New opportunity

Manager, Applied AI

Invenergy LLC · Chicago, United States of America

About this role

The Manager, Applied AI works within Enterprise Analytics & AI to design and deploy practical enterprise AI solutions that deliver measurable business impact. The role partners with teams to integrate AI into their workflows responsibly, serves as a subject-matter expert on enterprise AI platforms, and manages one or more employees supporting AI adoption.

Responsibilities

Design, build, deploy, and manage low-code AI agents and custom solutions, including multi-agent systems and advanced process automation. Work directly with business teams to understand workflows, identify opportunities, and co-develop solutions.
Maintain expertise in enterprise AI platform capabilities and limitations. Evaluate third-party AI applications for organizational fit, functionality, security, and responsible AI considerations, and recommend options to Business Operations.
Analyze AI usage and adoption data to identify gaps and improve impact. Partner with Talent Development on employee training and guidelines that support effective, responsible, self-service use of AI.
Track emerging capabilities and platform updates; collaborate with data scientists, data engineers, and analytics teams to align end-user solutions with broader AI initiatives.

Required qualifications

A bachelor’s degree in a technical or quantitative field, such as computer science, information systems, engineering, or data science; 8+ years of industry or technology-function experience with significant achievements and upward growth; and experience designing, building, or managing AI agents or similar solutions. Candidates need strong familiarity with Microsoft 365 and AI-enabled productivity tools, experience leading or mentoring others or readiness for people leadership, and the ability to explain technical concepts to nontechnical audiences and senior leadership. Independent time and project management, cross-functional collaboration, curiosity, and continuous learning are also required.

Preferred qualifications

Technology-team management; hands-on Microsoft Copilot Studio experience; enterprise technology adoption or change initiatives; technical training development or delivery; responsible AI, governance, security, or compliance knowledge; collaboration with data science or engineering teams; exposure to Microsoft Power Platform, Azure AI services, or Databricks; and Python or SQL proficiency.

The role follows a hybrid Chicago schedule: in office Monday through Thursday and remote Friday. Base pay is USD 134,000–155,000 annually, with a stated bonus of 25%–40%. Benefits include medical, dental and vision insurance, a 401(k), and paid time off.

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

Skills for this role

AI agentsMulti-agent systemsLow-code AI developmentProcess automationMicrosoft 365Microsoft Copilot StudioMicrosoft Power PlatformAzure AI servicesDatabricksPythonSQLResponsible AIAI governanceAI securityTechnology adoptionTechnical trainingProject managementPeople managementStakeholder communication

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Deliver useful AI solutions with measurable impact
The role designs, deploys, and manages low-code agents and custom solutions, including multi-agent systems and process automation. It works directly with business teams to understand workflows and co-develop solutions for specific needs.
Guide enterprise platform and software choices
The manager is expected to know enterprise AI platforms’ capabilities and limitations, track updates, and evaluate third-party applications for fit, functionality, security, and responsible AI considerations before recommending options to Business Operations.
Improve responsible adoption
The posting calls for analyzing AI usage and adoption data, identifying gaps, and partnering with Talent Development on training and guidelines that support effective, responsible self-service use.
Connect end-user work to broader AI initiatives
The role collaborates with data scientists, data engineers, and analytics teams to align user-facing solutions with wider initiatives, while explaining technical concepts to nontechnical audiences and senior leadership.
Lead delivery and adoption support
The manager oversees one or more employees supporting AI adoption and must manage multiple initiatives independently. People leadership and prioritization sit alongside hands-on solution work.

Likely challenges to think through

Choosing the right level of automation
Likely preparation hypothesis: complex agents may offer more capability than a workflow needs, while simpler approaches may fall short. Prepare to explain how you would scope a use case, compare low-code and more complex options, and define evidence of business impact before scaling it.
Balancing utility with responsible use
Likely preparation hypothesis: an attractive platform feature or third-party application may raise security or responsible AI concerns. Prepare a decision framework that weighs workflow value, platform limitations, organizational fit, and risk without assuming a particular Invenergy policy.
Turning access into effective self-service
Likely preparation hypothesis: usage alone may not show whether AI is helping employees. Prepare to discuss how adoption data and employee feedback could reveal gaps, inform training and guidelines, and distinguish meaningful impact from tool activity.
Protecting focus across hands-on and management work
Likely preparation hypothesis: building solutions, advising teams, tracking platform changes, and managing adoption staff may compete for time. Prepare an example of how you would prioritize concurrent work, delegate appropriately, and keep business and technical partners aligned.

Practice your answers

Technical practice

How would you identify a business workflow where an AI solution could deliver measurable value?

What to coverThe posting calls for working directly with business teams to understand workflows and identify opportunities. Suggested answer focus (inference): describe how you would map the current process, distinguish suitable tasks from poor fits, and agree on a measure of impact.

How would you design, deploy, and manage a low-code AI agent for an enterprise workflow?

What to coverThe role includes building and managing low-code agents with business teams. Suggested answer focus (inference): cover the agent’s purpose, testing with users, deployment, and how you would monitor and improve it after launch.

When would you choose a multi-agent system or advanced process automation over a simpler solution?

What to coverThe posting names both as possible custom solutions, not as defaults for every use case. Suggested answer focus (inference): compare task complexity, handoffs, reliability, and maintenance burden before justifying the added complexity.

How would you assess whether Microsoft Copilot Studio fits a proposed agent?

What to coverHands-on Copilot Studio experience is preferred, while familiarity with Microsoft 365 and AI-enabled productivity tools is required. Suggested answer focus (inference): explain how you would check the needed capabilities and limitations, and compare a low-code approach with another solution where appropriate.

How would you evaluate a third-party AI application for Business Operations?

What to coverThe role oversees evaluations covering organizational fit, capability, security, and responsible AI. Suggested answer focus (inference): describe a consistent comparison against the business use case, key risks, and the evidence behind a recommendation.

What responsible-AI considerations would shape an employee-facing AI solution?

What to coverThe posting emphasizes responsible integration into workflows and identifies governance, security, and compliance knowledge as preferred. Suggested answer focus (inference): discuss appropriate use, handling sensitive information, human review, and clear guidance for users without assuming a particular company policy.

How would you use AI usage and adoption data to improve business impact?

What to coverThe role monitors adoption data to find trends, gaps, and opportunities for targeted action. Suggested answer focus (inference): distinguish usage from useful outcomes, explain how you would investigate a gap, and propose a way to assess whether an intervention worked.

How would you keep enterprise AI solutions aligned with changing platform capabilities?

What to coverThe posting calls for expertise in platform capabilities and limitations and for tracking emerging features. Suggested answer focus (inference): explain how you would validate a new capability against a real need, weigh migration or maintenance costs, and decide whether to recommend it.

How would you design training and guidelines that support responsible self-service AI use?

What to coverThe role partners with Talent Development to create, deliver, and improve employee training and guidelines. Suggested answer focus (inference): address different user needs, practical workflow examples, responsible-use boundaries, and feedback that would inform revisions.

How would you align an end-user AI solution with broader analytics and AI initiatives?

What to coverThe posting calls for collaboration with data scientists, data engineers, and analytics teams. Suggested answer focus (inference): explain when to involve those teams, how to define responsibilities, and how to avoid building an isolated solution that conflicts with broader work.

Behavioural practice

Tell me about a time you led or mentored someone delivering a technical initiative.

What to coverThe role manages one or more employees supporting AI adoption; prior leadership, mentoring, or management is required. Suggested answer focus (inference): use a real situation, your actions in setting direction and supporting the person, and the resulting outcome.

Describe a time you co-developed a solution with stakeholders who had different priorities.

What to coverThe posting emphasizes collaboration across business, technical, and enablement teams. Suggested answer focus (inference): give the situation, how you surfaced and resolved competing needs, and the result for the shared work.

Tell me about a time you explained a technical recommendation to nontechnical senior leaders.

What to coverClear communication with nontechnical audiences, including senior leadership, is required. Suggested answer focus (inference): describe the decision at stake, how you explained benefits and limitations, and what resulted.

Describe a time you helped people adopt a new technology or way of working.

What to coverThe role supports employee AI adoption, self-sufficiency, training, and guidelines. Suggested answer focus (inference): use a real situation, the actions you took to address user needs or concerns, and evidence of the outcome.

Tell me about a time you had to prioritize several initiatives independently.

What to coverThe posting requires independent time and project management across multiple initiatives. Suggested answer focus (inference): explain the situation, how you made and communicated trade-offs, and the result.

Sharpen your knowledge

Topics to prepare

Agent and automation design
Review when a low-code agent, multi-agent system, or process automation fits a workflow, including how to handle limitations and failures. Practice suggestion: compare these approaches for one hypothetical business workflow and explain your choice. The posting explicitly calls for designing and managing these solutions.
Enterprise AI platform fit
Review Microsoft 365 and AI-enabled productivity capabilities and limitations; Copilot Studio is preferred, not required. Practice suggestion: map a proposed employee use case to available capabilities and identify what you would verify before deployment. The role serves as a platform subject-matter expert.
Responsible AI and application evaluation
Review organizational fit, functionality, security, and responsible AI as evaluation dimensions. Practice suggestion: define a short set of review questions and trade-offs for a hypothetical third-party AI application. The posting assigns oversight of these evaluations and recommendations to Business Operations.
Adoption and measurable impact
Review how usage trends, adoption gaps, and business outcomes differ. Practice suggestion: define measures for a hypothetical rollout and an action you would consider if usage rose without clear impact. The role analyzes adoption data and seeks measurable business impact.
Enablement and people leadership
Review how employee training, self-service guidance, cross-functional coordination, and mentoring support adoption. Practice suggestion: outline how you would divide responsibilities between a manager, an adoption-focused employee, and Talent Development. The posting includes managing one or more employees and partnering on training.

Skills to revise

Build an agent prototype
Practice suggestion: if Copilot Studio is available, prototype a small low-code agent for a hypothetical workflow; otherwise, sketch its steps, user handoffs, tests, and deployment considerations. Agent experience is required, while hands-on Copilot Studio experience is preferred.
Run workflow discovery
Practice suggestion: rehearse a 15-minute stakeholder conversation that identifies current steps, pain points, users, and criteria for a useful solution; summarize the opportunity in plain language. The role leads one-on-one and small-group engagement with business teams.
Assess a third-party AI application
Practice suggestion: draft a concise comparison of two hypothetical options against fit, capability, security, and responsible AI, noting unresolved questions before a recommendation. This mirrors an explicit responsibility, not a stated company evaluation process.
Interpret adoption data
Practice suggestion: use a small fictional table of usage by team and time period to identify a gap, propose a follow-up question, and explain a targeted action. The posting requires analyzing usage and adoption data; it does not specify an analysis tool.
Deliver an employee-facing explanation
Practice suggestion: give a three-minute, jargon-free explanation of an AI tool's useful tasks, limitations, and responsible-use guidance, then adapt it for senior leadership. The role supports training and requires clear communication with nontechnical audiences and leaders.
Prioritize and delegate concurrent work
Practice suggestion: draft a one-page plan for two hypothetical AI opportunities, with priorities, milestones, stakeholder check-ins, and a task to delegate or mentor. The posting requires independent management of multiple initiatives and management of adoption staff.

Your preparation checklist

Map the required qualifications to evidence
Prepare a one-page inventory of relevant education, career progression, AI-agent work, Microsoft 365 and AI-enabled productivity tools, and examples of communicating with nontechnical leaders. Note gaps rather than inventing examples.
Build an AI-agent project walkthrough
If you have a relevant project, prepare a concise account of the business workflow, stakeholder input, agent design, deployment, responsible-use controls, and measured results. Distinguish your contribution from the team’s.
Practice a low-code solution design
For a hypothetical business workflow, sketch an agent or process-automation design, including where a multi-agent approach would or would not help, platform limitations, human oversight, and measures of business impact. Copilot Studio experience is preferred, not required.
Draft a third-party AI evaluation matrix
Create a sample comparison covering organizational fit, functionality, security, and responsible AI considerations, ending with a recommendation and open questions for Business Operations. This is practice, not an Invenergy procurement process.
Outline an adoption and training plan
Prepare a sample plan showing which usage data you would review, how you would identify adoption gaps, and what employee guidance or training you might develop with Talent Development. Include a way to assess whether it improves effective use.
Prepare leadership and collaboration examples
Select examples, if available, of mentoring or managing others, balancing simultaneous initiatives, and working across business and technical teams. Practice explaining one decision in terms suitable for senior leadership.
Write questions for the interviewers
Ask which enterprise AI platforms are in use, how business teams and Enterprise Analytics & AI coordinate on solutions, what adoption measures matter most, and what the employee-management scope would be.

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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