It’s hard not to read something about artificial intelligence every day (and you may be reading something *written* by AI). AI is permeating everyday life at a rapid clip, especially with the emergence of ChatGPT. But we’ve only scratched the surface. SaaS applications will see strong growth in AI integrations, including machine learning. The combination of AI/ML in SaaS is powerful, and it will dramatically impact our productivity and capabilities for years to come.
Artificial Intelligence (AI) and Machine Learning (ML) are related concepts. Using large datasets, AI systems can be trained to perform tasks with methods comparable to human intelligence, such as making predictions and performing complex analytical tasks. ML is a subset of AI where algorithms enable systems to learn from data without human programming.
Customer Facing Application
AI/ML will affect how we interface with SaaS applications. We’re already living with many of these techniques today, but mass adoption will roll out quickly.
Many SaaS applications deliver personalized experiences based on past interactions. AI personalization tailors news feeds on social media, content on streaming platforms, and recommendations in healthcare.
Chatbots for Customer Service
Chatbots offer numerous benefits for SaaS customer service. They help organizations manage costs and deliver better customer service by offering fast, consistent responses to inbound queries.
AI algorithms can parse large datasets to identify patterns of fraudulent activity. Historical data helps build predictive models to identify potential fraud before it occurs.
Amazon and Netflix know a great deal about our tastes. Algorithms provide more personalized and relevant suggestions to us, delivering a better user experience and increasing engagement (and sales).
Internal Facing Application
Within SaaS solutions, organizations can tap AI/ML to strengthen performance, capability, and security.
AI can automate routine workflows to save time and reduce errors, and natural language processing (NLP) algorithms can generate responses to common questions or issues. ML can make those routines even better over time.
Predictive analytics can analyze user behavior to identify trends in customer behavior or find issues with system performance. For example, using NLP, sentiment analysis can help determine the attitude expressed in a user conversation.
AI can identify a security breach or attack and alert security teams in real-time. It can deter access where it senses suspicious activity (e.g., login attempts from unusual locations to protect sensitive information. While monitoring user behavior, AI can automatically respond by isolating accounts and blocking IP addresses.
Organizations can use AI/ML in SaaS when working on tasks outside the software too. That is, they can consume AI services to fuel growth through marketing, sales, and operations.
Historical sales and market data can drive AI forecasting to help businesses make better decisions that fuel growth. For example, SaaS companies can identify customers who may be at risk of churning out or predict future demand to better plan product updates.
AI can analyze historical data to identify ideal customer profiles, most effective messages, highest ROI channels, and best-converting offers.
The Impact of AI/ML in SaaS
The compelling reasons for AI/ML in SaaS applications are that it can improve user experiences, increase organizational efficiency, and enable better decision-making. SaaS products will run more effectively, allowing developers and engineers to focus on strategic initiatives. Growth of AI/ML in SaaS continues as more companies recognize these benefits. This trend will only accelerate as the technology evolves.