Telecom & Technology Consulting
When emerging technologies begin to deployed in mass scale, they tend be implemented as stand-alone solutions. Yet inevitably, over time, they often merge with other technologies to complement each other. Such is the case with SaaS and AI.
While AI provides many capabilities on its own, it’s now improving how SaaS platforms operate. This technical collaboration is transforming product development, an array of business models, and what customers expect from brand and user experiences.
AI-powered SaaS systems now automate many functions including predictive analytics, natural language processing, and context-aware recommendations. In turn, the SaaS/AI combination improves overall operational efficiency: decreased downtime, enhanced customer satisfaction, and fewer interventions.
The twin power of SaaS and AI is also fostering personalization, which is essential to meet 21st century customer demands. This manifests in tailored dashboards, customized workflows, and intelligent suggestions. Even more, interfaces can adapt to user behavior, which increases engagement.
AI also enhances SaaS systems by facilitating personalized pricing based on usage. For example, with AI’s behavior monitoring and analytics capabilities, SaaS-driven organizations can develop subscription models based on the value provided to the consumer. AI can deliver even more results by determining when to prompt a customer with a specific feature or offer, helping to grow revenue and decreasing churn.
With so many possibilities, the question for leaders is, how can you actually harness the power of SaaS/AI in a way that’s relevant to your enterprise’s strategic goals?
For one, you can now reimagine how AI can enhance your customer support operation, especially regarding costs. Traditionally, customer support has been the most expensive part of managing a SaaS solution. Cutting costs means losing support, which leads to loss in customer service, trust, and sales. But with AI you can improve service while reducing costs, all through sentiment-aware chatbots, automated issue classification, predictive ticket routing, and auto-generated troubleshooting steps.
AI complements SaaS by providing intelligent product analytics. Specifically, you can gain insight about characteristics that influence uptake, behaviors that may create customer attrition, user navigation patterns, and appealing pricing models.
Perhaps most valuable, AI can clarify the reasons why a specific event occurred, as well as accurately predict what event will occur next. This is predictive analytics, which allows leaders to forecast churn, identify feature roadblocks, identify upsell opportunities, and improve what offerings best match market and subscriber demand.
There are also immense internal benefits with the SaaS/AI combination. DevOps can reach new levels of productivity and efficiency through predictive load-balancing, automated deployment validation, and intelligent resource provisioning. As an example of the possible results, AI-based load forecasting may reduce infrastructure costs by more than 20% annually. This is all due to the fact thatAI allows SaaS platforms to learn as they evolve.
All opportunities, of course, comes with implementation challenges. There are potential data quality and governance issues, since AI requires clean, uniform, and secure data. This is critical, as SaaS-based organizations often have incomplete data, and/or a SaaS system that wasn’t developed to incorporate AI.
Privacy and ethical issues now go hand-in-hand with AI deployments. Consumers are concerned about how their data is used, the protection of their privacy, and how algorithms may be manipulating them unfairly. As such, it’s imperative that leaders ensure compliance with AI-focused legislation and all ethical and privacy standards.
Whenever you update your technology strategy, this inherently requires an update to your team’s skills. With the merging of SaaS and AI, you need, among other improvements, engineers knowledgeable about machine learning (ML), product-savvy ML specialists, and designers who can develop AI-centric architectures.
The marriage of SaaS and AI is no longer a consideration – enterprises must act now in order to ensure completive advantage and future growth. Whether it is transforming the customer experience, improving operational efficiency, generating more revenue, or building more powerful products, AI allows SaaS platforms to deliver more positive results for your organization.
