Learn to design, develop, and scale enterprise-grade AI products using modern AI technologies, automation, and responsible AI frameworks. Professional Certification Program Building Enterprise Products with AI Tools
AI-Driven Workflows Build intelligent systems powered by modern AI tools. Scalable Architecture Design enterprise-ready platforms and automation pipelines. Responsible AI Implement ethics, governance, and compliance frameworks.
In Building Enterprise Products with AI Tools, you will learn how to design, develop, and scale enterprise-grade AI products using modern automation, AI-driven architectures, and responsible AI frameworks. Gain practical skills to build real, scalable, production-ready AI solutions. What you will learn Build Intelligent Enterprise
Products with AI Tools
This course takes you end-to-end through the lifecycle of building enterprise products with AI tools. From understanding the AI product ecosystem to designing scalable architectures, managing delivery, and governing responsible AI, you will learn how to turn ideas into robust, production-ready enterprise solutions. Build a clear understanding of the AI product landscape, key enterprise use cases, and how AI tools, data, and platforms come together. This module sets the foundation for thinking like an AI product leader inside complex organisations. Learn how to architect AI-driven products that are resilient, secure, and scalable. Explore platform thinking, data infrastructure, integration patterns, and how to design AI systems that fit seamlessly into existing enterprise environments. Translate ideas into working AI products using agile practices, MLOps, and continuous delivery. Learn how to run experiments, ship features safely, and manage AI models in production for performance, reliability, and business impact. Discover how to manage AI risks, ethics, and compliance while still innovating at speed. Learn practical frameworks to govern AI products, define KPIs, and communicate value to stakeholders across the enterprise. There are 4 modules in this course
Module details The AI Product Ecosystem What's included 3 lessons
Module details Designing for Scale What's included 3 lessons
Module details Building & Managing AI Products What's included 3 lessons
Module details Governance, Risk & Responsible AI What's included 3 lessons
Earn a career certificate Add this credential to your LinkedIn profile, resume, or CV. Share it with your organisation, clients, and professional network to highlight your expertise in building enterprise AI products.
What professionals say about this AI products program
This course shows you how to design, build, and scale enterprise-grade products powered by AI tools. You’ll learn how to move from ideas and experiments to real, production-ready solutions that work inside complex organisations. We cover the full lifecycle: opportunity discovery, architecture, delivery, MLOps, and responsible AI so you can lead or contribute to serious AI product initiatives. No. You don’t need to be a data scientist or ML engineer to benefit from this course. We focus on product thinking, architecture choices, workflows, and decision-making rather than teaching you to build models from scratch. Basic familiarity with digital products, APIs, and data concepts will help, but the content is designed so product managers, tech leads, and business stakeholders can all follow along. Yes. Throughout the course you’ll be guided to define and shape a concrete AI product or feature for your own context – from use case selection and architecture sketch to rollout considerations. You’ll leave with a structured concept document and practical next steps you can use with your team or stakeholders, not just theory. The course is designed for cross-functional teams. Product managers, engineering leaders, architects, data leaders, and innovation owners will all find it relevant. Many organisations see the best results when at least two roles (e.g. product + tech) go through the material together and use the same language and frameworks. We use examples from popular AI platforms and tooling, but the core concepts are platform-agnostic. The focus is on principles you can apply whether your organisation uses OpenAI, Azure, AWS, Google Cloud, or internal models. This means you won’t be locked into one vendor’s way of working and can adapt the learning to your current tech stack. We go deep enough to help you make informed decisions and have meaningful conversations with engineering and data teams, without turning it into a pure engineering course. You’ll see reference architectures, workflows, and examples of pipelines and monitoring, but the emphasis is on “what and why” rather than detailed configuration. That’s completely fine. The course includes guidance on choosing realistic starting points, dealing with messy data, and building momentum with smaller, high-impact use cases. You’ll learn how to avoid over-engineering and instead focus on phased delivery that matches where your organisation is today. Most participants complete the course over 2–4 weeks with a commitment of around 3–4 hours per week. You can move faster or slower depending on your schedule. Because everything is on-demand, you can fit the lessons and exercises around your work rather than attending at fixed times. Yes. You’ll get practical templates for use case selection, product definition, architecture mapping, governance considerations, and rollout planning. Many learners adapt these templates directly into their internal documentation or planning processes with their teams. Yes. Organisations often enrol cohorts of product, technology, and data leaders so they can develop a shared view of how to build AI products. For group access, tailored sessions, or customised programs, please reach out and we’ll discuss options based on your needs. Frequently asked questions
What is “Building Enterprise Products with AI Tools” about?
Do I need to know coding, machine learning, or be a data scientist?
Will I actually build something tangible in this course?
Is this more suitable for product, engineering, or data teams?
Does the course focus on a specific AI platform or vendor?
How technical are the architecture and MLOps parts?
What if my company is still early in its data or AI journey?
How much time do I need to complete the course?
Do I get templates, checklists, or frameworks I can reuse?
Can my organisation enrol a cohort or run this in-house?
