AI Product Manager: Chicago

Hire an AI product manager in Chicago.

Chicago's large enterprises (United Airlines, Boeing, Walgreens, Discover Financial) have AI prototypes that work in demo and never ship. Engineering says the prototype is ready. The product team has not written a failure mode spec, defined an accuracy threshold, or gotten legal to sign off on the output format. The feature stays in prototype.

These blockers are product management problems, not engineering problems. A fractional AI PM takes ownership of the outstanding blockers and gets the feature to production. Fixed engagement: 8–12 weeks, specific deliverables.

How we work

Tell us about the AI feature that is stuck.

Why AI features stall after engineering says done.

When engineering says a feature is done, they mean the code works as specified. The problem is that the spec did not include several things that are required before the feature can ship in a regulated enterprise environment.

The accuracy threshold: what percentage of outputs need to be correct before the feature touches production users? If nobody wrote a number into the spec, engineering has no pass criterion. They built a working prototype. Whether it is production-ready is a product question.

The failure mode spec: what does the feature do when the model returns a low-confidence output, an API times out, or the input is malformed? At a company like Walgreens, a pharmacy workflow tool that handles errors silently is a patient safety issue, not a UX issue.

Legal sign-off: what disclaimer language appears with AI-generated content? What is the output format specification that legal reviewed? At Discover Financial, the legal team needs a written output spec before they approve the feature. A demo is not a specification.

What the fractional AI PM takes ownership of.

The specific blockers that keep AI features from shipping at Chicago enterprises. Each one needs a product owner, not a project tracker.

Failure mode specification

A written document covering all four failure mode categories: input failures, model failures, downstream API failures, and output validation failures. Each entry specifies the trigger condition, the fallback behavior, and the user-facing result.

Accuracy threshold and test set

A pass/fail criterion based on an error consequence analysis. The threshold is calibrated to the cost of an incorrect output in the specific workflow. A labeled test set of at least 50 examples that measures against the threshold.

Legal output format review

A written output format specification that legal can review without attending a demo. Includes the full set of possible output types, the disclaimer language for each, and the override path for high-risk outputs.

Go-live decision ownership

A pass/fail checklist for every production readiness criterion. The AI PM owns the go-live decision, when every item on the checklist is green, the feature ships. No more circular conversations about whether the feature is ready.

What the engagement produces.

Four deliverables. Each is a document your team can work from the day after the engagement closes.

Failure mode specification

All failure categories documented with trigger conditions, fallback behaviors, and user-facing results. Reviewed by engineering, legal, and product before sign-off.

Accuracy threshold document

Error consequence analysis, pass/fail threshold, labeled test set specification, and measurement protocol. The threshold is the definition of done for the AI feature.

Legal output format brief

Written output format specification with disclaimer language for each output type. Formatted for legal review, not for engineering.

Go-live checklist

Every production readiness criterion in a pass/fail format. The checklist replaces the circular conversation about whether the feature is ready.

How we work.

1
Discovery call (1 hour)
We map the current state of the stuck feature: what it does, which blockers are active, who the relevant stakeholders are, and what the approval sequence needs to be. We quote a fixed price at the end of this call.
2
Blocker resolution sprint (weeks 1–6)
We interview engineering, legal, and product. We write the failure mode spec, accuracy threshold document, and legal output format brief. We circulate for review and track revisions.
3
Go-live ownership (weeks 7–12)
We own the go-live checklist, manage the final approval sequence, and produce the monitoring spec. The engagement ends when the feature is in production.

Questions from Chicago clients.

What does a failure mode spec look like for an AI feature. What failure modes need to be documented?: A failure mode spec covers four categories. First, input failures: what happens when the user submits empty input, malformed input, or input in an unexpected language. Second, model failures: what the feature does when the model returns a malformed response, times out, or returns a confidence score below the threshold. Third, downstream failures: what happens when the feature depends on an external API (a database lookup, a pricing engine, a data feed) that returns an error or slow response. Fourth, output failures: what the feature displays when the model output fails a validation check. Each failure mode entry specifies the trigger condition, the fallback behavior, and whether the user is shown an error or a degraded output. Features that ship without this spec handle edge cases inconsistently across the codebase.
How do you get legal to sign off on AI output formats in a regulated industry like financial services?: Legal teams at Discover Financial and similar firms approve AI output formats when three conditions are met. First, the output format is documented: legal needs to see a written specification of what the feature can and cannot produce, not a live demo. Second, the disclaimer language is agreed in writing before the feature ships: what caveat appears with AI-generated content, what the content cannot be used for, and what the override path is. Third, the feature has a human review path for cases above a risk threshold. The AI PM writes the output format specification and the disclaimer brief, then runs it through legal review before engineering finalizes the UI. This takes three to four weeks when done in parallel with development. Done after development, it can delay launch by two months.
What accuracy threshold is good enough for an AI feature to ship. How do you determine this?: There is no universal threshold. The threshold depends on the consequence of an error. A search ranking feature that occasionally surfaces a slightly less relevant result can ship at 75% rubric pass rate. A risk classification feature at a company like Discover Financial that influences credit decisions needs 95%+ on a labeled dataset before it touches production traffic. The AI PM determines the threshold by working through the error consequence analysis: what is the worst realistic outcome when the model is wrong, who is affected, and what is the cost of that outcome. The threshold is the minimum quality where the expected benefit of the feature exceeds the expected cost of its errors. This analysis belongs in the product spec, not in a post-launch retrospective.
How is a fractional AI PM engagement different from a project manager who manages the AI project?: A project manager tracks tasks, manages timelines, and escalates blockers. A product manager decides what gets built, writes the spec, and owns the quality definition. For AI features specifically, the product function includes three things a project manager does not do: writing the evaluation criteria that define when the feature is done, making the build/buy/fine-tune decision, and owning the failure mode spec. At Boeing and United Airlines, the difference matters because the AI features that stall after engineering says done are not stalling because of project management failures. They are stalling because nobody wrote the accuracy threshold or the legal output spec. Project management cannot fix a missing product spec.

The blockers have answers.

Describe the AI feature that is stuck and the specific blockers preventing launch. We'll reply within one business day with a rough scope and price range.

Hire an AI product manager in Chicago.

Why AI features stall after engineering says done.

What the fractional AI PM takes ownership of.

The specific blockers that keep AI features from shipping at Chicago enterprises. Each one needs a product owner, not a project tracker.

Failure mode specification

Accuracy threshold and test set

Legal output format review

Go-live decision ownership

What the engagement produces.

Four deliverables. Each is a document your team can work from the day after the engagement closes.

Failure mode specification

All failure categories documented with trigger conditions, fallback behaviors, and user-facing results. Reviewed by engineering, legal, and product before sign-off.

Accuracy threshold document

Error consequence analysis, pass/fail threshold, labeled test set specification, and measurement protocol. The threshold is the definition of done for the AI feature.

Legal output format brief

Written output format specification with disclaimer language for each output type. Formatted for legal review, not for engineering.

Go-live checklist

Every production readiness criterion in a pass/fail format. The checklist replaces the circular conversation about whether the feature is ready.

How we work.

Discovery call (1 hour)

We map the current state of the stuck feature: what it does, which blockers are active, who the relevant stakeholders are, and what the approval sequence needs to be. We quote a fixed price at the end of this call.

Blocker resolution sprint (weeks 1–6)

We interview engineering, legal, and product. We write the failure mode spec, accuracy threshold document, and legal output format brief. We circulate for review and track revisions.

Go-live ownership (weeks 7–12)

We own the go-live checklist, manage the final approval sequence, and produce the monitoring spec. The engagement ends when the feature is in production.

Questions from Chicago clients.

What does a failure mode spec look like for an AI feature. What failure modes need to be documented?

A failure mode spec covers four categories. First, input failures: what happens when the user submits empty input, malformed input, or input in an unexpected language. Second, model failures: what the feature does when the model returns a malformed response, times out, or returns a confidence score below the threshold. Third, downstream failures: what happens when the feature depends on an external API (a database lookup, a pricing engine, a data feed) that returns an error or slow response. Fourth, output failures: what the feature displays when the model output fails a validation check. Each failure mode entry specifies the trigger condition, the fallback behavior, and whether the user is shown an error or a degraded output. Features that ship without this spec handle edge cases inconsistently across the codebase.

How do you get legal to sign off on AI output formats in a regulated industry like financial services?

Legal teams at Discover Financial and similar firms approve AI output formats when three conditions are met. First, the output format is documented: legal needs to see a written specification of what the feature can and cannot produce, not a live demo. Second, the disclaimer language is agreed in writing before the feature ships: what caveat appears with AI-generated content, what the content cannot be used for, and what the override path is. Third, the feature has a human review path for cases above a risk threshold. The AI PM writes the output format specification and the disclaimer brief, then runs it through legal review before engineering finalizes the UI. This takes three to four weeks when done in parallel with development. Done after development, it can delay launch by two months.

What accuracy threshold is good enough for an AI feature to ship. How do you determine this?

There is no universal threshold. The threshold depends on the consequence of an error. A search ranking feature that occasionally surfaces a slightly less relevant result can ship at 75% rubric pass rate. A risk classification feature at a company like Discover Financial that influences credit decisions needs 95%+ on a labeled dataset before it touches production traffic. The AI PM determines the threshold by working through the error consequence analysis: what is the worst realistic outcome when the model is wrong, who is affected, and what is the cost of that outcome. The threshold is the minimum quality where the expected benefit of the feature exceeds the expected cost of its errors. This analysis belongs in the product spec, not in a post-launch retrospective.

How is a fractional AI PM engagement different from a project manager who manages the AI project?

A project manager tracks tasks, manages timelines, and escalates blockers. A product manager decides what gets built, writes the spec, and owns the quality definition. For AI features specifically, the product function includes three things a project manager does not do: writing the evaluation criteria that define when the feature is done, making the build/buy/fine-tune decision, and owning the failure mode spec. At Boeing and United Airlines, the difference matters because the AI features that stall after engineering says done are not stalling because of project management failures. They are stalling because nobody wrote the accuracy threshold or the legal output spec. Project management cannot fix a missing product spec.

The blockers have answers.

Describe the AI feature that is stuck and the specific blockers preventing launch. We'll reply within one business day with a rough scope and price range.