Two leaders from Pega show how marketing and operations are being reshaped by AI – not just through automation, but through trust, intentionality, and human oversight.
The Two Fronts of AI Transformation
At this year’s PegaWorld, the conversations around AI signaled a shift from speculation to real progress. In separate interviews with Tara DeZao, Senior Director of Product Marketing for AdTech and MarTech, and Rebecca Miller, Senior Manager of Product Strategy for CRM, we explored how two distinct areas – marketing and customer operations – are converging through the thoughtful application of AI.
What’s taking shape isn’t just more intelligent systems, but a more intentional philosophy: using AI to support and extend human strengths. This shift is especially apparent in how organizations are scaling both creativity and operational consistency. Though Tara and Rebecca focus on different parts of the business, their insights reflect a common belief: AI becomes truly valuable when it enhances, rather than replaces, human decision-making.
Marketing’s Leap: Creativity at Scale Without Losing Control
Tara DeZao brought a grounded view to the much-discussed topic of GenAI in marketing. She sees dynamic creative optimization as both widely hyped and genuinely useful. The value lies in scaling messaging and creative output, but only if brands are prepared to ease up on rigid control.
“You have to be a brand that’s willing to take a calculated risk,” she said. “You still need oversight, but AI can absolutely help you scale your voice.”
This balance – between maintaining brand standards and embracing innovation – is where many marketing teams are working to find footing. GenAI tools are capable of generating content at speed and tailoring it to different audience segments, but they require a degree of trust and flexibility that not all organizations are ready to give.
At Pega, one response to this challenge has been “Intern Iris,” an internal AI tool treated like a junior staff member: resourceful, efficient, and still learning. Employees are reminded to check Iris’s work as they would with any intern. The result is a productive balance – AI is used as a lever for efficiency, but human oversight keeps quality intact.
Tara categorized marketers into three groups:
– Traditional: focused on manual, campaign-based efforts.
– Transitional: leveraging streaming data and customer journeys.
– Transformational: investing in AI to drive adaptive, real-time experiences.
For brands in the third group, AI isn’t just improving output – it’s helping teams respond more effectively to real-time customer behavior. That’s not just automation; it’s responsive strategy in action.
Service’s Shift: From Workflow to Autonomy
On the operations side, Rebecca Miller described how AI is enabling progress in areas like customer service, workflow automation, and employee experience. Her focus was on agentic AI – automated agents that perform tasks on behalf of human users within defined guardrails.
Her perspective emphasized pragmatism. “We encourage clients to start small – pick a process or a pain point, use tools like Blueprint to visualize the future experience, and then scale from there,” she noted.
That approach reflects Pega’s “center-out” business architecture, which enables teams to build core logic once and deploy it across channels. It also underscores a critical point: AI transformation doesn’t have to be disruptive to be effective. Incremental steps can lead to lasting change.
A key theme in Rebecca’s comments was the role of AI in improving employee experience. With engagement scores declining across industries, many organizations are looking to AI not just for efficiency, but for relief – from repetitive tasks, slow systems, and decision fatigue. When implemented well, AI can help employees focus on more meaningful, higher-impact work.
From Guardrails to Governance: Building Trust in AI
Across both conversations, one theme was consistent: trust is the real enabler of AI progress.
Whether experimenting with GenAI or piloting agentic workflows, organizations face a familiar challenge – confidence. Teams want to try new tools but need clarity on what’s safe, what’s tested, and what outcomes to expect.
Pega’s strategy here is refreshingly clear. Tools like Intern Iris and Blueprint don’t just provide functionality—they create structure. They let people test ideas, see outcomes, and adjust accordingly. They frame AI not as a black box, but as a collaborative process.
This matters especially in enterprise contexts, where complexity and risk aversion can delay adoption. Rebecca noted that you can’t realistically launch agentic AI across a large-scale contact center without testing and evidence. But you can simulate, evaluate, and let the success stories build organically.
What Enterprises Can Learn From Pega’s Approach
For business leaders evaluating how to bring AI into their organizations, a few lessons from Pega stand out:
1. Start with the use case, not the hype.
Focus on high-friction areas where AI can drive measurable improvement – whether that’s content creation, call handling, or employee onboarding.
2. Build before you bet.
Use modeling tools to create tangible experiences of what AI can do before making large-scale changes. This builds internal buy-in and reduces implementation risk.
3. Frame AI as a partnership.
Set expectations by treating AI outputs as starting points, not finished products. Establish workflows that include validation and refinement. The “intern” metaphor works because it encourages responsibility without fear.
The ROI of Realness: Beyond Metrics, Toward Momentum
AI isn’t magic – and it shouldn’t have to be. The most meaningful impact often comes from incremental progress: clearer processes, faster output, fewer mistakes, more thoughtful work.
Tara and Rebecca point to a future where AI is simply part of how work gets done – scaled, governed, and built around human capability. That doesn’t make headlines, but it does make organizations stronger.
From marketing to service to employee enablement, Pega is demonstrating that successful AI implementation isn’t about dramatic reinvention. It’s about disciplined adoption, practical experimentation, and a steady focus on outcomes that matter.
When AI becomes habit – not hype – you’re on the right track.
