Cognitive technologies – produced by artificial intelligence – are rising in popularity in a digital sophisticated era. Despite this inclination to such a lucrative technological sphere – the combination of artificial intelligence, machine learning, Internet of Things etcetera –, the success rates of these technological projects are still unpredictable and considered risky. Compared to the conventional undertakings towards business advancement, artificial intelligence is still located in an uncertain terrain.
AI and Business Needs
There are three fundamental aspects of cognitive technology: engagement, insight and automation. These key aspects of technology enable the robotic understanding of business procedures, subsequently learning from complex human thought processes and replicating them. However, rather than apprehending artificial intelligence merely from an abstract and technological standpoint, we can understand how artificial intelligence works in the business world. Hence, at the intersection of cognitive technology and business needs, they automate operational processes efficiently, provide better insights with increased accuracy in data analysis, and improve engagement with customers and employees alike.
Businesses now often utilise robotic process technologies (RPA) to automate their business processes. Robotic process technology is the digitalisation, and subsequently automation, of physical tasks without the need for human intervention. The robot in the system – which follows instructions based on a coded server – acts like a human that processes and internalises information from various sources at once. This well-defined code governs the system, creating a sturdy and self-sufficient architecture around its programme. Hence, this design enables the automation of repetitive and mundane tasks.
Some examples include transferring data, updating information in multiple records systems, data extraction, and analysing documents using natural language processing.
However, a robot is still a robot. In most cases, the RPA has yet to fully possess the capability to learn on its own and improve by itself – though software developers have gradually been improving its learning capabilities in recent years. Because of its manageability and ease in implementation, RPA is widely known to be a more affordable option.
RPA is the most appropriate to use in backend systems – especially replacing the need to outsource. While it is natural to equate automation to job losses, eradicating administrative roles is not a goal. Rather, it serves to reduce the reliance on outsourcing resources and enables the company to automate their backend processes more efficiently.
Big Data and Insights
With the immense magnitude of big data, machine learning is applied to make sense of a vast amount of information in a short period of time – instantly. Algorithms are used to identify patterns amongst these volumes of data and provide reliable predictions. From a multitude of data abstracted from various platforms, real time analysis is gathered with valuable insights.
Some examples include analysing and predicting customer behaviour, detecting fraud, and automating advertisements seen on social media dashboards.
In machine learning, the feedback loop allows the system to learn and improve on its own. When trained on a data set, the system takes note of successes and failures – retaining what is correct and disposing of what went wrong. This allows better predictions to be made overtime, and progressively enables new data to be stored and analysed immediately.
Machine learning can also produce accurate search results based on similar matches. For example, data submitted can be extrapolated to find other associated data on a variety of databases online – such as identifying similar people, related companies or even suggest similar interests. Ultimately, this gets rid of the troublesome and time-consuming act of manually sieving through countless of folders and documents.
Engagement and Relationships
Cognitive engagement in businesses is emulated through intelligent agents and smart chatbox, improving service and performance. Often programmed in internal sites for employees to troubleshoot problems or ask questions, or programmed as a recommendation system to communicate with customers with human-like behaviour, these engagement processes enable more interaction and foster amicable relationships.
These are enabled with natural language processing – where it analyses the language used by the employee or customer, determine their motives, and meet their requests quickly and efficiently. The nodes of connection within the system spreads swiftly to transmit information, reducing the cognitive burden on the user.
With cognitive engagement technologies, there is less reliance on “IT support departments” or customer service agents. Instead, companies can choose to programme their system to automatically respond to the relevant parties.
Our Future, Defined by Artificial Intelligence
Artificial intelligence, though well-established in the technological realm, will only continue to grow and advance further. The emergence of COVID-19 merely accelerated the rate of digitalisation – who knows what else will happen in the future? To embrace the evolving technological landscape, businesses can align cognitive technologies with the right business direction and be better prepared to face any unprecedented adversities in future. The first step is to understand cognitive computing through the right business lens, and situate artificial intelligence in the business world. After that, you’ll know what to do.
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