New opportunity

Product Manager, AI Solutions

Veralto Global · Wood Dale, United States of America

About this role

The Product Manager, AI Solutions works with Videojet, a Veralto company, to turn AI opportunities into production-ready solutions that reduce costs, improve customer experiences and drive growth. Based in Wood Dale, Illinois, this is a hybrid, full-time role with a 40-hour workweek. The manager guides solutions from identifying a business problem through launch and adoption, partnering with frontline experts, stakeholders and an AI Solutions Engineer.

Responsibilities

  • Discover high-value problems through observation and stakeholder engagement; translate expert knowledge into testable requirements.
  • Define minimum viable products, prioritize a portfolio of AI opportunities, and lead build-versus-buy evaluations and business cases.
  • Partner with engineering throughout delivery, establish success metrics, measure realized value, and drive stakeholder adoption and organizational change.
  • Deliver multiple launched and adopted AI solutions, measurable business outcomes, trusted cross-functional partnerships and clear investment priorities, with frontline teams actively using deployed solutions.

Required qualifications

  • Bachelor's degree and at least 5 years in Product Management, Program Management, Process Improvement or a related field.
  • Demonstrated ownership of a product or AI feature from problem definition through launch; strong stakeholder engagement and workshop facilitation skills.
  • Experience gathering requirements and defining acceptance criteria; working knowledge of modern AI technologies and applications; exposure to AI, analytics, automation or digital transformation initiatives.

Compensation and benefits: The posted US compensation range is $100,000–$140,000 USD per year, with eligibility for bonus pay. Benefits include permissive paid time off, flexible working hours, onboarding and training, career coaching and development, health benefits and a 401(k).

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Product ManagementProgram ManagementProcess ImprovementAI Product ManagementStakeholder EngagementWorkshop FacilitationRequirements GatheringAcceptance CriteriaMVP DefinitionPortfolio PrioritizationBuild-versus-Buy AnalysisBusiness Case DevelopmentSuccess MetricsChange ManagementAI TechnologiesAnalyticsAutomationDigital Transformation

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Get interview-ready for this role

A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Find valuable problems before proposing AI

    The posting calls for observation and stakeholder engagement to identify high-value problems. Prepare to explain how you would distinguish a consequential frontline need from an appealing AI use case.

  • Turn expert knowledge into testable scope

    The role translates expert knowledge into requirements and defines MVPs and acceptance criteria. Prepare an example of moving from a workshop or observation to a bounded, testable product scope.

  • Make clear investment choices

    The manager prioritizes a portfolio of AI opportunities and leads build-versus-buy evaluations and business cases. Prepare to describe the evidence and trade-offs you would use to compare opportunities.

  • Guide solutions through delivery and launch

    The posting expects ownership from problem definition through launch and close partnership with an AI Solutions Engineer. Prepare to explain how you would keep business requirements and engineering decisions aligned as a solution moves toward production.

  • Measure outcomes and secure adoption

    Success is defined by measurable business outcomes and frontline teams actively using deployed solutions, not launches alone. Prepare to distinguish a success metric from a usage metric and explain how you would check realized value.

Likely challenges to think through

  • Choosing where AI is worth the investment

    Possible challenge, not a reported employer problem: promising opportunities may compete for investment, while buying and building carry different costs and constraints. Prepare a decision framework that weighs expected business value, feasibility and the smallest useful MVP.

  • Balancing a small MVP with production readiness

    Possible challenge, not a reported employer problem: narrowing scope can speed delivery but leave important expert judgments or acceptance criteria unresolved. Prepare to show how you would test an MVP with frontline experts and revise requirements with engineering before launch.

  • Turning deployment into sustained use

    Possible challenge, not a reported employer problem: a launched solution may not change workflows or produce measurable savings, experience improvements or growth. Prepare to discuss stakeholder involvement, adoption measures and a plan to compare results with the original business case.

03

Practice your answers

Technical practice

How would you discover a high-value AI opportunity by observing frontline work?

What to coverThe posting calls for observation and stakeholder engagement to identify business problems. Preparation suggestion: explain how you would distinguish a recurring, costly problem from an appealing AI idea, and validate it with the people doing the work.

How would you turn an expert's workflow into testable requirements and acceptance criteria?

What to coverThe role translates expert knowledge into clear requirements and calls for experience defining acceptance criteria. Preparation suggestion: describe how you would capture exceptions and specify observable conditions for acceptance without prescribing a solution too early.

How would you decide whether a problem calls for AI, analytics, automation, or a simpler process change?

What to coverThe posting seeks working knowledge of modern AI applications and exposure to AI, analytics, automation, or digital transformation. Preparation suggestion: compare the value, limitations, and validation needs of each approach rather than assuming AI is the answer.

What would you include in the MVP for a proposed AI solution?

What to coverDefining MVPs is a stated responsibility. Preparation suggestion: explain which user need and outcome you would test first, what you would defer, and what evidence would justify expanding the solution.

How would you prioritize a portfolio of competing AI opportunities?

What to coverThe role calls for clear prioritization of opportunities and investments. Preparation suggestion: show how you would weigh potential business value against effort and uncertainty, and communicate why some opportunities should wait.

How would you lead a build-versus-buy evaluation?

What to coverBuild-versus-buy evaluations are explicitly part of the job. Preparation suggestion: compare the options against requirements, time to value, total cost, fit with existing workflows, and the ability to maintain the solution.

How would you make the business case for an AI solution?

What to coverThe posting asks the manager to lead business cases for opportunities intended to reduce costs, improve customer experiences, or drive growth. Preparation suggestion: state the expected outcome, baseline, investment, assumptions, and key uncertainties so the decision can be revisited.

How would you work with an AI Solutions Engineer to move a solution from concept to production?

What to coverThe role partners closely with engineering throughout delivery and aims to launch production-ready solutions. Preparation suggestion: discuss how you would resolve scope and quality trade-offs, check the solution against acceptance criteria, and address issues found before launch.

How would you determine whether a launched solution delivered measurable value?

What to coverEstablishing success metrics and measuring realized value are stated responsibilities. Preparation suggestion: distinguish usage from business outcomes, set a baseline, and explain how you would investigate a gap between expected and observed results.

How would you tell whether a deployed solution is being adopted by frontline teams?

What to coverThe posting defines success partly as frontline teams actively using deployed solutions. Preparation suggestion: combine evidence of actual use with frontline feedback, then identify changes to the workflow or solution if adoption is weak.

Behavioural practice

Tell me about a time you facilitated a workshop with stakeholders who disagreed on the problem.

What to coverStakeholder engagement and workshop facilitation are stated qualifications. For practice, use situation, action, and result to show how you surfaced differing views, reached a useful problem definition, and documented the outcome.

Describe a product or feature you owned from problem definition through launch.

What to coverEnd-to-end product or AI feature ownership is a stated qualification. For practice, use situation, action, and result to clarify your decisions, how you worked with delivery partners, and what happened at launch.

Tell me about a time you changed an investment priority when new evidence emerged.

What to coverThe role prioritizes a portfolio of opportunities and investments. As a preparation suggestion, use situation, action, and result to explain the evidence, the trade-off you made, and how you communicated the decision.

Describe a time you built trust with frontline experts while changing how work was done.

What to coverThe posting emphasizes trusted partnerships, frontline use, and organizational change. For practice, use situation, action, and result to show how you listened, addressed concerns, and learned whether the change took hold.

Tell me about a launched initiative whose results differed from expectations.

What to coverThe role measures realized value, not just launches. As a preparation suggestion, use situation, action, and result to explain how you identified the gap, investigated its cause, and adjusted your approach.

02

Sharpen your knowledge

Topics to prepare

  • Business value and problem selection

    The posting calls for finding high-value problems and delivering outcomes that reduce cost, improve customer experience, or drive growth. Revision exercise: for a hypothetical frontline problem, distinguish the observed problem from a proposed AI solution and state how solving it could create value.

  • AI application fit

    Working knowledge of modern AI technologies and applications is required. Revision exercise: compare two possible AI approaches for a hypothetical business problem, including where a non-AI approach might suffice. This comparison is preparation, not a specified employer framework.

  • MVP and portfolio decisions

    The role defines MVPs and prioritizes a portfolio of AI opportunities. Revision exercise: take three hypothetical opportunities, identify the smallest useful outcome for each, and explain which should be pursued first and why.

  • Build-versus-buy economics

    The posting explicitly assigns build-versus-buy evaluations and business cases. Revision exercise: outline the costs, expected benefits, uncertainties, and decision criteria for one hypothetical solution without assuming a particular vendor or tool.

  • Realized value and adoption

    Success means measurable business outcomes and frontline teams actively using launched solutions. Revision exercise: distinguish a launch milestone, a usage measure, and a business-outcome measure, then explain how each would be checked after release.

Skills to revise

  • Facilitate problem discovery

    Observation, stakeholder engagement, and workshop facilitation are stated in the posting. Practice exercise: draft a short workshop agenda and five questions for frontline experts that uncover current work, pain points, and exceptions without presuming AI is the answer.

  • Write testable requirements

    The role translates expert knowledge into requirements and defines acceptance criteria. Practice exercise: turn a hypothetical frontline request into three requirements, each with an observable acceptance criterion and a question to resolve ambiguity.

  • Present an investment recommendation

    The manager is expected to make business cases and establish clear investment priorities. Practice exercise: deliver a two-minute recommendation comparing two hypothetical opportunities, including the expected outcome, key uncertainty, and reason for the priority order.

  • Coordinate delivery with engineering

    The posting calls for close partnership with an AI Solutions Engineer throughout delivery and ownership through launch. Practice exercise: rehearse a handoff conversation covering the problem, MVP boundary, acceptance criteria, open decisions, and launch readiness.

  • Plan measurement and adoption

    The role establishes success metrics, measures realized value, and drives stakeholder adoption. Practice exercise: draft a brief post-launch plan naming a baseline, an outcome measure, a frontline-usage measure, and how stakeholder feedback would inform the next decision.

04

Your preparation checklist

  • Frame the business context

    Summarize Videojet’s product identification business and the posting’s stated AI goals: reducing cost, improving customer experience and driving growth. Prepare to explain why measurable outcomes matter more than deploying a tool.

  • Build a launch-to-adoption walkthrough

    Prepare one relevant product or AI-feature example, if available, covering problem definition, your ownership, stakeholders, requirements, engineering collaboration, launch and observed results. Distinguish measured results from expectations.

  • Rehearse a discovery workshop

    The role calls for observation, stakeholder engagement and workshop facilitation. Draft questions for frontline experts, a short workshop agenda and a sample problem statement that turns expert knowledge into testable requirements.

  • Scope a hypothetical AI MVP

    As interview practice, choose a plausible workflow in Videojet’s stated business without claiming it is an actual company project. Produce an MVP brief with users, boundaries, acceptance criteria and how you would work with an AI Solutions Engineer to test feasibility.

  • Prioritize opportunities and evaluate sourcing

    Create a sample comparison of two hypothetical AI opportunities using business value, effort and uncertainty. Add a build-versus-buy decision outline and a brief business case; the posting explicitly assigns the manager these decisions.

  • Plan measurement and adoption

    Draft a scorecard with a baseline, solution-use measures and business-outcome measures, plus a way to check realized value after launch. Outline how you would gather frontline feedback and address adoption barriers.

  • Prepare focused employer questions

    Ask how the AI Solutions team selects opportunities, divides work between the product manager and AI Solutions Engineer, measures realized value, and supports frontline adoption. These questions follow the posting; do not assume the answers.

AI-generated from the job information · Prepared Oct 1, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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