Notes from London by Giuseppe Donvito, Partner of P101
In 2017 investors poured in over $15.2B in funding to AI startups across industries. It was a 141% jump in funding from 2016. This monster figure will decrease when machine learning becomes “normal”, an essential feature of every product and service: at that point investors will start to be more picky about the AI companies they fund.
But let’s take a step back. What exactly is artificial intelligence in computer science? By definition, the field of AI research defines itself as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of success at some goal. Artificial Intelligence truly materializes when the solution has “the ability to learn, understand and think in a logical way”, “when the solution can adapt to its environment beyond its original design”. As such, AI solutions must embody two key capabilities, each with varying degrees of complexity: data acquisition & processing capabilities, and modelling capabilities.
AI startups in this landscape can be classified into two sub-sectors — infrastructure companies, which provide cloud infrastructure, algorithms, and libraries for creating AI based applications; and applications, which covers companies which integrate AI across different industry verticals. From a funding perspective, companies focused on AI applications have attracted significantly more investments than AI infrastructure businesses: $11.5B versus $2.2B. As tech giants endeavour to rebuild themselves around AI, M&A activity is on the rise.
With the ability to simulate human reasoning and scale typically human storage and classification tasks, artificial intelligence is first and foremost a workforce revolution. In a way that can be seen as negative for those who will be replaced by a robotic colleague but that actually has a lot of positive implications. The best way to enjoy the advantages of AI without feeling too overwhelmed is to think of it as a new workmate who will probably evolve into a more or less self-sufficient entity. The idea is that it will become a positive “amplifier” for the experience of human workers, both in terms of support for daily activities and in terms of improving customer experience.
This can only happen if corporate implementation of AI is embraced as a cultural change starting from top management, and in particular from the Board, in a holistic approach that does not dismiss any part or function of the company. Without this approach, without AI permeating organizations in their entireness, businesses would run the risk of leaving those areas lacking artificial intelligence behind, disconnected from the rest of the company, with the consequence of creating bottlenecks.
Artificial intelligence will transform all industries, but among them the one that seems to be more suited to the use of AI techniques “across the value chain” is that of financial services, owing to its structure – just think of the massive presence of data. All of this and much more, with specific attention to the world of finance, was discussed in London during the Artificial Intelligence in Financial Services conference. More than 120 market-leading players took part to the meeting, ranging from banking to asset management, insurance, software and hardware companies.
In the financial industry, artificial intelligence can disrupt or modify portions of the advisory value chain and contribute to the creation of new business models thanks to data analytics. The advantage of applying AI to this industry comes from the availability of historical and real time data that can be processed and used for machine learning. The main players already see immediate benefits in areas such as back office, customer experience and general improvement of operational efficiency.
According to the speakers at the conference, two areas will be impacted by artificial intelligence across all financial sectors: one is cybersecurity – on the one hand, AI applications will increasingly be able to solve problems of intrusion into corporate computer systems, on the other, companies will need to defend themselves from potential intrusion by smart-bots. The second area is that of ethics: the existence of autonomous systems having sophisticated artificial intelligence potentially means that they can bypass human control. And, therefore, thresholds for “responsibility” become blurred.
Finally, the conceivable applications of AI in asset and wealth management are endless and impact the entire value chain. Imagine sentient, real time optimization of sales and marketing interactions and client services, predictive market modeling based on instantaneous processing of teraflops of data, self-creating exchanges with complete price transparency. It is not unrealistic to think that research analysts will have their own bots using real time big data to do a majority of their market and financial analysis, and even assembly of reports and recommendations to the CIO.
At this level, AI initiatives are still a big bet to make. But no one working in this industry should ignore such a futuristic scenario. The world of finance should be aware of the necessity to change in order not to be disrupted by the development of AI, which is undoubtedly a game changer.