New opportunity

AI Adoption & Change Management Specialist

Biogen · Cambridge, United States of America

About this role

The Associate Director, AI Adoption & Change Management leads change management for AI, digital and technology initiatives across Biogen’s Project & Portfolio Management (PPM) and Development Asset Leads (DAL) organizations. The role supports adoption of AI-enabled capabilities, Planisware enhancements, workflow automation and reporting modernization in drug-development portfolio and program operations. It works with PPM, DAL, AI Innovation, Data Stewardship, IT, Legal, Compliance and Quality to turn new capabilities into measurable business value.

Responsibilities

Develop change strategies and plans addressing effects on roles, handoffs, decisions, data entry, processes and resources. Assess organizational readiness and change impacts, address resistance, and build change-champion networks across Development functions.
Lead stakeholder engagement, communications, role-based training, job aids, office hours and communities of practice. Define and report adoption, user-engagement, value-realization and solution-performance measures to leadership.
Guide responsible AI use, including understanding limitations, human-review requirements, governance and compliance obligations. Coordinate user acceptance testing, release readiness, go-live support and post-implementation adoption monitoring.

Required qualifications

Bachelor’s degree in a relevant discipline with 8+ years of experience, master’s degree with 6+ years, or equivalent experience in change management, technology adoption, digital transformation or portfolio operations.
Experience leading organizational change and the people, process and workflow changes associated with AI-enabled solutions, including readiness assessments, capability building, user education, feedback and sustained adoption. Ability to identify barriers to adoption and apply a structured methodology such as Prosci, ADKAR or Kotter.
Understanding of AI, generative AI, analytics and data-driven technologies, their business applications, limitations and risks. Strong leadership, matrix management, strategic planning and communication skills, including translating technical concepts for leaders and users, building consensus and defining adoption KPIs.

Preferred qualifications

Change management certification such as Prosci or ACMP; knowledge of life-sciences R&D portfolio, program and resource management and Planisware; experience with enterprise AI tools, Microsoft Copilot, Power Platform, SharePoint, Power BI or Tableau; understanding of AI governance, human-in-the-loop review and privacy in regulated settings. An advanced degree and biotech, pharmaceutical or R&D experience are preferred.

This is a hybrid, full-time role in Cambridge, MA, with a base compensation range of $144,000.00-$198,000.00. Regular employees are eligible for cash-bonus and equity incentives and benefits including insurance, paid leave, a matched 401(k), an employee stock purchase plan and tuition reimbursement of up to $10,000 per calendar year.

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

Skills for this role

AI adoptionChange managementDigital transformationStakeholder engagementOrganizational readiness assessmentStrategic communicationsRole-based trainingChange impact assessmentGenerative AIAnalyticsResponsible AIAI governanceHuman-in-the-loop reviewUser acceptance testingAdoption metricsValue realizationProsciADKARKotterPortfolio managementProgram managementResource managementPlaniswareMicrosoft CopilotPower PlatformSharePointPower BITableauMatrix managementStrategic planning

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

Translate technology changes into operating changes
The role leads change plans for AI initiatives, Planisware enhancements, workflow automation and reporting modernization across PPM and DAL. Plans must address effects on roles, handoffs, decision points, data entry, processes and resources.
Build readiness and sustained adoption
The posting calls for readiness and impact assessments, responses to resistance, and change-champion networks across Program Management, Program Planning, Development Asset Leads and broader Development functions.
Equip leaders and everyday users
The role leads stakeholder engagement, communications, role-based training, job aids, office hours and communities of practice. It also translates technical concepts into practical workflow implications for leaders and users.
Demonstrate measurable business value
The role defines and reports adoption metrics, user-engagement indicators, value-realization measures and solution-performance KPIs to leadership, rather than treating rollout alone as success.
Support responsible AI through launch and beyond
The role helps users understand appropriate AI use, limitations, human-review requirements, governance and compliance obligations. It also coordinates user acceptance testing, release readiness, go-live support and post-implementation adoption monitoring.

Likely challenges to think through

Aligning changes across a matrix
Preparation hypothesis, not a confirmed Biogen problem: PPM, DAL, AI Innovation, Data Stewardship, IT, Legal, Compliance and Quality may have different priorities for handoffs and decision ownership. Prepare a way to map affected decisions and negotiate a workable change plan without assuming one group controls the whole process.
Balancing AI uptake with appropriate review
Preparation hypothesis, not a confirmed Biogen problem: making AI-enabled workflows convenient could conflict with users' need to understand limitations and retain human review. Prepare to explain how role-based guidance and feedback could reinforce both useful adoption and responsible use.
Distinguishing usage from value
Preparation hypothesis, not a confirmed Biogen problem: engagement figures may rise without showing better portfolio or program operations. Prepare an approach that connects adoption and solution-performance indicators to defined workflow outcomes, then uses post-launch monitoring to test whether value persists.

Practice your answers

Technical practice

How would you structure a change plan for an AI-enabled capability across PPM and DAL?

What to coverSuggested answer focus: Explain how you would apply a structured change methodology to stakeholder impacts, readiness, communications, training and reinforcement. Show how the plan would adapt as roles and workflows change.

How would you assess changes to roles, handoffs and decision points before a rollout?

What to coverSuggested answer focus: Map the current and proposed workflows with business and IT stakeholders, including data-entry expectations and resource impacts. Explain how you would turn the findings into role-specific actions and resolve unclear ownership.

How would you diagnose low readiness or resistance to an AI initiative?

What to coverSuggested answer focus: Distinguish capability gaps, process complexity, uncertainty about roles and lack of confidence in AI outputs. Describe how feedback and readiness measures would guide different interventions rather than a single communications campaign.

How would you approach adoption of a Planisware enhancement in R&D portfolio operations?

What to coverSuggested answer focus: Discuss how you would learn the affected portfolio, program and resource-management workflows and identify changes for each user group. Planisware knowledge is preferred in the posting; avoid assuming a particular feature or configuration.

How would you explain where generative AI could help a portfolio workflow, and where it should not be relied on?

What to coverSuggested answer focus: Connect a proposed use to workflow value while explaining output limitations, uncertainty and effects on decisions. Distinguish AI-generated assistance from decisions that require accountable human review.

What would you include in responsible-AI guidance for users in a regulated setting?

What to coverSuggested answer focus: Cover appropriate use, human review, privacy, governance expectations and routes for resolving compliance questions with relevant partners. Explain how guidance would be usable in day-to-day work without claiming a specific Biogen policy.

How would you design enablement for leaders and everyday users of a new capability?

What to coverSuggested answer focus: Differentiate leadership communications from role-based training, job aids, office hours and communities of practice. Explain how feedback and change champions could reveal confusion and reinforce new behaviors.

How would you measure whether adoption is producing business value?

What to coverSuggested answer focus: Separate adoption, user engagement, value realization and solution performance measures, as the posting does. Discuss baselines, meaningful interpretation for leadership and how you would investigate high usage without improved outcomes.

How would you coordinate UAT, release readiness and post-launch monitoring?

What to coverSuggested answer focus: Describe how representative users would test affected workflows and how findings would inform training and go-live support. Explain what you would monitor after launch and how you would respond to unresolved adoption issues.

What change risks would you examine in workflow automation or reporting modernization?

What to coverSuggested answer focus: Consider data-entry expectations, data stewardship, handoffs and how people interpret reports or automated outputs. Explain how you would work with business, IT and data partners to address those risks without assuming a specific tool implementation.

Behavioural practice

Tell me about a time you built consensus across teams without direct authority.

What to coverSuggested answer focus: Use a real situation involving competing priorities, the actions you took to align stakeholders and the result. Clarify your own contribution and how you handled unresolved disagreement.

Tell me about a time you addressed resistance to a change in roles or workflows.

What to coverSuggested answer focus: Describe the situation and the specific concern you uncovered, then explain your response and its result. Include what evidence told you whether the concern had been addressed.

Tell me about a time you translated a complex technology concept for different audiences.

What to coverSuggested answer focus: Give the situation, how you adapted your explanation for leaders and users, and the result. Show how you checked understanding rather than relying on the communication alone.

Tell me about a time you built a network of advocates for a change.

What to coverSuggested answer focus: Describe the situation, how you engaged and supported advocates, and the result for adoption or feedback. Distinguish their contributions from actions you personally led.

Tell me about a time post-launch feedback changed your adoption plan.

What to coverSuggested answer focus: Explain the rollout situation, the feedback or measures you noticed, the adjustment you made and the result. If outcomes were mixed, describe what you learned and what you did next.

Sharpen your knowledge

Topics to prepare

Structured change management
Required: experience applying a methodology such as Prosci, ADKAR, Kotter or equivalent. Revise one framework by mapping its stages to a hypothetical AI-enabled workflow change, including readiness, resistance and reinforcement.
AI capabilities, limitations and responsible use
Required: understanding of AI, generative AI, analytics, business applications and risks; the role also promotes human review and compliance. Revise by explaining when an AI-generated portfolio summary would need human verification and how to communicate its limitations to users.
Portfolio and program workflow impacts
The role covers PPM and DAL operations, including Planisware enhancements, automation and reporting modernization; R&D portfolio knowledge and Planisware experience are preferred. Revise by tracing a hypothetical enhancement through roles, handoffs, decision points and data-entry expectations.
Adoption and value measurement
The posting calls for adoption, engagement, value-realization and solution-performance measures. Revise by distinguishing what each measures and drafting one illustrative indicator for each, with a baseline and a way to review results after go-live.
Barriers to adoption in a matrix organization
Required skills include addressing capability gaps, resistance, role uncertainty and distrust of AI outputs while building consensus. Revise by comparing responses to two different barriers across PPM, DAL and a partner function such as IT or Quality.

Skills to revise

Conduct a change-impact and readiness assessment
Practice drafting a short assessment for a hypothetical workflow automation: list affected roles, handoffs, decisions, data-entry changes, readiness questions and likely resistance. The role explicitly leads impact and readiness assessment.
Build a stakeholder and champion plan
Practice mapping PPM, DAL, AI Innovation, Data Stewardship, IT, Legal, Compliance and Quality to engagement needs, then outline how champions would gather feedback and reinforce new behaviors. The posting names these partners and calls for champion networks.
Write role-based adoption materials
Draft a brief leader update and a user job aid for the same hypothetical AI-enabled change, then outline an office-hours session. The role leads communications, role-based training, job aids and office hours and must translate technical concepts for different audiences.
Prepare an adoption and value report
Using illustrative figures, sketch a leadership update showing adoption, engagement, value and solution-performance measures, plus what feedback would prompt a change to the rollout. Defining, monitoring and reporting these measures is an explicit responsibility.
Coordinate testing through sustained adoption
Draft a checklist for a hypothetical release covering user acceptance testing, release readiness, go-live support, feedback collection and post-implementation monitoring. These stages are explicitly assigned to the role; the checklist is a preparation exercise, not a stated Biogen process.

Your preparation checklist

Prepare a change-leadership walkthrough
Choose a real organizational change you led, if available. Summarize the starting problem, your role, the structured method you used, barriers you addressed, stakeholder decisions and measured results. The posting calls for senior-level change leadership and experience applying a method such as Prosci, ADKAR or Kotter.
Map a sample change impact
As an interview exercise, sketch how a hypothetical AI capability or Planisware enhancement could affect PPM and DAL roles, handoffs, decisions, data entry and resources. Mark assumptions rather than presenting the scenario as a Biogen project.
Draft a readiness and engagement plan
Prepare a one-page plan showing how you would assess readiness, identify resistance, engage PPM, DAL and partner functions, and build a change-champion network. Include role-based communications, training, job aids, office hours and feedback loops.
Explain responsible AI in user-facing terms
Prepare a brief example of guidance for leaders and users covering appropriate use, AI-output limitations, human review, governance and compliance. Explain how you would seek input from Data Stewardship, IT, Legal, Compliance and Quality without assuming Biogen’s specific policies.
Outline release-to-adoption support
Draw a simple sequence from user acceptance testing and release readiness through go-live support and post-implementation monitoring. Note what user feedback you would collect and how it could change training or workflows.
Build a sample value-measurement scorecard
Propose a few clearly defined adoption, user-engagement, value-realization and solution-performance measures, with a baseline, reporting cadence and intended leadership audience. Label targets as illustrative, not Biogen commitments.
Write questions for the interviewers
Prepare questions about which AI and Planisware changes are priorities, where PPM and DAL users face adoption barriers, how decision-making works across the matrix, and how leadership currently defines successful adoption and business value.

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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