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Latest Insights on Artificial Intelligence

Founded as an academic discipline in 1955, Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions (Frankenfield, 2020). Machine Learning (ML) is a method of analyzing data using an analytical model that is built automatically, or ‘learned’, from training data (Taulli, 2020). When ML powers AI applications, the combination can be powerful. With the rapid development of technology, we are entering the era of AI & ML and we consider them as important tools in the technology arsenal for enterprises. The AI & ML market was valued at $59.4 billion in 2019 and is forecasted to grow at a 27.7% CAGR to $158.1 billion by 2023 (Martin, 2019). In the space of AI, we have seen 2064 deals taking place in 2020 (by end of Q2), out of which around 41% were in Software and SaaS, 9% in healthcare, 8% in media and entertainment, 6% in cybersecurity, 6% were in Financial Services, and 30% in all other spaces. The AI & ML sector is also known for having created multiple unicorns, including SenseTime ($7.5b valuation), UiPath ($7b valuation), Automation Anywhere ($6.8b valuation), YITU Technology ($2.4b valuation), Graphcore ($2.0b valuation), and C3.ai ($3.3b valuation).

AI & ML had an enormous impact on businesses throughout 2020 especially triggered by the COVID-19 pandemic and received increasing attention around the world. E.g. AI & ML start-up Bluedot had reported it detected COVID-19 on January 5, 2020, days before the WHO notified the public of its spread. A whitepaper by the European Commission on its approach to AI has been published on February 1, 2020. Besides, C3.ai Digital Transformation Institute has been formed by Microsoft and leading AI & ML research universities on March 26, 2020, to mitigate COVID-19 and support societal AI advancement. We identified various AI & ML areas that will be particularly progressing over the next five years and will have a continuous impact on how people live and work as well as what businesses will face from a technological point of view.

Information Technology:

We see a rise in the use of AI & ML in B2B services for boosting business efficiency and productivity in the coming years. AI as a Service (AIaaS) is an innovative service provided by an algorithm - a process or set of rules to be followed in calculations by a computer, with a set of instructions, to perform calculations, data processing, automated reasoning, etc.

AIaaS for enterprises has already the wide scope of applications including sales & marketing, office efficiency, process & operational efficiency and its commercialization have already become more mature in industries like retail and financial services. One area where AI & ML is having a significant impact on enterprise computing is IT automation. Gartner said autonomous AI will be among the top technology trends in 2020-2025 that will shape the future. The most recent largest deals we have seen in this space (including big-data platform-based systems) are Tradeshift’s €215m Series F, Automation Anywhere’s $290m Series B, and Onfido’s $100m Series D, Databricks $400m Series F, ThoughtSpot’s $248m Series E, SambaNova Systems $250m Series C, H-Visions’ $14m Series C, Encoo Tech’s $30m Series B, Gechuang Dongzhi’s $16m Series A, and Vecna Robotics’ $50m Series B.

AI applications in the manufacturing and logistics sectors including agricultural robots, automated material handling, hybrid fulfillment, and workflow optimization solutions enable companies to refine their performance in distribution, warehousing, e-commerce, and other areas. Examples for such AI deals are Headspin’s $60m Series C, and Samsara’s $643m Series F.

Financial Services:

We expect great opportunities for AI & ML being continuously adopted in the financial services space, in particular where there are two main pain points: cost pressure from repetitive work in its daily operation, and failure of financial institutions to access long-tail customers. According to reports from Autonomous NEXT, Bain, McKinsey, and Accenture, AI & ML could save nearly trillion-dollar costs in the financial service industry (Stanfill, 2019).

For institutions, automation is driven by AI & ML and intelligent customer service using voice recognition and semantic understanding could improve efficiency and lower operational cost. The most recent largest deals include 4Paradigm’s $230m Series C1, HighRadius$125m Series B, Thought Machine’s $83m Series B, and Yunzhangfang’s $85m Series D.

For individual consumers, AI powers the online banking start-ups (the neo-banks) by providing customized recommendations to each consumer at low cost, so consumers can control their personal finances from their mobile phones without having to see specialists. Besides, Roboadvisory is becoming another essential part of this space. The new financial advice service using AI provides clients with personal recommendations with enhanced analytics. Customers can enjoy cost benefits, especially when obtaining customized portfolios from Robo advisors. Notable recent deals include Oriente’s $50m Series B, ZestMoney’s $36m Series B, MoneyLion’s $26m Series C1, Vise’s $13.3m Series A, FalconX’s $17m Series A, and INDwealth’s $12m Series C.

Transportation:

We believe AI & ML is the most contributing technologies to be adopted in the transportation space by enabling companies to process substantial amounts of data sets. Following the first wave of basic ridesharing, advanced autonomous transportation will represent one of the most critical components in this sector for the upcoming years. Autonomous vehicles are equipped with sensors. With all these data inputs into the autonomous driving software, machine learning serves as the tool to analyze and learn from the data so autonomous vehicles can sense and evaluate their surroundings and environments and figure out a safe way to the destination without human efforts. Investments in this space increased in recent years and remain popular even during the COVID-19 pandemic, reflecting the high capital intensity required for R&D. In the future, higher accuracy is still required on-vehicle sensors and GPRS locating, and we believe autonomous vehicles have a long way to replace drivers and traditional vehicles, during which the co-existence of autonomous vehicles and human drivers should be accepted as a transition. Due to its capital- and data-intensive nature we expect limited high-value deals such as Waymo’s $2.3b at $18 billion post-money valuation, Didi Autonomous Driving’s $500m Series B, Pony.ai’s $462m Series B, Inceptio Technology’s $100m Series A, FiveAI’s $41m Series B, Neolix Technologies’ $29m Series A1.

Smart City & IoT:

Smart cities are already using AI at a greater pace than previously. Every smart city across the world is using or planning to use AI in mitigating traffic density and accidents. Sensors installed at parking lots, traffic signals, and at intersections use AI in accumulating useful data for the governments to plan their city initiatives efficiently. This raw data is unimaginably bigger than what humans can view, analyze, and process. This is where the role of AI comes in. AI can keep a count of any number of vehicles, pedestrians, or any other movements while keeping a track of their speeds. It can also carry out face recognition, read license plates, and process all satellite data to any extent to establish patterns necessary for city developments. In the next 3-5 years, AI & ML technology will be adopted even in wider coverage, for example, to provide an intelligent logistic system and fire control system. The most recent deals in this space are MiningLamp’s $300m Series E, Intellifusion’s $141m Series C, Cloudwalk Technology’s $253m Series C1, and Tpson’s $14m Series B.

AI Security:

With the progress of technology products such as applications, software, cloud, and big data, we’re now entering an era where we constantly exchange information and process payments online. Cybersecurity becomes critical because a large number of organizations like governments, military, corporates, financial institutions collect, process, and store unprecedented amounts of data on computers and other devices. As a result, the next frontier of AI-related security concerns is emerging as attackers begin to use machine learning and other AI techniques to power their attacks. According to Gartner’s insight, there are three key areas to explore when considering how AI is impacting security: (i) protecting AI-powered systems, (ii) leveraging AI to enhance security defense, and (iii) anticipating the dangerous use of AI by attackers. In the next 5 years, we consider AI-driven technology designed to enhance office, retail, and school security. Some of the recent and major deals include SentinelOne’s $200m Series E at a $1.1b valuation, Plume’s $85m Series D, Snyk’s $150m Series C, Privitar’s $80m Series C, Bigpanda’s $50m Series C, Arctic Wolf’s $60m Series D, and Uface’s $14m Series Pre-B.

Retail:

retail is one of the industries which has the highest AI penetration (Deloitte, 2019), is now one of the major evolutionary sectors in all value chains. The adoption of AI in retail space is growing rapidly. Data of Global Market Insights shows an over 40 percent of CAGR for global AI applications in the retail industry during 2018-2024, with a market size reaching $8 billion by 2024 (Deloitte, 2019). Deep learning and computer vision are the main pillar technologies for the smart retail transition. Deep learning is mainly applied in data analytics and modeling to optimize the industry chain. Through the accumulation of users’ consumption data, deep learning can analyze the shopping behavior and demands of consumers, and tag each consumer with different labels to segment them and make personalized recommendations. While computer vision, sensor fusion, and deep learning algorithms combined can be used for consumer behavior analysis and commodity identification (Deloitte, 2019). The technology automatically senses the store environment and can detect when products are taken from or return to the shelves and keep track of them. So consumers just leave the store without payment procedure or queueing and the store will automatically process payment and send the receipt via email. Notable recent deals in this space include DT Dream’s $84m Series B, SENSORS Data’s $30m Series C1, Standard Cognition’s $35m Series B, DeepNorth’s $26m Series A, SandStar’s $14m Series B, and AiFi’s $11m Series A.

Healthcare:

The Healthcare sector is a space that gradually deployed AI & ML technology in almost every subsector, from R&D to diagnosis and treatment. With the aging population becoming one of the major trends globally causing a rapid jump of healthcare costs, it’s not surprising that this space attracts considerable attention and investment with regards to AI technology. The global market of AI in healthcare was valued at USD 2.5 billion in 2018 and is expected to grow at a compound annual growth rate (CAGR) of 41.5% from 2019 to 2025. The growing need for lowering healthcare costs, growing importance of big data in healthcare, rising adoption of precision medicine, and declining hardware costs are some factors driving growth (Grand View Research, 2019).

Fragmentation of data in the healthcare sector also inhibited the widespread usage of AI & ML which structure and integrate data in order to drive automation, eliminate inefficiencies, and save costs benefiting not only healthcare providers and insurance companies but also patients. Some notable deals in these spaces are Olive’s $51m Series E, OM1’s $50m Series C, and Arterys’ $28m Series C. Another major adoption of AI & ML with improved healthcare data integration and access lies in precision medicine. Notable deals include Tempus Labs’s $100m Series G, Q Bio’s $40m Series B, and GNS Healthcare’s $23m Series D.

In the R&D field, AI & ML in healthcare could potentially offer exciting use cases in drug discovery. AI-powered drug discovery efforts are enabling big pharma and biotechnology companies to streamline R&D efforts, including calculating vast patient datasets into digestible, tangible information, identifying personalized / precision medicine opportunities, or forecasting potential responses to new drugs (Reiss, 2020). Recent notable deals are Verana Health’s $100m Series D, and Suzhou Ribo’s $28m Series C1.

Besides the above spaces, we also see rapid development in the semiconductor space, a segment for both the design and software-based optimization of computing hardware. With the optimization of computing hardware, AI & ML could be better adopted in each sector and space in the future.

Industries are facing different pain points and have to overcome their barriers regarding the application of AI technology. The adoption of AI in industries including financial services, retail, and education has provided the world a perfect reference to other industries that still lack some AI exposure, such as smart cities, AI security, and healthcare. In the future, AI technology will move its focus from B2C to B2B and reshape the way enterprises and the public sector operates.

For investors, it will be essential to have deep knowledge in those areas, to keep track of major developments, and most importantly to be able to differentiate those companies that just use the term “AI & ML” for marketing or valuation purposes from the real contributors in the sector.


Bibliography

Frankenfield, J. (2020, 3 13). Artificial Intelligence (AI). Retrieved from Investopedia: www.investopedia.com/terms/a/artificial-intelligence-ai.asp.

Pitchbook. (2020). Artificial Intelligence & Machine Learning. Pitchbook.

Taulli, T. (2020, 5 23). Machine Learning: What Is It Really Good For? Retrieved from Forbes: https://www.forbes.com/sites/tomtaulli/2020/05/23/machine-learning-what-is-it-really-good-for/#29a8f384500f

Martin, D. (2019, 11 18). 6 Emerging AI And Machine Learning Trends To Watch In 2020. Retrieved from CRN: https://www.crn.com/slide-shows/internet-of-things/6-emerging-ai-and-machine-learning-trends-to-watch-in-2020

Stanfill, C. (2019). Artificial Intelligence & Machine Learning. Pitchbook. Retrieved from Pitchbook.

Deloitte. (2019). Global artificial intelligence industry whitepaper.

Grand View Research, I. (2019, 10). Artificial Intelligence In Healthcare Market Size, Share & Trends Analysis Report 2019 – 2025. Retrieved from Grand View Research: www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-healthcare-market

Reiss, R. (2020, 3 3). Transforming Drug Discovery Through Artificial Intelligence. Retrieved from Forbes: https://www.forbes.com/sites/robertreiss/2020/03/03/transforming-drug-discovery-through-artificial-intelligence/#1be66f3a6a15

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