Select the business use case
Map the workflow and baseline its cost, delay, quality, and volume. Assess value, feasibility, data access, and the consequences of error.
The short answer
Pursuing Excellence helps operating businesses identify where AI can improve a workflow, test the business case, and implement a controlled pilot. The approach connects use-case selection, data readiness, human oversight, evaluation, adoption, and ongoing operating ownership.
When this
becomes a priority.
Start with the work: an expensive handoff, a reporting bottleneck, a recurring exception, or a decision that needs better information. Then determine whether AI fits, what it needs to access, how its output will be checked, and who remains accountable.
- AI experiments are multiplying without shared priorities or ownership.
- Leadership wants a practical roadmap tied to measurable business value.
- A promising workflow needs a controlled pilot and an adoption plan.
- Agents can act, but approval, monitoring, or rollback controls are unclear.
From the facts to the work
What the engagement
can cover.
The scope follows the business need. These workstreams define the decisions, controls, and delivery responsibilities that matter.
Design the operating controls
Define an owner, permitted actions, autonomy boundaries, human approvals, data restrictions, escalation triggers, and a fallback process.
Implement and evaluate a pilot
Coordinate a bounded implementation using approved systems. Test representative cases and failure conditions against explicit acceptance criteria before expanding access or autonomy.
Embed adoption and oversight
Train users, document the workflow, monitor performance, and review whether the change produces the intended result. Scale only after the pilot supports the case.
A concrete result
What your team
leaves with.
Practical decisions and usable operating tools, with owners, measures, and a path into execution.
- Prioritized AI opportunity map
- Readiness and workflow assessment
- Pilot charter and measurable business case
- Autonomy, approval, and escalation design
- Evaluation and fallback plan
- Adoption roadmap and governance cadence
The experience behind the work
Grounded in
operating reality.
The PX Agent Governance framework applies six practical controls: a named owner, defined autonomy, performance measures, approval and escalation paths, a tested fallback, and regular review. This service builds on Warren’s operating and transformation experience.
Explore Warren’s experienceResults reflect Warren's prior executive roles and the teams involved.
Questions leaders ask
Before you
make the call.
What is a practical AI strategy for a middle-market business?
It identifies a small set of valuable workflows, evaluates the readiness of data and systems, defines acceptable risk and human oversight, and sequences pilots with measurable success criteria. The result should guide investment and implementation decisions.
How do you measure an AI pilot?
Compare the pilot with a baseline for time, cost, quality, and exception rates. Include human review effort and the cost of errors. Define acceptance criteria before testing, and monitor the process after launch to confirm the benefit holds.
What does AI agent governance mean?
It means defining what an agent may do, the data it may use, who owns it, when a person must approve an action, how exceptions are escalated, and how work can be stopped or reversed. These controls are part of the operating design.
Does PX build every model or custom integration?
PX leads business design, use-case selection, implementation governance, evaluation, and adoption. Configuration, model engineering, security review, and custom integrations are scoped with the internal technology team or appropriate delivery specialists.
Start with a conversation
Identify your highest-value AI use case.
Start with a focused 30-minute conversation about your situation, the value at stake, and the right next step.