AI Project Manager

1. AI Strategy & Roadmap Development

• Define and maintain a comprehensive AI roadmap fully aligned to the company's business objectives and departmental needs.
• Identify automation opportunities across Finance, HR, Operations, Sales, Marketing, and Customer Support.
• Prioritise AI initiatives based on business impact, feasibility, and ROI potential.
• Develop phased implementation plans with clear milestones, timelines, and measurable success criteria.
• Stay ahead of market trends and translate emerging AI opportunities into actionable strategies.
• Present AI strategy updates to leadership on a regular cadence.

2. Workflow Platform Management

• Demonstrate strong hands-on knowledge of workflow automation platforms such as n8n, Make, Zapier, Microsoft Power Automate, or equivalent tools.
• Design and oversee end-to-end automation workflows that integrate seamlessly with existing business systems (CRM, HRMS, etc.).
• Evaluate and recommend the most suitable platforms based on scalability, cost-efficiency, and business needs.
• Ensure automation workflows are properly maintained, monitored, and continuously optimised for performance.
• Document all workflows with clear SOPs and handover guides for operational teams.

3. ROI Calculation & Business Case Development

• Build detailed ROI models for proposed AI projects, including cost savings, time reduction, and quality improvement projections.
• Track and report on actual vs. projected ROI post-implementation across all active AI initiatives.
• Develop compelling business cases to secure leadership buy-in and budget approvals.
• Define KPIs and measurement frameworks for every AI initiative and report performance monthly.
• Benchmark AI investment returns against industry standards and competitor data.

4. Designing Effective AI-Driven Platforms

• Architect scalable, user-friendly, and operationally integrated AI-driven platforms for enterprise use.
• Establish AI governance frameworks including data quality standards, model monitoring protocols, and ethical AI usage guidelines.
• Ensure platforms incorporate feedback loops for continuous learning, model retraining, and performance improvement.
• Design with security, privacy, regulatory compliance, and data governance requirements built in from day one.
• Evaluate build vs. buy decisions for AI tools and platforms with clear technical and commercial justifications.

5. Articulating & Evangelising 'Why AI'

• Clearly articulate the strategic value and business case for AI adoption to non-technical stakeholders in accessible language.
• Educate internal teams and management on AI benefits, limitations, risks, and realistic implementation expectations.
• Champion a data-driven, AI-first culture across the organisation through workshops, lunch-and-learns, and internal communications.
• Address change management challenges and resistance to AI adoption proactively through empathy and evidence.
• Produce internal thought leadership content to build organisational AI literacy.

6. Research, Innovation & Market Awareness

• Continuously research the latest AI tools, frameworks, large language models (LLMs), and industry-specific AI applications.
• Monitor competitor AI adoption and identify best practices from global industry leaders for potential adoption.
• Evaluate emerging technologies and present structured findings and recommendations to leadership regularly.
• Actively engage with AI communities, conferences, webinars, and research papers; maintain a strong professional network.
• Deliver a quarterly 'State of AI' report covering technology trends, vendor landscape, and strategic recommendations.

7. Vibe Coding & Low-Code / No-Code AI Development

• Demonstrate practical knowledge of vibe coding — using AI-assisted coding tools (Cursor, Replit AI, GitHub Copilot, v0, etc.) with natural language prompts to rapidly prototype solutions.
• Leverage low-code/no-code platforms and AI-augmented development to accelerate solution delivery without full engineering dependency.
• Bridge the gap between business requirements and technical implementation using AI-augmented development approaches.
• Evaluate vibe-coded prototypes for production-readiness and escalate appropriately to the engineering team.
• Maintain awareness of the capabilities and limitations of AI code generation tools to guide responsible adoption.

8. Business Requirement Analysis & Product Translation

• Conduct structured discovery sessions with department heads to deeply understand pain points, inefficiencies, and strategic objectives.
• Translate business requirements into detailed product specifications, user stories, process flows, and technical briefs.
• Facilitate workshops to map current-state processes and co-design AI-powered future-state workflows with stakeholders.
• Validate that all delivered AI solutions accurately and completely address the original business requirements.
• Maintain a centralised requirements repository ensuring full traceability from requirement through to delivery and sign-off.

9. Client & Vendor Communication

• Serve as the primary point of contact for all external AI vendors, SaaS providers, and technology partners.
• Negotiate contracts, SLAs, and deliverables with vendors; manage vendor performance throughout the full engagement lifecycle.
• Communicate project status, risks, changes, and outcomes to clients and internal stakeholders through regular structured updates.
• Manage expectations proactively and resolve conflicts between business needs and technical constraints diplomatically.
• Prepare and deliver polished executive-level presentations, live demo sessions, and formal project review reports.

10. Technical Implementation Oversight (Added Advantage)

• Oversee and review technical implementations to ensure alignment with architectural decisions, quality standards, and delivery timelines.
• Conduct or facilitate code reviews and technical walkthroughs in collaboration with the engineering team when required.
• Collaborate closely with developers, data scientists, and DevOps engineers to remove blockers and maintain delivery velocity.
• Understand API integrations, webhook configurations, and system architecture at a functional level to guide technical decision-making.

11. Technical Foundations – Database & Coding Literacy

• Maintain working knowledge of relational databases (MySQL, PostgreSQL) and NoSQL systems (MongoDB, Firebase) to support AI data pipelines.
• Understand fundamental programming concepts in Python, JavaScript, or similar languages to communicate effectively with technical teams.
• Ability to read and interpret code, SQL queries, and data schemas without needing to write production code independently.
• Familiar with REST APIs, JSON data formats, and core cloud infrastructure concepts (AWS, Azure, GCP).

12. Team Leadership & Management

• Build, mentor, and manage a cross-functional AI project team including developers, data analysts, UX designers, and QA engineers.
• Define clear roles, responsibilities, and performance expectations for all team members.
• Foster a collaborative, innovative, and psychologically safe team environment that encourages experimentation.
• Conduct regular 1:1s, performance reviews, and provide constructive and timely feedback.
• Resolve team conflicts and maintain high team morale, engagement, and productivity throughout the project lifecycle.
• Plan resource allocation and manage workload distribution proactively to prevent burnout and delivery risk.

13. Management Communication & Alignment

• Maintain a regular communication cadence with C-suite and senior leadership on AI project status, risks, and opportunities.
• Translate complex technical AI concepts into clear, strategic business language for non-technical executives.
• Present monthly and quarterly project dashboards showing progress against KPIs, delivery milestones, and ROI metrics.
• Proactively surface risks, blockers, and dependencies to management alongside recommended mitigations and contingency plans.
• Align AI project priorities with evolving company strategy and business direction during leadership planning cycles.