From fraud detection to predictive insights—learn how C-suite leaders are using deep learning for strategic advantage.
Deep learning applications can provide the C-suite executives with the opportunity to derive useful information that can be used in making their decisions to help them rise above their competitors by providing superior service or product. A survey conducted by Deloitte showed that 82 percent of executives think that advanced analytics (including deep learning) will have a significant impact on their sectors.
Table of Contents:
1. Decoding the Data Deluge
2. Revolutionary Breakthrough
3. Harnessing the Power of AI
Ethical Standpoint
1. Decoding the Data Deluge
Utilizing deep learning algorithms, organizations can get to know some complex patterns, correlations, and trends in their data. These insights enable executives to gain a thorough insight into customer behavior, market forces, and inefficiencies in operations. A McKinsey study, for instance, found that organizations taking advantage of deep learning to analyse their customers would record an overall lift in sales by 10 percent.
2. Revolutionary Breakthrough
An example of a case study that illustrates a remarkable effect of deep learning on decision-making is one that is found in the healthcare sector. A deep learning algorithm was created in 2018 by scientists at Stanford University and can detect skin cancer with the same amount of accuracy as a dermatologist. A dataset comprising more than 130,000 images was used to train the algorithm, which managed to detect skin cancer in images at an accuracy of 91%. Such an innovation in the field of technology can transform dermatological diagnosis and can guide doctors to make more matric decisions and propose treatments.
Besides the field of healthcare, deep learning has also contributed heavily to the fields of finance, retail, and manufacturing. As an example of the use of deep learning algorithms applied to financial institutions, one can distinguish the detection of fraudulent transactions and market information patterns (and, as a result, better risk management). The retail corporations implement deep learning to predict demands and suggest personal offers and interventions, which boost sales and customer satisfaction levels. Deep learning is used by manufacturing companies to perform predictive maintenance, where unplanned outages are minimized and performances of the equipment are optimized.
3. Harnessing the Power of AI
Decision makers should strive to utilize a strategic approach to make sure they incorporate AI technologies into their companies in a very efficient way. A survey revealed by EY states that 84 percent of executives want to use AI to acquire or maintain a competitive advantage. Nonetheless, the proportion of having the advanced AI capabilities implemented is only 23 percent. This underscores the significance of improving AI implementation plans.
Decision-makers ought to establish an explicit vision and plan for the use of AI, thus aligning them with other business plans. The companies that have managed to utilize AI have gained a great advantage. To give an illustration, one of the studies by McKinsey discovered that in manufacturing, the productivity of the adopters of AI improved by an average of 40%. It is essential to identify exact use cases in which AI can be applied in order to, e.g., automate routine processes and enhance customer experiences, and to implement it in particular areas.
It is critically important to invest in developing the appropriate AI potential of the personnel. According to a report by IBM, those companies that have an AI talent strategy developed have higher chances of beating their competitors. It is also imperative to upskill the current workforce that will also be transformed by AI. The reskilling program can also take advantage of an organization that invests in employee productivity and job satisfaction.
Information is the key component in AI, and decision-makers need an efficient data strategy. A survey compiled by the MIT Sloan Management Review established that companies that had an effective, definite data strategy had more than two times better performance when compared to industry rivals. Data governance, data privacy, and security are important elements of AI implementation.
Ethical Standpoint
Responsible Artificial Intelligence is key, as well as ethics. The trust in AI systems is critical to transparency and fairness. The advocacy of ethical concerns in AI activities will be more acceptable to the organizations. The result of the AI Trust Index shows that 79 percent of consumers would trust brands more if they could explain how their AI suggestions are generated (Edelman).
Decision-makers can realize the transformational nature of AI by adopting AI and activating the human enterprise. It is able to improve its decision-making processes, operational efficiencies, and achieve a competitive advantage over its peers in its respective industries with the right AI strategies and investments.
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