Built with the aim of connecting theory to practice, this course will help you gain valuable industry insights from world-leading experts through a variety of interactive teaching and learning methods.

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Delivered face-to-face at Alliance Manchester Business School’s Executive Education Centre, you will take part in collaborative workshops, group discussions and activities, and hear from a selection of inspiring guest speakers including Vlad Jiman, Director of Data at N Brown, and Rachael Hardman, Data Science Team Lead at Peak.

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You will also have the opportunity to explore Alliance Manchester Business School’s state of the art Data Visualisation Laboratory and experiment with your own data sets.

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Designed to break down access barriers and reduce complex language surrounding data and AI, this course uses case studies, prepared models, and dashboards to emphasise the practicality of AI models and business applications.

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We aim for several stand-out learning experiences, including:

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Applied Learning A learning environment created by the course facilitator that draws on the experience and input of your course peers.

Knowledge Transfer A steady stream of feedback from participating peers with diverse business backgrounds on, for instance, how they have implemented data and AI within their organisation.

Reflection A rare time-out to contemplate your own opportunities and challenges while engaging with our world-class faculty.

Network Expansion An expanding professional network drawn from your peers.

Personal Growth An expanding self-awareness of your own competencies and how they can be honed.

Behavioural Change An adoption of new rationales and mindsets that improve decision making.

Facilitated Challenge A regular raising of difficult questions by the course facilitator designed to take you away from your work environment into new work-territory.

• Strengthen your decision-making capabilities with data and AI models and outputs
• Understand the ethical, legal and social responsibility issues around the use of AI
• Develop data literacy and data storytelling capabilities
• Discover the technical terms and key questions to ask about AI models
• Gain expert insights through real-world case studies and industry speakers
• Become a member of the AMBS Executive Education Network and benefit from top-class speaker events, regular updates, and networking opportunities with like-minded professionals
• Take time out to reflect on your own skill set and engage with AMBS’s world-class faculty

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• Strengthen your ability to spot risks and opportunities for your organisation through data interpretation skills
• Streamline your organisation’s decision-making and discussions surrounding data and AI when working with and managing technical teams
• Exceed organisational objectives by improving productivity and performance through data and analytics
• Confront key issues your organisation may be facing around the deployment of data and AI models and understand how to resolve them

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This course would suit general or technical managers and leaders from a wide range of industries with a basic understanding of data. You should be looking to improve your ability to interpret and critically analyse business data and AI models to meet the needs of your organisation.

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No coding or programming experience is required to take part in this course.

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Bringing together an exceptional amount of expertise, research and refined knowledge in the subject area of Data and Artificial Intelligence, Julia Handl, Professor in Decision Sciences, is joined by Richard Allmendinger, Professor of Applied Artificial Intelligence, to deliver this insightful and data-focused course.

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Having worked with a wide range of world-renowned organisations such as Siemens, BAE Systems, IBM and Fujitsu, the team deliver this course based on a vast range of real-world experiences that showcase theories being put into practice.

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Julia’s core methodological expertise lies at the interface between optimisation and machine learning, and she has a keen interest in the development and use of these techniques in challenging application areas.

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Richard's research interests focus on the development and application of data optimisation, learning and analytics techniques to real-world problems arising in areas such as healthcare, manufacturing, economics, sports, music, and forensics.

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