The second industrial revolution generated many new jobs as firms began to mass produce. During the third industrial revolution, labour intensive processes became automated and moved the labour market from manual labour towards administrative/office positions, through electronics and IT (Schwab, 2016). During the third wave, techniques to collect tremendous amounts of data were developed and today data has been said to be the world’s most valuable resource. From this, complex Enterprise Resource Management (ERP) systems emerged, from which businesses can analyse collected data and make more informed decisions to optimized their business processes. Now, we stand before the fourth industrial revolution which involves the use of automated robots to analyse the data and develop improved business processes from such input. This leaves the question; what input will humans have in the future for designing improved business processes and will the large ERP and analytics firms as Oracle, SAP and Salesforce survive?

There are four driving elements in the fourth industrial revolution: Process Mining, Robotic Process Automation (RPA), Internet of Things (IoT), and Blockchain Technologies (van der Aalst, 2018). Theoretically, these four elements combined have the capability to connect a firm’s or industry’s entire value-chain, both vertically and horizontally, and from this make automated process enhancing decisions. However, robots are only as good as the algorithms they are built on and without constant human cognitive interactions to innovate and improve those algorithms, how can they create better solutions designed to serve human needs? Saunderson and Nejat (2019) explores how robots will be able to interpret non-verbal communications in social interactions, given that a substantial part of meaning is hidden behind conscious and unconscious bodily functions which are not explicitly stated or even understood. I think this limitation is the main barrier for Industry 4.0 to completely eliminate human day-to-day business application developers. Team meetings will be needed and interpreted on a conscious level, not yet graspable by robots. Given the many drawbacks of human individuals, such as the principal agent problem generating conflicting organizational interests, AIs may today objectively have the capacity to improve overall market efficiencies. However, until robots can assimilate such a consciousness; ERP and analytics firms will still be useful in the long term to build the groundwork for the models used to develop AI algorithms and interpret the data available.

References:

Saunderson, S. and Nejat, G., 2019. How Robots Influence Humans: A Survey of Nonverbal Communication in Social Human–Robot Interaction. International Journal of Social Robotics, 11(4), pp.575-608.

Schwab, 2016. https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/#:~:text=The%20First%20Industrial%20Revolution%20used,information%20technology%20to%20automate%20production.

van der Aalst, W., Becker, J., Bichler, M., Buhl, H., Dibbern, J., Frank, U., Hasenkamp, U., Heinzl, A., Hinz, O., Hui, K., Jarke, M., Karagiannis, D., Kliewer, N., König, W., Mendling, J., Mertens, P., Rossi, M., Voss, S., Weinhardt, C., Winter, R. and Zdravkovic, J., 2018. https://eur-on-worldcat-org.eur.idm.oclc.org/oclc/7925381332

Ever had an idea which you thought could be turned into an enterprise; then figured out that it would be too difficult to find sufficient funding in order to realize it? In developed economies, the existence of endless complex venture ideas must compete through structured and innovative business plans to gain the investors’ capital investment. This is also the case for developing economies, however, due to the nature of a developing market many ideas generated in such conditions are simpler and require much less finance. Despite simple business models with huge potential upside, entrepreneurs in developing countries struggle to find sources of finance. This is simply because there are multiple non-business related risk factors involved in providing credit in such less controllable conditions (Barrowclough, 2018). So here we are faced with an opportunity to solve poverty while also generating return on investment but the influence of uncontrollable, yet reliable, data has prohibited its success (Saini, 2019). The obvious solution? Big data analytics and AI.

AI can help financiers not only to provide more informed loans but also to comply with dynamic local laws, regulations which govern local financial markets (Saini, 2019). Moreover, blockchain technologies improves the availability of reliable and qualitative data from which AI could be fed to generate intelligent insight to help microfinanciers accurately distribute their funds. On the other side of the market, AI is also helping entrepreneurs discover possibilities of financing sources, through targeted ads and AI driven search engines.

Historically, microfinancing in developing countries has proven to generate increased business activity while also giving a return on capital for the investors, however, there is not substantial evidence proving that microfinancing has led to reductions in income and gender inequalities (Saini, 2019). Microfinancing has also not reached out to the poorest of the poor, due to their lack of knowledge and resources to ever discover such opportunities. Reaching out to such groups has become a possibility today, given that a portion of such groups have gained access to mobile phones and can transfer funds and access social media.

Usage of mobile phones in the poorest of the poor areas, has enabled the collection of data from previously unexamined areas. Individuals’ activities on social media are also fed to AI algorithms to provide a basis to the generation of creditworthiness and general character of a user (Saini, 2019). In order to not only achieve economical growth but also economic development such as gender equality; the AI algorithms must be developed from a socially responsible perspective, not with the purpose of maximizing monetary returns but achieving improved social development (Su, 2019). It is doubtful that private financial institutions will use AI in socially responsible ways; which is why local regulations need to adapt in order to respond to the endless advancements in AI and data analytics.

References:
Barrowclough, D. 2018, THE INS AND OUTS OF INCLUSIVE FINANCE: SOME LESSONS FROM MICROFINANCE AND BASIC INCOME. Viewed on 06 October, 2020 from: https://unctad.org/en/PublicationsLibrary/gdsmdp2017d3_en.pdf

Saini, M. 2019, AI’s role in the world of Microfinance. Viewed on 06 October, 2020 from: https://medium.com/@mridulasaini/ais-role-in-the-world-of-microfinance-12a1e2bcf5f0

Su, D. 2019, How social media marketing applied on MFIs. Viewed on 06 October 2020 from: https://www.academia.edu/40020730/How_social_media_marketing_applied_on_MFIs