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.