The most common application of AI is the automation of human tasks and business processes.
Robotic Process Automation (RPA) was introduced to the China market in 2017.
Now RPA has integrated with cognitive components and evolved to Robotics & Cognitive Automation (R&CA).
Robots can now handle both of these tasks according to predefined rules.
This includes doing ‘smart’ tasks, for example:
• recognizing named entities, such as a company or a person’s name
• extracting structured information from unstructured documents, such as contracts and financial statements
• automatically classifying invoices according to their product name in natural language, and
• searching websites to find target information.
These types of tasks are increasingly carried out by AI in fraud detection, for example, assisting forensic teams in searching unstructured documents for target terms and information.
This enables teams to deliver relevant work faster, more accurately and at a lower cost.
RPA and R&CA are very likely therefore to increase human unemployment from a long-term perspective, especially in the offshore business-process outsourcing industry.
If you have the ability to outsource a project, you can probably automate the whole process.
2. Assisting decision-making
By using machine learning algorithms to detect key patterns and relationships from billions of data sources, AI can derive deep and actionable insights to support the business decision-making process.
There is nothing new, of course, in using a wide variety of data sources to predict consumer behavior – there is a significant difference, however, in the scale, speed and accuracy with which AI can process data.
Big data technologies integrated with machine learning or deep learning algorithms can give surprising and useful insights.
Moreover, this process of insight generation is not driven by user queries but by a continuous monitoring mechanism that is proactive and real-time based.
AI colleagues who provide cognitive insights can be considered senior consultants, but they won’t pose threats to the existing employees since their tasks are far beyond human capabilities.
The cognitive insights that AI colleagues produce are useful to executives since they improve the quality of strategic decisions.
Executives need to understand the importance of, and make strategic investments in, this type of AI.
3. Facilitating engagement
Digital agents are becoming increasingly popular in daily work and life.
The Turing test is a test of AI’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
Although digital agents are, as yet, unable to pass the Turing test, they have already made our work and life easier in many areas.
Digital agents are already present in the following industries.
• Consumer and industrial goods. Digital agents enhance the customer experience by answering questions
contextually based on past behavior, preferences, weather, vacation plans, etc.
• Financial services. Robo-advisers give investment advice and provide personalized investment options to customers, and monitor and alert users about changes to portfolio risk.
• Healthcare. Digital agents deliver personalized, collaborative and timely medical advice to patients, such as providing prescription refill reminders, thereby improving patient satisfaction and health outcomes.
Challenges While AI is becoming an important element of the workforce in many different organisations, there are risks associated with the use of AI technology.
According to our State of AI in the Enterprise report mentioned above, cyber-security is viewed as the number one risk (see ‘AI challenges’). Cyber attacks targeting AI systems may cause data breaches and information security problems.
This is not a problem unique to AI, but as dependence on AI increases, addressing cyber-security concerns will become all the more important.
Failures of AI systems are another critical concern, particularly where mission-critical data is involved.
A photocopying error in an internal document might not pose a significant risk to an organisation, but an error in the key numbers in a financial statement would be another matter.
In the medical field there can be ‘life-and-death’ situations where the risk of an AI failure cannot be tolerated.
Human monitoring and control are still needed in many scenarios.
How should we get ready?
Despite the concerns discussed above, many companies that have invested in AI technology have already reaped economic benefits.
The key is to take a practical approach to the benefits available from these technologies.
This approach usually starts with implementing process automation via AI.
This can lead to the adoption of RPA or R&CA technology, which is relatively cheap and can be applied to business processes within a managed scope.
These benefits can be gained immediately and they can help establish confidence in the management team about
future use of AI.
The question of finding the right mix of talent to successfully adopt AI should be the first consideration.
Although China is in a leading position in the AI industry globally, there is a significant shortage of AI talent in China as compared with the US. According to a 2017 Linkedin report, Talent solutions, the US is ranked number one and China is in seventh position in terms of
the available talent. It is calculated that there are only around 50,000 AI engineers in China. Another report from McKinsey,
The future of artificial intelligence in China, shows that most US data scientists have more than 10 years of work experience, while 40% of Chinese data scientists have less than five years experience.
Due to the high demand in the market, the salary levels of the data scientists and AI engineers keeps increasing.
However, many companies still find it very hard to find the right talent to support the implementation of their AI strategy.
There are typically three different ways to address this problem.
1. Companies can target global talent, especially the established Chinese researchers working in the US, for recruitment.
2. Companies can train existing employees to become AI experts.
People with a background in mathematics, data mining and software development are well suited to a career in AI.
3. Companies can collaborate with university laboratories. Although fresh graduates from universities lack industry experience, their academic training gives them a good knowledge of AI.
They can be good candidates for a new AI research team in the company, with enough in-work training.
It seems certain that increasing numbers of AI colleagues will join the workforce in the future and take over many labour-intensive tasks, but they are unlikely to eliminate humans from the workforce as many tasks require high-level cognitive skills.
Further in the future it is harder to predict what may happen, but it seems likely that AI will continue to evolve and redefine the relationship between technology and humans.
The impact of these developments will be very significant for businesses and for the workforce.
Organisations that adapt quickly to the new digital business model by embracing AI technology can increase their competitive
As discussed above, AI can not only save on labour costs but can also assist executives with their strategy formulation and decision- making. Management teams need to assess what impact the increasing popularity of AI will have on the organisation’s workforce and plan ahead for the training that will be required to ensure the human workforce is adapted to the new AI era.
Opportunities and threats are coming together; it will be the survival of the fittest.
This article was first published in the January 2019 edition of CSj, the official journal of the Hong Kong Institute of Chartered
Reprinted with permission.