The Limitations of Al Agents & The Future of SaaS

1. The Limitations of Al Agents & The Future of SaaS What’s Inside 1. Referencing Dharmesh Shah 2. Referencing Aaron Levie 3. The Dilemma 4. The Nature of Services 5. Practical Limitations 6. Overcoming the Hype 7. Conclusion TL;DR: Will AI agents completely take over human jobs? The idea is compelling. However, this vision, largely championed by industry evangelists, may be overly optimistic. Dharmesh Shah, Founder of HubSpot—who I am personally a big fan of—predicts the rise of Results as a Service (RaaS): “Just like Software as a Service is really still software, Results as a Service is really still SaaS. What changes is what’s being purchased. With SaaS, you’re purchasing access to software. With RaaS, you’re paying for an actual result. Aaron Levie from Box calls this “Outcomes as a Service,” but I prefer RaaS because it’s easier to say out loud than OaaS. The other clever take I’ve seen is Service as a Software (SaaS). Which is clever, but confusing, since the acronym doesn’t change.”

2. To reference what Aaron Levie originally said through a tweet about the end of pay- per-seat pricing and the rise of Outcome-As-A-Service: “Right now, the dominant business model in SaaS is a per-seat model, which inevitably means that the total number of seats you can sell is limited to the number of employees in the organization that are relevant for your particular software. Legal software is roughly capped by the size of the legal team, audit software is capped by the size of the audit team, and so on. The implication of this is that the customer generally already has to have not only a need for your solution but also the existing headcount in the organization to become users of your software. Incidentally, this is often why so many SaaS products tend to go after horizontal productivity categories because this maximizes the number of potential users you have access to in an organization.” The Dilemma AI flips this on its head, especially with the power of AI Agents, and you get a new form of “outcome-as-a-service.” When AI is actually doing the work within the software itself, you're no longer constrained by the number of employees inside the organization to use the actual software. The software is quite literally bringing along the work with it and delivering a particular business outcome. It's clear the full potential of this playing out is not fully understood, as this represents a massive transformation of software as an industry. While I admire and respect these views - I also think there are significant reasons to believe that services, by their very nature, cannot be fully productized. There are significant reasons why Accenture has passed $2BN in AI consultancy revenue while we are still speculating on whether it should be called “Outcome as a Service” or “Service-as-a-software” or “Results as a Service” :) The Nature of Services: Inherently Human and Contextual Services are inherently different from products. They involve a high degree of human interaction, customization, and contextual understanding. While AI can automate

3. certain tasks and enhance efficiency, there are several aspects of services that remain uniquely human: Customization and Personalization: Services often need to be tailored to the specific needs of each client. AI lacks the nuanced understanding and adaptability that human professionals bring to tailor services in real-time based on complex and often unstructured requirements. Trust and Relationship Building: Many service industries rely on building trust and relationships with clients. For example, consulting, legal advice, and healthcare involve significant human interaction that AI cannot replicate. Contextual Awareness: Human service providers excel at understanding the broader context in which they operate, including cultural, social, and emotional nuances. AI, despite its advancements, still struggles with this level of contextual awareness.

4. Practical Limitations of AI in Service Delivery While AI can enhance service delivery, there are practical limitations to its application: Complex Problem-Solving: Services often require complex problem-solving and critical thinking. AI can assist with data analysis and routine tasks but struggles with the creative and strategic thinking required for complex issues. Human Judgment and Ethics: Many services require ethical considerations and human judgment that AI cannot fully comprehend or execute. For instance, legal and medical services involve decisions that have profound ethical implications. Overcoming the Hype: Realistic Expectations for AI While the potential of AI in transforming service models should not be dismissed, it is essential to temper expectations: Complementary Role of AI: AI will likely play a complementary role in enhancing service delivery rather than completely transforming it. AI can automate routine tasks, provide data-driven insights, and improve efficiency, but it will not replace the human elements that are critical to many services. Incremental Adoption: The adoption of AI in service industries will be incremental and will likely focus on areas where it can provide the most value without compromising the human touch that clients value. Joe Rogan - Elon Musk on Artificial Intelligence (Watch now) The vision of AI completely transforming service-based business models into productized outcomes is an exciting one, but it remains largely speculative. The inherent nature of services, the practical limitations of AI, and the continued success of traditional service models like Accenture's highlight the challenges and constraints of this vision.

5. To conclude my views on the topic Will AI agents completely take over human jobs? Partly. Will that lead to SaaS companies, running pay-per-seat pricing models, facing churn? Highly likely. Will we therefore see a shift in the SaaS pricing model from a pay-per-seat model to a pay-per-outcome? Possibly, but without guaranteed success. The key is for SaaS companies to not detach themselves from services. Does it mean all SaaS companies have to build large professional services teams? Not necessarily. SaaS companies could instead build a network of consultants and channel partners who are trained and certified to implement these tools and ensure customers realize value. What are your thoughts on the future of AI and human collaboration in the workplace? Source: https://app.saas22.com/resources/story/16/the- limitations-of-al-agents-and-the-future-of-saa-s