BM565 Digital Business And New Technologies Assignment Sample

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Introduction to Digital Business And New Technologies Assignment Sample

Artificial intelligence (AI) has significantly changed how organisations interact with customers in customer service. As digital technologies evolve, businesses are increasingly adopting AI and implementing it to enhance operational efficiency, personalise customer interactions, and achieve a competitive advantage.

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Q1: Impact on Existing Organisations

Two critical variables that affect the use of new technology are identified in the Technology Acceptance Model (TAM), developed by Davis (1989), which are Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) (Charness and Boot, 2020). These components directly impact users’ attitudes and intentions towards technology adoption. Amazon’s customer service AI tools, including chatbots and Alexa, are highly perceived to be helpful. Customers receive faster resolution of their queries, 24x7 availability and personalised assistance (Billanes and Enevoldsen, 2021). A good example of this is the use of AI in helping customers via voice activation through Alexa or the use of AI in Amazon’s recommendation engine, where their recommendation engine shows items relevant to the customer’s interests.

BM565 Digital Business And New Technologies Assignment Sample

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Ease of use is an aspect that Amazon has devised its AI tools on, ensuring they are very easy to use and easy to understand. The interface of its mobile app, website, and voice assistants is minimal and natural, making it easy for customers with different technological backgrounds to interact without difficulties (Billanes and Enevoldsen, 2021). The chatbot interface is human-like, and NLP-based systems interpret queries through minimal friction. This ease of interaction supports the PEOU dimension as Amazon’s AI-driven services are widely accepted by users worldwide.

Application of Kotter’s 8-Step Change Model

• This made Amazon feel urgent because the marketplace was rapidly digitalising, and it needed to lead in AI adoption. It helped build urgency for innovation by calling attention to the risks of stagnation and opening the door to automation and personalisation as opportunities.
• Form cross-functional teams: Cross-functional teams include engineers, AI specialists, customer experience experts, and senior managers to manage the AI integration of customer service systems (Robertson et al., 2025).
• This strategic vision and the initiatives let Amazon’s leadership, then CEO Jeff Bezos and now CEO and Jassy, communicate a strong vision to deeply embed AI at the heart of their business processes to improve efficiency and customer experience across all (Robertson et al., 2025).
• Empowerment: The employees were empowered to bring and deploy AI at various levels. Participation and buy-in came through upskilling programs and internal AI training.
• Removed barriers to enable action: Phased out legacy systems on their basis of flexibility, cloud-based (such as AWS) to facilitate scalable AI deployment (Robertson et al., 2025). Technical and service teams were broken down in the organisational silos to enable cooperation.
• Early success stories: Achievements in short-term wins, such as reducing costs around the edges of the customer service function, such as the costs associated with handling customer service calls, such as reducing the cost of handling telephone calls and reducing the response time from handling customer calls (Ledro et al., 2022).
• Always reinvest: Amazon continues to reinvest in AI research and product development, always committing to chatbots but going further with emotional AI and predictive analytics.
• This was institutionalised at Amazon. It is one of the core components of the company’s culture, strategy, and business model and is not treated as a project any more.

Q2: New Organisations Challenging Old Players

Amazon has shown that successful adaptation is possible as many legacy organisations fail to grasp the importance of AI and thus decline. This can be understood from Christensen’s Theory of Disruptive Innovation (1997). Disruptive innovation is a process through which small, resource-poor firms successfully disrupt previous large businesses by offering simpler, cheaper, or easier alternatives.

Sears is another good example: it was once America’s leading retail chain. Sears did not invest in digital transformation or use AI-based customer service tools. The reactive and traditional customer engagement of its customer engagement was far from the preferential and personal elements that AI systems provide (Ledro et al., 2022). Without being able to develop and offer a similar service model, Sears failed to emerge with Amazon's real-time support, customised suggestions and built-in AI tools.

On the contrary, Zendesk and Freshdesk are AI-native companies developed to disrupt the customer service software market. These banks offer their clients AI-based customer support platforms that empower small and medium businesses to deploy chatbots, automated ticketing, and cheaply analyse customer data (Akter et al., 2023). Although their technology is cloud-based, accessible, and scalable, it corresponds to a vast market. However, newer firms that took on AI from the start were more nimble than old, slow competitors and were customer-centric.

Section 3: Evidence from Secondary Data

An interview with Jeff Bezos, founder and former CEO of Amazon, is also used to help to support the analysis of AI’s impact on customer service and broader business transformation. In the 2023 interview “Jeff Bezos Interview on the Future of AI”, Bezos offers his view on the good, the bad, and the ugly of artificial intelligence about what they can do now, what might be problematic, and how they might impact society (Akter et al., 2023). Section 2 analyses the strategic integration of Amazon in AI from a Strategic context, clarifying his perspectives in line with the Technology Acceptance Model (TAM) and Kotter’s 8–Step Change Model.

Bezos starts by discussing the perceived dangers of AI, notably how autonomous weapons fit in, a perceived risk that he feels is significant and imminent (Needhi, 2024). He says such threats do not demand general AI but can be based on limited AI technologies like machine vision. This distinction is crucial to understanding why, under Bezos’s leadership, Amazon focused on practical and scalable AI applications like customer service automation instead of speculative, futuristic systems (Needhi, 2024). It has widely stressed the importance of ‘narrow AI’ as it broadly manifests machine learning and natural language processing in the real world to aid the company’s business operations. This corresponds with the “Perceived Usefulness” dimension of TAM, where the value of the AI depends on tangible improvements in customer interaction, efficiency or operational scale.

Instead of dystopian narratives that feature a general AI “overlord” enslaving humanity, Bezos does not seem to take the risk quite as seriously (Alexander, 2021). General AI is still not a reality, and he doesn’t even believe we have the composition of knowledge to work towards such systems. Instead, he discusses how current AI technologies can help and give humans power, not take their place. That aligns with Amazon’s approach to AI: Automation assists customer service agents by responding to routine queries and forwarding more difficult ones to human counterparts (Kopalle et al., 2021). This hybrid model further enhances service efficiency and quality through successfully integrating AI tools by embracing strategic change leadership based on Kotter’s model, especially in the stages of enabling action and generating short-term wins.

Bezos’s reflections on the future of work are a highly relevant part of the interview. He says fears of job displacement by artificial intelligence are grossly overstated. He also believes humans are natively adaptive and will continue to create more types of work as they have during past technological revolutions (Kopalle et al., 2021).

References

• Akter, S., Hossain, M.A., Sajib, S., Sultana, S., Rahman, M., Vrontis, D. and McCarthy, G. (2023). A framework for AI-powered service innovation capability: Review and agenda for future research. Technovation, 125, pp.102768–102768. doi:https://doi.org/10.1016/j.technovation.2023.102768.
• Alexander, S. (2021). Please Don’t Give Up On Having Kids Because Of Climate Change. [online] Astralcodexten.com. Available at: https://www.astralcodexten.com/p/please-dont-give-up-on-having-kids [Accessed 31 Mar. 2025].
• Basu, A. (2024). Amazon’s Impact on Global E-Commerce: A Deep Dive Analysis. [online] https://www.artisanfurniture.net/news/. Available at: https://www.artisanfurniture.net/news/amazons-impact-on-global-e-commerce-a-deep-dive-analysis/ [Accessed 31 Mar. 2025].
• Billanes, J. and Enevoldsen, P. (2021). A critical analysis of ten influential factors to energy technology acceptance and adoption. Energy Reports, 7, pp.6899–6907. doi:https://doi.org/10.1016/j.egyr.2021.09.118.
• Charness, N. and Boot, W. (2020). Technology Acceptance Model - an overview | ScienceDirect Topics. [online] Sciencedirect.com. Available at: https://www.sciencedirect.com/topics/social-sciences/technology-acceptance-model [Accessed 31 Mar. 2025].
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• Kopalle, P.K., Gangwar, M., Kaplan, A., Ramachandran, D., Reinartz, W. and Rindfleisch, A. (2021). Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities. International Journal of Research in Marketing, [online] 39(2), pp.522–540. Available at: https://www.sciencedirect.com/science/article/pii/S016781162100094X.
• Ledro, C., Nosella, A. and Vinelli, A. (2022). Artificial intelligence in customer relationship management: literature review and future research directions. Journal of Business & Industrial Marketing, 37(13), pp.48–63. doi:https://doi.org/10.1108/jbim-07-2021-0332.
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• Needhi, J. (2024). How AI Transformed Customer Service at Amazon: A Case Study. [online] Medium. Available at: https://medium.com/@jeyadev_needhi/how-ai-transformed-customer-service-at-amazon-a-case-study-30085a467d22 [Accessed 31 Mar. 2025].
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