Retail and Automotive Industries: The Use of Artificial Intelligence

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Table of Contents

Retail Industry Case Study Analysis
Automotive Industry Case Analysis
Role of Analytics in Solving Business Problems
Developing and Sourcing Analytics Capabilities
Reference List

Retail Industry Case Study Analysis

The main focus of the first case study is the use of Artificial Intelligence systems such as machine learning, computer vision, natural language processing, expert systems, and speech recognition systems in the retail sector. Analytics have been employed in the Retail Industry to address retail services (ML in retail: Practical examples & use cases to drive value, 2022). They include determining the best prices for products and services, providing personalized customer experiences, creating advanced monitored inventory systems, vendor management, and tracking customer touchpoints.

Customer experience is a potential area of focus for most retail businesses in the entire retail industry sector. Marking market segments has become challenging with continuous advancements in technology, influencing and altering customer behavior which consequently alters customer buying characteristics. Social media, for example, has transformed shopping strategies as multiple brands compete with outstanding marketing procedures. The main focus of private and public businesses is to make profits. As such, adopting strategies that facilitate this organizational strategic objective, is currently being given a lot of attention.

Discovery and predictive analytics have been used to address the ever-challenging predicament of customer experience. Discovery analytics utilization involves the creation, adoption, and implementation of new and advanced technologies that use artificial intelligence systems to address existing shortcomings in the provision of superior customer experience. Similarly, predictive analytics have been employed in analyzing past and present data to create environments that suit individual customer behaviors.

The main challenge, as with any artificial intelligence system is access to the proper data. Discovery analytics require access to multiple data from customers, and the retail industry to initiate learning algorithms that can create new concepts that facilitate a better customer experience. Similarly, obtaining sufficient data that can be analyzed in predictive analytics to yield suitable output and guide decision-making is still a huge challenge in the retail sector.

Stakeholders should obtain sufficient legal permissions to collect consumer data. This can be done by assuring stringent privacy policies to protect and mitigate sharing of such data. Additionally, stakeholders should invest in the installation of intelligent artificial systems that effectively collect and analyze consumer data, providing outputs that can be employed in decision-making.

Automotive Industry Case Analysis

The second case study dwells on the incorporation of artificial intelligence systems such as neural networks (used by BMW) and computer vision (used by Porsche) in the automotive industry. Analytics have been employed in the automotive industry in developing auto AI applications, automation tools, and maintenance tools, manufacturing tools, and revolutionizing the driving experience (How big data and AI are transforming the automotive industry, 2022).

Manufacturing vehicles that align with today’s technologies is a huge challenge for most automotive outlets. The current auto market demands vehicles installed with self-driving technology, with features such as auto piloting, self-parking, lane assist, vehicle designs, and body mechanics that align and facilitate these functions.

Discovery, operational, and automation analytics are used in the case study. Discovery analytics drive the creation of new strategies, particularly in robotics, which takes over most manufacturing functions from humans. With the advances in the need of creating superior vehicle designs, discovery analytics aid in the creation of better solutions that can stand to current industry requirements. Operational analytics support day-to-day manufacturing processes.

The main challenge in the use of discovery, operational and automatic analytics in the automotive industry is the development of solutions that are capable of adapting to the industrial environment’s mechanical changes. Creating solutions that learn fast to changing design models and mechanical tweaks is problematic. Moreover, such systems need frequent optimizations that enable them to achieve intended objectives during the manufacturing process.

Stakeholders must first collect industry and market data, and analyze available market products, demands, and existing challenges that can be used as opportunities. They must focus on upskilling employees, equipping them with technological skills that align with industry requirements.

Role of Analytics in Solving Business Problems

Operational Analytics in the finance sector

There are two types of analytics employed in businesses: operational and discovery analytics. Operational analytics, involve statistical analysis, data-driven techniques, and data mining methods employed in facilitating day-to-day decision-making processes and operations (Böhm et al., 2016). It comprises the usage of data analysis and business intelligence tools to streamline everyday business or industry processes in real-time. The approach is centered on establishing a trusted system that promotes sharing of highly accessible data between teams in the business. Operational analytics functionality is prevalent in areas that involve diagnostics, manufacturing, billing automation, and supply chain tasks. For operational analytics to be successful, the organization requires the employment of a robust team of experts in data and business analysis.

The finance industry has undergone a tremendous transformation with regard to technological advancements through the use of cloud computing, artificial intelligence, and robotics (Cornwell et al., 2022). Due to the changes, a lot of implications continue to rise in the industry, particularly due to the rising use of data in the provision of financial services and products to customers.

The financial sector is one of the industries that have access to multiple consumer data to collect, analyze and employ for organizational objectives. However, the processes for collecting, integrating, analyzing, and solving various problems in the sector, are challenging (Cornwell et al., 2022). In a single day, a financial institution processes millions of transactions. Thus extracting data from these transactions and keeping track of them can be overwhelming, and time-consuming. Despite this data being so insightful, inadequate sorting and improper structuring of the big data can be a substantial threat. Often giving competitors an advantage. To attend to the rising requirement of managing big data in financial institutions, and survive in the competitive market, where quality data analytics are key, many are adopting operational analytics technologies.

On the other hand, discovery analytics facilitate the creation of new creative solutions or products and services that, for instance, can influence robotics or customer purchase decisions. Discovery analytics comprise the utilization of rapid analytical techniques that aid in robust explorations of multiple data sets. Overall, the analytics aid in homogenous systems integration increased access to data, sophisticated analytics implementation, reduction in manual coding, and employment of superior computing systems.

In the financial industry, cyber-security is a rising concern for the big data collected and processed in these institutions. Loss of information, even for the transactions from a minute can lead to the closure of an institution. The value of financial institutions is well-known and the risk of loss is constantly an imminent threat to their data and the institution’s overall organizational objectives. Note, that with discovery analytics, the big data can offer significant insights into weaknesses that may be exploited by malicious data miners. It is important to note that installation of complex hardware peripheral devices and software are not assurances of data security. That is why discovery analytics may facilitate the analysis of data and using the outputs in identifying highly valuable data sets, and adopting measures that protect such. Preventing access to every bit of data, as noted before, can be time-consuming and overwhelming. Thus the use of discovery analytics to create new strategies for protecting valuable data first is an important milestone in mitigating cybersecurity in its entirety.

Developing and Sourcing Analytics Capabilities

How do we best ingrain analytics into the organization’s decision-making processes?

Adopting the analytics philosophy in the communications department and the whole business, decision-making processes will necessitate the statement of the department’ and the business strategic goals first. The department and the entire organization’s strategic objectives must agree that the inculcation of analytics will improve, in part the business processes, and consequently lead to the attainment of the objectives set (Knobbout and Van der Stappen, 2020). The decision-making policies must be in line with the fact that the department and the business require transformation, to not only achieve internal objectives but also gain a competitive advantage. Essentially, the establishment of analytics depends entirely on the policies that guide decision-making, being in line with required technological advancements.

To establish analytics in the decision-making process of the communications department and the business, they both must first define the most relevant data and analysis strategies and tools. In the communications department, the data and analysis methods should facilitate the achievement of the organization’s strategic objectives. As such, these methods and tools should be in line with the policies of the company, allowing the use of statistical and data mining methods in creating new solutions. The tools must be able to perform visualizations that are model based, on putting actions in the hands of the systems. When such measures are put in place, inculcating analytics becomes easier.

Additionally, to best ingrain analytics, it is salient that decisions are re-engineered to use outputs including insights and analysis obtained from the analytics. The current decision-making processes and policies are not in line with the recommended strategic objectives. Therefore, the decision-making policies must be reshaped, and designed such that the department and the business’s operations can run unaltered, and particularly achieve improvements in the intended areas.

How do we organize and coordinate analytics capabilities across the organization?

To effectively coordinate and organize analytics capabilities across the communications department and the entire organization; it would be statistically reasonable to adopt a cross-functional approach to activities, processes, roles, and responsibilities. The approach requires designing an appropriate departmental and company construct that allocates resources based on the level of need in a specified section. It involves the examination of the department and the entire business and identifying and assessing areas where analytics will facilitate significant value and that aligns with the decision-making policies. Indeed, it requires the evaluation of the demand and the effect of supply in areas that need the installation of Analytics.

However, the coordination of analytics has no one right strategy, and the communications department may require a different model of organizing analytics capabilities from that of the business. Despite this fact, several factors form the grounds for the formation of a suitable model that can allow organization and coordination for any area as deemed fit (Knobbout and Van der Stappen, 2020). They include outcome measurement processes, use of insight-driven decisions, information and data management tools, department and organization structure and talent management, governance and sponsorship, and capability development strategies.

The communication department and the company should lobby for executive support in leadership and decision-making. The leaders must align their leadership to the vision that analytics embodies. Both should assess their structural ability, including talent availability, and available skills, that can facilitate the establishment, and organization of the analytics transformation process. Both must have the ability to process insights, and effectively incorporate them into their decision-making procedures, to facilitate the creation of smarter decisions.

How should we source, train and deploy analytics talent?

Sourcing analytics talent begins with the understanding that such talent is scarce. Scarce because, with the ongoing transformations in the use of big data, data scientists, for example, are in high demand in every industry. However, with the realization of the growing need for experts in analytics, universities and institutions are offering programs in data science, with specializations in specific areas such as communication, health, or finance. Talent can be sourced internally, or externally. Internally, talent can have sourced by upskilling certain employees. Externally, talent can be sourced from public or private partnerships, or hired from offshore or onshore for specific periods.

Training talent may occur internally or externally. Internally, Google’s example can be emulated, where the company follows a 70-20-10 rule. 70 percent of the time is used in working on the main job task, one day a week to work on projects that advance one’s technical skills, and half a day per week to explore ideas, products, and business innovations. Employees may be trained through seminars and externally by sponsoring specific experts to advance their skills in colleges such as MIT. Talent deployment involves strategic employee planning. Establishing departmental and organizational goals that guide employees. Installing talent retention strategies such as compensation, career development programs, and motivator programs.

Reference List

Böhm, A., Dittrich, J., Mukherjee, N., Pandis, I. and Sen, R., (2016). Operational analytics data management systems. PVLDB, 9(13), pp.1601-1604.

Cornwell, N., Bilson, C., Gepp, A., Stern, S. and Vanstone, B. (2022.) The role of data analytics within operational risk management: A systematic review from the financial services and energy sectors. Journal of the Operational Research Society, pp.1-29.

Emeritus – Online Certificate Courses | Diploma Programs. (2022). How Big Data and AI Are Transforming the Automotive Industry. [Online]Web.

Knobbout, J. and Van der Stappen, E. (2020). A Capability Model for Learning Analytics Adoption: Identifying Organizational Capabilities from Literature on Learning Analytics, Big Data Analytics, and Business Analytics. International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI), 2(1), p.47.

Rishabh Software. (2022). ML in Retail: Practical Examples & Use Cases to Drive Value. [Online] Web.

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