Advanced Technologies That Drive Business Transformation


Advanced technologies capture and enrich data to generate insight and power intelligence at scale, freeing people up for more critical work. This technology drives business transformation and empowers organizations to move faster and wiser.

Automation technologies encompass various products such as robotic systems, numerically controlled machine tools, and semiconductor production and assembly machines. Virtual and augmented reality systems also fall within this realm of technology.

Artificial Intelligence (AI)

Artificial Intelligence (AI) is the technology behind machines we depend on daily to complete many of the tasks we take for granted. This field encompasses subfields such as machine learning and deep learning that use advanced algorithm-based systems to automate processes and solve complex problems.

AI may not seem like something we notice every day, but AI is already part of life. From smart speakers on our mantles that play music or provide weather updates to popular chatbots like OpenAI’s ChatGPT or Siri on our phones – AI has long since become part of daily life.

McKinsey report shows that many companies are dabbling with AI rather than diving deep, falling behind industry leaders making full use of this technology. Successful businesses of tomorrow will rely on AI to improve existing processes while finding innovative ways to tackle problems that would otherwise be too challenging to address manually.

AI can play an essential role in cybersecurity, helping businesses detect and block malicious activity before it threatens to compromise their bottom lines. AI improves customer service, speeds up credit scoring processes, and automates time-intensive data management tasks.

AI can also be applied to telemedicine, MRI scans, and self-driving cars in the healthcare and automotive industries. These tools help ease the workload on medical professionals so they can focus on more urgent cases; AI-powered MRI scanners, for example, can identify tumors much more rapidly and with lower margins of error than traditional radiologists could.

Machine Learning

Machine learning (ML) is a subfield of artificial intelligence (AI) used to automate data processing and make predictions without human intervention, detect patterns in large datasets, and accurately predict them. ML applications span from retail banking and retail stores, manufacturing plants, and automotive dealerships up to retail shops themselves – companies wanting to remain competitive must understand how ML works and its implications on their businesses to stay ahead.

Machine learning (ML) offers businesses many benefits for improving processes, creating tailored customer experiences, and developing innovative products and services. Unfortunately, however, machine learning requires significant investments of time and hardware as well as large amounts of accurate data free from noise or bias to work efficiently; this may present some businesses with difficulties; luckily open source frameworks like TensorFlow, sci-kit-learn, Caffe, and Torch are making it simpler to utilize machine learning among non-programmers.

AI technology has become a part of everyday life through voice assistants like Alexa, Siri, Cortana, and Google Assistant. AI also plays an essential role in self-driving cars, mobile phones, and medical imaging/diagnostics – for instance, ML can enable machines to learn to recognize specific markers of illness on mammograms.

Advanced technologies will profoundly affect our lives in the coming decades, from greener economies and supporting entirely new industries to encouraging entrepreneurship and job creation. The European Union (EU) is working hard to ensure its businesses fully utilize these technologies. Yet, the uptake across sectors, regions, and EU Member states varies significantly due to a lack of awareness around the potential benefits and opportunities they present.


Robotics refers to the use of robots to perform tasks. This technology enables machines to mimic human movements and actions using tools. Robots have become widely utilized across industries, including construction, entertainment, education, and medical settings for surgical procedures or rehabilitation, providing access to dangerous environments more difficult for humans to reach.

Robots have become a more prevalent workplace presence over the last several years. Their ability to complete repetitive tasks more precisely than humans makes them an appealing solution for many businesses, though some worry that robots will replace human jobs entirely. It’s important to remember that robots will work alongside humans rather than replacing them – increasing productivity and improving efficiency at all costs.

Robots are safer to work with than humans as they don’t require breaks or vacations. They can work around the clock without interruption, helping manufacturers lower production costs and become more competitive.

Most producers recognize that advanced robotics is an integral component of a future-ready factory, believing it will enable them to make faster decisions and respond more rapidly to changes. Unfortunately, however, they’re worried about its high costs and difficulty of integration into existing processes.

To overcome these difficulties, producers must determine how advanced robotics can fit into their current operations and identify enablers that will help implement and deploy robotics – including an idealized vision of their future factory, organizational competencies, and appropriate system architecture.

Big Data

Big Data refers to an abundance of complex information generated from sensors, devices, video/audio streams, networks, transactional applications, log files, and web usage that is too large or complex for traditional databases to store or requires intensive processing power to analyze.

Extensive data analysis can be performed in real-time to improve business performance and make informed decisions more quickly while helping reduce costs and risks. Trucking companies use big data from various sources to identify and remove high-risk policyholders for safer operations and reduced insurance premiums; police departments use it to predict crimes before they occur and stop them before they happen; businesses utilize extensive data analysis to optimize supply chains and anticipate customer needs.

There are three primary characteristics of Big Data, namely Volume, Velocity, and Variety. Velocity refers to the speed at which data is created and processed – sensors, social media platforms, and application logs continuously make vast volumes of data that need fast processors to process quickly.

Storage and processing these enormous volumes of data requires innovative solutions, spurring advancements in cloud, hybrid cloud, and data lakes technology, and advances in SQL query engines such as Drill, Hive, Impala, and Presto.

Big Data offers many advantages to both existing and emerging markets. By studying customer behavior online, businesses can analyze customers to predict their product or service needs more precisely – enabling personalized experiences and increasing customer satisfaction for all parties involved. This benefit benefits growth-oriented businesses that collect and analyze Big Data more actively than non-growth ones.

Internet of Things (IoT)

IoT directly connects internet connectivity, data processing, and analytics to physical objects. This technology can help businesses reduce manual processes costs while improving customer service and supporting new business models. Sensors connected via IoT may monitor industrial equipment in factories, warehouses, vehicles, home appliances, office facilities, retail spaces, or healthcare facilities.

Smart thermometers upload information about their activation times and temperatures to the internet with integrated data processing software, then use this information to self-calculate how to improve their functionality over time. This takes out much of the human element from this process and makes it more automated; this is just one example of how IoT can enhance our lives in many ways.

IoT technology can be applied across every area of business and domestic life, from monitoring mechanical and electrical systems in buildings to improving logistics for fleet management. Additionally, it can be utilized by the construction industry to detect structural changes on bridges or facilities or to enhance public safety by monitoring traffic congestion and decreasing energy consumption in cities.

One of the main challenges associated with IoT technology is securing its data transmission channels to prevent unauthorized access to sensitive information. Furthermore, its ecosystem must develop a cohesive security framework across devices and applications, such as creating protocols dictating when sensors transmit their knowledge and measures designed to prevent hackers from exploiting devices; a complex process that may take years before perfection can be reached.