As AI adoption accelerates, many enterprises are discovering a widening gap between promised innovation and measurable results. Fragmented decisions, opaque performance, and unclear cause–effect relationships have become common — especially inside complex, mission-critical environments like the contact center.
After 20 years of operating AI in production, Afiniti is defining a new category to address this gap: Outcome Orchestration.
Defining Outcome Orchestration
Outcome Orchestration deploys AI to unify and steer contact center data, intelligence, and decisioning across people, systems, and workflows — holding performance accountable to real business baselines.
Rather than replacing existing platforms, Afiniti operates as an intelligence layer within complex environments, orchestrating decisions that consistently drive outcomes. This approach reflects a foundational belief: AI only matters if it measurably improves outcomes in production.
Proven in Production
At the core of Outcome Orchestration is Afiniti Pairing, Afiniti’s patented AI technology that dynamically matches customers with the agents most likely to achieve a desired outcome.
Afiniti Pairing has delivered more than $2.5 billion in measurable value across enterprise contact centers of all sizes and platforms. In 2025, Afiniti achieved 100% client retention, reinforcing a model built on long-term performance rather than experimentation.
A Foundation for Responsible Expansion
In 2026, Afiniti will extend Outcome Orchestration beyond pairing to address broader contact center decisioning needs; including agent experiences, routing decisions, and intelligence. These capabilities are being introduced deliberately, informed by real operational challenges observed across Afiniti’s customer base.
What remains constant is Afiniti’s commitment to AI that earns trust, by integrating into real environments, delivering measurable outcomes, and proving its value over time.
Read the full announcement: https://www.afiniti.com/afiniti-introduces-outcome-orchestration-defining-a-new-standard-for-enterprise-ai/