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Generative AI’s Current State, Challenges, and Industry Adoption

Generative AI’s Current State, Challenges, and Industry Adoption

The News: Late in the third quarter (Q3) 2023, Pegasystems announced that Pega Infinity 23, the latest version of its low-code enterprise development platform became generally available to customers. The platform incorporates deeper intelligence, low-code, and autonomy capabilities within its entire portfolio of products in a variety of markets, including Pega Platform, Pega Customer Decision Hub, Pega Customer Service, and Pega Sales Automation. You can read the press release announcing the news on the Pegasystems’ website.

I recently had a conversation with Ken Parmelee, senior director, intelligent automation strategy, with Pegasystems, where we spoke about the excitement around the use of generative AI, the demand for more explainability and transparency when using large language models (LLMs) and predictive AI models, the key industries that seem to be on the forefront of adopting AI, and Pega’s approach to rolling out tools and features to help enterprises develop the applications and workflows needed to operate in a data-intensive and world.

Generative AI’s Current State, Challenges, and Industry Adoption

Analyst Take: At the end of Q3 2023, Pegasystems announced the general availability of Pega Infinity 23, the latest version of its low-code enterprise development platform, which infuses generative AI, support for more autonomous capabilities, and the incorporation of low-code tools that allow fusion teams (skilled developers and non-technical workers) to interact and jointly develop new applications, processes, and workflows. Not surprisingly, our conversation was dominated by the topic of AI, and, more specifically, the use of generative AI.

Excitement About Generative AI, But a Cautious Approach to Deployment

One of the interesting insights raised by Parmelee was the level of excitement around generative AI among Pega’s customers and prospects, particularly with respect to the capabilities of generative AI, from the basic summarization and text-generation use cases, to more sophisticated use cases, such as code generation. However,  for companies that are not using the Pega platform, there is still trepidation around deployment due to issues around model hallucination, data privacy and security, and the real-world capabilities of the technology versus the hype.

“We’ve done a roadshow with clients and non-clients on those features, and one of the things that I would tell you is I think people are super excited about [generative AI capabilities], but they’re also super scared, right?” Parmelee said. “And I do think what we’ve seen with a number of clients is that the market is confused, because you’ve got vendors out there [predicting] that generative AI will mark the end of development. And as anybody who’s been in this business for a while knows, that’s not real.”

Parmelee added that he believes that the key to reducing or eliminating that fear is by leveraging generative AI now to create new applications or processes quickly that can be shared with, vetted, and tested by IT. IT has the expertise, experience, and know-how to ensure that the big issues – data privacy, IT security, and scalability – are properly addressed and managed.

Desire for More Explainability and Transparency When Using Generative AI

Despite the success that many of Pega’s initial pilot customers are having with generative AI tools that are deployed within Pega Infinity 23, Parmelee admits that many enterprises, particularly those in regulated industries, are insisting upon more explainability with AI models and greater transparency in terms of which data sources are being used to ground generative AI prompts. According to Parmelee, the steps and processes that will need to be deployed to handle this type of work likely will create new work opportunities within enterprises, which might actually address the fear that AI will eliminate workers.

“I think, trust, transparency, and then bias are the three things that we consistently see where people are wanting to make sure that as they use regular AI or generative AI, that it’s a consistent [ability] of being able to say, ‘Well, what was the data set? Why did they come to that conclusion?’,” Parmelee said. “With generative AI, there’s also a worry about [personally identifying information] PII; the fear that [a generative AI algorithm] is going to glom on to somebody’s personal information from a source.”

Further, “explainability is still a big issue; [people] want to know where that result came from,” Parmelee said, noting that the ability to source and audit the content, information, and outputs from generative AI tools will be increasingly important as the use cases become more complex.

Parmelee adds that these concerns about generative AI models should spur the use of human checkers to verify that the information generated or returned from a prompt is accurate, free from bias or toxicity, and not using information that should be off-limits. As a result, he says, the fear that AI might replace human workers could be unfounded; instead, Parmelee says that a large number of workers could be required to handle this sort of generative AI verification work.

Three Industries Are Actively Taking Steps to Deploy Generative AI

Another interesting insight from our conversation was focused on the industries that are actively testing or using generative AI. Parmelee highlighted companies within the government, healthcare, and financial services industries as the organizations that are most willing to embrace AI.

According to Parmelee, government entities are interested in generative AI because it can help them to do more with fewer resources. These federal, state, and local government organizations are often overwhelmed with service inquiries, requests, or other administrative issues that cannot be handled via their current staff or within current budgets.

“Government doesn’t know that much about generative in general, but they want it because they have such budgeting challenges these days,” Parmelee said. “They’re looking [at generative AI] from the perspective of ‘if I can do more or go faster with less [cost], then I’m better off.’”

Parmelee also cites healthcare and financial services as two other industries that are embracing AI, for similar reasons, including the ability to innovate fast and quickly get things to people who are buyers or constituents.

“Government, financial services, and healthcare are actively using generative AI as much as they can right now, although a lot of that [activity] is internal,” Parmelee said. Whereas retail is using generative AI to improve customer experience, Parmelee says companies within these industries are devoting a lot of resources to using generative AI with their own internal data, so they can reduce the potential for PII exposure and the likelihood of hallucinations more effectively.

Pega’s Approach to Generative AI

For its part, Pegasystems is focusing on helping companies automate more quickly via generative AI. It is introducing dynamic dashboards and buddies under the Autopilot brand, which are basically chat or voice interfaces that allow users to ask questions and get answers.

“So the idea is we supply a kind of interface for you, which is just a chat or voice interface that you can either speak to, or type into, and get this responsive system,” Parmelee said. “It’s not all that different than what you think of as generative today, except that because it’s tied to cases and processes in the back end, it means that it is constantly learning what worked and what didn’t, and then reapplying that to the way an issue is handled the next time.”

In a real-world context, that means a buddy could be configured to serve as a natural language assistant that is grounded in whatever domain is required. Parmelee notes that salespeople could have a sales buddy configured to be grounded in past sales and customer data, whereas a customer experience buddy could be grounded in a knowledge base of past user queries, product information, or other relevant data source, letting the user ask questions and get their answer, instead of trolling through lots of documentation. The company is also enhancing its fabric to make it easier to use streaming data and inject AI across operations capabilities and is making it easier to connect with external systems, such as SAP and Salesforce.

Perhaps most important, Pega is positioning itself as a source of knowledge about generative AI and how it should be deployed. Parmelee estimates that only about 20% of organizations truly understand generative AI and how to use it, 30% that know enough to be dangerous, and the remaining portion of the market that likely is seeking information and guidance on how to deploy generative AI. Pega, as a platform vendor that has rolled out its own generative AI tools, as well as embraced integration with other large enterprise software vendors, is well positioned to serve as that trusted advisor.

Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.

Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.

Other Insights from The Futurum Group:

Low-Code/No-Code Development Platforms: Democratizing and Speeding Application Development Via Code Abstraction Tools – Enterprising Insights, Episode 2

Pega Streamlines Chargeback Processes via Process AI

Generative AI Capabilities Coming to Pega Infinity in Q3

 

Author Information

Keith has over 25 years of experience in research, marketing, and consulting-based fields.

He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.

In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.

He is a member of the Association of Independent Information Professionals (AIIP).

Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.

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