Artificial Intelligence (AI) has been around for the past 50 years, but it has seen tremendous development in the past twenty years, partly due to new dedicated hardware that makes certain lengthy operations feasible. AI has departed from specialised, technical academia to become a prominent, commercialised product.
These days, AI is at the forefront of various new technologies in the market. For instance, your smartphone, which for many is the first and last thing you see every day, uses AI for its facial and voice recognition. But AI isn’t just shaping our lifestyle; it is revolutionising the way we work.
According to a survey done by Gartner in 2019, AI implementation in enterprises has grown by 270% in the past four years. It is either exciting or worrying, depending on who you ask. Either way, AI has already made its way into industries such as health care, financial services, and retail. Naturally, this development has not gone unnoticed.
There are many people who have negative preconceived notions about AI. They either worry that AI will take over their jobs, or they think that AI is useless, like in the case of chatbots that don’t provide useful answers, there is no middle ground. I believe that these fears are often unfounded, and opinions are misguided. AI won’t be entirely replacing us nor is it pointless, instead they’re likely to augment our work. According to research done by Accenture, AI technologies are projected to increase labour productivity by up to 40% and enable people to make better use of their time.
One of the reasons is that AI accelerates the process of repetitive tasks. It is often done through Machine Learning, which is a branch of AI that helps the system to learn from data by identifying patterns. Other AI technologies besides Machine Learning and Natural Language Processing, are also needed. Combined, they give AI the ability to make decisions in situations where humans were the only players before. AI technologies can now somewhat mimic human abilities as they can achieve similar or better outcomes in many tasks. So, today, instead of doing mundane, repetitive tasks, you would have the support of AI to automate such tasks so you can have a better oversight of the project without worrying about repetitive, time-consuming tasks.
AI provides endless possibilities and opportunities that you should capitalise on. Upon implementation, its impact is immediately palpable. AI can help you to extract information from data with speed and accuracy, saving your time and resources. By embracing AI, you’ll be able to reap the benefits of AI and use it to your advantage. For example, if you’re doing research, you could apply AI to automate information extraction, fill in your database, gather better insights from the data, and it's all done much faster.
Some might think of chatbots as incompetent, pseudo-AI. However, chatbots are increasingly becoming more effective and are saving resources and manpower by providing immediate, unmanned assistance to customers. There are several tasks where a chatbot can be used to provide assistance. One of them is when they’re applied to a particular problem space. Based on what you ask, the chatbot will provide links for you to get to the information that you need. So you won’t have to go through the inconvenience of calling the hotline.
There are strong AI chatbots, which are capable of initiating a conversation with you. For instance, if you buy one of those devices with voice assistants like Amazon’s Alexa and Apple’s Siri, they can execute commands and sustain a conversation with you. You could ask these voice assistants almost anything, and they would execute what you need (provided your instructions are clear, and the given commands are within their repertoire).
There is no stopping the development of chatbots as they are gradually being integrated into more consumer products. 46% of US adults have used digital voice assistants, according to a survey done by Pew Research Center in 2017, and this number is growing. The use of voice assistants is expected to triple by 2023, according to a study by Juniper Research in 2018.
So while you may have had a simple chatbot in the past, you can be rest assured that this technology is improving significantly.
AI is a hot topic in business these days as it’s seen as an indication of the company’s progress, particularly in the eyes of the company’s shareholders, because the use of AI can add brand value and equity to the company. The biggest tech companies conscientiously infuse R&D elements in their keynote announcements to reinforce themselves as both consumer-centric as well as cutting edge. Many MNCs and investment forums today boast about their in-house ‘Tech and AI lab’ or incubator that often generates high profile news which serves as a useful buffer against negative news.
Businesses have embraced AI with open arms and intend to build a future with it as the centre of focus. In a study done by MIT Sloan Management Review, close to 85% of executives believe that AI will give their business a competitive edge. It’s just a matter of time before AI becomes a key player in most tech-dependent businesses.
While companies are eager to ride on the AI wave, they should know that AI generally takes time to be implemented. Not only does it take time to integrate AI solutions to the current system, but employees also need to be assimilated and trained to operate the new systems. The behaviour of the employees often needs to change, taking into account new processes AI implementation leads to.
AI isn’t a standalone field of study like many think it is. It has become so niche that the set of technology used could vary from industry to industry, and it needs to be mentioned within the context of its industry for better clarity. For example, AI technology that is used in logistics isn’t the same as AI used in medicine. Rolls Royce’s autonomous ships use AI to sense and navigate safely at sea, whereas Moorfields Eye Hospital in London uses AI to diagnose and prescribe the right treatment for 50 eye diseases with 94% accuracy.
Every industry has its own set of priorities and problems. So if you want to implement AI for your organisation’s needs, look at AI solutions that are specific to the industry that your company is in. As AI continues to progress and expand, you can expect to see more AI specialists in a particular sector.
In businesses, there are types of jobs that require working with large volumes of information, where each step of this work is usually rather simple. The efficiency of your work depends on how quickly you process that information and extract what is needed to proceed to the next work stage or make decisions.
An AI technology that may quicken up the process is the Robotic Process Automation (RPA). RPA is part of the whole business automation process that has been gaining traction over the past few years. RPA helps a company to reduce staffing costs and human error. List down the step-by-step procedure of the process that you want to automate, and by inputting these steps for the RPA system to follow, it can automate multiple actions and tasks that are repetitive. So this means that RPA will be able to smoothly perform what would have been a time-consuming task for you.
However, the RPA has its shortfall. The information that you want to extract has to be from a document that follows a standard structure which the RPA recognises. And this isn’t always going to be the case. Additionally, RPA takes time to be implemented and can be rather costly.
Knowledge Process Optimisation is a newer area of AI developed to deal with automating complex cognitive tasks that RPA couldn't achieve, until recently, and were well beyond the reach of the software. The technological foundation and enabler of KPO is Artificial Intelligence. The confluence and advancements of AI fields such as Knowledge Representation, Automatic Reasoning, Natural Language Processing, Perception (i.e. Image & Audio Processing), and to a lesser extent Machine Learning, have made possible this nascent industrial revolution in the workplace. For instance, TAIGER’s solutions are trained to read, understand and interpret the information to speed up the unstructured document processing. Powers of attorney, non-disclosure agreements, board meeting resolutions, or employment contracts are all the examples of such documents.
Coupled with optimisation of simple tasks, intelligent automation helps businesses increase efficiency of information processing in a smart way and throughout the whole value creation chain.
There are three key steps when implementing AI solutions.
Firstly, identify the pain points that you would like to correct. It is recommended that you discuss this with a department head or someone who understands the company and its inner workings inside out. For example, one of the largest banks globally, which is a customer of ours, identified that non-customer SME onboarding was a tedious and manual process which turns customers off and digital solutions would need to be explored to reduce the drop-off.
Next, take a look at the track record of your intended AI solutions that you believe might help your organisation resolve the pain points identified. How have these solutions benefited the organisations that have implemented them? Based on the AI solution provider’s library of use cases, you will be able to see a clearer picture of whether the AI solution could work for your company. If it’s a pain point that is not in the library, then it might take longer for the development of an AI solution.
AI solutions these days are customised towards the organisation’s needs. There is no one-size-fits-all solution that meets the needs of every organisation. More often than not, it’s a process of tinkering, checking, and testing to get the solution that works best for you.
So the third step is to consult with specialists and get concrete proposals for the solutions. You not only want to be discussing the product but also the timelines and costs involved. After the initial steps, you’ll be well-prepared for this talk. Many companies work with system integrators and consulting houses to go in-depth to discuss operational viability and project management matters.
Whichever AI solution the organisation deploys, the following rules of successful digitalisation should be taken into account: always know what exactly you want to achieve with this solution; do not separate core business and its IT processes from new AI-driven processes - instead, integrate them; focus on people, new skills and new processes at least as much as on new technologies.
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Councillor and Chairman of Digital Transformation chapter | Former COO
SGTech | TAIGER