Don Schuerman is chief technology officer and vice-president of product marketing at Pegasystems, answerable 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 give attention to digital transformation, mobility, analytics, business process management, cloud and CRM.
Pegasystems offers a sturdy platform designed to assist organizations achieve business-transforming results through real-time optimization. The platform enables clients to deal with 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 versatile 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 within 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 feel there are three things that set us apart:
Most enterprise software vendors have rolled out various gen AI bots, agents, or co-pilot features, but the reality is these look-alike tools is not going to drive competitive differentiation. We enable our clients to reimagine how their entire business runs with unique tools comparable to Pega GenAI Blueprint, which provides best-of-breed app designs in seconds. We’re not only automating tasks; we’re fundamentally reimagining how businesses operate and innovate.
. Other vendors sprinkle in these gen AI bot features and hope that’s enough to extend efficiency. Our platform is rooted in our industry-leading case management and orchestration, which enables us to not only automate with gen AI but additionally orchestrate and optimize all the process from end to finish.
– we’re focused on driving higher client engagement and workflow automation through AI. Sometimes, the issue 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 method.
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 firms?
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 assist him reimagine over 800 separate business processes within the HR services department. He says the most important bottleneck in attempting to improve these processes was that the businesspeople and IT don’t speak the identical language, which ends up in unrealized expectations. Pega GenAI Blueprint helps each stakeholders understand the method and tips on how to improve it much faster than traditional methods, resulting in simpler solutions.
The identical article discusses the restrictions of current generative AI applications. How can firms 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 elements of the method. But these kinds of features are commonplace now, providing little competitive advantage. Productivity hacks like summarization and text generation are table stakes – what businesses must advance out there is to make use of generative AI to innovate all recent ways of doing business at a high level. For instance, Gartner has identified a brand new technology category they call Business Orchestration and Automation Technologies (BOAT) that appears at driving business outcomes more holistically, from streamlining costs, to improving decision making, to reducing operational costs and using the appropriate automation technologies for the job at hand. One-off gen AI features have their place, however it’s just a bit of the puzzle and never the silver bullet to resolve all problems.
What are essentially the most promising enterprise use cases for generative AI that transcend typical productivity enhancements, and the way can businesses best implement these?
Probably the most exciting generative AI opportunity is the potential to inject best practices right into a process. Those which might be using gen AI to simply write more code could possibly be setting themselves up for more technical debt down the road. 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 since it’s developed as a visible model and not only 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 of 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 construct more and buy far fewer ‘off the shelf’ apps. I feel it is a big transformation, and we imagine with Pega GenAI Blueprint we’re well positioned to be the platform of selection for our enterprise clients.
You’ve previously suggested that generative AI can aid in product development by identifying market gaps. Are you able to 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 motion with their customers, whether meaning up selling a product, fixing a service issue, or sometimes doing nothing in any respect. This enables us to attach with customers 1:1 with actions that best serve their individual needs. But operating in a 1:1 way means you would like a terrific quantity of tailored offers – it’s much better than spamming everyone with the identical message, however it requires marketing organizations to create more messages which might be unique to different customer groups. Now with gen AI, we are able to uncover which customers have been underserved after which suggest recent actions and construct recent treatments that will be more useful to those groups. This has the potential to assist organizations expand into market audiences they’ve typically not been in a position to address.
How can established firms with legacy systems effectively integrate generative AI to stay competitive against more agile startups, particularly in reimagining their core operations?
I feel we’re entering a tipping point for legacy systems. For many years, 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 elemental drain that legacy systems place on enterprises – they siphon off IT spend that could possibly be going to innovation, they introduce risk, they usually prevent enterprises from moving fast in changing markets. Luckily, I imagine one in all the superpowers of gen AI is that it can allow us to dramatically speed up the speed at which we redesign and retire our legacy systems – not by simply recoding them, but by rethinking the workflows and processes themselves to each 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 technique to actionable outcomes. Are you able to provide guidance on how businesses can align their AI goals with tangible business results?
Too many firms start by specializing in a shiny recent tool like AI fairly than starting by determining what their business objectives are and what problem they need to resolve. By specializing in the tool fairly than the issue, they pigeonhole themselves right into a path which may not be optimal for his or her business. As a substitute, they should step back and ask themselves what they’re really trying to perform. Sometimes gen AI isn’t the appropriate solution and will be higher served by applying AI decisioning as an alternative. They should remember there are several types of AI which might be higher suited to solving different business problems.
How can businesses leverage generative AI to revolutionize their operations fairly than simply automating routine tasks? What strategies should they employ to maximise ROI on this area?
Don’t just give attention to the person tasks – this may prevent you from seeing the forest for the trees. Step back and understand your overall business workflows and the outcomes you are attempting to drive from them. Generative AI may be used to investigate your processes and infuse best practices in any number of various industries. This could drive profound changes by enabling firms to rethink and redesign their core workflows. For instance, AI might help design recent operational models from scratch or re-engineer existing ones to enhance efficiency and innovation. Establish clear metrics to measure success and usually refine your approach based on these insights. By leveraging AI to drive meaningful change fairly than incremental improvements, businesses can unlock significant value and stay ahead of the competition.
What industries do you suspect are most poised to learn from redesigning workflows using AI, and the way should they start implementing this approach?
Nearly any organization can universally profit from improving their workflows, particularly in fast-changing markets. Services industries comparable to financial services, telco, and healthcare can likely realize essentially the most gains to assist streamline how they engage with their customers. These sectors handle complex, data-intensive processes and are under increasing pressure to enhance efficiency, reduce costs, and deliver higher outcomes. As well as, any industry with large amounts of legacy services – comparable to banking – can profit 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 usually not at all times accurate or appropriate. By integrating human oversight, we are able to mitigate risks comparable to AI-generated content inaccuracies or ethical issues.
For example, in customer support, AI can generate responses and suggestions, 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 within the loop lets you take one in all the weaknesses of gen AI – it’s inherently non-predictable or non-deterministic, which implies it doesn’t give you an identical answer twice – and switch that right into a strength. With Pega GenAI Blueprint, we use gen AI as a brainstorming partner, suggesting recent approaches to workflow design. The human is at all times the ultimate decider, but by continually suggesting recent approaches, gen AI pushes original considering and helps humans avoid ‘repaving the cow path.’
