Don Schuerman is chief technology officer and vice-president of product marketing at Pegasystems, responsible for Pega’s platform and customer relationship management (CRM) applications.
He has 20 years’ experience delivering enterprise software solutions for Fortune 500 organisations, with a focus on digital transformation, mobility, analytics, business process management, cloud and CRM.
Pegasystems offers a robust platform designed to help organizations achieve business-transforming results through real-time optimization. The platform enables clients to address key business challenges using enterprise AI decision-making and workflow automation, including personalizing customer engagement, automating services, and improving operational efficiency. Established in 1983, Pegasystems has developed a scalable and flexible architecture that supports enterprises in meeting current customer demands while adapting to future needs.
Given your extensive experience as CTO at Pegasystems, how does Pega GenAI distinguish itself in the rapidly evolving landscape of generative AI for enterprises?
Pega has been innovating AI solutions for years, including exploring generative AI well before it broke into the mainstream. I think there are three things that set us apart:
First, we’re not just speeding processes, we’re driving innovation. Most enterprise software vendors have rolled out various gen AI bots, agents, or co-pilot features, but the truth is these look-alike tools will not drive competitive differentiation. We enable our clients to reimagine how their entire business runs with unique tools such as Pega GenAI Blueprint, which provides best-of-breed app designs in seconds. We’re not just automating tasks; we’re fundamentally reimagining how businesses operate and innovate.
Second, we’re not just automating in isolation, we’re orchestrating how work gets done from start to finish. Other vendors sprinkle in these gen AI bot features and hope that’s enough to increase efficiency. Our platform is rooted in our industry-leading case management and orchestration, which enables us to not only automate with gen AI but also orchestrate and optimize the entire process from end to end.
Third, we’re not just a generic gen AI engine – we’re focused on driving better client engagement and workflow automation through AI. Sometimes, the problem at hand calls for the creative power of generative AI, whereas other issues might require predictive AI or decisioning AI to infuse more logic into the process.
In your Forbes article, “Unlocking The Potential Of Advanced AI For Business Innovation,” you mention the potential of generative AI to reimagine business operations. What are some specific examples where AI could catalyze legacy transformation in established companies?
Deutsche Telekom’s SVP of Design Authorities, Daniel Wenzel, described to the audience at PegaWorld iNspire this summer how he’s currently using Pega GenAI Blueprint to help him reimagine over 800 separate business processes in the HR services department. He says the biggest bottleneck in trying to improve these processes was that the businesspeople and IT don’t speak the same language, which leads to unrealized expectations. Pega GenAI Blueprint helps both stakeholders understand the process and how to improve it much faster than traditional methods, leading to more effective solutions.
The same article discusses the limitations of current generative AI applications. How can companies move beyond incremental productivity improvements to harness AI’s full transformative potential?
Most generative AI in enterprise software is applied as one-off features that help speed specific aspects of the process. But these types of features are commonplace now, providing little competitive advantage. Productivity hacks like summarization and text generation are table stakes – what businesses need to advance in the market is to use generative AI to innovate all new ways of doing business at a high level. For example, Gartner has identified a new technology category they call Business Orchestration and Automation Technologies (BOAT) that looks at driving business outcomes more holistically, from streamlining costs, to improving decision making, to reducing operational costs and using the right automation technologies for the job at hand. One-off gen AI features have their place, but it’s just a piece of the puzzle and not the silver bullet to solve all problems.
What are the most promising enterprise use cases for generative AI that go beyond typical productivity enhancements, and how can businesses best implement these?
The most exciting generative AI opportunity is the potential to inject best practices into a process. Those that are using gen AI to just write more code could be setting themselves up for more technical debt down the line. The injection of IP into the software design process is a game changer, enabling organizations to get to an optimal solution much faster based on years of experience. And because it’s developed as a visual model and not just lines of code, it’s easier to collaborate and refine it over time across technical and non-technical stakeholders. Previously, finalizing an app design could take weeks and required very specialized skill sets; now, these gen AI-powered tools enable business users to type in their specific needs in plain language and quickly move from concept to comprehensive design. Forrester recently published some research predicting that using AI to inject IP into low-code or model-based design systems will fundamentally shift how enterprises use software – allowing them to build more and buy far fewer ‘off the shelf’ apps. I think this is a big transformation, and we believe with Pega GenAI Blueprint we are well positioned to be the platform of choice for our enterprise clients.
You’ve previously suggested that generative AI can aid in product development by identifying market gaps. Can you elaborate on how this process works and share a real-world example?
Our Pega Customer Decision Hub is a predictive AI solution that helps our clients make the next-best action with their customers, whether that means up selling a product, fixing a service issue, or sometimes doing nothing at all. This allows us to connect with customers 1:1 with actions that best serve their individual needs. But operating in a 1:1 way means you need a great quantity of tailored offers – it’s far better than spamming everyone with the same message, but it requires marketing organizations to create more messages that are unique to different customer groups. Now with gen AI, we can uncover which customers have been underserved and then suggest new actions and build new treatments that would be more beneficial to these groups. This has the potential to help organizations expand into market audiences they have typically not been able to address.
How can established companies with legacy systems effectively integrate generative AI to remain competitive against more agile startups, particularly in reimagining their core operations?
I think we are entering a tipping point for legacy systems. For decades, large enterprises have been kicking the technical debt can down the road. We spent years applying band aid solutions like RPA that didn’t address the fundamental drain that legacy systems place on enterprises – they siphon off IT spend that could be going to innovation, they introduce risk, and they prevent enterprises from moving fast in changing markets. Luckily, I believe one of the superpowers of gen AI is that it will let us dramatically accelerate the rate at which we redesign and retire our legacy systems – not by simply recoding them, but by rethinking the workflows and processes themselves to both run on modern cloud architectures and deliver the digital experiences customers and employees expect.
In a separate article on establishing an AI manifesto, you emphasize the importance of tying AI strategy to actionable outcomes. Can you provide guidance on how businesses can align their AI goals with tangible business results?
Too many companies start by focusing on a shiny new tool like AI rather than starting by figuring out what their business objectives are and what problem they need to solve. By focusing on the tool rather than the problem, they pigeonhole themselves into a path that might not be optimal for their business. Instead, they need to step back and ask themselves what they are really trying to accomplish. Sometimes gen AI isn’t the right solution and may be better served by applying AI decisioning instead. They need to remember there are different types of AI that are better suited to solving different business problems.
How can businesses leverage generative AI to revolutionize their operations rather than just automating routine tasks? What strategies should they employ to maximize ROI in this area?
Don’t just focus on the individual tasks – this will prevent you from seeing the forest for the trees. Step back and understand your overall business workflows and the outcomes you are trying to drive from them. Generative AI can be used to analyze your processes and infuse best practices in any number of different industries. This can drive profound changes by enabling companies to rethink and redesign their core workflows. For example, AI can help design new operational models from scratch or re-engineer existing ones to improve efficiency and innovation. Establish clear metrics to measure success and regularly refine your approach based on these insights. By leveraging AI to drive meaningful change rather than incremental improvements, businesses can unlock significant value and stay ahead of the competition.
What industries do you believe are most poised to benefit from redesigning workflows using AI, and how should they begin implementing this approach?
Nearly any organization can universally benefit from improving their workflows, particularly in fast-changing markets. Services industries such as financial services, telco, and healthcare can likely realize the most gains to help streamline how they engage with their customers. These sectors handle complex, data-intensive processes and are under increasing pressure to improve efficiency, reduce costs, and deliver better outcomes. In addition, any industry with large amounts of legacy services – such as banking – can benefit by examining their processes likely established years ago to modernize them and ensure they keep pace with newer competition.
How does the ‘human-in-the-loop’ approach enhance the effectiveness and ethical deployment of AI, particularly in customer-facing roles?
Generative AI, while powerful, can produce outputs that are not always accurate or appropriate. By integrating human oversight, we can mitigate risks such as AI-generated content inaccuracies or ethical issues.
For instance, in customer service, AI can generate responses and recommendations, but having a human review these outputs ensures they align with company values and customer needs. This oversight is crucial for maintaining transparency and accountability, particularly when AI models produce plausible but incorrect or misleading information.
Interestingly, having a human in the loop allows you to take one of the weaknesses of gen AI – it is inherently non-predictable or non-deterministic, which means it doesn’t give you the same answer twice – and turn that into a strength. With Pega GenAI Blueprint, we use gen AI as a brainstorming partner, suggesting new approaches to workflow design. The human is always the final decider, but by constantly suggesting new approaches, gen AI pushes original thinking and helps humans avoid ‘repaving the cow path.’
Thank you for the great interview, readers who wish to learn more should visit Pegasystems.
