to foster new skills and promote an AI culture.

In brief

• It’s essential for companies to emphasize reskilling, training and experimentation while integrating AI experts for enhanced organizational outcomes.
• Having decisive leadership that is committed to AI will be the difference between success and struggle.
• With the use of AI, it’s critical that SaaS companies take six steps now to future-proof product and engineering teams.

The push to incorporate generative AI (GenAI) and autonomous Agentic AI systems into software-as-a-service (SaaS) offerings has ushered in a new era of product development, with SaaS companies racing to launch products that capture the benefits of GenAI.

But thriving in an artificial intelligence (AI) world requires more than just technology. SaaS companies must undergo significant transformation across the enterprise, beginning with their product and engineering teams. SaaS research and development (R&D) teams need new skills, team structures and ways of working to keep pace with the market to create a competitive advantage and drive enterprise value.

New skills required for the AI-driven future

Building and integrating AI capabilities requires a wide range of new technical competencies that SaaS companies may currently lack. Traditional coding skills remain important, but SaaS developers need to become adept at AI-augmented development, which requires prompt engineering (crafting effective inputs for AI models) and reviewing AI-generated code for quality and compatibility. Working with AI as a collaborator — and knowing how to coax the best results from it — is key.

SaaS companies also need skills in machine learning (ML) DevOps platforms (MLOps) and infrastructure: handling model deployment, cloud cost management, scalable data pipelines and hardware optimization for AI workloads.

Product managers will need new skills, too. It’s important for them to use GenAI tools and even design Agentic AI frameworks where AI agents collaborate to complete tasks. Perhaps what is most important is that product managers have strong empathy and change management skills to drive user adoption and trust in AI features. Addressing user concerns about reliability — whether through mistrust or over-trust in AI — requires thoughtful onboarding, education and risk mitigation in the product design.

As AI automates routine tasks — even within R&D — human skills like creativity, communication and adaptability will be more prized than ever. The developers and product professionals who thrive will be problem-solvers first and coders second.

As a result of these shifts, hiring profiles will change. Organizations will seek candidates experienced in application programming interface (API) development, model integration and data analytics. Job postings for software engineers increasingly list familiarity with AI and ML or specific tools (like GPT APIs) as a plus. We will likely see new hybrid roles emerging, such as AI product managers, prompt engineers or user experience (UX) designers specializing in AI-driven experiences.

Upskilling current staff is equally important, and many firms are launching internal training programs and AI bootcamps. However, a major hurdle is the skills gap and resistance to learning; some employees are hesitant to learn entirely new tools or worry that their hard-earned expertise will be sidelined. Overcoming that resistance through training incentives and mentorship — such as pairing junior staff with AI-proficient experts — will be key.

Organizational design shifts in an AI-first world

Adopting GenAI at scale often requires rethinking the organizational chart of product development. Traditional SaaS R&D organizations — typically organized by product modules or functional expertise — may need to shift toward AI-first team structures.

One emerging pattern is the creation of multidisciplinary teams that embed AI expertise directly into product teams. Rather than a separate “AI team” operating as a silo, AI specialists (data scientists, ML engineers) are distributed across teams to work alongside feature developers and product managers. This integration confirms that AI capabilities are woven into every feature from the ground up. For example, when developing a new SaaS feature, the team can immediately consider how an AI model might enhance user experience or automate part of the workflow, rather than treating it as an afterthought.

Some companies are also introducing new leadership roles to signal the shift to AI-centric development. The rise of the chief AI officer (CAIO) or chief science officer (CSO) is designed to help make sure that AI isn’t just an R&D experiment, but a core component of product strategy.

Another trend is centralizing AI expertise and resources so that advancements can be quickly shared across the enterprise. For example, some SaaS companies are creating internal AI centers of excellence that build common services, such as an in-house large language model (LLM) platform or prompt libraries, for use across all product teams.

As the industry continues to change, expect some roles to evolve or merge. As GenAI tools become part of daily work, the line between product manager and developer might blur. We may see a role for a “product developer” — a person who uses GenAI tools to handle everything from requirements and design to generating code.

The common thread in these organizational transformations is flexibility. To succeed, rigid department boundaries must give way to cross-functional collaboration focused on leveraging AI wherever it adds value.

Don’t wait: steps SaaS companies must take now

To future-proof product and engineering teams in light of AI, SaaS companies should take proactive steps now. Here are six key strategic recommendations:

Shaping the SaaS future requires deep commitment

The evolution of SaaS businesses in response to generative and Agentic AI is a multifaceted challenge — touching talent, culture, organizational structure, technology and strategy. It is reminiscent of past shifts like the move to cloud or the adoption of DevOps but also unique in its pace and the breadth of change.

Organizations that proactively acquire new skills, reorganize for agility, address challenges head-on and commit to a long-term transformation mindset may be best positioned to thrive in the AI-first era.

SaaS leaders must act decisively — skilling up their people, re-wiring processes and learning from each experiment — to make sure they aren’t just observers but shapers of this new era of software. With the right strategy and execution, embracing AI can become not a disruption to fear, but a transformation to wield for greater innovation and value creation in SaaS.