As AI is seeing more and more mainstream adoption, the debate on the influence of AI on the future of jobs intensifying as well. On the one side, there is the view that AI is going to replace a lot of jobs, whereas the other side argues that AI will be just a way to complement humans – but inevitably changing the way how jobs look like. Although I believe that there’s truths in both views, I have a stronger belief in the role of AI being complementary. A recent article which I found outlines a really interesting perspective on how AI makes recruiting more human.

One area which has been particularly susceptible to AI is recruiting, as AI and advanced technology can parse through an avalanche of resumes much faster than humans can. Not only can AI do this repetitive task much faster, it can also do it much more accurate than humans. The last benefit I would like to point out is that AI will be able to this screening unbiased, something which humans easily fall for (Jiang, 2018). So as AI can obviously play a huge role in recruiting, I do not believe AI will replace recruiters – but it will change their job to a more purposeful, human job. Here are a few examples of how AI will make recruiting more human:

1) Personalized candidate outreach: better able to match candidates to a job which really fits their wants & needs
2) AI can identify targets, but a human needs to take the decision to hire them. Recruiters are able to establish real connections with candidates, as they have access to more in-depth profiles
3) Focus on employer branding, as AI frees up their time of repetitive tasks. Recruiters can create consisting and compelling experiences across all their channels.
Source: (Jiang, 2018)

All in all, I believe the possibilities to make jobs more human through AI is a trend which will become visible in other industries as well. AI will definitely change the way we do our jobs, but I don’t think it will always replace them. As AI can do a lot of repetitive tasks for us, it allows us to focus on more purposeful jobs and tasks. I personally really like two of Microsoft’s videos, which are in similar fashion to what I believe in; and comes down to: “What’s a hammer without someone who swings it”? If you are interested, have a look at them:
https://www.youtube.com/watch?v=9tucY7Jhhs4

Sources:
Jiang, S. (2018). How AI makes recruiting more human. Retrieved from: https://www.forbes.com/sites/forbeshumanresourcescouncil/2018/09/27/how-ai-makes-recruiting-more-human/

When it comes to payments in e-commerce, most European countries, the US, Canada and Australia strongly rely on online banking (iDeal in the Netherlands for example) Credit Card (Visa or Mastercard) payment or usage of PayPal (Adyen, 2015). The future of digital payments looks very interesting, there are many different solutions coming from all kinds of directions.

Alipay
In China, only 1% of the e-commerce payments are processed with Credit Card, whereas 48% of transactions are completed with Alipay. Unlike credit cards or PayPal, Alipay is completely integrated in Chinese lifestyles – which make it irreplaceable for a Chinese citizen. For example, you could split bills with QR codes, pay utility bills, get food delivered with WeChat, top up phone credit and buy train/metro tickets (Hendrichs, 2015). Even during Chinese New Year, people can use Alipay to send each other “red envelopes”, a traditional gift during Chinese New Year.

TechFin companies
Another new payment solutions comes from the big tech companies, with solutions such as Apple Pay and Google Pay – called TechFin solutions (Krishnakumar, 2018). These companies have enormous user bases, making it easier to penetrate to existing users of their user base. Based on their network effects, they can pose a real threat to current payment methods.

Blockchain payment methods
Also, blockchain based solutions may prove to be competition to PayPal. Solutions such as Request Network – often praised as the ‘new PayPal’ on internet fora or blogs (Levenson, 2017) – are still in its early stages. Using blockchain based solutions is also very beneficial for merchants, providing automated accounting and less need for expensive accountants or expensive accounting systems (Levenson, 2017; Yermack, 2017).

All in all, in my opinion it’s not a question if digital payments will change, but rather how it will change. With so many different new possibilities, I’m curious to see where this goes.

References:
Adyen. (2015). The Global E-Commerce Payments Guide. Retrieved from: https://www.adyen.com/blog/the-global-e-commerce-payments-guide

Hendrichs, M. (2015). Why Alipay is more than just the Chinese equivalent of PayPal. Retrieved from: https://www.techinasia.com/talk/online-payment-provider-alipay-chinese-equivalent-paypal

Krishnakumar, A. (2018). From Fintech to TechFin – Who should banks be more worried about?. Retrieved from: https://dailyfintech.com/2018/03/16/from-fintech-to-techfin-three-trends-that-banks-will-be-worried-about/

Levenson, N. (2017). Request Network is more than just PayPal 2.0 — It could revolutionize the finance world. Retrieved from: https://hackernoon.com/request-network-is-more-than-just-paypal-2-0-it-could-revolutionize-the-finance-world-87b54bb455

Yermack, D. (2017). Corporate governance and blockchains. Review of Finance 21, pp. 7 – 31.

A very interesting application of the development of AI and machine learning can be found within the legal sector. Traditionally, the legal sector is quite old-fashioned and not adapting to new technologies. Furthermore, the legal sector is heavily dependent on large documents of jurisdiction, old cases and law books. Some examples of what AI and machine learning can do for the legal sector are:
• Review documents and research: A company called ROSS Intelligence is working on systems to process natural language to review large documents and match them to similar cases. This can help to reduce the load of human hours quite dramatically.
• Perform due diligence: When taking new corporate decisions, a lot due diligence has to be performed; confirming facts and figures to thoroughly evaluate decisions.
• Contract review: Companies such as LawGeex and Kira Systems are developing software which can rapidly review contracts, identify potential risks and highlight these risks.
• Automated divorces: The costs of using divorce lawyers are very high. Wevorce is able to do automated, online divorces for much less of the price traditional lawyers cost.
Source: (Marr, 2018)

In my opinion, these technologies are very interesting to be adopted within courts or lawyer’s offices. These technologies, of course, do require significant investments to adopt. Unfortunately, sufficient funds are something the public law sector is missing. Dutch judges and public prosecutors even raised alarm with regards to the inability to innovate within the legal sector (NOS, 2018). There is a massive shortage of millions of funds, and these lawyers and prosecutors are even asking for money (NOS, 2018).

So even though there seems to be a lot of potential for AI and machine learning within the legal sector. For now, it only seems like law firms, who might be able to invest into new technologies. This could create strong disadvantage to the public sector, and therefore I can see why judges and public prosecutors raised alarm.

References:
Marr, B. (2018). How AI And Machine Learning Are Transforming Law Firms And The Legal Sector Retrieved from: https://www.forbes.com/sites/bernardmarr/2018/05/23/how-ai-and-machine-learning-are-transforming-law-firms-and-the-legal-sector/

NOS (2018). Unieke waarschuwing rechters en OM: ‘Rechtspraak wordt uitgehold’. Retrieved from: https://nos.nl/nieuwsuur/artikel/2250340-unieke-waarschuwing-rechters-en-om-rechtspraak-wordt-uitgehold.html

After reading a few posts here on Picnic and the Dutch supermarket landscape, I would like to share something on Amazon and the supermarket landscape. Last year, Amazon bought the supermarket chain Whole Foods for $13.7 billion and seemed to be on its way to disrupt a new industry: grocery shopping. A video in which Amazon introduced the ‘Shop and Go’ concept, with automatic payments and skipping the cashier went viral. (https://www.youtube.com/watch?v=NrmMk1Myrxc) A few days ago, this became a bit more specific: it is planning on opening 3000 supermarkets called AmazonGo (Bloomberg, 2018). If it manages to live up to this ambition, it would be one of the biggest supermarkets in the US within a few years (Majid, 2018).

While the concept of AmazonGo currently seems to target concepts such as 7-Eleven and other small convenience stores rather than the big supermarkets, the technology behind AmazonGo is very promising. The technology is based on computer vision, sensor fusion and deep learning: it automatically detects when products are taken shelves and added to a visual ‘cart’ (Amazon, n.d.). This technology also has a major impact on the way supermarkets can use data. With real time tracking of which products are bought and who buys them, there is a lot of potential for targeting specific customers, optimized product placement and assortment decisions. Of course, Amazon has to keep the consumer’s privacy in mind, which is inevitable these days.

Looking at the way Amazon targeted other industries before, I am really curious to see whether we will be shopping at AmazonGo in the future instead of Albert Heijn. Even though this does not sound very likely, I do believe AmazonGo’s technology is here to stay. Not only can this technology be applicable to supermarkets, but it can be used to a lot brick-and-mortar stores in general, especially for commoditized goods with low differentiation.

References:
Amazon (n.d.) Amazon Go. Retrieved from: https://www.amazon.com/b?ie=UTF8&node=16008589011

Bloomberg (2018) Amazon Will Consider Opening Up to 3,000 Cashierless Stores by 2021. Retrieved from: https://www.bloomberg.com/news/articles/2018-09-19/amazon-is-said-to-plan-up-to-3-000-cashierless-stores-by-2021

Majid, A. (2018). Amazon Go wil 3000 kassaloze supermarkten openen – en dat is slecht nieuws voor Ahold Delhaize. Retrieved from: https://www.volkskrant.nl/nieuws-achtergrond/amazon-go-wil-3000-kassaloze-supermarkten-openen-en-dat-is-slecht-nieuws-voor-ahold-delhaize~b24c37ed/

If you have read the news in the past 24 hours, you might already know – but there are talks of Uber trying to acquire the food delivery company Deliveroo (Ram, 2018). From a two-sided market perspective, this seems like a very interesting move. However, it looks like bad news to other food delivery companies; Just Eat’s share price is aready down 7% since the news got out (Cocco, 2018). The acquisition motive from Uber and Just Eat’s drop in share price can be explained from two principles on two-sided markets: network effects and cross-sided externalities.

Like in most two-sided markets (e.g. Uber, Airbnb, Seedrs), companies are strongly dependent on network effects. Network effects are defined as: the positive effect described in economics and business that an additional user of a good or service has on the value of that product to others. In other words, the value of the platform increases as the number of members of this platform increase. With an increased user base on Uber Eats because of the acquisition, the value of the platform increased as well.

Another characteristic of two-sided markets is that the effects of ‘cross-sided externalities’ are highly important, this comes down to that an increase of people on one side of the market has a positive/negative effect on the other group. In the case of food delivery, there are positive cross-sided externalities present:
• An increase in the amount of users (the ones who order food), makes it more attractive to the restaurants to be on this platform (larger target pool)
• An increase in the amount of restaurants has a positive effect on users, as they can find better matches in terms of their food preferences

All in all, both network effects and externalities are two of the concepts through which natural monopolies often emerge; e.g. Airbnb. While the food delivery industry is still dispersed with Deliveroo, Uber Eats, Foodora and Thuisbezorgd in the Netherlands, this move from Uber will definitely shake up the competitive landscape.

References:
Ram, A. (2018). Uber looks to Deliveroo to expand food delivery in Europe. Retrieved from: https://www.ft.com/content/9bcfcbaa-bd8f-11e8-8274-55b72926558f

Cocco, F. (2018). Just Eat shares fall 7% after reports Uber looking at Deliveroo.
Retrieved from: https://www.ft.com/content/600e8d40-bd6c-11e8-8274-55b72926558f

In line with today’s lecture on pricing innovations, I want to share with you the emergence of the Anything-as-a-Service (XaaS) model. In times of digitalization, lots of opportunities are out there to move towards a service-based model as opposed to a product based model. Over the years, we have seen the rise of Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS). Some well known examples of SaaS are: Dropbox, Google Apps, Salesforce and Cisco WebEx. In the Netherlands, we have recently even seen the rise of the Bike-as-a-Service (BaaS) model; brought to the market by Swapfiets.

The main principle of the XaaS business model is that it shifts the customer’s focus from high fixed costs and capital expenditures to variable costs and operational expenditures. In the case of software, in the past, companies had to buy very expensive software products and the software had to be run on the companies’ premises, which also required hardware investments. Moreover, software updates were not always included and in case of defects, service was not always included. With the emergence of SaaS, most of these disadvantages are tackled by this new model:
• A company pays a monthly/weekly/yearly fee (instead of one big lump-sum)
• The software runs on the cloud (so expensive hardware is not necessary anymore)
• Software updates and maintenance are usually included

All in all, the emergence of SaaS really changed the way software is distributed. An interesting overview of what companies had to previously manage and what they have to manage now can be found here: https://www.bmc.com/blogs/saas-vs-paas-vs-iaas-whats-the-difference-and-how-to-choose/

The future of the XaaS business models look really promising, IBM (2014) even believes we are entering an ‘XaaS Era’. Currently, in India, Farming-as-a-Service (FaaS) is emerging as well (e.g. Mitchell & Sehgal (2018), from Bain & Company wrote a really interesting piece on this) and Artificial Intelligence-as-a-service (AIaaS) is on the rise as well. I’m curious how the future of XaaS models will be like, but it seems inevitable that we are moving towards a service based economy.

References:
IBM (2014) The XaaS family: Understanding Iaas, PaaS and SaaS. Retrieved from: https://www.ibm.com/blogs/cloud-computing/2014/10/31/xaas-family-iaas-paas-saas-explained/

Mitchell & Sehgal (2018) Indian Farming’s Next Big Moment: Farming as a Service. Retrieved from: https://www.bain.com/insights/indian-farmings-next-big-moment-farming-as-a-service/

When it comes to personal finances, many developing countries (Kenya in this example) still make use of their informal networks to send remittances to one another (Jack & Suri, 2014). In other words, if someone wants to send money to their cousin in a city 200km away, one has to take a 10$ bus (roundtrip) or use friends who are coincidentally going to this city to take care of the money transfer (Jack & Suri, 2014). Transaction costs, can therefore, be considered very high – as these kinds of transfer possess high risks.

Without a stable financial system in place, M-Pesa was launched by Safaricom in 2007 and turned out to be a huge success; mobile money was invented. M-Pesa allowed individuals to do several personal financing tasks just by sending SMS messages on basic mobile phones. In just four years after launching M-Pesa, already 70% of the Kenyan population adopted M-Pesa – mainly for sending P2P remittances or saving money (Jack & Suri, 2014).

The effects on the Kenyan economy have been strong, and for M-Pesa users: average consumption increased (+18.5%), extreme poverty (-22%) and general poverty was reduced (Jack & Suri, 2016). But not only did it impact the wealth of users, it changed the way economy functioned on three levels:
1. Household level: a survey found that M-Pesa users feel more safe keeping funds in their accounts for a longer time, and they finally have cheap way of sending money.
2. Community level: it allowed for economic expansion within communities and strengthened the local business environment.
3. National level: Gencer (2011) argues that a 10% increase in adoption rate, can increase the GDP with 0.6% to 1.2%. Moreover, it completely alters the way the supply of the national currency is managed by its central bank.

To conclude, not only is the concept of M-Pesa’s mobile money technologically very interesting – its economical consequences might be even more interesting. Being such a big success in Kenya, throughout the years it expanded to Tanzania, South Africa, Afghanistan and India. I’m personally very curious to see how this technology will impact other developing countries, what do you think?

Gencer, M. (2011). The mobile money movement: catalyst to jumpstart emerging markets. Retrieved on 11-09-2018, from: https://www.slideshare.net/mpayconnect/the-mobile-money-movement-by-mpay-connect-dec-2010-innovations-publication-winter-2011

Jack, W., and Suri, T. (2014). Risk sharing and transactions costs: Evidence from Kenya’s mobile money revolution. American Economic Review 104, pp. 183-223.

Jack & Suri (2016) The long-run poverty and gender impacts of mobile money, Science. 1288-1292.