Pegasystems announced Pega GenAI – a set of 20 new generative AI-powered boosters to be integrated across Pega Infinity™ ‘23, the latest version of Pega’s product suite built on its low-code platform for AI-powered decisioning and workflow automation. It will be showcased next month at PegaWorld iNspire and available in Q3 2023. Pega GenAI will provide organizations with the architecture and integrated use cases to drive value from generative AI now and into the future.
For example, a bank looking to automate their loan processing operations would traditionally need to start by identifying, designing, and developing dozens of workflows from scratch. With Pega GenAI, they simply tell Pega they are building a “loan processing application,” and Pega will automatically create the related workflows, data models, user interfaces, sample data, and more based on responses from generative AI models like those from OpenAI. Because the responses from generative AI are mapped directly into Pega’s powerful model-driven architecture, low-code developers can easily configure and change these suggested starting points to rapidly deliver a completed application. Pega GenAI boosters like these will be infused throughout Pega Infinity, allowing users to accelerate their low-code application development, enhance customer service, and improve customer engagement.
A new API abstraction layer, called Connect Generative AI, will allow organizations to get immediate value from generative AI with a plug-and-play architecture that allows for low-code development of AI prompts. Rather than directly calling OpenAI, or other APIs directly from UIs or workflow steps, Pega uniquely provides an API abstraction layer so developers can easily swap out large language models running on both public and
private clouds and build reusable generative AI components that can be leveraged across applications. Connect Generative AI will be able to automatically replace personally identifiable information (PII) data with placeholders in generative AI prompts, helping organizations enforce their data protection policies and advancing secure use of public and private models.
Generative AI powered boosters in Pega Infinity ’23 facilitate rapid development of innovative new capabilities and give low-code developers the power to infuse generative AI functionality into decision-making and workflow automation. As large language models, cloud services, and data privacy needs continue to evolve, this “AI choice” architecture allows Pega and its clients to continuously innovate new secure solutions. Pega will initially offer connectors to OpenAI’s API and Microsoft Azure’s OpenAI APIs and will be supplemented by additional downloadable connectors to other providers on Pega Marketplace.
The 20 new Pega GenAI boosters will help make Pega Infinity ‘23 even easier, faster, and more powerful to use, including:
Faster low-code application development in Pega Platform
- AI-prompted workflows: When users type in the name and short description of the new application they want to build, Pega GenAI will provide relevant suggestions for different workflows and build out their associated stages and steps.
- AI-generated personas: Pega GenAI will provide definitions for relevant users and personas associated with a workflow, allowing low-code developers to route work appropriately and generate the relevant user interfaces.
- Automatic data modeling: Pega GenAI will provide suggestions for different types of data likely needed for an application and model it into the workflow and user interfaces.
- Back-end integration assistance: Pega GenAI will speed application integration by automatically understanding the target system’s APIs and mapping the corresponding workflow data fields.
- Sample data generation: Users will be able to test and demo their application using sample data automatically generated by Pega GenAI, reducing hours of low value work by developers and improving the quality of testing.
Enhanced customer engagement relevancy in Pega Customer Decision Hub
- Treatment creation assistant: Pega GenAI will help users produce better treatments by providing text and image suggestions. Users can select from Cialdini’s principles of persuasion to create variations in tone for the copy to appeal to different customers.
- Autonomously recommend higher impact actions: Pega GenAI will present a list of opportunities to improve the Next-Best-Action library along with automatically generated suggestions for how to make those improvements. Suggestions will come from analyzing underserved customers and identifying actions that don’t have enough treatment variation for the AI to optimally work.
- Explainable AI analytics: Pega GenAI will provide visibility into how AI decisions are made by analyzing decisioning data and providing easy-to-understand explanations of why certain actions would (or would not) be presented to a customer.
- Population targeting and validation: By automatically generating sample personas for different populations, Pega GenAI will help users more quickly test the logic in their engagement policies.
Improved customer service in Pega Customer Service
- Automatic interaction summaries: Pega GenAI will automatically summarize message transcripts between agents and customers. This will reduce the need for customer service agents to manually write notes and significantly improve handle times in contact centers.
- Accelerated chatbot training: Pega GenAI will generate labeled training data for chatbots, anticipating likely utterances and phrases from customers. This will reduce the time and effort needed to deploy new customer service bots.
- Customer interaction simulator: Organizations will be able to use Pega GenAI to role play with their customer service agents to train their staff, identify agents in need of additional training, find process bottlenecks, and evaluate staffing levels.
- Advanced chatbot response: Pega GenAI will assist a chatbot that can’t identify a suitable answer on its own. For example, it could help the chatbot better understand the customer’s intent so it can improve its chances of a relevant response. Generative AI output can further train and enhance the natural language models to teach the chatbots how to respond to new inquiries.
More productivity in Pega Sales Automation
- Email reply generator: Pega GenAI will scan incoming emails and offer suggested replies.
- Meeting summary generator: Salespeople can use Pega GenAI to generate sales meetings summaries and action items for meeting attendees by analyzing meeting transcripts from Pega Voice AI, Zoom, or WebEx.
Faster bot creation with Robot Studio
- Script generation: Pega GenAI will automatically generate custom scripts that can be called directly from the automation surface, which helps simplify development and debugging.
- Test data generation: Robotics developers will save time by using Pega GenAI to automatically create input parameters for testing bot automations.
Enhanced knowledge management libraries in Pega Knowledge
- Semantic search: Users will be able to ask Pega GenAI natural language questions and receive concise, summarized answers based on knowledge management articles.
- Generation of new answers: Knowledge management content authors will be able to use Pega GenAI to help create new, succinct articles based on summaries of existing content.
Easier access to robust operational insights
- Automatic insight generation: Users will be able to generate reports via Pega GenAI by making requests such as, “Show me the sales figures for the Midwest region in Q2.”
Pega’s approach to generative AI allows organizations to confidently deploy their AI models of choice in a responsible and governed way while minimizing risk. It incorporates auditing, rules-based governance, and workflow-managed human approval to advance safety, security, and reliability. Pega will allow for all AI-generated text to be reviewed, edited, and approved by authorized staff to mitigate the risk of inaccurate or biased text from being exposed to customers.
Pega GenAI builds on Pega’s decades of experience in applying computer intelligence, including rules and data-driven AI, in responsible and effective ways. It complements the already powerful AI-powered decisioning engine in Pega’s low-code platform, which brings together decision management, predictive and adaptive analytics, natural language processing, voice recognition, business rules, and a robust set of MLOps and testing capabilities for monitoring and governing AI models.
