Fractional AI Product Manager · Dallas, TX

Hire an AI product manager in Dallas.

AT&T, Texas Instruments, Kimberly-Clark, and Tenet Healthcare all have AI roadmaps. Most of those roadmaps have features that have been in backlog for six months or more with no production deployment to show for it.

The bottleneck is almost never the engineering team. It is a spec that is not clear enough to start, success criteria that were never defined, or three stakeholders who each have a different understanding of what the feature does.

A fractional AI PM takes ownership of the spec, runs the stakeholder alignment meeting, writes the production criteria, and unblocks engineering. Fixed 8-12 week engagement. The engagement ends when the feature is in production or the handoff to a full-time PM is complete. Pricing is scoped to the feature after the discovery call.

How we work

Tell us about the AI feature stuck in your backlog.

The backlog bottleneck.

A Dallas financial services company has an AI feature on the roadmap for automating credit memo summarization. It was scoped in Q1. Engineering has not started because the spec says "AI will summarize credit memos" and stops there. What model? Deployed where? What does a passing summary look like? Who approves it before it touches a customer record? These questions were never answered.

A telecom vendor in the DFW market wants to build an AI feature for network anomaly detection. The business owner wants real-time alerts. Engineering says real-time is a six-month project. Both sides have been in the same meeting four times without resolution because there is no one whose job it is to make the decision.

At Tenet Healthcare, a digital health initiative has been pending since a vendor demo last year. Three clinical stakeholders want different outputs from the same AI feature. The feature has not moved to engineering because nobody has produced a spec all three can sign off on.

These are not technology problems. They are product problems. Engineering teams in Dallas are not slow. They are waiting on a clear, complete, agreed-upon specification before they can write the first line of code. A fractional AI PM writes that specification and gets the signatures on it.

What the fractional AI PM owns.

Stakeholder alignment meeting

One structured meeting with a written pre-read, a defined decision-owner for each open question, and a published scope statement with names attached. Disagreements that remain after 60 minutes escalate to a named executive with a deadline.

Production-ready feature spec

A written spec with data inputs, expected outputs, edge case handling, and the non-functional requirements engineering needs to start: latency targets, error handling, model selection rationale, and deployment environment.

Defined production criteria

A table with three columns: criterion, measurement method, and threshold. Every row has a number. Business stakeholders and engineering both sign off on it before work starts.

Stakeholder communication cadence

Weekly written status updates to all stakeholders. No surprise scope changes. Every decision logged in writing with the name of who made it and why.

Engineering unblock

When engineering has a question the spec does not answer, the AI PM answers it the same day or schedules the decision meeting. No question sits in Slack for three days waiting for a stakeholder to respond.

Full-time PM handoff

A decision log, the committed spec, a vendor rationale document, a monitoring plan, and a backlog of follow-on items. Every document transfers to the incoming PM at close.

Defining production criteria that both sides agree to.

Criteria are numbers, not descriptions

"The model performs well" is not a production criterion. "Precision above 92% on the held-out test set, latency below 800ms at p95, false-positive rate below 3% on Category A" is. Every criterion has a number. Engineering can test against it. Business can verify it.

Both sides sign before engineering starts

The criteria document requires signatures from the business owner and the engineering lead before work begins. This is not a formality. It prevents the scenario where a feature passes every engineering test and still fails the business demo because the standard was never stated.

Criteria account for Dallas industry context

Healthcare features at Tenet or a Dallas health system need a HIPAA-compliance criterion in the criteria table, not just a performance metric. Financial services features need an audit trail criterion. The criteria template adapts to the regulatory environment of the company.

Criteria travel with the handoff

When the fractional engagement ends, the criteria document goes into the handoff package. The incoming PM or engineering team knows exactly what bar the feature was built to and what to monitor post-launch.

How we work.

Discovery call, written scope, fixed price

One call to understand the feature and the current state of stakeholder alignment. Within 48 hours: a written scope, the list of deliverables, a timeline, and a fixed price. You approve the scope before we start.

Remote with a Dallas-time working rhythm

We work Central Time and are available during Dallas business hours for same-day responses. Shared Slack channel, written updates every Friday, Loom walkthroughs for every major deliverable.

Two-week start, no recruiting cycle

No job posting, no interviewing, no three-month ramp. We can typically start within two weeks of a signed agreement. The feature that has been stuck in backlog since Q1 can be in engineering by next month.

Questions about AI product management in Dallas.

What does a stakeholder alignment meeting for an AI feature look like. How do you resolve disagreements about scope?: The meeting runs on a written document, not slides. Before the call, we circulate a one-page scope statement with four sections: the business problem in one sentence, the proposed output the AI produces, the data inputs required, and the three criteria we will use to declare the feature done. Disagreements about scope almost always come down to different mental models of what the AI actually does. The scope statement forces those models into writing before the call. In the meeting, we work through each section with a defined decision-owner for each line. At the end of the call, we publish an updated scope statement with names attached to each decision. Disagreements that cannot be resolved in 60 minutes are escalated to a named executive with a deadline.
How do you write production criteria for an AI feature that stakeholders in both business and engineering agree to?: Production criteria need to be measurable by both sides without a debate about what counts. For a classification AI feature at a financial services company, that might be: precision above 92% on the held-out test set, latency below 800ms at the 95th percentile, and a false-positive rate on a defined category below 3%. Those are numbers engineering can test against and numbers a business stakeholder can understand. Vague criteria like 'the model performs well' do not work because they shift the definition of 'well' to after deployment. We draft production criteria as a table with three columns: criterion, measurement method, and threshold. Every row needs a number.
What is the right sequence: define success criteria first, or start building and measure afterward?: Define success criteria first. Every time. Starting to build first produces two consistent problems. First, engineering makes architectural decisions that are hard to reverse based on an implied success bar that the business stakeholder never agreed to. Second, when the feature is demoed, the business stakeholder evaluates it against a standard they have in their head but never stated, and the demo fails for reasons engineering could have addressed if they had known the criteria. The process is: write the criteria, get written sign-off from both the business owner and engineering lead, then write the spec, then build. The criteria document takes one day to produce. It saves weeks of rework.
What does the handoff package to a full-time PM look like at the end of a fractional engagement?: The handoff package has five components. First, a decision log covering every scope decision made during the engagement, who made it, and why. Second, the production spec as committed at the start of engineering. Third, a vendor and tooling rationale document if we evaluated any third-party services. Fourth, a monitoring plan covering which metrics to track post-launch and at what thresholds to escalate. Fifth, a backlog of known follow-on work items that did not fit the current engagement scope. The new PM gets everything in writing, not in someone's head. Transition calls are included in the engagement fee.

Get the stuck feature into engineering.

Tell us the feature, how long it has been in backlog, and what the current blocker is. We respond within one business day with a scope and price range. No commitment required.

Hire an AI product manager in Dallas.

The backlog bottleneck.

What the fractional AI PM owns.

Stakeholder alignment meeting

Production-ready feature spec

Defined production criteria

A table with three columns: criterion, measurement method, and threshold. Every row has a number. Business stakeholders and engineering both sign off on it before work starts.

Stakeholder communication cadence

Weekly written status updates to all stakeholders. No surprise scope changes. Every decision logged in writing with the name of who made it and why.

Engineering unblock

Full-time PM handoff

A decision log, the committed spec, a vendor rationale document, a monitoring plan, and a backlog of follow-on items. Every document transfers to the incoming PM at close.

Defining production criteria that both sides agree to.

Criteria are numbers, not descriptions

Both sides sign before engineering starts

Criteria account for Dallas industry context

Criteria travel with the handoff

How we work.

Discovery call, written scope, fixed price

Remote with a Dallas-time working rhythm

We work Central Time and are available during Dallas business hours for same-day responses. Shared Slack channel, written updates every Friday, Loom walkthroughs for every major deliverable.

Two-week start, no recruiting cycle

Questions about AI product management in Dallas.

What does a stakeholder alignment meeting for an AI feature look like. How do you resolve disagreements about scope?

The meeting runs on a written document, not slides. Before the call, we circulate a one-page scope statement with four sections: the business problem in one sentence, the proposed output the AI produces, the data inputs required, and the three criteria we will use to declare the feature done. Disagreements about scope almost always come down to different mental models of what the AI actually does. The scope statement forces those models into writing before the call. In the meeting, we work through each section with a defined decision-owner for each line. At the end of the call, we publish an updated scope statement with names attached to each decision. Disagreements that cannot be resolved in 60 minutes are escalated to a named executive with a deadline.

How do you write production criteria for an AI feature that stakeholders in both business and engineering agree to?

Production criteria need to be measurable by both sides without a debate about what counts. For a classification AI feature at a financial services company, that might be: precision above 92% on the held-out test set, latency below 800ms at the 95th percentile, and a false-positive rate on a defined category below 3%. Those are numbers engineering can test against and numbers a business stakeholder can understand. Vague criteria like 'the model performs well' do not work because they shift the definition of 'well' to after deployment. We draft production criteria as a table with three columns: criterion, measurement method, and threshold. Every row needs a number.

What is the right sequence: define success criteria first, or start building and measure afterward?

Define success criteria first. Every time. Starting to build first produces two consistent problems. First, engineering makes architectural decisions that are hard to reverse based on an implied success bar that the business stakeholder never agreed to. Second, when the feature is demoed, the business stakeholder evaluates it against a standard they have in their head but never stated, and the demo fails for reasons engineering could have addressed if they had known the criteria. The process is: write the criteria, get written sign-off from both the business owner and engineering lead, then write the spec, then build. The criteria document takes one day to produce. It saves weeks of rework.

What does the handoff package to a full-time PM look like at the end of a fractional engagement?

The handoff package has five components. First, a decision log covering every scope decision made during the engagement, who made it, and why. Second, the production spec as committed at the start of engineering. Third, a vendor and tooling rationale document if we evaluated any third-party services. Fourth, a monitoring plan covering which metrics to track post-launch and at what thresholds to escalate. Fifth, a backlog of known follow-on work items that did not fit the current engagement scope. The new PM gets everything in writing, not in someone's head. Transition calls are included in the engagement fee.

Get the stuck feature into engineering.

Tell us the feature, how long it has been in backlog, and what the current blocker is. We respond within one business day with a scope and price range. No commitment required.