The potential AI demonstrated through the years has been so remarkable that it has now entered the boardroom of C-Suites of organizations around the globe regardless of sectors. Moreover, there are several batteries of studies conducted in recent times that point to the fact that investment in AI for the most part comprises of industries outside the tech space. As organizations are increasingly seeing the operational efficiency of artificial intelligence, for instance, AI powered Kiva warehouse robots, they are rushing to jump on the bandwagon to elevate their business. Though AI is in its infancy when it comes to business settings, its business implications are huge.
At several recent conferences, including RoboBusiness 2017 in Silicon Valley, a major topic of concern was how AI can help business today. This article will seek to answer this question.
AI & Business
Organizations should ideally look at AI through the perspective of business capabilities rather than technologies. Broadly, AI can deliver three important business needs which are discussed below: Cognitive Engagement, Robotic Process Automation and Cognitive Insights.
Robotic Process Automation
According to the researchers, the potential savings that organizations can experience with RPA will reach between $5 trillion to $7 trillion by 2025. It’s also predicted that the tasks performed by RPA software will be on par with output of 140 million FTEs by the same year. As we can see, there is a clear indication of the potential RPA can reach in the coming years. From automating tasks that could previously be performed manually, this technology will go a long way in affecting change in the way business operates.
Recent studies indicate that the most common automation is in the area of digital and physical tasks, namely in finance and back office administration. As compared to the earlier business automation tools, RPA is more efficient as robots mimic human inputting and consuming of information more effectively from numerous IT systems.
Some of the tasks performed by RPA include:
- Data transfer from email and call center systems to recording systems
- Handling customer communication
- Replacing lost credit or ATM records and updating records
- Rectifying errors in the billing system by extracting information from various documents.
- Using Natural Language Processing (NLP) to extract provisions from legal and contractual documents.
Among the three cognitive technologies discussed in this article, RPA is the least expensive and easiest to implement. It also brings high and quick returns on investment.
Cognitive insight
The second way in which AI, or machine learning to be exact, is used in business is for cognitive insights. Algorithms are used to identify patterns from multiple data sets and extract meaning from them. This information is used for the following:
- Make a prediction on a particular customer’s buying preference
- Detect fraudulent credit card transactions and insurance claims in real time
- Automating personalized directed digital ads
- Examining warranty data to detect any quality or safety problems in automobiles and other manufactured products.
The cognitive insights that are delivered by machine learning differ from those that are provided by traditional analytics mainly in some aspects: ML provided insights are much more data-intensive and detailed, their ability to predict using new data improves over time and as the models are usually trained using some part of the data set, the models keeps getting better.
Applications using cognitive insights are used to enhance the performance of tasks carried out by machines such as programmatic ad buying that require rapid automation and data crunching that are well beyond the ability of humans.
Cognitive Engagement
To survive in the competitive marketplace, organizations should adopt a cognitive approach to customer engagement. AI should be incorporated in their cognitive engagement strategy in order to ensure their competitive advantage..
A cognitive engagement strategy typically involves the input of customer data. This data is processed by predictive analytics to deliver insights about the customer’s behavior in the future. These insights can then be used by marketers to make smart and well-informed customer engagement choices and to produce highly effective campaigns using analytics. These campaigns, though they provide personalized engagement to each customer, they are labor intensive. This is where AI has a role to play. As AI has its own decision making capability, it can aid the marketers to deliver personalized content for each customer rapidly. This would bring about “extreme personalization” which denotes engaging the right customers at the right time with the right content.
However, there are numerous studies that vouch for the fact that cognitive engagement technologies are usually employed more to interact with employees than with customers. This can change as the organization becomes more comfortable turning customer interactions over to machines. For instance, Vanguard is in the process of creating an intelligent agent that aids the customer service personnel in answering the frequently asked questions. Here the aim being to eventually enable the cognitive agent to direct interact with the customers .
About the Author
Natasha Manuel is a Content Manager at SpringPeople. She has been in the edu-tech industry for 7+ years. With a aim to provide the best bona fide information on tech trends, she is associated with SpringPeople. SpringPeople is a global premier training provider for high-end and emerging technologies, methodologies and products. Partnered with parent organizations behind these technologies, SpringPeople delivers authentic and most comprehensive training on related topics.
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You’re very rightly saying that “It [RPA] also brings high and quick returns on investment.” I believe the quick ROI is, in fact, one of the main competitive advantages of automation technology. If you consider that bot deployments free bright human minds to focus on endeavours that are most likely to create high value, then the quick and substantial ROI comes as no surprise. In fact, according to McKinsey, the revenue in the first year upon RPA implementation shows a growth of 30 – 200%.
Besides the tasks that you mention, automation can also be taken as a trustworthy ally in business because it facilitates compliance, and it helps to better manage the growing security risks of our times.
RPA minimises human error, which is a main cause of compliance breaches, and consequently, a source of never-ending compliance stress for organisations. Software robots are perfectly suited to multiple-source data management and report generation, which are unavoidable steps in regulatory compliance.
When it comes to cybersecurity, both data or access security are threats that should not be ignored in order to stay on the market, and to stay competitive. Encryption, segregated access to data within an RPA team, provision of a ‘zero touch environment’, active directory integration, or managing alerts, are just some ways by which RPA actively supports security.
So long live the (software) robots, right?