Take 5 Report

From ambition to AI Scale: The control framework AI leaders need

We are in the early stages of scaling AI and agentic processes, but momentum is accelerating.

Reaching the tipping point of rapid scale requires robust orchestration and disciplined control frameworks in data privacy and enforceable business rules. These are non-negotiable foundations for scaling AI. Executives prioritize customer impact, improved outcomes, and deploying talent to high-value work. Investment urgency is real. AI scale is a growth lever, not a technology experiment, but the urgency carries risk. Control frameworks are immature. In the interim, human-in-the-loop oversight and risk analytics will serve as compensating safeguards. Modernization and legacy retirement will continue as consequences of AI scale, but they are not seen as its drivers. Layering AI onto already fragile legacy estates risks increased complexity and may limit the ability to fully leverage proprietary data for competitive advantage.

HFS Research, in partnership with Pega®, surveyed 101 senior enterprise leaders in North America to understand how enterprises are seeking to scale AI and agentic solutions and the implications for IT modernization and legacy retirement.

The survey uncovered five key takeaways:
    • Confidence in unified orchestration of data privacy and business rules is key to AI scale
      Eighty percent (80%) of executives cite unified orchestration as critical. For now, they will rely on human-in-the-loop (HITL), auditing, and rollback.
    • Governance confidence is the gate to AI scale and workflow modernization
      Forty-five percent (45%) of executives say AI governance is inadequate. Slowing workflow and IT modernization risks more complexity.
    • Auditability exposes the fault line in governance
      Sixty percent (60%) claim they can avoid duplicate AI pipelines. Forty percent (40%) cannot trace data end-to-end, and just half can justify automated decisions.
    • Scalability is judged at the customer interface
      If customers don’t feel it, it isn’t scale. First-contact resolution is recognized as the clearest proof of AI transformation.
    • Legacy retirement is sidelined
      Enterprises are stacking AI on fragile foundations, risking poor data quality and lost competitive advantage. Without modernization, AI is another layer of complexity.

The Bottom Line: AI scaling safely is elusive. Confidence, not intent, is the constraint. A unified orchestration fabric underpinning strong governance, data privacy, traceability, and auditability is a milestone on the path to the AI scale tipping point.

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