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What is Artificial Intelligence?

A guide to its business application

Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, mainly computer systems. The term can be applied to any system that perceives its environment and challenges and can then decide to achieve a goal. AI applied to businesses follows the principle, where it analyses business data and takes decisions based on this data. In its most advanced form, AI is similar to a human brain and can cognitively analyse information and take appropriate actions. Francois Chollet, AI researcher at Google and creator of the machine-learning software library Keras, has said intelligence is tied to a system's ability to adapt and improvise in a new environment, to generalise its knowledge and apply it to unfamiliar scenarios.

How does it differ from deep learning and machine learning?

Machine learning is a subset of AI, and it consists of techniques that enable computers to figure things out from the data and deliver them to AI applications.

Machine learning is an application of AI in which algorithms break up data, learn from that data and then use what they have learnt to make informed decisions. If you take the example of Netflix, you can see machine learning in use here. The algorithm uses the data it has collected on your view preferences and those with similar preferences to recommend what to watch next.

Deep learning is a subfield of machine learning that structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions independently. While deep learning is a subset of machine learning and functions similarly, the capabilities are different. Machine learning models get progressively better as they analyse more and more data, but an engineer needs to step in and make adjustments if they produce an incorrect outcome. With a deep learning model, however, an algorithm can determine whether an outcome is accurate or not through its neural network.

There is a lot of buzz around AI today and we interact with these cognitive systems daily, without even realising we are doing so. An example of this is our daily interaction with advanced web search engines or recommendation tools such as Google, Amazon and Netflix. However, AI is not a new technology and was founded as an academic discipline in 1956, although its application to resolve business problems is relatively new.

Artificial intelligence (AI) is steadily passing into everyday business use. From workflow management to trend predictions, AI has many different uses in business. Various use cases of AI include improving customer services with virtual assistant programs, automating workloads by using algorithms to categorise work, and predicting performance or behaviour.

AI offers a massive opportunity for businesses. A recent global market survey suggests that the global market for Artificial Intelligence (AI) estimated at US$43.1 Billion in the year 2020, is projected to reach a revised size of US$228.3 Billion by 2026, growing at a CAGR of 32.7% . Contradictory to this, analysts also believe the adoption of AI is being slowed down because of various factors, including lack of expertise, non-supportive data platforms and high perceived costs.

Why adopting and integrating AI Technologies is difficult?

Even though AI is not a new technology, its application to businesses is relatively new. So there is a common perception that the value AI delivers is questionable while the technology is still in its infancy.

A recent survey report from Delloitte, states that 47% of senior decision-makers who were part of the survey found it challenging to integrate cognitive capabilities into existing projects.

Businesses such as Amazon, who have successfully managed to integrate cognitive capabilities into their business operations, can gain the competitive edge by providing better customer service and using predictive data to maintain a lean supply chain.

The report also lists some of the top challenges slowing the adoption of AI tech:

Although the cost of implementing an AI has declined in recent years, it is still considered prohibitive by most businesses. Most AI enthusiasts who have secured a budget for cognitive technologies are being very cautious in its usage. Hence, the use cases are scarce and primarily used for less risky operations such as chatbots, virtual assistants and converting speech to text or vice versa.

There is no AI without IA (Information Architecture)

Another major factor holding back businesses from implementing AI models successfully is the absence of reliable data models. The growth of unstructured data poses a significant threat to businesses as they are being forced to rethink their data management strategies.

The opportunity AI offers

In more recent times, big companies have been more and more invested in the use of AI. Amazon has been using AI to predict what its customers are likely to buy before they have even thought about buying it. They can then use this data to ship items to a warehouse close to your address and reduce the delivery time when you do order them. Netflix had been using AI algorithms to recommend new content to its users; it has now evolved to using AI during the preproduction of its shows to scan the availability of actors and locations for shooting.

As these larger companies invest more and more into AI research and development, we start to see a trickle-down of the technology becoming available to a broader range of businesses. There are many reasons companies of all sizes are excited about implementing AI. The main aims when employing AI is to reduce operational costs, increase efficiency, grow revenue and improve customer experience. By successful employing AI correctly, a business can expect to;

· Save time and money by automating routine and manual processes.

· Increase productivity whilst decreasing operational costs.

· Use the output from AI technologies to make faster, more accurate business decisions.

· Cut out human errors such as miscalculations or typing mistakes.

· Use insights to predict customer behaviour and preferences to then offer them a more personalised experience.

· Data mine at an increased capacity to generate new leads and increase sales opportunities.

An example of an AI as a service platform is the chatbot technology company “Certainly.” They have been empowering customer service teams, in big brands worldwide, with their conversational AI technology. The platform comes with a database of over 24,000 sentences and 1 billion variations, meaning it can be quickly implemented and personalised for businesses to use. Being an automated process, the platform can always be on hand to deliver support to customers 24/7. It can also deal with various issues from complaints, returns, refunds, tracking and tracing.

IBM has also developed many applications, using IBM Watson AI, which business can access as a service. One of these, Watson Speech to Text, uses advanced statistics and cognitive thinking to transcribe high and low-quality audio to text. One of the business sectors that has taken advantage of this technology is call centres. Using the technology, they are now able to transcribe millions of hours of calls into text. They can then analyse this text to look for common problems or issues their customers call in about and work to resolve them before they arise. As the technology grows more and more accessible, speech to text technology will be implemented in every workplace and meeting. Using the technology will mean employees no longer have to worry about taking notes and actively engaging in the discussions.

ABP sees the future potential of AI

At ABP, we recognise that AI will become be a powerful tool for businesses to implement. That is why we are now building our own AI practice based on our experiences dealing with data and automation. We will be working with our clients to rethink how their work gets done and by whom. Our ready to use services will help to support businesses as they embrace and drive their digital transformations. By leveraging emerging technologies, we can create fully automated, transparent, end to end processes to be deployed across a range of sectors within a business. From finance, IT, marketing, HR to legal, all can benefit from successfully applying the right technology to the right processes.


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