According to recent research, over 80% of global businesses are either using AI or exploring its potential in their operations. Meanwhile, some 40% of businesses cite “improving customer experience” as the top motivator for implementing AI.  This all while more than half of consumers express concerns about AI. 

This paradox is where B2B software leaders now find themselves– navigating the trendy but uncertain world of GenAI, or generative artificial intelligence. 

With that in mind, our team assembled a panel of leading voices in analytics and artificial intelligence to discuss the implications of GenAI in B2B software.

In the first in our new webinar series, An Afternoon with the Analysts, we held a spirited conversation on “The State of GenAI in B2B Software.” This roundtable discussion aims to help engineering and product leaders cut through the noise and get a clearer understanding of how and why Gen AI is being utilized in SaaS products

The panel includes:

  • Howard Dresner, Chief Research Officer and Founder, Dresner Advisory Services
  • Myles Suer, Research Director, Dresner Advisory Services
  • Erin Peck, Senior Product Manager, Resolver, Inc.
  • Arman Eshraghi, Founder & CEO, Qrvey

You can watch the recording or read the transcript, and we’ve covered some highlights below.

Recent Industry Research on Generative AI

Dresner Advisory Services collects data twice per year and publishes “Wisdom of Crowds®” market reports. Howard began by sharing their most recent industry research on generative AI, which shows that interest in GenAI has spiked dramatically from Q1 to Q4 of 2024.

Almost 50% of respondents express excitement about the possibilities associated with generative AI and want to be an early adopter. Howard expects this to increase even further, “Because it’s very approachable. A lot of folks are playing with various generative AI applications out there, and they can see the value.”

Researchers also asked, “What are your plans surrounding GenAI?” and found that a large majority are either already in production or experimenting with GenAI, which spans all industries, functions, and geographies.

What is contributing to the increases in excitement around & use of GenAI?

Myles responded, “I think the first thing that excited people was the ability for generative AI to go after unstructured data. There are a lot of interesting use cases that people really couldn’t do well before.”

  • Pharmaceuticals: Tracking and analyzing data about testing new drugs and therapies
  • Marketing: Experimenting with GenAI for campaigns and writing 

Myles also listed two important use cases moving forward:

  1. Productivity: It will change how we do different things in our jobs
  2. Transformation: Using generative AI to extend products, which includes customer experience, manufacturing, and agriculture; detecting where there’s a weed instead of putting pesticide everywhere.

How do you incorporate GenAI into your product today & what drove it? 

Erin responded, “At Resolver, when we’re thinking about GenAI, we’re looking to target the most intensive and repetitive tasks that users come to our system to complete.

  1. “We brought our first GenAI feature to market in 2021, so I think pretty early. With the security customers, we’re focused on streamlining the incident triage process. They get a high volume of very repetitive incidents. We’ve found some good fit with AI capabilities helping them make sure they have complete data and triage as efficiently as possible. The tool helps users make connections within their data, really ensuring complete, accurate, efficiently triaged incidents.
  1. “In the area of governance, risk, and compliance, it’s dealing with complex, fast-changing, regulatory frameworks. So, we’re leveraging AI to reduce the mental load and help them recognize patterns within their data. We believe that this is going to help risk teams be more efficient and work together better.”

What’s the most common reason SaaS companies incorporate GenAI?

Arman responded, “I think it’s mostly because users are asking for it. Maybe a year or two ago, people were looking at GenAI as a luxury item. So, if your application had some GenAI capabilities, it was impressive and viewed as very advanced. That perspective is changing now. People expect GenAI to be there.

“In the midterm, about five years, AI will definitely be a must-have extension for analytics. End users will expect easier, better ways, not a replacement, but an extension of what they have with AI to offer much better self-service.”

Budget Allocation to GenAI

Howard shared more research insights on GenAI adoption, top uses cases, and budget allocation.

Howard stated, “The promise of generative AI is so compelling that organizations are definitely putting their money where their mouths are, so to speak, and they’re investing, even if it’s early days, just to experiment and better understand the use cases, and where to get the most value.”

What are some emerging use cases that you anticipate gaining traction in the next year?

Myles mentioned personal productivity and marketing, as well as the enormous ability to transform customer service interactions, particularly AI agents accelerating the time required to resolve calls. “We’re just at the beginning of this thing, but I do think this is as big as the Internet, when it all nets itself out.”

Challenges with New GenAI Features? Users Skeptical of AI

Erin explained, “Our users met the new incident triage tool with a little bit of skepticism because we work in very highly regulated security industries. So, one of the initial hurdles was getting them to trust and opt in to these features. Our legal and sales team did a great job of laying out really easy-to-interpret documentation for our users to opt in which covered four main topics:

  • “Data usage. How will the AI tool leverage their data to train the model and deliver better services?
  • Data regionality. Where will their data be processed? Especially working with a lot of government organizations, we need to ensure that the data is not leaving their geographical region.
  • Data security. We need to prove that any AI sub-processors we’re using are still compliant with our security standards.
  • Third-party access. Anywhere AI gets involved, we need to ensure that they’re processing their data with the same kind of rigor that we use around security.”

What are Top Concerns About GenAI?

Kerry asked the panelists, “Which area of concern do you take the most seriously, and which one would you toss?”

Erin responded that she’d toss talent and skills availability. “I think it’s a very teachable skill, so anyone who’s well experienced in the software industry can likely apply those product skills to an AI feature.”

Let’s Freakin’ Gooooo!

Myles cited poorly defined use cases as a top concern. “I’ve had a number of discussions with CIOs, and one of the fears they have is that organizations have forgotten everything they’ve done over the last many years of developing concrete use cases, and they just want to get into action. And every time that happens, things fail. In fact, there are some research numbers I’ve seen that indicate that this particular issue is one of the reasons people have failed. They’ve got a bunch of prototypes going, but they don’t go into production because they don’t have value.”

Concerns Over Security, Privacy, & Cost

Arman noted that SaaS companies deal primarily with multi-tenancy and external use cases, so data security and privacy concerns would multiply. Cost would also be magnified since they’re working with multiple tenants, sometimes tens of thousands. “The cost needs to be justified, especially if they are looking at analytics and GenAI as a revenue source.” 

How can you minimize risk & improve your chance of success when embedding GenAI?

Erin responded, “First and foremost, let’s not forget to do the PM job. You need to be very close with your users and how they operate both within your system and in the market. So, make sure you’re taking them along for the ride throughout your development process. The worst thing we can do is just shove AI into our product, without being thoughtful about it. So, make sure you’re solving real user problems, and you’re having that impact you intended.

“I think we also need to consider AI as a companion and tool for users to be successful in our systems, but it’s really important that they maintain a sense of autonomy. You’re not replacing their critical thinking skills. You’re just augmenting it and allowing them to be more efficient.

“My last tip for product managers is to start small. The first AI feature that we brought to market took a really long time to build. And by the time we did get it out, it was trickier to nudge that functionality in the right direction.”

Howard said organizations should view GenAI as a strategic initiative and invest accordingly. He also noted that GenAI makes data and analytics much more accessible. 

“One of the things that I’ve been talking about since 1993 is what I call ‘information democracy.’ How do we get timely, relevant, data-driven insights to all of an organization’s user constituents, both internally and externally? Right now, penetration is hovering around 40%, and GenAI makes it so much more approachable. And by embedding technology, it gets us much closer to where we need to be in terms of information democracy.”

Watch the recording for additional insights, such as responses to our Q&A.

  • What was Resolver’s process for experimenting and rolling out GenAI features?
  • Has DeepSeek changed your working assumptions on GenAI?
  • What’s the role of open source?
  • What do companies need to make sure they get right about adding Gen AI into their product?
  • How are people adding new revenue from GenAI?

How can GenAI be leveraged in your SaaS product’s analytics? Get a demo of Qrvey’s AI-driven self-service analytics platform today.

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