With the never-ending potential of technology to disrupt everyday processes, more and more industries are deciding to adapt to one exciting area of innovation today: artificial intelligence (AI). In fact, Global Industry Analysts Inc. predicts that AI will be worth 164.03 billion GBP by 2026, and here, we look at four industries set to be disrupted by AI.
Since the healthcare sector collects and greatly depends on personal data from their patients, AI will play a crucial role in data management. The collection, storage, formatting, and tracing of data are easily done through health chatbots, and diagnosis can be made even remotely.
Case in point: British entrepreneur Ali Parsa founded Babylon, a healthcare service that launched an AI-based app designed to reduce doctors’ workload. The app can receive data about a patient’s symptoms, compare this information to a database of illnesses, identify an initial diagnosis, and recommend a possible course of action, based on the patient’s history.
Aside from patient monitoring, AI also speeds up repetitive tasks such as analysing tests, X-Rays, ultrasounds, MRIs, and CT scans. Moreover, robots are slowly being included in operating rooms for surgery purposes. Because they are more precise and more efficient, they are able to perform suturing and stitching faster than doctors do, decreasing the time allotment for the surgery and relieving the doctors of additional workload.
Autonomous driving may be one of the most revolutionary AI applications in the real world, with companies like Tesla, Uber, and Google developing self-driving cars. Through this innovation, transportation of goods and services will be quicker and more efficient, since the need for rest stops will be eliminated and the cost of hiring drivers will be removed, too. However, there is more to AI in the transportation sector than just autonomous vehicles.
Innovators today are developing connected vehicle systems through AI, which Verizon Connect categorises into V2V, V2I, and V2X technology. V2V, or vehicle-to-vehicle communication technology, enables vehicles to exchange data, like speed and position, with each other to improve safety applications. Meanwhile, V2I, or vehicle-to-infrastructure technology, captures data surrounding the vehicles, such as traffic and road conditions and weather advisories to help drivers reach their destinations safely.
Last and most crucially, V2X, or vehicle-to-everything technology, empowers every automobile on the road to connect with the wider world around it, allowing drivers to prepare for possible dangers and automating the driving process as a whole. Together, these systems are transforming the way we think about transport, and how roads and cities will be built in the future. Already, these technologies are already being tested by Peachtree Corners in Georgia, USA.
The retail sector also benefits from AI through customer data collection and analysis. This can be done with the implementation of chatbots that can capture clients’ reactions and suggestions, turning them into suggestions that can be used in restocking and rearranging products in stores. Because these retail experiences can be personalised, customers will be more satisfied, and consequently, sales will increase.
Aside from renovating brick-and-mortar establishments, another way AI boosts retail is through predictive analytics in online shopping. By analysing browsing patterns and site purchases, e-commerce companies, like Amazon, can accurately predict what customers will buy next, therefore maximising their sales. The company also does this with their self-shop stores, Amazon Go, which provides customers with an autonomous shopping experience through machine learning and image recognition.
The potential of AI also extends to all the steps of a software development life cycle (SDLC). In a previous Logit.io blog post, the six stages of SDLC were thoroughly discussed, proving just how lengthy and detail-oriented the entire process is. But with AI technologies like advanced machine learning, developers can shorten the time and planning often required. AI assistants can also help in saving time used for coding preparation, debugging codes, and preparing project documentation.
The analysis of system logs can also be made easier with the early detection and flagging of errors. In the future, there is a possibility that these errors can also be corrected even without human supervision. Lastly, using data from previous and existing projects, AI can identify loopholes in the process, decreasing the risks the next time developers create another project. With these, the software can be built faster.
While AI is already widely used, it has yet to reach its full potential. We are simply at the beginning of the adoption curve for this technology, and what it achieves in the future is something we're all excited to see.