AI and Other Technologies in Business

AI and Other Technologies in Business

DOI: 10.4018/978-1-7998-2036-9.ch004
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In the previous chapters, the authors discussed a switch from a traditional business model towards the modern digital business model, which seems to follow a specific pattern, as highlighted by strategist Tom Goodwin. In this economy, knowledge and data have an important role that can be compared to that of technology itself. Among other things, the authors discuss how companies need to overcome Polanyi's paradox as well as the so-called curse of knowledge—or status quo bias—according to which they might not understand how to innovate themselves because of it. In particular, some organisations might be so proficient and knowledgeable that they risk not seeing what is coming and preparing themselves for the disruption in their sector. The authors also discuss the use of AI and other technologies in business and how to use them efficiently.
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An Introduction To The Role Of Knowledge In The Digital Business

Knowledge is a key component of an organisation. Whether we are discussing knowledge gathered from its employees, or the historical knowledge that is part of the organisation itself, knowledge has a fundamental role in the success of any business. Data represents one of the most concrete forms of knowledge we can find. We will see in the next Chapter which approaches to data can be used by managers.

At this stage, we can affirm that the modern economy is based on three major trends. These can be seen as counterparts of the more traditional trilogy given by the human mind, products and the core. The first trend we should take into account is the circumstance that machines are more capable than ever. This is the case of AlphaGo, for example, which aims to replicate how the human brain works, adding speed, velocity and the ability to ‘ingest’ a great amount of data. The second trend that can be spotted relates to the use of platforms to sell products or physical goods that firms might not have. We have already mentioned how companies like Uber, Facebook, and Airbnb are offering products that they do not own, disrupting the traditional business model. At the same time, the crowd is involved in the designing process, to add expertise, needs and other information to the mix. So in the modern era, the so called core - meaning knowledge, processes and expertise that companies have internally - is substituted with external fonts of knowledge, as in the cases of GE and Indiegogo (Hurst, 2015; Shieber, 2015). Furthermore, Indiegogo can be described as an online crowdfunding community, where people provide financial support to ideas in exchange of rewards, not ownership of new products. In other terms, a reward can mean they will be the first to receive a product, that does not exist yet and might never exist.

To prove such point, according to Karim Lakhani for example, managers and in general businesses that want to innovate should not go to experts, they should go to externals. “In more than 700 challenges we have run on crowds for NASA, for the medical school, for companies - you name it - over the past five years, we’ve only had one failure [where] the crowd did not show up or did not work on the problem. In all other circumstances, we either met or vastly exceeded the internal solutions that existed” (as quoted in McAfee & Brynjolfsson, 2017, p. 255). If anyone is interested in involving the crowd, Topcoder, an online platform for computationally intensive problems, might be a useful tool to look at.

When previously discussing the abilities new machines have, we have mentioned AlphaGo. This is particular important in relation to the role of knowledge, and we will soon understand why. While trying to program an AI machine to play Go, researchers have to deal with Polanyi’s Paradox - which basically states that we know more than we can tell - and in general terms, the fact that humans use tacit knowledge to deal with tasks. To avoid such paradox, a team at Google DeepMind built AlphaGo, which learnt to play studying millions of positions and simulate only the moves that it thought lead to victory. When the team thought AlphaGo was ready, it challenged the European Go Champion Fan Hui in 2015, winning 5 matches against 0. Critics were not that impressed, so the team challenged Lee Sedol, considered the best human Go player on the planet in 2016. Sedol thought it would be easy to win, however AlphaGo won the four matches in total, thus beating Sedol during the first three matches as well as the last one.

Such an accomplishment was already a great victory in itself, however it is important to highlight that it happened even though AI programs built for games do not learn very fast, because they require a huge amount of data to be trained. This is perfectly expressed by Kasparov, after he was beaten in 1997 by IBM’s Deep Blue computer in a world-changing game of chess. Kasparov himself is recorded to have said “I had played a lot of computers but had never experienced anything like this. I could feel - I could smell - a new kind of intelligence across the table. While I played through the rest of the game as best I could, I was lost; it played beautiful, flawless chess the rest of the way and won easily” (Kasparov, 1996). What does this leave us with? After such a historical moment, now chess computers offer professional coaching to human amateurs.

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