Pega's research shows that organizations getting value from agentic AI first re-engineered their processes. They moved from siloed, channel-based departments to smaller, agile teams with end-to-end campaign control, using AI agents as force multipliers for specific tasks.
A robust AI strategy separates creative, generative tasks (the 'sculptor') from precise, high-scale execution (the 'watchmaker'). Generative AI is best used at design time to ideate, while faster, explainable machine learning models are superior for real-time, regulated customer decisions.
Instead of replacing skilled roles like copywriters, AI transforms them into strategic enablers. Their job evolves from creating individual assets to building the underlying AI prompts and frameworks that allow the entire organization to produce high-quality, brand-compliant work at scale.
The true differentiator for successful AI implementation isn't the latest model version, but rather the 'grindy work' of traditional change management. This includes aligning on success metrics, redesigning processes, and managing the cultural shift required for new ways of working.
To combat the unpredictable costs of token-based AI usage, Pega is adopting a value-based pricing model. Instead of charging per token, they charge based on work completed (e.g., per loan funded or service request processed), aligning costs directly with business outcomes and enabling forecasting.
The idea of a single orchestration hub is outdated. A more effective model is federated, where specialized agents (e.g., an agent that embodies brand guidelines 'as code') are exposed as reusable services. This allows different departments like sales, marketing, and HR to plug into the same expertise.