New opportunity

AI Enablement Partner

Janus Henderson Investors · New York, United States

About this role

Janus Henderson Investors is seeking an AI Enablement Partner for its firm-wide AI transformation. Embedded within a business unit and rotating across departments over time, the partner will identify valuable AI opportunities, build practical solutions and help teams adopt them. The role sits in the Innovation team and works within the AI Academy framework, primarily using the Microsoft Copilot ecosystem while evaluating other tools as they emerge.

Responsibilities

Work with business teams to understand workflows and pain points, prioritise use cases, define measures of success and decide whether delivery belongs with business users, AI Business Partners, automation teams, Technology or an external partner.
Prototype and build solutions using Copilot Studio, Power Automate and Microsoft 365, including agents and automations that can scale reliably. Research alternatives such as Claude desktop, agentic AI platforms and custom LLM applications.
Link initiatives to measurable benefits, including hours saved, quality improvements, risk reduction and revenue growth; track and report outcomes.
Coach users in prompt engineering, AI tool use and solution design. Mentor departmental AI champions and advanced users so they can sustain adoption. Document reusable prompts, patterns, agents and lessons learned for use across departments.
Coordinate with Technology and control partners to support secure, privacy-conscious and responsible adoption.

Required capabilities include hands-on experience building agents or automations with Copilot Studio, Power Platform or similar low-code/no-code AI tools; experience evaluating multiple AI or GenAI platforms; business analysis, communication, stakeholder management and change-management skills; knowledge of prompt engineering and GenAI application design; and clear documentation. The role calls for independent work in ambiguous business settings.

Preferred qualifications include financial services or asset-management experience; familiarity with SharePoint, Teams, Dataverse and Azure AI services; ROI tracking, data governance and responsible AI knowledge; experience coaching non-technical users; product-management sensibility; and Microsoft AI, Power Platform or Copilot Studio certifications. The role has no supervisory responsibilities. It is based in New York with hybrid working. The estimated base salary range is $110,000–$200,000; the position may also be eligible for a discretionary annual bonus. Benefits described include leave, professional development, retirement and health and wellbeing offerings.

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

Skills for this role

Microsoft Copilot StudioPower AutomatePower AppsMicrosoft 365Generative AIPrompt engineeringAI agent developmentLow-code developmentBusiness analysisBusiness process mappingChange managementStakeholder managementSolution designAI adoptionROI measurementDocumentationCoachingSharePointMicrosoft TeamsDataverseAzure AI servicesData governanceResponsible AIProduct management

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

Find and route valuable use cases
The role embeds with business teams to map workflows, prioritise pain points and decide whether delivery belongs with users, AI Business Partners, automation teams, Technology or an external partner.
Turn ideas into usable AI solutions
The posting calls for hands-on prototyping and reliable agents or automations using Copilot Studio, Power Automate and Microsoft 365, while evaluating other AI tools when they may fit better.
Make benefits measurable
Initiatives need practical success measures, value cases and outcome tracking tied to measures such as time saved, quality, risk reduction or revenue growth.
Build adoption that lasts
The partner coaches business users, mentors departmental AI champions and advanced users, and helps teams sustain AI use after the initial enablement phase.
Enable safe reuse across departments
The role coordinates with Technology and control partners on security, privacy and responsible adoption, then documents prompts, agents and lessons clearly enough to adapt during departmental rotations.

Likely challenges to think through

Choosing AI only where it adds value
Preparation hypothesis: teams may bring more ideas than can be pursued, and some may need process improvement rather than AI. Prepare a way to compare workflow pain, expected benefit and delivery ownership before committing to a build.
Balancing prototype speed with safe scaling
Preparation hypothesis: a quick agent may demonstrate value before its reliability, data handling or ownership is settled. Prepare to explain how an experiment would be tested and coordinated with Technology and control partners before wider adoption.
Showing impact beyond usage
Preparation hypothesis: adoption counts alone may not establish business value. Prepare to define a baseline, choose a relevant outcome measure and distinguish estimated benefits from tracked results.
Leaving capability behind when rotating
Preparation hypothesis: solutions may depend too heavily on the embedded partner unless local users can maintain them. Prepare an approach to coaching champions and documenting reusable patterns without assuming every department has the same workflow.

Practice your answers

Technical practice

How would you identify AI opportunities in an unfamiliar business unit?

What to coverSuggested preparation: Explain how you would map workflows with the people doing the work, identify pain points and establish a baseline. Distinguish opportunities suited to AI from problems better addressed by changing the process.

How would you prioritise a use case and choose its delivery owner?

What to coverSuggested preparation: Compare expected value, feasibility, risk, adoption effort and potential to scale. Explain how those factors would inform delivery by business users, an AI Business Partner, an automation team, Technology or an external partner.

How would you turn a workflow problem into a Copilot Studio agent or Power Automate solution?

What to coverSuggested preparation: Describe the user task, inputs, multi-step flow, hand-offs and a small prototype that can be tested with users. Address what would need to change before the agent or automation could be used reliably at scale.

How would you use Microsoft 365 components in a practical AI solution?

What to coverSuggested preparation: Discuss where tools such as Teams, SharePoint or Dataverse might fit when they serve the workflow; familiarity with these components is listed as preferred. Explain how you would choose connections and data sources rather than adding components by default.

How would you diagnose an agent that gives inconsistent or unhelpful results?

What to coverSuggested preparation: Separate possible causes such as unclear instructions, unsuitable source material and a poorly designed workflow. Describe how you would test representative tasks, review failures with users and decide whether to revise, constrain or stop the solution.

How would you design prompts and a conversational experience for business users?

What to coverSuggested preparation: Cover clear task instructions, relevant context, multi-step interactions and usable outputs. Explain how you would test whether non-technical users can get dependable results and know when to review an answer themselves.

How would you address security, privacy and responsible AI while moving from prototype to adoption?

What to coverSuggested preparation: Identify the data involved, who needs access and potential failure or misuse scenarios. Explain how you would involve Technology and relevant control partners in decisions about safe use without assuming a particular approval process.

How would you evaluate an emerging AI tool against the Microsoft Copilot ecosystem?

What to coverSuggested preparation: Propose a bounded experiment using the same business task to compare usefulness, reliability, integration, data handling and effort. Explain what evidence would justify adopting an alternative, including an agentic platform or custom LLM application.

How would you demonstrate that an AI initiative delivered value?

What to coverSuggested preparation: Define a baseline, an owner and practical measures tied to the use case, such as hours saved, quality improvement or risk reduction. Explain how you would track actual outcomes and report uncertainty rather than treating estimated benefits as achieved results.

How would you make a successful solution reusable in another department?

What to coverSuggested preparation: Describe what to document about prompts, agent design, inputs, limitations and lessons learned so another team can understand the pattern. Distinguish what can be replicated from what must be adapted to a different workflow and its controls.

Behavioural practice

Tell me about a time you found direction in an ambiguous business problem.

What to coverSuggested preparation: Use a situation, action and result example showing how you learned what mattered, set priorities and made progress without a detailed playbook. Be clear about your own decisions and what changed.

Describe a time you earned support for a change without direct authority.

What to coverSuggested preparation: Use a situation, action and result example involving stakeholders with different concerns. Explain how you built credibility, handled disagreement and assessed whether the change was adopted.

Tell me about a time you helped non-technical colleagues become self-sufficient with a new tool.

What to coverSuggested preparation: Use a situation, action and result example covering how you adapted coaching to their work and checked understanding. Include what you left behind—such as guidance or a capable peer—to sustain use after your involvement.

Describe a time you worked with control or technology partners to resolve a delivery concern.

What to coverSuggested preparation: Use a situation, action and result example that identifies the concern, your collaboration and any trade-off you made. Explain the outcome without implying that speed took precedence over safe adoption.

Tell me about a time you adapted a proven solution for a different team.

What to coverSuggested preparation: Use a situation, action and result example showing what was reusable and what the new team's workflow required you to change. Explain how you gathered feedback and established whether the adapted solution worked.

Sharpen your knowledge

Topics to prepare

AI opportunity selection and delivery ownership
On paper, rank three hypothetical workflow problems by value, feasibility and whether AI is needed; assign each to business users, an AI Business Partner, automation teams, Technology or an external partner. The posting explicitly calls for this triage and for distinguishing AI opportunities from process problems.
GenAI application design
Sketch a multi-step agent and conversational flow, including where user review belongs. The role requires working knowledge of prompt engineering, agent architectures and conversational experiences.
Benefits and success measures
For a hypothetical use case, define a baseline, an owner and a method for measuring hours saved, quality, risk or revenue impact. The posting asks for value cases, measurable KPIs and reported outcomes.
Safe adoption and data governance
Review a hypothetical data-sensitive use case and list privacy, security and responsible-AI questions to resolve with Technology and control partners before adoption. That coordination is an explicit responsibility; data-governance knowledge is preferred.
Adoption across rotating business units
Outline how an initial solution could be handed to departmental AI champions and advanced users, then adapted for another department. The role sits within the AI Academy framework and is expected to leave teams able to sustain adoption.

Skills to revise

Build a working agent or automation
Using synthetic information, prototype a small workflow in Copilot Studio and Power Automate, then test failure paths and describe what would need to change for reliable team use. Building agents or automations with these or similar low-code tools is a must-have capability.
Map and scope a business workflow
Interview a colleague about a hypothetical process, draw its steps and handoffs, and write a concise problem statement with a prioritised use case. The posting requires embedded workflow discovery, business analysis and stakeholder communication.
Compare AI platforms for a use case
Set criteria for a hypothetical task and compare a Microsoft Copilot approach with an alternative named in the posting, such as Claude desktop; record trade-offs and what would require further validation. Experience evaluating multiple AI/GenAI platforms is required.
Track outcomes and explain the value case
Create a simple before-and-after results sheet for a hypothetical pilot and give a brief explanation of its assumptions and limitations to a senior stakeholder. The role calls for transparent benefit tracking and communication of ROI cases.
Coach prompt use and solution design
Run a short practice session in which a non-technical user improves a prompt, checks its output and identifies when a repeatable workflow would be more useful. Coaching users and building lasting departmental capability are stated responsibilities.
Write a reusable handover
Document the prototype’s purpose, prompt or agent pattern, inputs, limitations, owner and adaptation steps so a different department could assess it. The posting explicitly asks for clear, context-independent prompts, patterns and lessons learned.

Your preparation checklist

Map a workflow and prioritise opportunities
Prepare an anonymized example or hypothetical business-unit workflow showing pain points, candidate AI use cases, one priority choice and a case where process improvement would be better than AI.
Walk through a working solution
Prepare a project walkthrough or small demo of an agent or automation built with Copilot Studio, Power Automate, Power Apps or similar tooling. Explain the user need, design, testing and path from prototype to reliable use.
Compare delivery routes and tools
For one proposed use case, make a brief decision table covering business-user ownership, the AI Business Partner, automation teams, Technology and an external partner. Include how you would evaluate Copilot against an alternative AI tool.
Draft a measurable value case
Show a sample baseline, expected benefit, owner and tracking method for a use case. The posting names hours saved, quality, risk reduction and revenue growth as possible measures; choose only those relevant to your example.
Plan for safe adoption
Outline the security, data-privacy and responsible-AI questions you would raise with Technology and control partners before moving a prototype into a business workflow.
Prepare a coaching example
Sketch a short session for non-technical users on prompting or effective AI tool use, plus a handover plan for departmental AI champions and advanced users to sustain adoption.
Create a reusable handover artifact
Draft a one-page template for a prompt, agent or automation that records its purpose, workflow, limitations, owner and lessons learned so another department could assess whether to adapt it.
Bring questions about the new role
Ask how the AI Business Partner model is being established, how rotations and AI Academy partners work in practice, and how teams agree ownership and report benefits after deployment.

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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