The best Change Agent is not a Change Agent
Co-authored with Nina Weingarten, Senior Consultant in our Munich office Reflecting on Change Few words have recently been used as ambiguously and non-specifically as…
Companies collect huge amounts of data on all kinds of business processes. Analyzing this data and turning it into tangible business value is uncertain, complex and time-consuming. A rapidly growing number of artificial intelligence-based software solutions aim to solve this challenge by using intelligent algorithms to analyze data sets, derive insights, and automatically recommend actions. Beneficial use cases can be found along all stages of the value chain; getting started is often easier than it seems. Examples show that ROI can be achieved as early as after just one day.
Artificial intelligence (AI) is a subfield of computer science that aims to enable machines to replicate human thinking and decision making. This is often achieved with machine learning (ML) algorithms, that continuously learn from datasets. In many cases the current discussion about AI is in fact a discussion about its subfield ML.
In comparison to classical systems (hardcoded and simple rule-based), ML algorithms can learn from existing data to react to previously unknown situations. Example: AI can learn what a person looks like from several existing images and then apply that rule to new images and recognize that person. This is possible due to the ML algorithms’ ability to recognize patterns in data and derive rules and statements from it. ML can be used in situations where large quantities of complex and unstructured data must be analysed and searched for patterns. It is faster and more accurate than human analyses of the same.
The potential use cases for AI are numerous but have yet to be fully understood. Here are some relevant business examples:
In many cases, companies are hesitant to introduce investment-intensive technologies – and often wait until best practices appear on the market and risks are foreseeable. However, when it comes to AI, hesitation might lead to a significant loss of opportunity. Experts go as far, as to call AI as an essential building block for the future viability of companies. Within the last two years we observed 5 major challenges, that companies are facing when implementing AI technology:
Despite these challenges, AI usage is on the rise. In a 2021 McKinsey study with over 1.800 international companies, 56% of all respondents reported AI adoption in at least one business function. Furthermore, 27% of respondents reported a minimum of 5% EBIT that is attributable to AI.
Today, the benefits of AI outweigh the costs in a wide range of use cases. Using the Digital Value Canvas® (read here more about the methodology) the benefits of AI usage in the example of industrial manufacturing can be mapped as followed:
Excubate supports companies in identifying technology use cases, formulating respective strategies, and identifying the right partners. In this article we want to give a short introduction to our standardized, six-step process for strategic (AI) partner selection. It enables a fast understanding of any technology as well as an implementation kickstart.
Our process results in a wide market overview and a top 5 player recommendation for each respective use case in scope. It enables our clients to directly engage best fit partners and quickly introduce value-adding solutions to the core business. We directly tackle the most common challenges companies have (as were stated above):
Output example: Best-fit players mapped along client sales value chain (each coming with respective detail one pager)
Solution example: dismiss unplanned machine downtimes by analyzing vibrations
Rotating equipment in manufacturing environments is often subject to issues, such as bearing damage, imbalance faults, cavitations, misalignments, and gearbox faults which often aren’t detected until it’s too late.
These typical problems result in inefficiencies and downtimes which, in general, can lead up to 20% of total production costs and 10% of global production losses. In the automotive sector, for example, average costs of 2.5 million Euro accumulate from just a one-hour outage.
In a past research project, Excubate applied the described research approach and identified a company that can solve such challenges. This particular solution provides AI-powered vibration-based sensors and associated software to avoid unplanned downtime of rotating equipment. The predictive maintenance solution supports early fault detection and thus avoidance of damage, production downtime, and loss of production. Testing of this solution is possible even in just a couple of days. Through a fast sensor installation, subsequent data collection and intelligent analysis, it is possible to detect first anomalies after a couple of days. This can lead in the best case to a return on investment after the first day of solution deployment.
Have we piqued your interest?
Contact us (jan.hartung@excubate.de) to make AI-based business benefits a reality for your company. Realize a fast ROI with the right partners.