Data is undoubtedly the central player in today’s business world. The different markets have reached a level of competitiveness that companies have been looking for the killer idea or even the best understanding of the market that will provide it with a competitive edge. Artificial intelligence (AI) has become an integral component in the operations of any business. Business analysts, data scientists and engineers are now the common hires for most of the companies, in a variety of industries. How can AI help businesses and take them to the next level is a question that is not confined to one answer. To be more accurate, AI has changed the operational structure of a business to the extent that AI is now the uncontestable driving force.
From Predictions to Prescriptions
Artificial intelligence and the subcategorized machine learning algorithms have long been used to allow companies to make predictions, that is, looking at intricate relations among previously collected data samples to basically predict what is going to happen next. As a notable example is the time-series analysis of bitcoin prices to predict the trend of the fabled cryptocurrency.
Predictions are no longer enough as generating insights and action plans from predictions requires significant efforts in an era where the time factor is no longer a luxury. Therefore, the current role of AI in the business world is into forecasting or as known in the data science circles as prescriptive analysis. Gaining a competitive edge in a particular industry depends heavily on selecting proper strategy before the competition does to capture the lion’s market share and take the business to the next level. As a success story involving AI, the luxury brand Burberry adopted a data-driven strategy as early as 2006 when it involved its customers in a data-based customized experience that tailors apparel suggestions based on their interest. This basically disrupted traditional in-store shopping for good as the adoption of AI in the luxury industry has been on the rise and will continue to do so.
AI to Streamline Business Operations
According to a report by Deloitte in 2017, 36 percent of surveyed business leanders have linked AI to the optimization of internal business operations. AI algorithms are indeed among the best techniques to improve operations within a given business, starting with the automation of several digital and physical tasks. Efficiency is therefore one of the key benefits AI brings to the business world. McKinsey and Company estimates in its “AI, automation, and future of work” report that around 50 percent of work activities can be automated using AI. This will eventually lead to a considerable shift in the workforce as several jobs will be deemed unnecessary while other job opportunities will emerge to usher the automation process.
AI-based Hyperautomation of Business Processes
The COVID-19 pandemic has accelerated automation endeavors. The term hyperautomation has been created to denote the approach of identifying most business processes that can be automated. Identified by Gartner as one of the top strategic technology trends for 2022, the discipline relies on several cutting edge technologies including AI, robotic process automation (RPA) and advanced software practices. RPA has been a precursor of today’s business bots. By observing how a worker interacts with a machines to complete tasks, a software code is developed to mimic the process itself enabling an automated replication of the same tasks. RPA however suffers from many limitations that could have an effect on the business operations. Among others, RPA lacks scalability as an increased workload requires a significantly larger number of bots, with an increased management burden. Moreover, RPA emphasizes problematic business processes. Since the developed software relies on how humans solve problems, any error will be subsequently magnified when automated. Hyperautomation tries to iron out the imperfections of the RPA methodology by incorporating a myriad of emerging technologies, notably in artificial and process intelligence to identify the best automation opportunities and then optimize the process.
Natural language processing (NLP) has been extensively used in combination with optical character recognition to develop solutions that understand text and voice data and act accordingly. NLP-based chatbots is an example of such technology created by companies to act as intelligent virtual assistants that provide customers with human-like interactions.
Less Costs, And a Skilled Workforce
The automation of several time-consuming work activities will in general result in reduced costs and a higher return on investments. When combined with the advanced analytics provided by AI, the developed solution provides the business in question with the weapons to explore various opportunities for revenue growth. This is supported by a team of skilled employees who are capable of synergizing their know-how and ability to develop creative solutions with the output of the learning algorithms.
When it comes to security, AI can provide an added value to businesses. The data-driven philosophy would allow companies to better manage threats and security breaches. The real-time data analysis should allow employees to detect potential threats before they even occur. Any change in data pattern which has been encountered in previous security breaches can be detected instantaneously, with enough time to take preventive measures.
Limitations of the Use of AI in Businesses
As in any technology, AI shouldn’t be embraced blindly by companies. The use of learning algorithms entails a complete shift of the structure and workflow inside the company, in addition to a re-allocation of the resources to incorporate the new technology and hire skilled workers to operate the new system. Companies should also learn to work with limited data availability and many other data-related problems seen in the data science process. Integration and interoperability are two other classical limitations of AI solutions that could also hinder the progress of businesses.
Ethics is another debatable issue at the core of AI solutions. The developing field of AI ethics encompasses the human bias in developing AI algorithms, the workforce shifts and unemployment resulting from the automation process, the transparency and explainability in the AI development process. Regarding the last item, the new area of explainable or interpretable AI has emerged, allowing business leaders to fathom how the utilized algorithms are actually working, thus improving the decision making process and avoid potential risks and pitfalls. In addition to addressing ethical, transparency and fairness concerns, explainable AI can help businesses generate better insights and strategies.
Artificial intelligence has constituted a disruptive force in all possible areas of our life that involve data. The most precious asset of the current century has allowed businesses to radically change their operational methodology. As it is becoming data driven, AI algorithms has been extensively employed to automate several of the work activities, and generate business strategies that will allow a company to gain a significant edge over its competitors. The AI technology is evolving at an incredible pace trying to match the human’s brain learning capability with the highest fidelity. Businesses are equipping themselves to adapt accordingly. With this evolution comes limitations and concerns, notably of ethical nature. These however are being addressed by the wider AI community through proper regulatory measures.
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