Introduction
Firm strategy and business paradigms vary because of technological breakthroughs, such as PCs, the Internet, and cell phones. As a result of these improvements, firms are better able to manage consumer needs and deliver services. Aside from these findings, it has been found that knowledge-based organisations develop, share, and utilise knowledge as a fundamental source of competitive advantage (Allal-Chérif et al, 2021). New frontiers in AI’s ability to provide value to users are being opened every day. Artificial intelligence (AI) is a wide term that describes the idea that computers can understand and act like humans through the application of software and algorithms. Using personalisation, they actively mould human lifestyles in nearly every facet of daily life. A system’s ability to accurately comprehend and learn from external data, and then use that knowledge to fulfil specified goals or adjusts its behavior accordingly is defined as AI in this study (Deshpande et al, 2018).
Automated and continuous learning, as well as data-focused analytics and decision-making, are the primary functions of artificial intelligence (AI). Data collected, processing, maintenance, and retrieval are all made easier thanks to AI, which can assist in the development and management of company services (De Bruyn et al. 2020). Machines may be trained to recognise patterns in enormous amounts of data using technologies like machine learning, genetic programming, and natural language processing. For personal usage, popular AI technologies include Alexa, Siri, and Cortana, as well as Mezi, Pandora, Olivia, and Liv for language translation and financial planning (Nest). Fluid AI, Sentient, Amazon MTurk, face-recognition (Haystack), legal linguistic assistant (Legal Robot), and credit reporting are some of the most often used AI products for business application today (Lenddo) (Giubulini et al. 2017).
Adopting Artificial Intelligence in Marketing
While personalization and customisation are logically connected, their practical applications are somewhat different. Customers can customise their marketing mix if they choose to do so, whereas personalisation occurs when the company chooses, based on consumer data, which marketing mix is best suited for the individual. Nearly all experts agree that personalisation is almost entirely under the control of firms and is powered by customer-level data, whereas customisation is almost entirely under the control of customers and is concerned with both the development and distribution of the product or service (Prentice et al. 2020).
In both digital and non-digital domains, personalization has been proven to be effective. “Recommended for you” sections on websites like Amazon, Pandora, and Netflix are classic instances of digital customisation (Kumar et al. 2019). Sprint employs predictive analytics to target customers at risk of churning with personalised retention offers. Consumers can also expect personalised care through intelligent call routing services, which connect customers with service professionals based on their abilities and personalities. As a process, personalization strengthens the bond between customers and marketers. Relationships with emotional connection proceed to a state of engagement, and positive relationships influence customer engagement (CE) behaviours (Lin et al. 2017).
As a result, the attitude, behaviour, and degree of interconnectedness among customers, between customers and employees, and between employees and customers within a company have all been defined as “engagement (Huang et al. 2018). In addition, the greater the degree of connectedness and positivity, the greater the degree of involvement. The appeal of AI is attributed to its high degree of personalisation. An expert systems-based method has been replaced with a data-driven, deep learning-based strategy thanks to AI. In most cases, users have no idea they’ve engaged with AI because it is so stealthy (Matt Ward et al. 2019).
When technology connects with its users on an individual basis, it develops a strong connection with them. As a result, the potential for consumer value creation is huge when marketers exploit such a link. Although customization initiatives can be successful, they are hampered by the size and intensity of consumer information, the ability of enterprises to produce insights from customer data, and the efficient execution of those insights. Companies are turning to AI-powered solutions to overcome these three limitations and go above and beyond the present level of customised offers (Rodgers et al. 2021).
Consumer Decision-Making and Personal Preferences
Consumers have a choice of two or more possible alternatives, conflict between those options, and a thought-driven strategy to resolving the conflict in nearly all marketing actions. When confronted with a difficult decision, most people turn to the internet for guidance. Nondigital sources like traditional media and published reports, as well as digital sources like sponsored media and earned media can all play a significant role in minimising decision regret and helping customers feel secure about their decisions (Kumar et al. 2019).
When confronted with a decision that is out of the ordinary, customers are more prone to look for additional information to allay their fears. “Information fatigue” and a shaky decision-making process might result from excessive information-seeking and processing. Overwhelmed customers may incur dysfunctional outcomes because of their exposure to too much information. The way companies customise their offers can be influenced by how customers evaluate alternatives and arrive at credible selections (Prentice et al. 2020).
Knowledge is constantly being accumulated and (re)organized, which shows that it is continually being used. However, this is a difficult task because it implies predicting when or how knowledge will be accessible and utilised. Companies that have managed their knowledge successfully have done so by selecting, interpreting, then integrating key insights to increase overall value rather than by just implementing newer technology tools (Kumar et al. 2019).
Companies’ Performance and Artificial Intelligence
AI has several advantages that are directly related to the marketing strategy that are incredibly appealing to businesses. In the first place, companies are often unable to choose between increasing income or cutting expenditures (Deshpande et al. 2018). Even though AI has the potential to cut costs by streamlining processes, firms are investing extensively in AI with the anticipation of seeing revenue increases in the near future. While Toyota has invested $4 billion to produce driverless vehicles, Baidu has secured $1.9 billion for an innovative payment services division that will employ AI to deliver short-term loans and investment opportunities tailored to the needs of customers (Kumar et al, 2019).
Secondly, because of AI, product curation can be done on a scale that is impossible for humans to do. With IBM’s new AI product, for example, deep learning has been made scalable. The new AI product can greatly scale up without losing accuracy in findings because it can connect to numerous servers at once to enhance computational speed and power (Prentice et al. 2020). This allows for accurate, real-time curation without interfering with the customer’s experience. When it comes to delivering the best possible customer experience, AI can help. Data-driven predictions are also a benefit of AI, since they allow for more exact matching of inclinations to company offerings (Kumar et al. 2019).
Such capabilities make AI extremely powerful and help consumers make better decisions. Customers can get near-exact matches to the things they are looking for based on pricing and other criteria by using eBay’s descriptive and predictive AI models, for example (De Bruyn et al. 2020). Third, as more and more tasks are being automated, AI can help managers think more creatively. In the past, it would have taken months or even years for a company to come up with a new product or service because of the time and resources required. L’Oréal, for example, utilises AI for customer engagement in the UK, which allows it to recognise photographs on social media and detect trends across the board (Huang et al. 2018).
Stéphane Bérubé, the brand’s CMO, says, “By customising our contacts with consumers, we get to comprehend them and react accordingly.” For the benefit of the customer, we will be able to anticipate and forecast market trends like never before. With the help of IBM Watson, the R&D teams at McCormick Foods are creating new spice blends based on information gleaned from client demand and social listening (Kumar et al, 2019).
Finally, the range of industries and circumstances in which AI can be used is extensive. According to industry estimates, supply chain management/manufacturing, as well as marketing and sales, will be the most affected by AI. Firms like GE and 3M are linking devices and using Internet of Things data as inputs for AI algorithms that automatically forecast the wear out of spare parts in the business-to-business area (Rodgers et al. 2021). Customer retention is improved because of the predictive maintenance services based on artificial intelligence (AI). Marketing and sales can generate $1.4 trillion to $2.6 trillion in value for firms throughout the world, according to McKinsey & Co.’s estimations, while supply chain and manufacturing may generate $1.2 trillion to $2.0 trillion (Rodgers et al, 2021).
Integrating AI into the workplace will necessitate a readiness assessment by companies. Preparation is mostly strategic in nature, with a focus on the long term rather than the immediate. Firms need to take certain steps to see if they’re ready for AI. Data maturity is essential to AI’s success (Allal-Chérif et al. 2021). To reap the full benefits of deep learning and AI, a robust data ecosystem must be in place (Kumar et al. 2019). As a result, companies will need to hire data scientists to help them make sense of their data and find new ways to turn it into useful information. In emerging economies, where data are not standardised, the ability of businesses to use AI is limited. Secondly, for AI projects to be a success, they must be linked with the aims of the company. As a result, AI must be a company-wide effort, including all levels of the organization’s hierarchy, as well as all its operations and stakeholders (Giubilini et al, 2017).
With AI’s multidisciplinary character, organisations may even want to contemplate an interdisciplinary style of operation rather than a typical hierarchy-based, top-down format. This could help companies better prepare for the inevitable shifts in business that will occur because of AI. As a result, it is imperative that clear expectations be established surrounding the use of artificial intelligence (AI). To give one example, a McKinsey study indicated that low or uncertain business returns were regarded as the top reason for not implementing AI, especially in smaller companies (Prentice et al, 2020). This should be taken seriously as a call to investigate and learn more about the strategic consequences and requirements of implementing AI.
Lastly, it is crucial for companies to set explicit control criteria and rules for the use of AI. The Turing test—”Can robots think?”—serves as a reminder. When answering this topic, Turing says, “the definitions of the terms “machine,” and “thought,” should be the first place to start.” 45 Using an AI tool can be difficult if you’re not familiar with the inner workings of the AI tool, have difficulty deciphering the output, or are unsure how to deal with randomness (or lack thereof) in the AI tool (Lin et al. 2017). This is especially true if you’re using a human fallibility rather than “true” learning. Who is in charge and how will the company respond in such a situation? For the fourth time in a row, artificial intelligence (AI) is likely to change how people work, creating new job roles, and creating operational and functional (global) teams. What steps are companies taking to get ready for the new realities of the workplace?
The ethical and privacy issues raised by AI also require further investigation. Firms are allowed to utilise certain types of data but not others. Are there ways to remove data from an AI application if users have privacy concerns? If that’s the case, how exactly do you go about it? If human biases are input into AI processes as data, they may provide output that suggests confirmation bias. Such concerns can have a significant impact on a company’s ability to compete (Kumar et al, 2019).
Conclusion
There has been a long history of interdependence between modern culture and the corporate sector. Periodic revolutions in business view and assessing business growth and expansion have emerged in this interacting interaction. As an example, while enterprises were operating in the era of the market economy, they focused on growing returns to scale. When it came to increasing outputs, factories aimed to do so at a greater rate than the change in inputs. Because of this, the importance of improving production efficiency and effectively managing all the production-related variables was highlighted. We evolved into an exchange economy, where firm-customer relationships were the primary focus, because of changes in the marketplace, such as greater market access. This meant that companies were more worried about increasing investment returns than any other issue. To optimise the return on the original expenditure of resources, of course (financial and nonfinancial). In this case, bottom-line growth (rather than just top-line growth) was the driving force, and rate of return became the most significant performance metric. Again, we’ve entered a new domain. Focus has shifted to information management this time around. We live in a knowledge – based economy, where information is currency, and the performance metric is the rate at which knowledge is returned. The rise of this change can be attributed in large part to the influence of technology. Using technology, we are now able to obtain, store, process and (re)use a wide variety of information. As a result, we’ve gained momentum for investigating new frontiers, including artificial intelligence (AI).
References
Allal-Chérif, O., Simón-Moya, V. & Ballester, A.C.C., 2021. Intelligent purchasing: How artificial intelligence can redefine the purchasing function. Journal of business research, 124, pp.69–76.
Deshpande, A. & Kumar, M., 2018. Artificial Intelligence for Big Data, Birmingham: Packt Publishing, Limited.
De Bruyn, A. et al., 2020. Artificial Intelligence and Marketing: Pitfalls and Opportunities. Journal of interactive marketing, 51(1), pp.91–105.
Prentice, C., Dominique Lopes, S. & Wang, X., 2020. Emotional intelligence or artificial intelligence- an employee perspective. Journal of hospitality marketing & management, 29(4), pp.377–403.
Giubilini, A. & Savulescu, J., 2017. The Artificial Moral Advisor. The “Ideal Observer” Meets Artificial Intelligence. Philosophy & technology, 31(2), pp.169–188.
Huang, M.-H. & Rust, R.T., 2018. Artificial Intelligence in Service. Journal of service research: JSR, 21(2), pp.155–172.
Kumar, V. et al., 2019. Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing. California management review, 61(4), pp.135–155.
Lin, W.-K., Lin, S.-J. & Yang, T.-N., 2017. Integrated Business Prestige and Artificial Intelligence for Corporate Decision Making in Dynamic Environments. Cybernetics and systems, 48(4), pp.303–324.
Matt Ward, W. & Bernard Marr, M., 2019. Artificial intelligence in practice: how 50 successful companies used artificial intelligence to solve problems, Wiley-Blackwell.
Rodgers, W. et al., 2021. Artificial intelligence-driven music biometrics influencing customers’ retail buying behavior. Journal of business research, 126, pp.401–414.
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