The primary function of AI (AI) and machine learning (ML) is to teach computers to recognise patterns within unstructured data, it then transforms this into structured data that can be drawn upon for automated responses.
From the Board room to the factory floor, AI is already being used today to automate many everyday operational tasks, freeing up time and helping to complete the tedious and repetitive tasks that humans have had to complete in the past.
There are countless examples of the technology within almost every industry. The medical field uses AI for image recognition with X-Rays or diagnosis, retail vendors utilise machine learning to monitor customer behaviour and HD cameras are used for image recognition to carry out inventory management. Industry analysts estimate AI will be built into nearly every new software update to hit the market by 2020. Organisations that make the shift earlier will have a major competitive advantage.
As well as the healthcare industry using AI for better diagnosis or research, the insurance industry is another area that’s embraced AI. Using the technology, insurers can help estimate analysis on claims. Using a picture of the damaged item, the company can run it through the damage photo database in near real-time to give an estimate claim payout. A great example of how AI eliminates the need for tedious tasks.
Despite the positive impact AI has on many industries, one of the biggest concerns is employment, with some workers concerned that AI will replace them. Strategically, AI helps businesses to carry out tasks more efficiently and bring cost savings. However, the “robot takeover revolution” is far too over-played.
Instead, workers should be embracing the technology to unlock new opportunities and concepts that weren’t economically viable in the past. AI and machine learning enables businesses to embark on projects and innovations that were once thought to be too costly or time-consuming. If a project cost is 100% manual, it isn’t cost effective to move forward with. However, if businesses can reduce this to 50% manual cost by utilising AI it now becomes a possibility.
Harnessing AI opens up innovators to a wider range of opportunities. When deciding on how to put it to work, IT professionals must consider the value it brings to day-to-day business. The technology is best placed to solve repetitive tasks that drive up cost, so identifying these projects is key to significantly helping a business.
Putting AI to work on automated businesses tasks can result in better quality of service, better customer experience and lower costs. Therefore, when analysing the benefit to drawback ratio businesses must evaluate the overall impact to operations. For example, using automation within GP surgeries to sift through large quantities of patient data is much more cost effective than using a human. Furthermore, giving the task to technology will enable workers to focus on more important jobs which better customer experience.
Gartner research revealed that those businesses failing to apply AI and machine learning will cease to be operationally and economically viable by 2020. This demonstrates that these technologies are no longer futuristic, and are in fact ready to go now and critical to the future viability of almost any organisation, regardless of industry.
Short term, IT is likely to not currently be the driving force sending businesses towards AI. Instead, it’s those in touch with the businesses current demands who are currently driving IT to use AI. Many organisations begin with one-off projects using AI and once these types begin to succeed, and the cost comes down, more projects will move to IT for scale and integration with structured data processing.
Whilst AI processes unstructured data, it produces structured data that needs integrating with other structured data to carry out a complete solution. Take an automated “greeter” for example. A customer can walk in, hold up the product they are looking for in front of a camera and it will tell them the location of the object and whether it’s in stock. The image recognition needs AI-enabled training, but the system needs to integrate with the product catalogue and the inventory control and POS systems as well.
Let data guide your AI path
The underpinning element to effectively deploying AI is data. Without sufficient data, AI cannot operate to its fullest potential and bring the best results. Large data sets ultimately win when it comes to training neural networks. However data has depth; it is expensive, time consuming and complex to move, so the project has to move to the data. In order to do so, businesses must decide where they are going to run these tasks – public or private cloud – and carry out AI operations where data exists.
Whether you’re ready for AI or not, it is coming. Every industry – without exception – are going to be impacted, it’s the why and how which isn’t still challenging to accurately predict.
The biggest companies won’t necessarily be the winners in deploying AI, or the best in marketing the technology, but rather those businesses that are continuously adaptable forward-thinking. AI is already beginning to level the barrier to entry by giving companies of all sizes and varieties tools to innovate, and the future AI leaders will be the ones to re-shape the way business is conducted.
This article is from the CBROnline archive: some formatting and images may not be present.
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