The digital world has been inexorably marching forward over the last 20 years, providing ever more efficient services to business and the public. Yet, only now is a new technological revolution occurring. Just as the Industrial Revolution transformed the nature of manual work in the late 18th century, AI is set to dramatically change the roles of white-collar workers and the service industry.
Chatbots are already a familiar feature in call centres, but now even accountants, lawyers and truck drivers are finding themselves substituted for AI technology. Greater computer power, the availability of huge volumes of data and the fact that digital means of interaction are preferred by consumers have led AI to the point at which it can take off.
AI now has the data it needs and is no longer a disembodied brain; it has the means to interact with people, businesses and even machinery. There is little need for physical infrastructure and, as such, computer power can be harnessed in minutes, by anyone and at low costs.
As with all revolutions, AI will bring opportunities to those who embrace change, and those who get there first will reap the biggest benefits. In today’s business environment, competition is intense, and, now more than ever, it occurs on a global scale.
Costs are plummeting as businesses around the world take advantage of fixed-income arbitrage. Meanwhile, government policy is being designed to positively encourage competition. Consequently, businesses must work even harder to differentiate themselves from the rest of the pack; AI provides one such means of differentiation.
There are a number of steps that organisations should follow when looking into the use of AI, and after its implementation.
Understand what AI is capable of
AI has the ability to process huge volumes of data, learn from the data, be taught, spot patterns, make predictions and draw conclusions rapidly, 24/7 and without faltering. In a business context, it can make accurate and reliable decisions of ever-increasing complexity based on the data it receives.
It can also communicate in both text and voice. AI can understand customer and supplier behaviour and will draw conclusions about segments and markets based on this knowledge. By monitoring data, AI is capable of understanding processes and operations, predicting issues and determining how to optimise, research, collect and summarise data.
Consider current business practices
In businesses where teams of people are carrying out very similar, human-intensive tasks, AI can help to significant cut costs. Whether it’s communicating with customers, completing back office tasks or considering data to make decisions, many processes carried out by AI systems at a fraction of the cost and with less chance of error.
Businesses must work even harder to differentiate themselves from the rest of the pack; AI provides one such means of differentiation.
Identify opportunities for improvement
Businesses should take the time to identify the tasks that AI could do better than current systems. This may be decision-making, understanding customer behaviour or providing faster service for a greater number of hours. These are the types of value-added services that give businesses an edge over their competitors.
Additionally, AI makes it significantly easier for companies to reach new or wider markets. It can complete processes in new languages, work in different time zones and deal with the volume of mass markets in a way that humans simply cannot.
If AI technology is adopted, it’s important that businesses continue to assess if there are new AI offerings that could be used to provide even better services; they must keep up with the technology as it changes and not fall behind.
Evaluate the feasibility of adopting AI
Once the opportunities offered by AI have been identified, businesses will need to act quickly to evaluate the feasibility of using such technology. The challenge of AI-driven solutions is that they are very dependent upon data and it’s not guaranteed that they will deliver the accuracy and benefits required.
There are two ways to address this issue. The first is to find a similar case study in which the implementation of AI was successful. The second is to run a ‘proof of concept’ that uses data to mathematically prove AI’s capability. For example, if the AI system needs to interact with customers, focus groups or A/B testing may be a useful way to collect proof of concept data.
Continue to evolve after implementation
Not all AI solutions work completely independently of humans; many of them augment the tasks performed by humans. The design of AI systems should be carefully considered to ensure they aren’t too ‘black box’, that is, that deep learning doesn’t occur in a system that is opaque to outside scrutiny. By mixing expert system approaches with machine learning techniques, the decisions made by AI systems can be understood by users.
It’s also important to understand just how quickly the field of AI is moving, to adopt open platforms and focus on reviewing and improving AI decision-making models. By collecting new data, automated decision-making processes can be refined and made more effective, bringing even greater benefits to businesses.