AI Interaction Architecture for Enterprise Decision Workflows — Ask AT&T / Decision Intelligence Layer

Established a cross‑product AI experience architecture: reusable interaction primitives, measurable rollout (instrumentation + experimentation), and governance‑by‑design so AI could be trustworthy, controllable, and enterprise‑grade.

Designed how enterprise AI surfaces insights, explains recommendations, and enables safe action in real workflows.

Define and scale a trustworthy, enterprise‑grade AI experience for AT&T’s Ask Platform — optimizing adoption, usability, and decision quality while ensuring governability.
Ask risked low adoption and untrusted AI outputs, slowing enterprise decision cycles and limiting measurable operational value.

Multi‑persona enterprise workflows (executives, analysts, technical users), cross‑functional teams, and measurable rollout workflows (instrumentation + experimentation).

Role: AI UX Architecture Lead — defined cross‑product interaction standards for copilots/contextual assistants, translated platform + policy constraints into UX guardrails, and aligned Product/Engineering/Data on reusable patterns that scale.

Leadership Footprint:

Cross-org alignment: Aligned multiple product and data science partners under a single AI interaction model and governance standard.
Hard decisions: Established human-in-the-loop and transparency requirements to protect trust when stakeholders pushed for deeper automation.
Tradeoffs: Balanced speed vs risk by standardizing reusable patterns so teams shipped faster without fragmenting the platform experience.

AI Interaction Model: Assist → Recommend → Act (Enterprise Decision Architecture).
Implemented an experimentation + instrumentation approach to validate changes and guide rollouts.
Delivered reusable UX foundations supporting secure scaling and operational efficiency.

AT&T’s Ask Platform needed to mature from “AI feature delivery” to an enterprise capability:

Enterprise usability at scale: A consistent, intuitive experience across diverse personas and workflows.
Trust + decision confidence: AI recommendations had to be understandable and actionable — not black‑box outputs.
Measurable rollout: Teams needed a reliable framework (instrumentation + experimentation) to validate changes and avoid shipping AI users wouldn’t adopt.
Operational scalability: Experiences had to remain performant and consistent as usage expanded and workloads increased.

ASSIST → RECOMMEND → ACT (+ disclosure • evidence • human control)

AI Interaction Model: Assist → Recommend → Act (Enterprise Decision Architecture). This became the shared standard used to design, review, and govern AI experiences across teams.

1) Experience Architecture (Reusable AI Patterns)

Defined a reusable interaction framework (disclosure → evidence → recommendation → action).
Implemented persona‑based workflows so the capability stayed coherent across roles.
Reduced cognitive load by pairing conversational entry + structured guidance for safe action.

2) Measurement System (Experimentation + Instrumentation)

Established a repeatable experimentation model: what to test, how to measure, how to decide.
Aligned teams on shared success metrics (engagement, retention, task success).

3) Governance‑by‑Design (Trust, Control, Auditability)

Built UX patterns that show what the system used, what it couldn’t access, and the safest next step.
Ensured higher‑risk actions remained human‑controlled (preview/confirm) and accountable.

Governance-by-Design (Enterprise Requirements):

Transparency: source attribution + what was/wasn’t used.
Explainability: confidence/limits + escalation paths.
Human control: preview/confirm gates for high-impact actions.

Unified decision framework: Replaced fragmented experimentation with a single rollout and measurement model—so teams shipped changes with shared success criteria, not opinions.
Outcome-based prioritization: Shifted roadmap prioritization from feature delivery to outcome validation (task success, adoption signals, trust indicators).
Governance as product UX: Established human-in-control constraints and transparency patterns (what was used, what was denied, next safe action) as non-negotiable standards for enterprise adoption.
Architectural authority: Set review standards and mentored senior designers/engineers to enforce consistent AI interaction patterns across teams.

Workflow validation: iterated with enterprise personas (executive, analyst, technical users) to align the interaction contract to real decision paths.
Rollout validation: used instrumentation and experimentation to validate adoption and task success before expanding access.
Trust validation: tracked trust signals (corrections/overrides, satisfaction indicators) to keep recommendations usable and governable.

How leadership shows up: decision velocity, speed-to-ship, and enterprise trust—measured and governed as a system.

Decision Velocity: Improved time-to-insight and time-to-decision by increasing query resolution efficiency (+40%, internal) and accelerating data-driven actions (internal value attribution).
Operational Efficiency (Speed to Ship): Reduced feature rollout cycle time (−15%, internal) by standardizing experimentation and using instrumentation as the decision mechanism.
Adoption & Trust (Enterprise Utilization): Increased utilization of AI-assisted workflows (+25% adoption; 70%+ satisfaction signal, internal) by enforcing human-in-control patterns and transparency as UX requirements.
Scale & Reliability: Supported growth to 100K+ monthly users (internal) while reducing latency (−20%, internal), keeping the experience stable under load.

ASK ATT Utilization

Note: Internal/FPO visuals; included to demonstrate measurement & governance framework.

AI adoption doesn’t fail on model quality—it fails on trust, control, and decision clarity. Experience architecture is the control surface for all three.