AI & customer experience

Where AI meets the contact center.

I spent twenty years inside the operations that AI is now being sold into — social and digital care, global partner delivery, high-trust customer portfolios, enterprise platform migrations. I now build and evaluate AI systems on owned infrastructure.

That combination is the point. Understanding what a model does is one problem. Understanding the operating environment it lands in — and what has to be true before it works — is the other.

20+
Years in customer operations
50 → 5,000
Partner agents in one year
60+
Teams in the portfolio operating model
73
Controlled AI experiments
The bridge

Operator first, then the AI.

Contact-center and service-delivery organizations are where AI either earns its business case or quietly fails to. These four cases run in that order — what the systems do, then the operating environment they land in.

01 · Answer-engine measurement

Two controlled answer collections

exposed low recurrence and changed the methodology

Measuring how AI answer engines represent an organization

Condition

Organizations measure what they publish and what customers say back. Almost none measure what AI answer engines tell people who ask about them.

Role

Architect and sole operator — spec, schema decision, hand-curated panel, collection, and the companion value model.

Operating change

Sampling standard set empirically rather than by instinct: measured 9% recurrence at three samples, which killed single-shot collection and triggered a staged convergence experiment.

What it demonstrates

That he can evaluate model behavior, contain over-claiming, and build a business case without inventing a number — in his own words rather than a vendor's.

  • three gaps
  • two lanes
  • measurement ladder
View the evidence
Actions

Defined machine answers as a third collection surface rather than folding them into social listening. Scored three gaps: divergence from the organization's own account, from its ecosystem's, and assertions neither supports. Recorded two lanes per answer — trained belief versus live retrieval with citations. Derived prompts from the organization's own ecosystem, then curated by hand.

Outcome

324 answers collected in the first run, 400 in the second, across four engines. A companion value model — Decision Exposure, not cost — with a measurement ladder that must resolve to material mismatch before any figure touches money, and a published validation cascade naming which of its own stages remain unproven.

02 · AI system evaluation

73 experiments

produced deterministic scoring and identified a more reliable model architecture

Building a diagnostic that questions its own output

Condition

An LLM pipeline that scores organizational evidence is only useful if it is diagnostic rather than stochastic. Ask it the same question twice and get two answers, and it is a generator wearing an instrument's clothes.

Role

Designer, builder and operator. Published under his own name.

Operating change

Model and prompt changes stopped being judgment calls and became measured decisions with go/no-go gates — schema pass rate, unclassified rate, minimum finding counts.

What it demonstrates

That he can hold a technical conversation about model behavior, limitations and deployment architecture without being an engineer — and that when his own system produced plausible but inaccurate output, he stopped using it and rebuilt it rather than shipping around the problem.

  • deterministic scoring
  • model comparisons
  • reproducibility
View the evidence
Actions

Built extract, score, adversarial-skeptic and synthesize stages, where findings must survive challenge before entering the evidence ledger. Defined a correctness metric weighting reproducibility across repeated trials, evidence verification that checks whether cited identifiers actually exist, diagnostic value by finding type, and cross-run stability. Ran controlled model comparisons on a fixed corpus. Locked a production model standard with an explicit rationale, and a rule that scoring stays on the calibrated model even when extraction moves.

Outcome

Scoring determinism reached zero variance across identical runs. 73 experiments across four research tracks. A published head-to-head finding that the larger model did not win, and that prompt edits raising acceptance rates broke reproducibility — a tradeoff most teams never measure.

03 · Global delivery

50 → 5,000

partner agents in one year, including a new site in Egypt

Scaling Xfinity Mobile care from pilot to global delivery

Condition

A mobile product growing faster than the operation supporting it. Fifty agents needed to become five thousand within a year, across global partners, without customer experience degrading during the transition.

Role

Led Xfinity Mobile care across sales, service, retention, digital and social channels, including the scaling and the new-market partner launch.

Operating change

A partner delivery model that held its quality bar while multiplying a hundredfold.

What it demonstrates

That he can build the delivery system behind a growth promise — the half most scaling stories skip.

  • work routing
  • quality ownership
  • partner operation
View the evidence
Actions

Treated it as a design problem rather than a hiring problem — establishing what had to be true before capacity was allowed to land: work routing, quality ownership, how a new site reaches standard rather than merely opening, and escalation paths across time zones. Stood up an entirely new partner operation in a market the organization had not operated in.

Outcome

50 to 5,000 agents in one year, including a new site in Egypt. During this period the business earned consecutive J.D. Power and ACSI customer-experience recognitions.

04 · Operating model

60+ teams

under one portfolio operating model; the portfolio simplified from 111 queues to 61

Building the portfolio operating model across sixty-plus teams

Condition

A specialty customer-service portfolio of more than 60 teams, from regulatory response, privacy and executive escalation to social care and global partner operations, with new work arriving from across the enterprise.

Role

Built and led the roughly 70-person strategy and operations organization, stood up in 2023, and designed the operating model.

Operating change

Every request entered one intake and prioritization system. Requesting teams developed the financial case and assessed customer, business, employee, capacity and implementation impacts, which strengthened business acumen while creating portfolio-wide visibility, clear decision rights and a defensible way to make trade-offs or say “not now.”

What it demonstrates

That he installs structure rather than becoming it — the difference between holding an organization together personally and building something that holds without him.

  • intake and prioritization
  • decision rights
  • portfolio visibility
View the evidence
Actions

Integrated portfolio management, insights and analytics, quality and performance, financial and workforce planning, incentives, employee engagement, product response, technology enablement and global partner operations into one organization. Mapped the portfolio by risk tier and designed governance to match, with explicit decision rights, defined escalation and named ownership. Used the resulting portfolio view to align budget, workforce and incentives to measurable outcomes.

Outcome

The portfolio simplified from 111 queues to 61, freeing operations leaders to focus on delivery and performance, with an operating budget in the $200M range and thousands of partner agents across six countries.

Enterprise platform adoption

A multi-year enterprise CX platform lifecycle, from the buyer's side

Owned the buyer's side of a multi-year enterprise CX platform adoption — the seat a platform vendor sells into. Migration completed against a dated 2019 target, forums transitioned on schedule in 2021, expansion into further operating groups the year after.

Canonical technical record

Experiments, benchmarks and published limits.

Greenbaum Labs holds the full record — methodology, results, and what each system does not do.

All selected work Experience LinkedIn Contact