What AI means for the future of SaaS: Reality vs. hype

There is no shortage of recent articles and blog posts seeking to explain how artificial intelligence will revolutionize the software-as-a-service industry, and the tech landscape more broadly.

With headlines like “Is SaaS in Crisis? How AI Changes Everything,” these posts and reports helped make generative AI the buzziest tech concept of 2023.

It can be difficult to discern the expert voices amid all this noise. When it comes to AI and SaaS, what’s real versus hype? What potential dangers do these technological advancements pose? What business and product development strategies will prove to be the winning ones?

We were able to glean a view of the uncertain future from Rick Nucci, a serial SaaS founder with multiple successes.

Nucci is the cofounder and CEO of Guru Technologies, Inc., a next-generation knowledge platform that replaces legacy tools like wikis and intranets, transforming scattered, siloed information into a single resource. Prior to Guru, Rick was the founder and chief technology officer of Boomi, which defined and led a new segment as the first-ever cloud integration platform-as-a-service and was acquired by Dell in 2010. He frequently speaks at industry events about startups, SaaS and cloud computing.

Here, Nucci discusses recent trends in AI and the enterprise software industry, including technological developments, ethical dilemmas, and business considerations.

This Q&A has been edited for length and clarity.

In your view, what are the most significant AI trends impacting the SaaS industry today? How are they reshaping the landscape?

We’re seeing the first wave of AI SaaS products hit the market and signals are clear: AI is going to transform the efficiency of teams everywhere.

As more companies realize this, they’re looking to adopt AI with more and more urgency. They want to capitalize on the opportunity, and they also don’t want to be left behind. There’s another trend emerging too, and it relates to data.

As companies adopt AI-powered SaaS products, many are realizing that AI is only as effective as the data it’s trained on. And they are turning their attention to improving the quality and accuracy of their company data — they know that this is a critical step towards unlocking the potential of AI for their business.

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How do you see advancements in natural language processing and understanding influencing the development of SaaS products?

Recent advancements in natural language processing (NLP) have the potential to upend the way we interact with information at work.

This shift is primarily driven by LLMs, or “Large Language Models,” such as those created by OpenAI (GPT4), Meta (Llama) and Anthropic (Claude). These models have fundamentally changed the field of NLP by drastically simplifying language tasks such as summarization, formatting, and translation.

Typically, when you need to find information at work, you open an application, conduct a search using basic keywords, and go through a list of results. With NLP, however, it’s now possible to simply ask a question to an enterprise AI search product like Guru and get a personalized answer based on attributes like your role, seniority, and geography. This allows you to find the exact information you need much faster.

It’s also leading to a change in the user experience (UX) of many SaaS products. Guru, for example, allows employees to ask questions and get answers without leaving the app they’re working in. I think we’ll see more SaaS buyers expect this type of experience as NLP continues to develop.

How do you foresee the intersection of AI and cybersecurity impacting the development and deployment of SaaS solutions in the future?

One of the biggest barriers to companies adopting AI is concerns about security. These concerns tend to show up in two ways: how data is shared externally, and how data is shared internally.

When a company is deploying an AI SaaS product, it needs to have complete confidence that its data will not be shared externally with third parties or used to train LLMs.  The company also needs to have confidence that data which is shared internally will only be seen by employees who have permission to access it.

This is a core aspect of Guru. For example, when an employee uses Guru to search across their company’s apps, the answers they get are based only on information they have access to. That way, sensitive information is never inadvertently shared with the wrong people. The intersection of AI and security is becoming increasingly influential on how AI is deployed at work. Providers that don’t have robust security standards will struggle to see their products adopted in the enterprise.

Could you discuss any ethical considerations or dilemmas that arise when integrating AI technologies into SaaS (and other technology) products — and solutions for addressing them?

When integrating AI into SaaS products, it’s important to remember that AI is not a replacement for human reasoning or ingenuity. After all, an LLM’s output is only as reliable as the information humans have provided it with.

So when using AI in high-stakes contexts like hiring decisions and answering important customer questions, it’s critical to safeguard against biases and inaccuracies that may exist in the data.

And the best way to do that is to have a human in the loop to refine, verify, and improve AI’s output.

What are the implications of machine learning’s ability to personalize UX for customer retention and acquisition in the SaaS and consumer tech space?

Teams that use AI to personalize the experience of their customers will see improvements in retention and acquisition rates.

Take a typical customer support team, for example. On any given day, they field a wide range of questions each one with its own nuances and context. A question from a high-paying, long-term customer about pricing might require a subtly different response to the same question from a brand new customer.

When customer-facing teams use AI to help them provide personalized responses to each customer, that team is going to save a lot of time. And that customer is going to have a more satisfying experience. Leveraging AI for this use case has the potential to raise the bar for customer experiences everywhere.

As AI continues to evolve, how do you stay informed and ensure your company’s offerings and resources remain aligned with market demands and user expectations?

At Guru, our approach is to always start with the problems that customers are trying to solve and work back to the technology. AI has already created countless new use cases, but the key is identifying the use cases that solve real business problems.

We do this in a few ways at Guru:

1. Gather feedback. We continually gather customer feedback, closely monitor feature adoption trends, and regularly meet customers in person to gather insights into how we can best help them solve problems.
2. Experiment with solutions. We are constantly experimenting with new AI technologies that can address real-world problems in effective ways.
3. Test and refine. We run early access programs around new generative AI capabilities, which have proven to be a great source of feedback. For example, the beta program for Guru’s enterprise AI search capability saw over 2,000 companies use Guru to answer more than 3 million questions. This showed us just how effective AI can be at helping employees find information more efficiently, but also exactly where we needed to improve the experience to ensure the AI capabilities are accurate and trustworthy.

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Kim’s Korner is a series of articles by Ballard Spahr’s emerging company and venture capital attorneys. The column is not legal advice. The substance of the column is derived from our experience working with founders and details many of the current critical issues facing startups.

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