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Target Audience

Innovation Managers, Chief Information/Digital Officers, Computer Scientists Developers with business interest, Management/Business Administration Experts, Talent program participants aspiring for C level.

Learning Target

Participants shall gain a practical overview on how to define and implement artificial intelligence driven projects in business environment. The goal is to become an internal AI implementation consultant/champion who can initiate, facilitate and manage the adaption of AI powered technologies. By the end of the course participants will have elaborated the implementation plan of an AI solution "bought from market" and one developed “in house” for their organisations. They will also be able to:

  • Understand and manage the data capital of the organisation
  • Choose the right project with the right technology (make or buy? platform or start up? general or niche project?)
  • Define the learning cycle for both the company and the technology
  • Understand how to build and manage a data science team
  • Manage the data sources and business goals to succeed with technology
  • Build a culture necessary to make data science projects and teams successful

Contents

  • Building a data driven organisation
    • Data is the source of insight not control
    • Maturity model of a digital company
    • AI on the digitalisation scale

  • Identifying a first AI project
    • The Data - Technology - Business KPI triangle
    • The make or buy dilemma: providers, start-ups, platforms, developers
    • Evaluating project opportunities within your organisation: pain points, data source, stakeholders, impacts, quick wins
    • Understanding the machine learning canvas

  • Managing the data capital
    • Understanding the “SISO” problem
    • New roles in an organisation: data manager, data engineer, data scientist
    • Dilemmas of the place of the data team in the organisation or project
    • Descriptive, predictive, prescriptive analytics
    • The layers of data management: big data, data cleaning, validation
    • Setting up the team for a first project

  • Building the learning cycle
    • How does an AI learn - teaching data and feedback loop
    • Assisted intelligence - helping people not replacing them
    • How does an organisation learn - system theory, mental models, controlled experiment and reflection loop
    • Cooperating with a new type of intelligence - the culture of adapting the technology driven future at scale
    • Designing the implementation process and quick wins

  • Presenting and winning an AI project
    • Expectation management - not inflating expectations
    • Quick wins - it is not AI that is going to bring the first benefits, but the data overview
    • Showing the scale - designing organisations for the future. A project is just one step
    • Active engagement of a feedback loop

Methodology

Interactive lecture, group work, working on projects brought by participants, best practice sharing, reflection

Duration

5 days

Dates and Locations

Functional Questions

Organisational Questions