EN | DE

Enterprise Guide for Make-or-Buy Decisions

November 25, 2021

Applying AI

Enterprise Guide for Make-or-Buy Decisions

“The field of AI is developing at a rapid pace, and hardly any company is (or should be) able to tackle all the issues on its own. A systematic approach to the make-or-buy decision is needed. However, to date most companies have not approached this question systematically at all or, even worse, they have simply delegated this decision to their standard IT purchasing process.”


With the increasing adoption of AI applications across all areas of a company — from marketing and customer service to production control — one question is becoming increasingly pressing: Should we develop AI solutions in-house or purchase commercial software? In short, this is the “good ole” question of make or buy. This report is intended to provide helpful guidance for make-or-buy decisions with AI. It contains the following topics:

  • General structure of the make-or-buy question for AI.
  • A framework together with decision criteria for solving the make-or-buy question for an individual use case from a lifecycle perspective.
  • Selection of the optimal partner, potential pitfalls of different partner types, and aspects of a good partnering strategy.
  • Elements for contracting in the context of AI use cases including a set of guiding questions.

Make or Buy Whitepaper


Download the whitepaper

Contributors

This report is the result of the appliedAI working group “Make-or-Buy decisions in AI” and has drawn on the experience of leading experts from appliedAI partner companies:


Dr. Stefan Dierks
, Senior Development Engineer, Corporate R&D, Rohde&Schwarz GmbH & Co. KG

Matthias Neuenhofer
, Project Manager Corporate Strategy, BayWa AG

Daniela Rittmeier, Head of AI Hub, Artificial Intelligence Center of Excellence, BMW Group

Thorsten Stein
, Purchaser for Data Science, BMW Group


Authors

Philipp Hartmann serves appliedAI as Director of AI Strategy. Prior to joining appliedAI, he spent four years at McKinsey&Company as a strategy consultant. Philipp holds a PhD from Technical University of Munich where he investigated factors of competitive advantage in Artificial Intelligence.

Andreas Liebl
is Managing Director at UnternehmerTUM as well as appliedAI. Before joining UnternehmerTUM, he worked for McKinsey&Company for five years and completed his PhD at the Entrepreneurship Research Institute at the Technical University of Munich.

Maria Schamberger
is a Senior AI Strategist at appliedAI. She has a rich background within the financial services industry from her former role as Vice President at the Allianz Group as well as consulting and research experience from McKinsey&Company. Maria studied Corporate Innovation at Stanford University and Banking at the Frankfurt School of Finance and Management.

Relevant whitepapers for you

How to find and prioritize AI use cases

Relevant whitepapers for you

Building the organization for scaling AI

Subscribe to the newsletter

Subscribe to appliedAIs newsletter to get more practical insights into applying AI