Five rules for integrating AI into your business model
Artificial intelligence is a growth factor for businesses. There is no master plan for using it – but these basic rules apply in every case.
7 Dec 2017Share
Intelligent robots and self-learning computers will have a huge impact on German industry in the coming years: Artificial intelligence (AI) could increase the gross domestic product by ten billion euros a year by 2030,
1. Business case: Argue for AI opportunities
Investing quickly in a trend is not advisable when it comes to artificial intelligence. First you have to understand what AI can do and how it brings added value to your company. Key parameters here are the technical feasibility, the available budget and the optimization potential within the company. Defining a business case is no easy task when it comes to artificial intelligence, McKinsey warns. Information about AI remains incomplete and doubts can be widespread among employees.
2. Competencies: Connect internal and external skill bases
AI experts are still far too scarce: In Germany there is a gap of
3. Raw material: Store granular data
Data forms the foundation for every artificial intelligence. Companies must therefore store as much granular (i.e. sufficiently detailed) data as possible and make this information available in a relational, table-based database. Such data might be images or language-based, as well as data from sensors and machines – a heterogeneous ensemble that needs to be adapted and aligned.
4. Expertise: Combine existing knowledge with AI
AI will not lead to successful outcomes without careful attention to context. A company that wants to introduce AI knows their own sector, their technological environment and the interplay of systems, technologies and employees best. Outsourcing such assessments is not advisable. McKinsey recommends codifying your company's own knowledge, i.e. recording and storing it, and integrating it into the AI algorithm, to best support machines' self-learning.
5. Practice: Test as an agile process
Introducing AI should be an agile process. Simulations and tests in subsections of the company serve to continuously optimize results. AI gains more knowledge (learning), learns to understand it (understanding) and later, applies this knowledge (solving). Errors form the basis for future improvement. The scope of the project determines how long the pilot process lasts. An AI task force might monitor the processes across functions and make decisions about next steps.
However, there is one other thing a company must consider before integrating artificial intelligence into its processes: cultural factors. Many employees may have concerns about security and data protection, worry about jobs being lost, or believe that machine intelligence can never equal human discernment.
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