AI Project Manager / AI Business Solutions Manager

Position: AI Project Manager / AI Business Solutions Manager
Experience: 7–10+ Years
Employment Type: Full-Time

Location: Tampa, FL

Department: Data & Artificial Intelligence
Reports To: Head of AI / Director of Technology

Position Overview

We are seeking an experienced AI Project Manager who will serve as the primary liaison between business stakeholders, technical architecture teams, and senior management to identify, evaluate, prioritize, and plan AI initiatives across the organization.

The ideal candidate will have strong business analysis, requirements gathering, stakeholder management, and project planning skills, combined with a practical understanding of Generative AI, Agentic AI, Machine Learning, and enterprise technology architectures.

This is primarily a business-facing role rather than a hands-on technical development position. The successful candidate must be able to understand business processes, translate business challenges into well-documented AI opportunities, collaborate with technical teams to evaluate solution options, develop business cases, and create actionable project plans.

Key Responsibilities

1. Business Engagement & Requirements Gathering (30%)

• Conduct workshops, interviews, and discovery sessions with business users, process owners, and department leaders.
• Understand existing business processes, operational challenges, inefficiencies, and opportunities for AI-driven improvements.
• Identify and document potential AI use cases across business functions such as Finance, Procurement, Supply Chain, Manufacturing, Sales, Customer Service, and HR.
• Gather and document functional and non-functional requirements, business rules, workflows, integration needs, and expected outcomes.
• Develop Business Requirements Documents (BRDs), use case definitions, process maps, user stories, and acceptance criteria.
• Identify business data sources, system dependencies, stakeholders, and operational constraints.
• Validate documented requirements with business stakeholders to ensure completeness and accuracy.
• Establish measurable business objectives and success criteria for each AI initiative.

2. AI Solution Evaluation & Technical Collaboration (20%)

• Work closely with AI Architects, Data Architects, Enterprise Architects, and engineering teams to evaluate potential solutions.
• Translate business requirements into clear inputs for technical feasibility assessments.
• Understand and document different technical approaches, including Generative AI, RAG, Agentic AI, Machine Learning, and traditional automation.
• Collaborate with technical teams to assess solution complexity, architecture options, integration requirements, data availability, and infrastructure needs.
• Compare alternative approaches based on cost, complexity, scalability, security, maintainability, and implementation timelines.
• Identify dependencies on enterprise applications such as ERP, CRM, data warehouses, document repositories, and external systems.
• Ensure security, data privacy, governance, and responsible AI considerations are incorporated into solution assessments.
• Document technical recommendations in a business-friendly format for stakeholder review.

3. Business Case Development, ROI & Prioritization (25%)

• Work with business stakeholders to quantify the expected benefits of each AI initiative.
• Develop business cases covering potential cost savings, productivity improvements, revenue opportunities, risk reduction, and operational efficiencies.
• Establish baseline metrics and define measurable KPIs for tracking expected business outcomes.
• Collaborate with Finance and technical teams to estimate implementation costs, ongoing operating costs, and expected return on investment.
• Develop ROI, payback-period, and cost-benefit analyses for proposed AI initiatives.
• Create prioritization frameworks based on business value, technical feasibility, implementation complexity, cost, risk, and strategic alignment.
• Maintain a consolidated portfolio of AI opportunities, including business justification, estimated benefits, dependencies, and recommendations.
• Prepare executive presentations and recommendations for senior management.
• Facilitate prioritization discussions with business leaders and executives to determine which initiatives should proceed.

4. Project Planning & Execution Readiness (20%)

• Once initiatives are approved, collaborate with internal technical teams to develop comprehensive implementation plans.
• Define project scope, objectives, deliverables, milestones, timelines, and resource requirements.
• Coordinate with AI Engineering, Data Engineering, Infrastructure, Security, and Application teams to develop realistic project estimates.
• Identify project dependencies, technical risks, assumptions, and constraints.
• Develop project plans covering discovery, solution design, development, testing, user acceptance testing, deployment, and post-production support.
• Define resource allocation, project budgets, governance structures, and stakeholder responsibilities.
• Establish project success criteria, acceptance milestones, and benefits realization plans.
• Facilitate project handoffs to delivery teams and support execution through milestone tracking, risk management, and stakeholder communication.
• Ensure project plans incorporate user training, change management, and business adoption requirements.

5. Executive Communication & Portfolio Reporting (5%)

• Develop and maintain an AI initiative portfolio and roadmap.
• Prepare concise executive dashboards showing initiative status, projected ROI, estimated costs, implementation timelines, and risks.
• Present business cases, architecture alternatives, and prioritization recommendations to senior leadership.
• Communicate technical concepts and trade-offs clearly to nontechnical stakeholders.
• Track progress against approved business objectives and report realized benefits following implementation.

Required Qualifications

Experience

• Bachelor's degree in Business, Information Technology, Computer Science, Engineering, or a related field.
• 7–10+ years of experience in IT project management, business analysis, technology consulting, or digital transformation.
• At least 2 years of exposure to AI, analytics, intelligent automation, or related technology initiatives preferred.
• Demonstrated experience conducting business discovery sessions, documenting requirements, and translating business needs into technology initiatives.
• Experience collaborating with solution architects, software engineering teams, and enterprise IT organizations.
• Proven ability to develop business cases, financial justifications, ROI assessments, and executive presentations.
• Experience managing multiple initiatives and facilitating prioritization with senior business stakeholders.
• Familiarity with enterprise applications such as ERP, CRM, and business intelligence platforms.

Technical Knowledge

The candidate is not expected to develop AI applications but should possess a working understanding of:

• Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG).
• Agentic AI, AI assistants, workflow automation, and intelligent process automation.
• Machine Learning and predictive analytics use cases.
• Enterprise data architecture, APIs, integration patterns, and data governance.
• Cloud-based AI platforms such as Microsoft Azure AI, AWS, or Google Cloud.
• AI solution considerations including security, scalability, model accuracy, operating costs, and human-in-the-loop controls.

Business & Project Management Skills

• Excellent requirements gathering, process analysis, and documentation capabilities.
• Strong analytical and problem-solving skills.
• Ability to develop ROI models, cost-benefit analyses, and business value assessments.
• Strong stakeholder engagement, workshop facilitation, and executive communication skills.
• Experience creating project plans, work breakdown structures, schedules, and resource estimates.
• Proficiency with tools such as Microsoft Project, Jira, Azure DevOps, Visio, PowerPoint, and Excel.
• Ability to manage competing priorities and work across business and technology teams.

Preferred Qualifications

• PMP, PMI-PBA, CBAP, or Agile/Scrum certification.
• Experience with AI transformation programs or enterprise AI adoption initiatives.
• Background in consulting, business transformation, or enterprise technology strategy.
• Experience in Govt. is a plus
• Familiarity with Microsoft Azure AI Foundry, Microsoft Fabric, Power BI, or comparable enterprise AI and data platforms.
• Experience establishing AI opportunity pipelines and enterprise AI roadmaps.

Key Deliverables

The AI Project Manager will be responsible for producing and maintaining the following deliverables:

1. AI Use Case Inventory: Consolidated repository of identified AI opportunities across business functions.
2. Business Requirements Documents: Detailed documentation of business processes, requirements, pain points, and expected outcomes.
3. Solution Assessment Matrix: Comparison of architecture alternatives, technical complexity, costs, risks, and implementation approaches, developed with technical architects.
4. Business Case & ROI Analysis: Quantified financial and operational justification for each proposed initiative.
5. AI Prioritization Matrix: Ranked initiatives based on business impact, feasibility, cost, complexity, and strategic value.
6. Executive Recommendation Presentations: Decision-ready summaries for senior management.
7. AI Implementation Roadmap: Phased roadmap showing approved initiatives, dependencies, and target timelines.
8. Individual Project Plans: Scope, deliverables, resources, budget, milestones, risks, and timelines for each approved initiative.

Key Performance Indicators (KPIs)

Success in this role will be measured through:

• Quality, accuracy, and completeness of documented business requirements.
• Number of qualified AI opportunities assessed and presented for prioritization.
• Accuracy and credibility of estimated costs, timelines, and business benefits.
• Stakeholder satisfaction and alignment between business and technical teams.
• Timeliness of business case development and executive decision-making.
• Percentage of approved initiatives with complete, actionable project plans.
• Realization of projected business benefits from implemented AI initiatives.

 Best Regards,
Praveen