Essay Management
Essay Management
Essay Management
Knowledge Management (KM) has been a key discipline for over decades disclosing
how knowledge is created, developed, retained and applied within an organisation or country
and enables innovation and learning from past knowledge (Soto-Acosta et al., 2014). KM has
been receiving interest for a long time and evidence shows that it is an academic discipline,
however it can mature to organisational level where it benefits organisations and workers, as
briefly mentioned KM is now one of the key components to providing a competitive advantage
to organisational success. Despite KM being a key component, organisational theory has not
fully been developed to provide its significance, in addition, its significance has not been
defined to an appropriate universal definition to draw in organisations attention.
Novel sequences of work and business practices have been developed in order to better
understand KM through technological advancements where the industry is referring to this as
the fourth industrial revolution. Industry 4.0 includes technological advances such as Artificial
Intelligence (AI) which is believed to enable many tasks especially in the managerial role as
well as labour workforce to go through simpler processes and have efficient production. A
knowledge-based economy tends to be a source of competitiveness for organisations and
countries which showcase their creativity and suggests that fresh and new connections are
being made and new ways and processes are being formed. In this digital era, there is a need
for business establishments and organisational structures to develop innovative and problem-
solving processes to encourage the growth of awareness of the digitisation of the construction
industry (Jallow, 2020). Yigitcanlar et al. (2020) through a systematic review of literature
observed that AI may improve productivity by automation of the data management process and
removing the need for middle party, resulting in increasing profit.
In businesses, the role of Artificial Intelligence (AI) is getting more prominent.
Examples of the use of artificial intelligence in the business environment are increasingly
diverse, ranging from object recognition to preventing the threat of cyber-attacks. Netflix's AI-
powered recommendation engine contributes $1 billion per year. Exxon Mobil leverages AI to
support the search for deep-sea petroleum resources. Pharmaceutical company Roche is using
deep learning to gain insight into Parkinson's disease. Meanwhile, the AI developed by Huawei
is deployed by the non-profit organization Rainforest Connection to combat illegal logging.
The Semrush Blog summarizes statistical facts from various sources that highlight the benefits
of AI for businesses. For example, 54% of executives say that implementing AI in the work
environment has increased productivity. And 79% of executives think AI will make their jobs
simpler and more efficient. By 2021, the continued use of AI in various business sectors will
create US$2.9 trillion of business value and 6.2 billion hours of productivity. Not only for
business, but AI also has the potential to contribute US$15.7 trillion to the global economy by
2030.
Although AI is increasingly popular in the business scene, many business people are
still hesitant to implement it. An e-book published by O'Reilly in 2019 mentions several factors
that prevent organizations/companies from adopting AI. The top five are corporate culture that
does not recognize the need for AI (23%), data availability issues (19%), lack of competent AI
staff (18%), difficulty identifying business use cases for AI (17%), and infrastructure
challenges technology (7%).
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Miftah Farid KD-570
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Miftah Farid KD-570
rules and is efficient so that financial institutions can effectively reduce potential
disputes and review the success rate of collection. Individuals and financial institutions
are also starting to explore Robo-Advisory, namely chatbots and assistant applications
that work together to monitor personal finances. Users simply set a target for how much
money to save or determine expenses. Next, leave it to the financial assistant who will
provide insight so that users can achieve the desired financial targets. Companies can
also take advantage of AI for travel & expense management. This system will utilize
deep learning to improve the quality of data extraction for various types of receipts,
such as receipts for hotel stays, purchases of gasoline, tolls and others. This utilization
will help companies cut approval workflows and processing costs per unit.
Seeing how AI has massively infiltrate and are applied in our society and life, it is
essentially prominent to observe how it will impact the existing working market. A study by
Microsoft and IDC shows that 85% of jobs in Asia Pacific will undergo transformation in the
next three years (Jimenez et al., 2018). Respondents in the study said that more than 50% of
jobs would be re-assigned to new positions and/or retrained and upskilled for digital
transformation. What's interesting is that this study shows that 26% of jobs are new types of
jobs created by digital transformation, which would offset the 27% of jobs that would be
outsourced or automated. In other words, the impact of AI on overall employment will be
neutral.
This is a clear indication of how the way businesses organize work, how people find
work and the skills they need to prepare for the workforce will change drastically. These
changes are likely to intensify in the next decade. As AI continues to change the nature of work,
we need to rethink education, skills and training to ensure that everyone is prepared for the jobs
of tomorrow and businesses have access to the people who will make them successful.
The future of AI can be bright or dim. My view is that technology replacement is
inevitable, and the ability to adapt to this phenomenon is what defines us all. And in order to
adapt to the future of AI that will emerge and evolve rapidly, everyone, from workers to
companies to governments, needs to start listening to one another more, work together and
keep learning new things and skills.
Georgi Todorov. (2021, February 26). 65 Artificial Intelligence Statistics for 2021 and Beyond.
https://www.semrush.com/blog/artificial-intelligence-stats/
Jallow, H. (2020). Knowledge Management and Artificial Intelligence (AI). 9.
Jimenez, D.-Z., Lim, V., Cheok, L., & Ng, H. (2018). Unlocking the Economic Impact of Digital
Transformation in Asia Pacific. 31.
Soto-Acosta, P., Colomo-Palacios, R., & Popa, S. (2014). Web knowledge sharing and its effect on innovation:
An empirical investigation in SMEs. Knowledge Management Research & Practice, 12(1), 103–113.
Tsang, E. W., & Zahra, S. A. (2008). Organizational unlearning. Human Relations, 61(10), 1435–1462.
Yigitcanlar, T., Desouza, K. C., Butler, L., & Roozkhosh, F. (2020). Contributions and risks of artificial
intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies,
13(6), 1473.