Generating Impact with AI

How data analytics and AI change the way industrial assets are managed
Joachim Kirsch | Claus Lintz | Fabian Schmidt | Kevin Lin | Henrik Roß
Nov. 2021 | White Paper | Englisch

Generating Impact with AI

How data analytics and AI change the way industrial assets are managed 

To cut through today’s complexity, companies are increasingly turning to data analytics and AI. However, despite the large spendings, around 70 percent of the executives confirm that their AI projects deliver no business impact. Reasons for failure are manifold and reach from unclear business objectives to insufficient organizational capabilities.

This White Paper presents four guiding principles for successfully generating impact by establishing data analytics and AI in asset operations. It provides guidance on how to elevate data analytics to a usable daily asset management, leading to tangible results. Examples from various industries illustrate business benefits and outline how to avoid the most common pitfalls.

$85 bn

were spent worldwide on data analytics and AI in 2021 alone.

30%

only of AI projects deliver a true business impact.

4 principles

are essential to make the application of data analytics and AI a success.

Management Summary
  • Studies reveal that 70 percent of all AI projects deliver little to no business impact
  • Four guiding principles are essential including a business impact focus, a transparent and explainable analytics approach, realistic expectations, and a process-oriented business setup
  • Setting a relevant business-oriented focus is essential when defining the scope and target of data analytics and AI projects
  • Using crystal box algorithms like Causal AI as a target-oriented analytic approach helps business and process experts understand the AI calculations
  • Setting realistic expectations is key: Skill level and experience of the people involved, knowledge of the physical process as well as availability and quality of data must be considered
  • By making process owners responsible for AI projects, a close collaboration between business, process, and IT experts is fostered
  • Integrating data analytics and AI in daily management routines helps to anchor the topic in the organization

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