How to handle Intelligent Automation
A few points to consider when reshaping the future of work and business by Viktor Weber, consultant, WEF- author and speaker on matters of digitalization, as well as innovation in the built environment and beyond.
Point 1: Values
Data generated from the “built world” has an enormous value- added potential. New business models are emerging based on the use of these collected data. But, we should perhaps also be prepared for stages of disillusionment resulting from data rush, e.g. when it becomes clear that not every data point has to be collected in real time and that some models can’t be constantly optimized. The data market will therefore become more differentiated.
Point 2: Data
How do we capture the world, robustly and reliably, and map it digitally so that the data flow remains meaningful across system boundaries?
that are as dynamic as the market environment and one’s own business reality i.e. they adapt constantly (sometimes hard to develop if, for example, the amount of data is insufficient).
MACHINE–2– MACHINE MACHINE–2–HUMAN HUMAN–2–HUMAN:
How to prevent data loss due to interruptions in the system, and how to capture the required data along the value chain?
Technologies can never be considered alone. In artificial intelligence, for example, sensor technology is crucial for being able to use data from the “built world,” as well as for training and implementing models for such things as machine learning.
Technologies can never be considered alone.«
Point 3: Beware of the Myth Trio
#1: IT IS SMARTER
Just because something claims to be smart or intelligent, the technology can still be far from intelligent. As things stand today, there is no machine and no system that has human cognition.
#2: WE NEED TO AUTOMATE EVERYTHING.
There are applications, especially in the repetitive, numerical range or in the field of mass data processing, where automation is extremely useful. But there are exceptions where it doesn’t make any sense.
#3: THE FUTURE IS EXTREME.
That debate should be more constructive, characterized by less showmanship or non-informatics, which simply give incorrect information when it comes to blockchains and artificial intelligence. Half-truths are not helpful.
Point 4: Digital Competence
Recognizing digital trends, understanding the fundamentals of digital technologies, such as AI, and applying this knowledge to business models, will become decisive factors in an increasingly competitive environment. So, intelligent automation needs digital competence based on five steps.
1. UNDERSTANDING: the business environment, employees, future trends, and problems.
2. LEARNING: a fundamental part of the business strategy, from intern to CEO.
3. CREATIVITY: Collect ideas and inspirations from the company in order to develop a sustainable vision and break it down into milestones.
4. TECHNOLOGIES: Use where meaningful, digitize and automate where it makes sense, and don’t forget that innovation can also be analog.
5. REFLECT: Collecting and gauging strategies, knowledge, and feedback without having worked out the master plan from the start (which doesn’t exist, anyway).
Point 5: Checks & Balances
IT’S GREAT TO DREAM of a world in which technology can reverse today’s problems. In doing so, however, we too often treat only the symptoms of a problem, not its cause.
INNOVATION can be painful, in the sense that when we realize that a previous behavior was wrong and that the money has gone. Then we have to change it, even if it’s uncomfortable.
TEST YOUR OWN JUDGEMENT, for example, with the simple game of thought: would you finance a project that claims that the error rate in image recognition can be minimized by x percent in two years? Or would you prefer a project that promises to revolutionize the industry with artificial intelligence and blockchain?