
Shows a technically sound AI chatbot failing to improve customer experience and how updating it to address the customer journey restores value, plus four pre-automation questions.
Start from the business problem, define the data requirements, and design the data store around them to harness big data’s volume, velocity, and variety, applying MVP to deliver early insights.
Technical projects fail in their own particular ways. The system works in the test lab and falls over in the field. The vendor's roadmap changes halfway through. The new product launches, and nobody can support it at three o'clock in the morning. Managing a technical project needs everything a normal project needs, plus a set of decisions and habits that most project management courses leave out. This course covers them.
You will start by choosing the right delivery approach. You will compare waterfall, agile and hybrid, and learn to justify your choice to a sponsor. You will then capture functional and non-functional requirements properly, and manage change across the four dimensions that always move together: product, process, integration and people. A scenario on replacing a legacy system puts these ideas to work.
In the middle of the course you will look at delivering technical solutions: services, tools and serviceability, the real risks of brand-new technology, working with vendors, cloud and edge, designing for a 24/7 world, and testing technical components before users find the problems for you. You will then look at product development, including MVP, minimum viable service and the human element, and at how to choose products you can actually support.
The final section is a set of challenges based on common real-world situations: an AI chatbot nobody used, a service that has to work for anyone, anywhere, at any time, and a year of data that produced no new insight. Pause each challenge, decide what you would do, and then compare your answer with mine.
Quizzes at the end of each section help you check your understanding.