Forward Deployment Strategist

ILLUIN Technology

. Build and maintain the AI roadmap for a business area .

Posted 9/18/2026full-timeParis • FranceMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI deployment, project management, and stakeholder alignment, with a strong focus on developing business cases and adoption strategies. Proficient in data governance, security, and AI ethics, while effectively leading teams and facilitating communication across diverse groups.

Highest-signal resume keywords

AI Deployment Project ManagementBusiness Case DevelopmentData Governance and SecurityStakeholder AlignmentChange Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI FundamentalsData StorytellingQuantified Business CasesProject ManagementAgile Methodologies

Soft Skills

Results-Oriented MindsetAbility to Navigate AmbiguityMentoring and Support

Tools & Technologies

TableauPower BILookerAWSGCPAzure

Certifications & Qualifications

Master's DegreeSpecialization in Data/AI

Industry Keywords

AI RoadmapChange ManagementDigital TransformationGovernanceUser Feedback Loops

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformTableauTypeScriptGo

About the role

Key responsibilities & impact

Build and maintain the AI roadmap for a business area
Assess and prioritize the use-case portfolio based on value, feasibility, and risk
Design target processes incorporating AI and agents, as well as human–AI interactions
Build business cases and define Go / No-Go criteria with decision-makers
Identify security, governance, and data-quality prerequisites
Provide guidance on Make vs. Buy decisions
Work with the Delivery Manager on progress, risks, and priority/timeline/scope trade-offs
Lead low-volume engagements end to end when necessary
Align leadership, business, IT, and Data teams around shared objectives and a common plan
Sequence and plan deployments, anticipate dependencies, and remove blockers
Lead the transition from pilots to scaled deployment with ILLUIN delivery teams
Prepare and facilitate steering and project governance meetings (Coproj, Copil)
Design the adoption strategy, including training, communications, business champions, and operating rituals
Develop upskilling plans
Establish and facilitate AI ambassador networks within client organizations
Identify resistance and implement appropriate responses
Define and track adoption and impact KPIs
Measure results and present them to decision-makers
Establish user feedback loops and continuous improvement processes
Adjust the deployment roadmap based on field feedback and signals
Mentor and support the development of junior consultants
Contribute to business development, proposals, and pitch presentations
Capture lessons learned and enhance the firm's methodologies and playbooks

Requirements

What you’ll need

Master's degree from an engineering school, business school, or university
Specialization or additional training in Data / AI and/or change management is strongly preferred
3 to 7 years of experience, including at least 2 years working on AI, data, or digital transformation projects
Experience supporting at least two end-to-end AI deployment projects, from scoping through adoption
Experience preparing and facilitating project management and steering meetings with senior decision-makers
Experience in a large-enterprise environment or consulting firm is strongly preferred
Fluency in English is a plus
Sufficient knowledge of AI fundamentals (LLMs, RAG, agents) to assess feasibility and engage effectively with Data Scientists
Experience building quantified business cases and prioritization frameworks
Experience building and presenting dashboards (Tableau, Power BI, Looker)
Strong understanding of data, governance, security, and AI ethics challenges
General understanding of cloud architectures (AWS, GCP, Azure)
Sufficient project management knowledge (planning, milestones, risk management, Agile methodologies) to work effectively with a Delivery Manager and, when necessary, independently lead a smaller-scale project
Ability to bring together stakeholders with potentially diverging interests
Data storytelling skills
Ability to navigate ambiguity in large organizations
Active monitoring of AI developments
Results-oriented mindset