Tuesday, 18 Feb 2020


Many may be familiar with virtual assistants such as Apple’s Siri, Amazon’s Alexa, or even Samsung’s Bixby, but what some may not know is these innovations are a reality thanks to artificial intelligence, and that these examples represent just a fraction of what artificial intelligence (AI) can do.

Some may associate AI with humanoid androids in movies, robots in factories or Matrix-like sci-fi dystopias, but one way of looking at AI is in the context of business, and its power to transform your business.

What is AI; where did it come from?

AI is certainly not a new concept, having been introduced in scientific fields as early as the mid-1950s, but the awareness of AI within mainstream society certainly is a millennial phenomenon. 2018 was the year that AI truly became a widespread essential element in many industries.

The journey from AI as a concept to the foundation of merely a system and finally the realisation of AI as a technological reality, has taken decades.

Today, AI is quite commonplace, not just in consumer households (in forms other than virtual assistants but also in the business sphere.

AI, at its simplest, is the term used broadly to refer to any type of computer software that utilises, mimics or replicates human-like activity, specifically, the human ability to solve problems, to learn and even to plan.

To use an analogy, we can refer to a Lamborghini as a car much as how a Honda is also a car, and so the same can be said of AI – it is a general term that does not cover the full spectrum of specifics of usage or application of this revolutionary technology.

To understand AI in the context of business is to look at sub-areas of AI, such as machine learning, and to understand that AI has been part of our business lives as early as smartphones were conceived.

The AI revolution is mainstream

According to a report from PwC, AI is set to contribute more than USD15 trillion to the global economy by the year 2030, with the world’s two largest superpower economies, the United States and China, set to benefit from three-quarters of this GDP growth.

Whittling down the boom in the use and integration of AI technology into business, we will see, firstly, in which industries AI can be and is being used, and secondly, how businesses can realise the potential of AI.4

Using AI to solve business problems

Businesses of all shapes and sizes can benefit from the integration of AI into their real-world real-time business processes and solutions. From increasing sales, detecting fraud, improving customer experience, automating work processes and providing predictive analyses, AI can contribute to efficiency and the bottom-line in some degree and form.

For instance, AI-based algorithms form the basis for chatbots and virtual assistants such as Siri and Alexa. Apart from making life measurably better for consumers in terms of time- and energy-saving, these innovations are the precursor for greater technological assistance in the form of other products, for example, if we imagine a refrigerator that can detect when food is past its expiry date. For the manufacturer, AI spells potential for future profits as consumer demand and tastes evolve.

    For the business owner and operator, AI technology can automate processes, which may seem like an obvious application, but the real benefit is the minimisation of errors often associated with human error.

    Where accuracy and speed are the ideal benchmarks for a business operator, AI provides that potential. In deploying an AI-based solution to automate processes, and with the progress of time, systems can learn and produce smarter, more consistent outcomes. With human capital, there is always the possibility of error, miscalculation or a costly oversight.

    More data, more machine-brain power

    As many business leaders know, data is paramount in today’s modern era of business. Data provides insight, helps with predictive analysis and data can influence future business decisions. 

    Machine learning is a common type of AI deployed for business purposes, specifically when large amounts of data need to be processed quickly.

    With such large volumes of data present in artificial intelligence and in business, businesses must protect this data and its integrity. (A loss or corruption of such data can mean costly errors.) Investing in protection for enterprise data should be considered a valuable business investment, and solutions such as Celcom’s Cloud Secure Web Security and Cloud Secure Email Security provide businesses with these safe tools. Businesses will find these reliably provide adequate protection against cyberthreat and cyber-risk, an ever-growing risk, while the cloud-based solution means the business need not worry about extra infrastructure for such solutions. The compatibility with Windows, iOs and mobile interfaces ensures flexibility and ease of use.

    The protection of such data contributes to the smooth flow and protected continuity of data analysis. Without secure data, artificial intelligence programmes may not function as desired.

      Machine learning functions primarily on algorithms (defined as a process, rules, and calculations used by a computer for problem-solving).

      The difference with machine learning, a type of AI, is these algorithms ‘learn’ over time, getting better at what they’re doing the more often they do it. 

      When more data is fed into the machine learning algorithm, its modelling should, ideally, improve.

      When vast pools of data are captured by devices – especially connected devices and IoT-enabled devices – machine learning then comes in handy for processing all this data for humans to use.

      For instance, a manufacturer may have connected the machinery in the plant to a network. These machines and other supplementary or ancillary devices will collect and churn data about production, function and more, into a central repository.

      Machine learning then analyses this data streaming in – this is AI at work – and patterns and anomalies are identified. For the manufacturer, having this done quickly, quicker than a human could compute, and with minimal error – errors that are possible with human capital – means costly mistakes can be avoided, or even, ideally, prevented.

      A more advanced form of AI is deep learning, a specific form of machine learning that can be deployed in fraud detection. Again, the root of this activity is analysis, based on data capture and data reading for patterns and anomalies, but with more factors and non-linear reasoning.

      Will AI replace my human teams?

      The answer is not a simple, straightforward answer as we have to consider the context. 

      While some business leaders stress that AI is a supporting tool rather than a one-size-fits-all blanket replacement for human intelligence, ingenuity and effort, other business leaders recognise AI’s capability in processing and analysing large troves of data far quicker than a human brain could, returning with information to support the next cause of action. 

      That this is done quickly is an advantage over human intelligence. 

        Being able to streamline decision-making with every possible scenario of possible outcomes – based on data – can help business enormously.

        Other business analysts term AI as the next generation of software, in that AI is smarter, faster, able to learn and make decisions on its own, while recognising the limitations of AI insofar that it has difficulty completing common-sense tasks in the real world. 

        In that respect, depending on the industry, the sub-sector, the nature of business as well specific business objectives (revenue generation, process automation, etc), AI can complement human intelligence for business improvement and advancement, but business still needs to be populated by human intelligence in areas such as service or customised services.

        The future of business is exciting and filled with possibility, and AI looks to be a commonplace fixture of that future.