What Future "The Literature Review" in an AI World

Introduction

The linchpin of all academic research is "The Literature Review".

PhD students could easily spend six months to a year or more undertaking a "comprehensive review of the existing academic literature" relevant to their topic. Even at the undergraduate dissertation level, the literature review chapter is sacrosanct. You won't get past the first hurdle for peer-reviewed journals without a detailed review of the relevant literature.

From a personal perspective, one of the main reasons i 'volunteered' for early retirement from academia in 2010 was the myopic focus on "the previous academic literature". In my area (digital), there was almost no "previous academic literature". For inspiration, I relied on the more future-looking perspectives of non-academic authors such as Don Tapscott, Brian Solis and numerous others. But this didn't count as their writings were not peer-reviewed :-(

So what does the future hold for "The Literature Review" in an AI world?

Last evening I decided to have a quick look at scite_ - described as "an award-winning platform for discovering and evaluating scientific articles via Smart Citations. Smart Citations allow users to see how a publication has been cited by providing the context of the citation and a classification describing whether it provides supporting or contrasting evidence for the cited claim."

Within a few minutes, and using a very simple prompt it returned the short transcript shown below. I could have improved the response by providing a more detailed prompt and/or entering into a two-way conversation with the agent, but very impressive nevertheless.

Apart from formatting, the below is unedited. I have also not checked all of the references for hallucination but at first glance they look fine.

Prompt

"The role of AI in business strategy development."

scite assistant response

Artificial Intelligence (AI) has become a transformative force in shaping modern business strategies, revolutionizing decision-making processes, operational efficiency, and strategic development.

The integration of AI technologies in business operations has been a focal point for researchers and practitioners alike, with numerous studies highlighting the pivotal role of AI in driving innovation and competitiveness (Edilia, 2023; Ekellem, 2023; Ekellem, 2023; Kaggwa, 2024). As businesses navigate an era of rapid technological advancement, the strategic implications of AI adoption are becoming increasingly apparent, with AI being recognized as a cornerstone of modern business strategies (Ida, 2018; Farayola, 2023; Alet, 2023; Osasona, 2024).

The influence of AI on business strategy extends beyond mere automation, encompassing predictive analytics, real-time monitoring, and collaborative robotics, thereby enhancing business efficacy and agility (Ekellem, 2023). By harnessing AI capabilities strategically, organizations can not only improve marketing effectiveness but also foster sustainable growth in dynamic business environments (Anjorin, 2024). Moreover, the development of competitive intelligence through emerging technologies like AI has the potential to revolutionize strategic decision-making processes and drive innovation (Ebrahimi, 2022).

AI's impact on business models and transformation management is profound, offering new entrepreneurial ventures innovative opportunities and effective pathways for growth and development (Fang, 2023). The integration of AI and automation is prompting strategists to rethink traditional business models and explore novel approaches to meet evolving customer expectations (Singh & Chouhan, 2021). Furthermore, AI is driving significant positive changes in business operations, pushing enterprises towards greater efficiency and innovation (Bruno, 2024).

As businesses strive to leverage AI effectively in their strategic endeavors, it is crucial to develop critical AI literacy among stakeholders, including decision-makers, leaders, and employees (Wisniewski, 2020; DeVasto, 2024). Educating current and future leaders on AI technologies, data science, and their implications for business is essential for fostering a culture of innovation and adaptability within organizations. Additionally, understanding the ethical and regulatory dimensions of AI is paramount in defining responsible AI business strategies (Salih & Bhatti, 2020).

The role of AI in business strategy development is multifaceted, encompassing not only technological advancements but also organizational capabilities, marketing strategies, and operational performance (Nuseir & Refae, 2022). AI's ability to enhance organizational learning, improve marketing effectiveness, and optimize business capabilities underscores its significance as a strategic enabler in today's competitive landscape (Nuseir & Refae, 2022).

By aligning AI initiatives with overarching business goals and tasks, organizations can effectively leverage AI service systems to drive innovation and achieve sustainable growth (Takeuchi, 2022).

In conclusion, the integration of AI technologies in business strategy development represents a paradigm shift in how organizations operate and compete in the digital age. By embracing AI as a strategic enabler, businesses can unlock new opportunities for growth, innovation, and competitive advantage. However, to realize the full potential of AI in business strategy, organizations must prioritize AI literacy, ethical considerations, and strategic alignment to navigate the complexities of the AI-driven business landscape successfully.

References:

Alet, J. (2023). Effective integration of artificial intelligence: key axes for business strategy. Journal of Business Strategy, 45(2), 107-114. https://doi.org/10.1108/jbs-01-2023-0005

Anjorin, K. (2024). Harnessing artificial intelligence to develop strategic marketing goals. International Journal of Management & Entrepreneurship Research, 6(5), 1625-1650. https://doi.org/10.51594/ijmer.v6i5.1127

Bruno, Z. (2024). The impact of artificial intelligence on business operations. Global Journal of Management and Business Research, 1-8. https://doi.org/10.34257/gjmbrdvol24is1pg1

DeVasto, D. (2024). Building critical ai literacy in the business communication classroom. Business and Professional Communication Quarterly. https://doi.org/10.1177/23294906241253199

Ebrahimi, M. (2022). Development of competitive intelligence to formulate business strategy using emerging technologies., 124-143. https://doi.org/10.4018/978-1-6684-5235-6.ch006

Edilia, S. (2023). Innovative approaches in business development strategies through artificial intelligence technology. Iaic Transactions on Sustainable Digital Innovation (Itsdi), 5(1), 84-90. https://doi.org/10.34306/itsdi.v5i1.612

Ekellem, E. (2023). Operational renaissance: harnessing ai for enhanced business efficacy.. https://doi.org/10.36227/techrxiv.24721143

Ekellem, E. (2023). Strategic alchemy: the role of ai in transforming business decision-making.. https://doi.org/10.36227/techrxiv.24707151

Fang, J. (2023). Research on the design of business models and transformation management of new entrepreneurial ventures driven by artificial intelligence. BCP Business & Management, 49, 36-41. https://doi.org/10.54691/bcpbm.v49i.5383

Farayola, O. (2023). Innovative business models driven by ai technologies: a review. Computer Science & It Research Journal, 4(2), 85-110. https://doi.org/10.51594/csitrj.v4i2.608

Ida, Y. (2018). Advanced learning technologies for deep learning. NTT Technical Review, 16(8), 37-41. https://doi.org/10.53829/ntr201808fa6

Kaggwa, S. (2024). Ai in decision making: transforming business strategies. International Journal of Research and Scientific Innovation, X(XII), 423-444. https://doi.org/10.51244/ijrsi.2023.1012032

Nuseir, M. and Refae, G. (2022). The role of artificial intelligence, marketing strategies, and organizational capabilities in organizational performance: the moderating role of organizational behavior. Uncertain Supply Chain Management, 10(4), 1457-1466. https://doi.org/10.5267/j.uscm.2022.6.010

Osasona, F. (2024). Ai integration in business analytics: a review of usa and african trends. Computer Science & It Research Journal, 5(2), 432-446. https://doi.org/10.51594/csitrj.v5i2.793

Salih, C. and Bhatti, F. (2020). A conceptual framework on defining businesses strategy for artificial intelligence. Contemporary Management Research, 16(3), 175-206. https://doi.org/10.7903/cmr.19970

Singh, N. and Chouhan, S. (2021). Role of artificial intelligence for development of intelligent business systems.. https://doi.org/10.1109/ises52644.2021.00092

Takeuchi, H. (2022). Method for identifying business goals and tasks for ai service system. Information Engineering Express, 8(2), 1. https://doi.org/10.52731/iee.v8.i2.712

Wisniewski, H. (2020). What is the business with ai? preparing future decision makers and leaders. Technology & Innovation, 21(4), 1-14. https://doi.org/10.21300/21.4.2020.4

Thoughts?

I would very much welcome your thoughts and comments on this.

Will academics be told that you "must not use AI in your research" in the same way that students were told "you must not use Wikipedia"?

As often heard from me - "i used to be an academic but i am alright now" :-)

But if it is going to become so much easier, as suggested above, i might just become one again :-)

Take care.

Jim H