Artificial Intelligence and Machine Learning Considerations for SaaS Business
By: Brad Johnson
A topic that we’re discussing more often with software investors is artificial intelligence (AI) and machine learning (ML) within SaaS applications. While most of the capital directed at AI/ML is still venture-based, growth equity firms are seeing an increased number of businesses leveraging these algorithms. We’ve attempted to summarize the perspective of thought leaders in the space in order to give a high-level overview on AI/ML as well as some ideas for implementing these concepts.
• Artificial Intelligence (AI): AI is a branch of computer science dealing with the capability of a machine to imitate human intelligence. This broad term can be used to characterize something as simple as recognizing an object in an image, to the ability to apply vision and decision-making in real time to guide vehicles and drones.
• Machine Learning (ML): As the name suggests, machine learning can be loosely interpreted to mean empowering computer systems with the ability to “learn”. As a subset of AI, the intention of ML is to enable machines to learn by themselves using the provided data and make accurate predictions.
Areas to Explore:
• Personalization: Leverage AI to learn from users’ previous interactions to configure interfaces that better cater to individual users. This might direct the feature/function roadmap and make the adoption cycle for new offerings shorter. Automation: Consider leveraging bots to reply to basic customer support requests (e.g. login reset) with an automated response, freeing up support reps to focus on more challenging tasks.
• Predictive Analytics: AI/ML can analyze user behaviors and general preferences, and over time, trigger alerts when it appears a user is disengaging. Using AI/ML to track leading indicators of churn could radically alter future performance.
• Release Management: These algorithms can augment developer coding abilities by providing the necessary checks to ensure accuracy, quality, and scalability.
• Enhanced Security: AI/ML give security devices and services the ability to replicate and learn from new security threats automatically, where historical measures have been static or highly human-dependent.
Thoughts & Recommendations:
• Start small, perhaps by extending existing functionality with ML techniques. Tap into leading customers for direct “voice of the customer” input on what they would find to be of value.
• Gartner expects AI to change the SaaS pricing model, as customers start to use their own AI. As much as 40% of SaaS companies with per-named-user pricing are expected to change their pricing models by 2025 with companies requiring fewer seats to complete the same work. One potential solution here is to move to a usage-based pricing model, where the customer’s fees are commensurate with the value received.
• What makes the promise of ML so great is the capability to generalize and learn. Getting the technology to do that requires leveraging your data and some highly trained data scientists. Many leaders expect a continued evolution in talent from software developers to data engineers to data scientists.
Information sourced from these resources & thought leaders: Chief Executive, JAXenter, Medium, Moor Insights & Strategy, Towards Data Science