Rather, we should encourage debate, communication and learning between technology experts, business and the profession to envision the way we solve fundamental business problems with the help of new technologies.

THINK RADICALLY

In doing this, the profession also needs to be open to more apprehensive change. Where AI enhances greater understanding from data, it enables experts to make take decisions and provide better directions. Evidence suggests that humans and computers working together in areas as diverse as chess and medicine produce better results than human or computers in solitude. Furthermore, it needs to focus on the specific skills and qualities that accountants have. It goes beyond technical knowledge to integrate qualities such as professional suspicion, acquiring and applying insight from numbers, and deriving that numbers can be trustworthy. Humans have peculiar characteristics, such as management, sympathy, flexibility, imagination, and innovativeness, which can never be replaced by computers. Humans make judgments often due to the replacement for lack of data.  However, powerful computers with access to varied sources of data may well replace the need for human judgment in the vast majority of cases.

BE ADAPTABLE

It is not possible to foresee the degree to which computers will replace human decision-making over the next 20 to 30 years. The long-term future of accountancy will eventually demonstrate how we, as humans, see and shape our correspondence with powerful systems. This will be determined by the span of economic, social and political factors. As a result, an open-ended approach is necessary when thinking about the future of accounting. The skills and learning program for the future accounting professional in specific has been subject to a lot of argument among professionals. Most would accept that accountants will need more skills in areas such as technology and data. And greater significance would be required for soft skills, critical thinking, and adaptability. There also needs to be greater attention on life-long learning. When the business needs modification, and all the stakeholders agree, the profession will be able to accept. Accountancy organizations introduce new practices all the time in business to provide more value. This quality will become more and more important for all executives involved in the profession. (CA-today-news, 2016)

AI and Humans working together

AI systems today can be very robust and are upgrading tremendously. They provide extremely accurate outputs, replacing human efforts. However, they do not replicate human intelligence. There is a need to identify the strengths and weaknesses of this different form of intelligence and develop an understanding of the best possible ways for humans and computers to work together. (Icaew)

APPROACHES TO AI

Research in AI concentrated for many years on mirroring human reasoning capabilities, for example, representing knowledge and encoding logic-based rules and decision trees. This was the approach taken in expert systems, which became popular in the 1980s and 1990s. These systems attempted to capture the explicit knowledge of experts and build it into rules engines that would make decisions or recommendations. This approach had some success but it rarely produced results that could be seen as akin to human intelligence. While there were a variety of technical issues with such systems, they were ultimately defeated by the complexity of the real world, and the extent to which we rely on intuitive thinking. We were unable to articulate our knowledge and decision-making rules clearly enough. This meant that systems could not cope with complex or ambiguous circumstances, or where things changed. Recent successes in AI take a very unique approach. Rather than trying to impose a top-down model of rules, they take a bottom-up approach and learn rules based on observation of what happened previously. This uses pattern recognition and is known as machine learning. While there are many fields of research into AI, improvements in machine learning are the main drivers behind the hype around AI today and the focus of this report. By attaching approaches in machine learning with developments in other areas of AI, such as knowledge representation and reasoning, computers can be used to complement and increasingly enhance on both ways of human thinking. (Accountingweb, Aug 2017)

STRENGTHS OF AI

Machine learning techniques drained into our own perceptual strengths i.e., pattern recognition and learning, instead of attempting to define complex rules. This approach could also enable computers into decision-making processes than was possible in the past when they were made complex by pre-defined rules. Now it is not new that algorithms performing better and more frequently than many experts, AI systems ‘turbo charge’ this ability and lead to much more powerful decision tools that have previously been possible. This reflects three features about models and the algorithms that AI contains.

·         LARGE DATA VOLUMES: They can process large amounts of data (structured and unstructured), which are much more than humans ever could; for example, the results of every piece of financial regulation. This provides a more powerful base for learning.

·         COMPLEX AND CHANGING PATTERNS: They can pick up poor or more complex patterns in data than humans can. Therefore, machines may be better in domains that we cannot anticipate. They can also be highly adjustable and learn from errors or new cases.

·         CONSISTENCY: They can be great consistent decision-makers. They do not suffer from fatigue or boredom. They also do not show human biases.

These capabilities are specifically important for organizations directing to utilize the increasing amount of big data that is available to them. Humans alone simply cannot scrutinize and extract intuition from the quantity of data being created each day. It is therefore essential to work with machine learning techniques. (Forbes, 2017) 

Accountants using AI capabilities

Although AI techniques such as machine learning have it’s far outreach, and the speed of change is fast. It is widely adopted in business and accounting. In order to build a positive insight of the future, we need to have an understanding of how AI can solve accounting and business problems, the challenges and the skills accountants need to work with intelligent systems.

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