Technological innovation definition
Digitalization and technological innovation offer countless advantages to both service and industrial companies – but at the same time presents them with new challenges. In particular, the processes running in the background are increasingly coming into focus. Modernizing existing IT infrastructures is therefore a top priority. There is no way around technologies such as cloud computing, big data, artificial intelligence (AI) or blockchain. These pave the way for digital transformation, enable new business models and thus secure future viability.
For banks and financial service providers, the focus in recent years has been primarily on regulatory requirements and improvements at the customer interface. However, since the entire value chain must be considered with regard to digitization, the processes running in the background are becoming increasingly important. Many companies are struggling with the limitations imposed by outdated IT infrastructures. These have often developed over years and led to intransparent as well as inefficient processes, which massively restricts agility and the ability to innovate.
Innovative technology and technological innovation examples
In our article innovation vs. invention, we explained the differences between the two terms. Based on this understanding, we have compiled some breakthrough technological innovation examples based on previous inventions.
1. Technological innovation example: electricity
2. Technological innovation example: the laser
Discovered in 1960, lasers were so ahead of their time that scientists were not even sure exactly where they could be used. Over the years, lasers have found their way into almost every field. From medicine to manufacturing to consumer electronics. In fact, almost everyone today comes into contact with a laser in some form on an average day.
3. Technological innovation example: quantum computing
4. Technological innovation examples in the banking industry
While many traditional financial institutions are still struggling to find their digital direction, digital players such as Apple, Google and PayPal are gaining an ever-stronger influence on payment transactions. More than ever, banks are therefore dependent on agile partners with digital DNA who are able to provide faster upgrades for existing business processes or develop innovative applications.
Yesterday's success with innovative technology says absolutely nothing about tomorrow's success
Unfortunately, many companies prefer to optimize their past instead of tackling something fundamentally new. Their managers are not creators, but administrators, because the system in which they bear responsibility does not reward daring. They are expected to land on target – and are rewarded for it. The result is a lack of ideas, risk aversion and hesitancy.
In such an environment, people prefer to turn small screws, but not the big wheel. People prefer to innovate in a three-step mode, but not to reorient themselves. In this way, groundbreaking innovations have very bad cards in established organizations. But with bad cards you lose a game.
Every technological improvement means that the next improvement can be achieved more quickly. With such unpredictable dynamics, it is impossible to know in advance what will work and what will not. Those who procrastinate and wait to see how things develop will miss the boat. Timely innovation is therefore a must.
In our book “Creating Innovation” you will have the opportunity to learn more in a totally unprecedented format.
Wave towards technological innovation in medium-sized companies
On the other side more and more medium-sized industrial companies are also recognizing the enormous potential of innovative technology. Cloud solutions in particular are becoming increasingly popular. They make it possible to open up new business areas that take advantage of AI and Machine Learning. For example, the evaluation of all production data in real time opens up the opportunity to completely rethink complex processes – for example, using a digital twin that simulates production. In addition, machine learning methods help to ensure consistently high quality and to design maintenance cycles in such a way that the risks of failure are reduced to a minimum – keyword predictive maintenance. The more digitally processed operating data is available, the more accurate statements and forecasts are possible. Even processes that become products and machines that control material inventories or settle supplier invoices via blockchain are no longer utopia in the Internet of Things of today. But here, too, the key to successful digitization is the appropriate IT expertise.
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