- Home >
- Services >
- Access to Knowledge >
- Trend Monitor >
- Domain of Application >
- Trend snippet: 80 percent of large companies have adopted AI to enhance their core business operations
Trends in Security Information
The HSD Trendmonitor is designed to provide access to relevant content on various subjects in the safety and security domain, to identify relevant developments and to connect knowledge and organisations. The safety and security domain encompasses a vast number of subjects. Four relevant taxonomies (type of threat or opportunity, victim, source of threat and domain of application) have been constructed in order to visualize all of these subjects. The taxonomies and related category descriptions have been carefully composed according to other taxonomies, European and international standards and our own expertise.
In order to identify safety and security related trends, relevant reports and HSD news articles are continuously scanned, analysed and classified by hand according to the four taxonomies. This results in a wide array of observations, which we call ‘Trend Snippets’. Multiple Trend Snippets combined can provide insights into safety and security trends. The size of the circles shows the relative weight of the topic, the filters can be used to further select the most relevant content for you. If you have an addition, question or remark, drop us a line at info@securitydelta.nl.
visible on larger screens only
Please expand your browser window.
Or enjoy this interactive application on your desktop or laptop.
80 percent of large companies have adopted AI to enhance their core business operations
Evolution of Artificial Intelligence
Today, approximately 80 percent of large companies have adopted examples of machine learning and other forms of AI to enhance their core business operations. Five years ago, this figure was less than 10 percent. Nevertheless, most companies still only use AI tools as point solutions – for fraud prevention, for instance – discrete applications that are isolated from the enterprise’s wider IT architecture. Every day, AI is getting closer and closer to human-like intelligence, and at some point we may no longer be able to tell the difference between machines’ capabilities and those of people. That said, since AI currently still isn’t able to grasp the full meaning of concepts, its scope of application is slightly limited. That might change, though. After all, when AI was first pioneered, it could only perform tasks that were easy and repetitive. It has since evolved and can now tackle far more multifaceted and complex activities – accurately predicting travel times in navigation apps, for instance. So, who knows what it will be capable of in the future?
Narrow AI versus Broad AI
At present, most AI applications fall under the Narrow AI category – meaning, they’re built for a specific task. In the future, we expect the rise of Broad AI solutions – systems that can work on a broad range of problems. Even though it’s relatively early days as far as AI goes, businesses worldwide are already using AI and deep learning in several unique ways as invaluable tools. Thanks to developments in computer hardware, performance improvements, the advent of cloud computing and advancements in AI technology itself, AI has become accessible to enterprises of all sizes. That said, bigger technology companies are still the biggest investors in this field. The movements in this arena bode well for the future: today, AI is already capable of making highly accurate predictions, and going forward, it will take over more and more activities that humans are currently responsible for.
The other side of the Artificial Intelligence coin
As is the case with various other technologies, data quality and privacy are both still challenging issues when it comes to AI. Perhaps more than any other innovation, though, AI instills much fear in people. There are the more widely expressed concerns – ‘Will we lose our jobs to robots?’ – and then there’s also anxiety about losing control over AI, its outcomes and impacts. As AI models become increasingly advanced, intelligent and complex, we as humans tend to understand them less – we need to be wary of possible biases that we don’t immediately recognise and can’t explain or even interpret. It’s even been found that AI-driven models can make choices that us humans aren’t capable of understanding – the “Computer says no” scenario is an example. Naturally, for many people, that’s a scary thought