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What Does EV hype Teach Us, and How Can We Consider the Lessons in AI’s Development?




Drawing parallels between the initial exuberance for Electric Vehicles (EVs) and Artificial Intelligence (AI), this paper argues for paying heed to learning from the adoption of EVs. It underscores the need for strategic foresight, ethics, and robust digital infrastructures following the ‘learn to walk before running’ manner that AI should take to be sustainable yet transformative.


“The two most powerful warriors are patience and time.” — Leo Tolstoy.


Two of the greatest things that will make great changes in our lives and work and help our planet are Electric Cars and AI. Both will bring about a great revolution with new technology, changing our world in a way that impacts how problems get solved and how businesses, among other areas, grow well. But, just to underscore how innovation does help shape the future, look at Electric Vehicles (EVs): they are leading the most sustainable way of mobility and are a perfect example of using consciousness to advance in technology. 


The Paths of Electric Cars and AI  

Significant technological progressions revolve around AI and electric cars. The initial wave of EVs generated excitement among the public, and with this enthusiasm for cars, many anticipated a swift adoption by the public. However, this expectation did not materialise as quickly as initially thought. It took a considerable amount of time before individuals started buying EVs. The public’s acceptance of cars did not align with sparkling projections. AI serves as a technology that replicates human actions on computers. It encompasses roles such as customer service representatives, virtual assistants, and personalised recommendations. AI systems are driven by the increased access to data.


Electric Vehicles (EVs): Pioneering Sustainable Transportation

Electric Vehicles (EVs) have emerged as leaders in revolutionising 21st-century transportation, notably reducing environmental impacts. Modern EVs, with advanced technology, ensure efficient commuting experiences and significantly contribute to cleaner air by lowering greenhouse emissions [1]. However, challenges such as the high initial costs and environmental concerns over battery life and disposal persist.[2] Yet, the expanding charging infrastructure highlights a move towards overcoming these hurdles. While we go through the change with EVs, a similar shift is happening with AI that impacts our lifestyle and business operations.


Artificial Intelligence (AI): Transforming the Fabric of Society

AI is reshaping our world in more ways than one, if not literally. A new development of such advanced technology is bound to drastically change our way of living. AI, mainly through intelligent assistants and automation, will just keep taking off huge chunks of our daily interactivity and jobs, making everything easier. It will improve essential fields, such as healthcare, with the help of people staying healthy. AI will give a chance to new opportunities by taking routine jobs so that humans can focus on creative and strategic pursuits[3]. AI will power the service to make it more efficient and personalised by providing all services, hence meeting our needs and wants.[4]

AI is an astonishing innovation that society needs to use efficiently to unleash human potential. Now, It becomes imperative to examine the lessons learned from the journey of Electric Vehicles (EVs).


What are the lessons learned from EVs for AI?

This brings us to the interweaving journey of Electric Vehicles (EVs) and Artificial Intelligence (AI) Competence, with findings and strategic recommendations arising from compelling narratives. The EV journey has been storying and has critical milestones and challenges to share learning that can deftly be applied to the domain of AI to chalk a roadmap that is both progressive and pragmatic.


·         Advanced infrastructures and reliable networks

The charging systems of electric vehicles are being designed and made in such a way that they become convenient and accessible for people. This has been done so people are encouraged to use electric vehicles more frequently and find them convenient. The lesson of electric cars is to build all the systems and structures that would allow an electric vehicle system to spread and work at a large scale.[5]

However, it might even slow growth or make it difficult to do business if systems were not put in place, such as the charging infrastructure for electric car systems that followed later. 

We should understand that investment in the digital core systems for AI can make it part of everyday running businesses. If the digital system is rightly put in place, then artificial intelligence will be incorporated and commonly used.[6]


·         Investments and availability gap

The other typical area would be in the cost discussion; one level above the debate on costs would be the discussion of long-term value[7]The former underlines their value in the long run, and the latter develops strategies that could bring those financial barriers down to the accessibility and attractiveness of technologies in the larger sense.[8] The other examples of closing the investment gaps for AI are the growing availability of small language models, cloud service, and off-the-shelf and data-as-a-service solutions [9].


·         Consumers /Stakeholders’ trust, ethics, and regulatory

Electric vehicles and artificial intelligence both have complex challenges to deal with. For electric cars, some of the early concerns were about how far they could go on one charge and not having enough places to charge them. [10]

For artificial intelligence, some of the main concerns are about ethics, privacy, and people possibly losing their jobs but also getting better capabilities and skills. Like electric vehicles, it is imperative to be open and honest with people, teach them about artificial intelligence, and show them the benefits [11]. But again, it will also require working closely with groups like the government that make rules to ensure innovations with artificial intelligence do not happen faster than considering the ethical issues[12] and ensuring employees, people involved, and society as a whole entity .

Learning from the challenge and associated strategies within EV adoption, AI companies can arm themselves with a repertoire of tools to help them begin to traverse the complexity of landscapes to drive technological innovation [13]with strategic foresight and ethical consideration, making the implementation impactful.


Conclusion: Hype vs Effective Adoption

There is the last point that regroups the above connections explained: Hype vs effective adoption. An opinion writer ( myself) would say, ‘Many people are now as excited about AI as they were in the beginning with Evs. However, business leaders need to learn that AI, like electric cars, initially had practical issues solving problems that slowed consumers from using them.’ In terms of integrating AI, leaders should come up with realistic plans with enough time and money to do things right[14]. That would surely help in the sustainable benefits of AI among stakeholders rather than fizzing out when the hype has died down. If corporations have to use AI to their advantage, there are no shortcuts.

Companies that would like to adopt AI must learn from the slow transition time to popularise electric cars. Moving too fast with new technologies often comes with unexpected problems [15]. Instead, careful methodologies should be applied to design AI to solve business problems, not just the flashy technology innovations that don’t help much [16].


Bringing innovations like AI means that companies must learn to walk before run



The main point is that though promising, AI needs careful management, which did not take place fully for EVs’. Promising innovations like AI take time, massive shifts that change many things. Realistic expectations and patience are the keywords, with progressive evolution from meeting immediate problems and opportunities while creating building blocks of benefits and performances[17] for the future using AI systems. Moving carefully and tactfully is better than rushing forward without properly thinking it through.

A wise man doesn’t look for the path to success; he paves it (anonymous)


References

1.   Hawkins, T., Singh, B., Majeau‐Bettez, G., & Strømman, A., 2013. Comparative Environmental Life Cycle Assessment of Conventional and Electric Vehicles. Journal of Industrial Ecology, 17.

2.   Cox, B., Mutel, C., Bauer, C., Beltran, A., & Vuuren, D., 2018. Uncertain Environmental Footprint of Current and Future Battery Electric Vehicles.. Environmental science & technology, 52 8, pp. 4989-4995 .

3.   Lakhani, K.( 2023) “AI Won’t Replace Humans — But Humans With AI Will Replace Humans Without AI” HBR

4.   Danaher, J., 2018. Toward an Ethics of AI Assistants: an Initial Framework. Philosophy & Technology, 31, pp. 629-653.

5.   Straka, M., Falco, P., Ferruzzi, G., Proto, D., Poel, G., Khormali, S., & Buzna, L., 2020. Predicting Popularity of Electric Vehicle Charging Infrastructure in Urban Context. IEEE Access, 8, pp. 11315-11327.

6.   Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., & Ghosh, S., 2021. Adoption of artificial intelligence-integrated CRM systems in agile organizations in India.

8.   Enholm, I., Papagiannidis, E., Mikalef, P., & Krogstie, J., 2021. Artificial Intelligence and Business Value: a Literature Review. Information Systems Frontiers, 24, pp. 1709-1734.

9.   Alshamaila, Y., Papagiannidis, S., & Li, F., 2013. Cloud computing adoption by SMEs in the northeast of England: A multi-perspective framework. J. Enterp. Inf. Manag., 26, pp. 250-275.

10.         Aduama, P., Al‐Sumaiti, A., & Al-Hosani, K., 2023. Electric Vehicle Charging Infrastructure and Energy Resources: A Review. Energies.

11.         Ahmad, S., Rahmat, M., Mubarik, M., Alam, M., & Hyder, S., 2021. Artificial Intelligence and Its Role in Education. Sustainability.

12.         Brendel, A., Mirbabaie, M., Lembcke, T., & Hofeditz, L., 2021. Ethical Management of Artificial Sustainability

13.         Li, X., Wu, T., Zhang, H., & Yang, D., 2022. Digital Technology Adoption and Sustainable Development Performance of Strategic Emerging Industries. Journal of Organizational and End User Computing.

14.         Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., & Ghosh, S., 2021. Adoption of artificial intelligence-integrated CRM systems in agile organisations in India. Technological Forecasting and Social Change.

15.         Demirkesen, S., & Tezel, A., 2021. Investigating major challenges for industry 4.0 adoption among construction companies. Engineering, Construction, and Architectural Management.

16.         Leesakul, N., Oostveen, A., Eimontaite, I., Wilson, M., & Hyde, R., 2022. Workplace 4.0: Exploring the Implications of Technology Adoption in Digital Manufacturing on a Sustainable Sustainability..

17.         Tse, T. , Esposito, M., Goh,D. , Lee, P., 2024 – Why Adopting GenAI Is So Difficult-HBR

 

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