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AI Implementation Manager
Job Posted: 7 hours ago
Salary: £ 35,000 - 55,000 / Annum
Job Type: Permanent
Location: Bridgend
Expire in: a month
Job Description
AI Implementation Manager
Location: Bridgend
Salary: £35(Apply online only)-£55(Apply online only)
About Us
We are a collective of four UK-based manufacturing companies. United by a shared vision for innovation, we are launching a transformative programme to embed artificial intelligence, smart technologies, and automation across our operations, supply chain, planning, procurement, quality assurance, and administrative functions.
Purpose of the Role
This newly established position will spearhead the deployment of AI-powered solutions across the group. The AI Implementation Manager will collaborate with senior leadership and departmental teams to modernize workflows, enhance operational intelligence, and integrate AI into everyday business practices.
We’re looking for a dynamic, forward-thinking professional who can bridge strategy and execution. You’ll be responsible for identifying high-value opportunities, designing and implementing AI tools, and driving measurable improvements across multiple manufacturing sites. This is a hands-on role for someone who thrives on solving complex problems and delivering tangible results.
This position plays a pivotal role in our 3-year AI transformation roadmap, which aims to build foundational capabilities, deliver operational impact, improve data governance, and foster a culture of digital innovation. By the end of the programme, we aim to be recognized as a leader in AI adoption within UK manufacturing, with scalable systems, an internal Centre of Excellence, and AI embedded into core decision-making processes.
Key Responsibilities
1. Strategic Planning & Roadmap Development
Create a comprehensive AI and automation strategy aligned with group-wide business objectives.
Evaluate all departments — including production, logistics, commercial, HR, finance, and customer service — to uncover AI opportunities.
Prioritize initiatives based on ROI, scalability, and operational relevance.
Stay informed on emerging AI trends in manufacturing, logistics, and consumer goods.
Contribute to long-term goals such as establishing a Centre of Excellence and embedding AI into planning and forecasting cycles.
2. Use Case Discovery & Prioritization
Identify and assess AI use cases with the greatest potential for impact.
Balance quick-win projects with strategic initiatives that support long-term transformation.
3. Solution Design & Deployment
Architect and implement AI and automation solutions that integrate with existing systems.
Lead pilots and scale successful prototypes across:
Manufacturing: predictive maintenance, scheduling, quality assurance
Packaging: AI-assisted design, simulation tools
Sales & Marketing: segmentation, forecasting, lead automation
Finance: reporting, cost analysis, document processing
Supply Chain: planning, routing, supplier analytics
4. Toolkits & Frameworks
Develop a repository of reusable AI models, prompts, and tools to accelerate future deployments.
Maintain a centralized, accessible library for group-wide use.
5. Data Infrastructure & Readiness
Collaborate with teams to ensure data is structured, accessible, and AI-ready.
Address gaps in data quality and implement robust data management processes.
Support the rollout of cloud-based environments and automated validation systems.
6. Change Enablement & Training
Lead change management efforts to embed AI into daily operations.
Promote AI literacy and train staff on practical usage.
Deliver training to at least 50 employees and support citizen developer initiatives.
Foster an internal AI community for collaboration and innovation.
7. Governance & Compliance
Define ethical standards and governance frameworks for responsible AI use.
Monitor compliance with data privacy, bias mitigation, and transparency regulations.
Ensure all AI initiatives align with legal and ethical standards.
8. Impact Measurement & Reporting
Establish KPIs and success metrics for each AI initiative.
Report quarterly progress to leadership, highlighting adoption, outcomes, and ROI.
Track improvements in forecasting, waste reduction, and tool usage.
9. Collaboration & Capability Building
Work closely with IT, operations, and commercial teams to ensure seamless integration.
Build internal capabilities to reduce reliance on external vendors (target: 80% self-sufficiency by Year 3).
Support external partnerships with universities and tech providers to drive innovation.
Success Indicators (First 12–24 Months)
Delivery of 2–3 high-impact AI projects with measurable ROI.
Efficiency gains in manufacturing, supply chain, and administration.
Forecasting accuracy improvements and reduced operational waste.
Tangible cost savings within 6–12 months of deployment.
20% staff adoption of AI tools by Year 2.
Established governance framework and internal AI enablement team.
Centralized AI prompt/tool library and training of 50+ staff with >70% engagement.
Candidate Profile
Required Skills & Experience
Proven track record in leading AI or digital transformation projects, ideally in manufacturing or FMCG.
Practical experience with AI tools like ChatGPT, Microsoft Copilot, and predictive analytics platforms.
Familiarity with AI/ML frameworks (e.g., OpenAI API, TensorFlow, PyTorch) and automation platforms.
Strong understanding of data integration, APIs, and enterprise architecture.
Ability to translate business challenges into scalable technical solutions.
Project management expertise across ideation, execution, and stakeholder coordination.
Excellent communication skills with the ability to convey technical concepts to non-technical audiences.
Demonstrated success in delivering measurable business improvements through AI.
Preferred Qualifications
Experience in manufacturing, supply chain, or industrial operations.
Hands-on with ERP systems (e.g., SAP, Odoo, NetSuite) and AI integration.
Knowledge of RPA, digital twins, and predictive analytics for process optimization.
Proficiency in analytics platforms like Power BI or Tableau.
Understanding of AI governance and ethical deployment practices.
Passion for emerging AI technologies and their business applications.
Degree in Computer Science, Data Science, or related field.
If you have any questions, please contact Jake Norfolk-Lee at Interaction RecruitmentBefore You Apply
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