Agentic AI is shifting from experimentation into production-grade workflows with measurable business lift. Three recent deployments illustrate the pattern across logistics, health plan member services, and payments. Each case pairs autonomous execution with operational metrics that boards can evaluate in plain terms.

For CT Labs, the implication is practical. Enterprise leaders can now plan for governed scaling from pilot to verified ROI by leveraging clear workflow ownership, risk guardrails, and talent grids that align with the operating realities of agentic systems.

What changed in the last 90 days

Early enterprise automation often delivered task-level efficiency while leaving end-to-end outcomes ambiguous. These deployments look different because they operate across full workflows, run continuously, and produce signals that finance and operations teams recognize.

Three signals matter most:

• End-to-end execution inside real operating environments, not demo lanes
• Measurable lift tied to margin, customer satisfaction, and transaction integrity
• Embedded controls such as validation, fraud detection, and safe authorization paths

Logistics agentic execution inside live freight operations

Hwy Haul commercially launched an agentic AI freight operating system called Miles, built and proven inside live brokerage operations. The company reports that Miles eliminates 25+ manual touches per load by autonomously quoting, booking, dispatching, monitoring, and fraud-checking loads. It also reports a 20-50 percent gross margin uplift during deployment.

Why this case is a milestone:

• Workflow coverage spans the freight lifecycle, not a single micro task.
• Margin lift links agentic execution to board-level value creation.
• Risk controls, including validation and fraud detection, are built into the system rather than applied after the fact.

For operators, this is a template for evaluating agentic ROI in any function. Map manual touches, assign an economic value to each touch, then measure lift at the workflow level rather than at the tool level.

Health plan member services with always-on voice resolution

Stellarus, formed from Blue Shield of California’s technology unit, partnered with Sierra to deploy an AI voice agent in a Blue Shield member support pilot. The agent handled benefits and coverage questions around the clock and delivered a 4.4 out of 5 customer satisfaction score across thousands of member interactions.

Why this case matters for enterprise leaders:

• Customer satisfaction gives an immediate quality signal.
• The agent handles high volume, repeatable questions while routing complex needs to humans, supporting a blended operating model.
• The pilot demonstrates that regulated, high-trust environments can deploy agentic voice interfaces with measurable outcomes.

For boards, this creates a clear evaluation frame. Member services and contact centers can be assessed using CSAT, containment, resolution rates, escalation quality, and compliance adherence, all tied to staffing models and service levels.

Payments and commerce enter secure agent-initiated transaction territory.

Payments are the hardest place to fake progress because production systems enforce security, authorization, and liability rules.

FIS announced an issuer-facing platform, designed with Visa and Mastercard, to help banks support agentic commerce, including enabling issuers to leverage relevant KYA data to reduce fraud and chargebacks.

In parallel, Visa reported progress toward agentic commerce in production contexts through its Intelligent Commerce framework and partner activity, framing secure agent-initiated transactions as a near-term reality.

Why this case matters:

• Agent-initiated commerce requires explicit network-level and issuer-level controls.
• Know your agent concepts create a governance pattern that other industries can reuse, especially where actions carry financial or safety risk.
• Production-grade transaction flows clarify who owns risk, what signals are logged, and how exceptions are handled.

The common pattern across all three deployments

Across freight, member services, and payments, agentic systems succeed when they combine autonomy with operational instrumentation and control surfaces.

CT Labs frames this as three layers that must be designed together:

1. Workflow layer
2. The agent covers a full business outcome, such as booking a load, resolving a benefits question, or completing a purchase authorization.
3. Control layer
4. Guardrails, validation, exception routing, audit logs, and fraud checks are embedded in the execution path.
5. Value layer
6. ROI is measured in metrics that align with enterprise value, such as margin uplift, CSAT improvement, fewer chargebacks, and higher approval quality.

What boards should do now with Agentic ROI Discovery?

CT Labs uses Agentic ROI Discovery as a board-aligned program to move from pilots to verified outcomes. The goal is fast clarity on where agentic workflows create defensible lift and how to govern them at scale.

Step 1: Select workflows that map cleanly to economics

Prioritize workflows with clear unit economics and measurable cycle time.

Examples by function:

• Revenue and commercial operations
• Quoting, contracting, renewals, deal desk triage, pricing approvals
• Operations and supply chain
• Scheduling, dispatch, exception management, compliance checks
• Customer operations
• Member support, claims triage, onboarding, service requests
• Finance and risk
• Transaction review, reconciliation, fraud investigation, dispute handling

The point is alignment between the workflow and KPI boundaries.

Step 2: Assign a single executive owner per workflow.

Agentic execution breaks traditional handoffs, leading to slow decision-making. CT Labs recommends explicit ownership per workflow:

• CRO owns revenue-facing workflows that affect win rates, pricing integrity, and pipeline conversion
• CPO owns product-embedded agentic experiences and the data signals that drive them
• COO owns operational workflows tied to cost, throughput, and service levels
• CISO owns control surfaces tied to identity, fraud, audit, and policy enforcement

Ownership becomes the accountability unit for both lift and risk.

Step 3: Define guardrails that match the action class.

Guardrails should be designed around what the agent can do, not around what the model can generate.

A practical taxonomy:

• Informational actions
• Provide coverage details, status, explanations, and guided flows.
• Transactional actions
• Book, dispatch, authorize, refund, submit, approve.
• Irreversible actions
• Funds movement, contract execution, access escalation, and regulated disclosures

As actions become more consequential, governance shifts toward stronger identity, approval pathways, and auditability, similar to issuer-grade approaches in payments.

Step 4: Build a measurement system that proves lift

For each workflow, define the baseline, deployment period, and steady-state targets.

Metrics that boards typically accept:

• Margin impact per unit, gross margin per transaction, contribution margin
• Cost to serve, touches per case, cycle time, exception rate.
• CSAT, first call resolution, containment, escalation quality
• Fraud losses, chargebacks, false declines, and approval rates

Use a single scorecard per workflow. Treat the agent as a production operator with KPIs and incident reviews.

Talent grids for scaling agentic workflows

As agentic systems enter production, the talent question shifts from model building to operationalization.

CT Labs sees three roles becoming pivotal:

Head of Agent Ops

Owns agent runbooks, exception handling, continuous improvement, and day two operations across agent fleets. This role sits close to the COO level outcomes and works closely with security and platform teams.

CRO and revenue operations leadership with agentic literacy

In logistics, margin lift emerges from autonomous execution across quoting, booking, and dispatch. Revenue leaders who understand how autonomy changes the sales and pricing system become central to capturing.

CPO leadership that treats agents as product surfaces

In member services, voice agents become a customer-facing product experience that requires quality, compliance, and carefully designed escalation processes.

A practical board question is simple. Which executive owns the workflow outcome, and which leader owns the control surface?

What this means for the next 12 months

These deployments show agentic systems operating as continuous execution layers, with measurable lift and embedded controls. Freight proves margin expansion in production operations. Member services prove quality signals at scale. Payments prove the governance path for safe agent-initiated transactions.

CT Labs expects the next wave to focus on governed scaling:

• Portfolio-level selection of agentic workflows that tie directly to enterprise value
• Explicit executive ownership per workflow
• Stronger identity, authorization, and audit standards as action classes expand
• Talent strategies that elevate Agent Ops and agent-aware functional leadership

What is an agentic workflow in an enterprise setting

An agentic workflow is a business process in which an AI system can plan, decide, and execute actions across multiple steps, while operating within defined guardrails and reporting outcomes through operational metrics.

How do boards evaluate agentic ROI beyond pilot success?

Boards evaluate agentic ROI by linking the workflow to financial and operational KPIs, tracking baseline versus steady-state performance, and reviewing risk controls, such as audit logs, validation checks, and exception handling.

What governance controls matter most for agent-initiated transactions

The highest leverage controls include strong agent identity signals, secure authorization pathways, fraud detection, chargeback reduction mechanisms, and auditable decision logs that clarify accountability across issuers, merchants, and networks.