Augment with Intelligence
AI belongs where it creates genuine leverage — and the hard part isn't deploying it, it's planning for what happens after. Most AI business cases ignore the second-order effects that erode half the projected savings.
This is the phase everyone rushes and most get wrong. Deploying intelligent self-service, virtual agents, and agent assist is the easy part. The hard part — the part vendor business cases routinely skip — is planning for what happens after deflection. Contained volume rebounds. The contacts that remain are harder, not just fewer. Savings projected on a straight line erode by half against the curve.
The questions this answers
- Where does AI actually help — self-service, agent assist, real-time insights?
- What gets contained, and what rebounds? (Both always happen.)
- How do we staff the work that’s left, which concentrates complexity rather than removing it?
- Where do we keep the human firmly in the loop because judgment and empathy decide the outcome?
How I work it
The deployment side is well understood: intelligent self-service and virtual agents for high-volume intents; agent assist and next-best-action to lift resolution, speed, and associate confidence; speech and interaction analytics for real-time insight. The differentiator is the discipline most programs lack — quantifying second-order effects. My FOW-Value Model plans across three pools (Autonomous AI, Collaborative Human+AI, Specialist), and my Service Demand Rebound Model proves why 50% containment ≠ 50% savings. I size the prize honestly, so the business case survives Year 2.
What good looks like
AI that measurably improves resolution, speed, and associate confidence; savings projections that hold up because they account for rebound and complexity concentration; and a human-AI work distribution designed to rebalance as the technology matures — adaptive, not frozen to today’s snapshot.
Backed by a published value model (207 research sources, 94 calibrated parameters), a co-invented patent in intelligent queue optimization, and three years building practical AI infrastructure.