Walmart’s acquisition of Flipkart demonstrates both Indian e-commerce’s coming of age and a repetition of history.
U.S. giants will spend billions in India because they see huge opportunities, and this will produce a short-term boon for Indian consumers. When the dust settles, though, prices will rise and consumer choices will become more limited than they had been. Foreign companies will mine data and manipulate consumer preferences. They will have once again colonized India’s retail industry.
Protectionism for physical goods and services is usually a bad thing, as it limits the incentive to innovate and evolve, stifling a country’s competitiveness and productivity. India’s protected domestic companies became lethargic, offered substandard products and services at high prices, and hobbled India’s economy.
In a digital economy, though, things are very different. The value resides in the ideas, which spread instantaneously via the internet. Entrepreneurs in one country can easily learn of the innovations and business models of another country and duplicate them.
As core technologies advance, they become faster, smaller and cheaper — and accessible to everyone, everywhere. Startups constantly emerge, putting established players out of business. So, speed and execution are key to business survival and competitiveness.
Valuable competition and innovation can arise from within the domestic economy itself, without having to invite foreign companies to the table.
Technology-based industries, such as retail, electronics and distribution, that require large capital investments handicap the small players, because money provides an unfair advantage to the larger ones.
The latter can use capital to put emerging competitors out of business — or to acquire them. It is what U.S. technology giants do as a matter of course.
Amazon, for example, has been losing money, or earning razor-thin margins, for more than two decades. But because it was gaining market share and killing off its brick-and-mortar competition, investors rewarded it with a high stock price.
With this inflated capitalization, Amazon raised money at below-market interest rates and used it to increase its market share. It also acquired dozens of competitors — just as it tried to do with Flipkart.
Having become the dominant player in the U.S. e-commerce industry, Amazon has its eye on India. A company that it left in the dust, Walmart, is desperate not to also lose the Indian market. Both are doing whatever they must to own Indian retail and then split the spoils between them.
That is why controls are desperately needed on this kind of capital dumping. And such controls won’t reduce competition or throttle innovation. As they did in China, they will stimulate competition and, through that, innovation.
Chinese technology companies are now among the most valuable and innovative in the world. In addition to having a valuation that rivals Facebook’s, Tencent’s WeChat e-commerce platform is far more advanced than any rival in the West.
Baidu is building highly advanced artificial intelligence (AI) technologies as well as self-driving cars. And DJI (Dà-Jia ng Innovations) has become a global leader in drone technologies. Had China not imposed controls, these companies may not have survived at all.
It is probably too late to save Indian e-commerce from modern-day East India Company-style colonization. But there are many other industries in which Indian startups can still lead the world.
With the exponentially accelerating advances occurring in technologies such as sensors, AI, robotics, medicine and 3D printing, practically every industry is about to be disrupted, and there are opportunities for Indian entrepreneurs to create solutions that benefit India and the rest of the world.
India urgently needs to wake up and protect its entrepreneurs from foreign-capital dumping. And it needs to provide incentives for Indian — and foreign — companies to invest in its startups, just as China did for its own.
As we reach the end of 2017, the first full 12 months where insurtech has been recognized as a standalone investment segment, we wanted to reflect on what has been an incredible year.
From the start, we at Eos believed that insurtech would be driven globally, and that has certainly played out. This year, we’ve visited: Hong Kong, Amsterdam, New York, Las Vegas, Nigeria, Dubai, India, Singapore, Bermuda, Milan, St. Louis, Munich, Vienna, Paris, Zurich, Cologne, Chicago, San Francisco, Silicon Valley, Seattle and Toronto. We’ve expanded our geographic footprint to include the East and West coasts of the U.S. and India and have seen fantastic progress across our expanding portfolio. We’ve welcomed a number of new strategic partners, including Clickfox, ConVista and Dillon Kane Group, and launched our innovation center, EoSphere, with a focus on developing markets
At the start of the year, we published a series of articles looking at the key trends that we believed would influence insurtech and have incorporated these in our review of the year.
We hope you enjoy it! Comments, challenges and other perspectives, as always, would be greatly received.
2017: The year innovation became integral to the insurance sector
How are incumbents responding?
We are seeing a mixed response, but the direction of travel is hugely positive. A small number of top-tier players are embracing the opportunity and investing hundreds of millions, and many smaller incumbents with more modest budgets are opening up to innovation and driving an active agenda. The number sitting on the side lines, with a “wait and see” strategy is diminishing.
“If 2016 was the year when ‘some’ insurers started innovating, 2017 will be remembered as the year when ‘all’ insurers jumped on the bandwagon. And not a minute too soon! When I joined 3,800 insurance innovators in Las Vegas, we all realized that the industry is now moving forward at light speed, and the few remaining insurers who stay in the offline world risk falling behind.” Erik Abrahamson, CEO of Digital Fineprint
We are more convinced than ever that the insurance industry is at the start of an unprecedented period of change driven by technology that will result in a $1 trillion shift in value between those that embrace innovation and those that don’t.
Has anyone cracked the code yet? We don’t think so, but there are a small number of very impressive programs that will deliver huge benefits over the next two to three years to their organizations.
“We were pleased to see some of the hype surrounding insurtech die down in 2017. We’re now seeing a more considered reaction from (re)insurers. For example, there is less talk about the ‘Uber moment’ and more analysis of how technology can support execution of the corporate strategy. We have long argued that this is the right approach.” Chris Sandilands, partner at Oxbow Partners
Have insurers worked out how to work with startups? We think more work may be needed in this area….
“Investors are scrambling for a piece of China’s largest online-only insurer… the hype could be explained by the ‘stars’ behind ZhongAn and its offering. Its major shareholders — Ping An Insurance (Group) Co., Alibaba Group Holding Ltd., Tencent Holdings Ltd.” – ChinaGoAbroad.com
“Tencent Establishes Insurance Platform WeSure Through WeChat and QQ” – YiCai Global
“Amazon is coming for the insurance industry — should we be worried?” – Insurance Business Magazine
“Aviva turns digital in Hong Kong with Tencent deal” – Financial Times
“Quarter of customers willing to trust Facebook for insurance” – Insurance Business Magazine
“Chinese Tech Giant Baidu Is Launching a $1 Billion Fund with China Life” – Fortune
We are already well past the point of wondering whether tech giants like Google, Amazon, Facebook, Apple (GAFA) and Baidu, Alibaba, Tencent (BAT) are going to enter insurance. They are already here.
Notice the amount of activity being driven by the Chinese tech giants. Baidu, Alibaba and Tencent are transforming the market, and don’t expect them to stop at China.
The tech giants bring money, customer relationships, huge amounts of data and ability to interact with people at moments of truth and have distribution power that incumbents can only dream about. Is insurance a distraction to their core businesses? Perhaps — but they realize the potential in the assets that they have built. Regulatory complexity may drive a partnership approach, but we expect to see increasing levels of involvement from these players.
Role of developing markets
It’s been exciting to play an active role in the development of insurtech in developing markets. These markets are going to play a pivotal role in driving innovation in insurance and in many instances, will move ahead of more mature markets as a less constraining legacy environment allows companies to leapfrog to the most innovation solutions.
Importantly, new technologies will encourage financial inclusion and reduce under-insurance by lowering the cost of insurance, allowing more affordable coverage, extending distribution to reach those most at need (particularly through mobiles, where penetration rates are high) and launching tailored product solutions.
Interesting examples include unemployment insurance in Nigeria, policies for migrant workers in the Middle East, micro credit and health insurance in Kenya, a blockchain platform for markets in Asia and a mobile health platform in India.
Protection to prevention
At the heart of much of the technology-driven change and potential is the shift of insurance from a purely protection-based product to one that can help predict, mitigate or prevent negative events. This is possible with the ever-increasing amount of internal and external data being created and captured, but, more crucially, sophisticated artificial intelligence and machine learning tools that drive actionable insights from the data. In fact, insurers already own a vast amount of historical unstructured data, and we are seeing more companies unlocking value from this data through collaboration and partnerships with technology companies. Insurers are now starting to see data as a valuable asset.
The ability to understand specific risk characteristics in real time and monitor how they change over time rather than rely on historic and proxy information is now a reality in many areas, and this allows a proactive rather than reactive approach.
During 2017, we’ve been involved in this area in two very different product lines, life and health and marine insurance.
The convergence of life and health insurance and application of artificial intelligence combined with health tech and genomics is creating an opportunity to transform the life and health insurance market. We hope to see survival rates improving, tailored insurance solutions, an inclusion-based approach and reduced costs for insurers.
Marine insurance is also experiencing a shift due to technology
In the marine space, the ability to use available information from a multitude of sources to enhance underwriting, risk selection and pricing and drive active claims management practices is reshaping one of the oldest insurance lines. Concirrus, a U.K.-based startup, launched a marine analytics solution platform to spearhead this opportunity.
The emergence of the full stack digital insurer
Perhaps reflecting the challenges of working with incumbents, several companies have decided to launch a full-stack digital insurer.
We believe that this model can be successful if executed in the right way but remain convinced that a partnership-driven approach will generate the most impact in the sector in the short to medium term.
“A surprise for us has been the emergence of full-stack digital insurers. When Lemonade launched in 2016, the big story was that it had its own balance sheet. In 2017, we’ve seen a number of other digital insurers launch — Coya, One, Element, Ottonova in Germany, Alan in France, for example. Given the structure of U.K. distribution, we’re both surprised and not surprised that no full-stack digital insurers have launched in the U.K. (Gryphon appears to have branded itself a startup insurer, but we’ve not had confirmation of its business model).”– Chris Sandilands, partner, Oxbow Partners
Long term, what will a “full stack” insurer look like? We are already seeing players within the value chain striving to stay relevant, and startups challenging existing business models. Will the influence of tech giants and corporates in adjacent sectors change the insurance sector as we know it today?
Role of MGAs and intermediaries
Insurtech is threatening the role of the traditional broker in the value chain. Customers are able to connect directly, and the technology supports the gathering, analysis and exchange of high-quality information. Standard covers are increasingly data-driven, and the large reinsurers are taking advantage by going direct.
We expected to see disintermediation for simple covers, and this has started to happen. In addition, blockchain initiatives have been announced by companies like Maersk, Prudential and Allianz that will enable direct interaction between customers and insurers.
However, insurtech is not just bad news for brokers. In fact, we believe significant opportunities are being created by the emergence of technology and the associated volatility in the market place.
New risks, new products and new markets are being created, and the brokers are ideally placed to capitalize given their skills and capabilities. Furthermore, the rising rate environment represents an opportunity for leading brokers to demonstrate the value they can bring for more complex risks.
MGAs have always been a key part of the value chain, and we are now seeing the emergence of digital MGAs.
Digital MGAs are carving out new customer segments, channels and products. Traditional MGAs are digitizing their business models, while several new startups are testing new grounds. Four elements are coming together to create a perfect storm:
Continuing excess underwriting capacity, especially in the P&C markets, is galvanizing reinsurers to test direct models. Direct distribution of personal lines covers in motor and household is already pervasive in many markets. A recent example is Sywfft direct Home MGA with partnerships with six brokers. Direct MGA models for commercial lines risks in aviation, marine, construction and energy are also being tested and taking root.
Insurers and reinsurers are using balance sheet capital to provide back-stop to MGA startups. Startups like Laka are creating new models using excess of loss structures for personal lines products.
Digital platforms are permitting MGAs to go direct to customers.
New sources of data and machine learning are permitting MGAs to test new underwriting and claims capabilities and take on more balance sheet risk. Underwriting, and not distribution, is emerging as the core competency of MGAs.
Three of the trends driving innovation that we highlighted at the start of the year centered on the customer and how technology will allow insurers to connect with customers at the “moment of truth”:
Insurance will be bought, sold, underwritten and serviced in fundamentally different ways.
External data and contextual information will become increasingly important.
Just-in-time, need- and exposure-based protection through mobile will be available.
Over time, we expect the traditional approach to be replaced with a customer-centric view that will drive convergence of traditional product lines and a breakdown of silo organization structures. We’ve been working with Clickfox on bringing journey sciences to insurance, and significant benefits are being realized by those insurers supporting this fundamental change in approach.
Interesting ideas that were launched or gained traction this year include Kasko, which provides insurance at point of sale; Cytora, which enables analysis of internal and external data both structured and unstructured to support underwriting; and Neosurance, providing insurance coverage through push notifications at time of need.
As discussed above, we believe partnerships and alliances will be key to driving success. Relying purely on internal capabilities will not be enough.
“The fascinating element for me to witness is the genuine surprise by insurance companies that tech firms are interested in ‘their’ market. The positive element for me is the evolving discovery of pockets of value that can be addressed and the initial engagement that is received from insurers. It’s still also a surprise that insurers measure progress in years, not quarters, months or weeks.” – Andrew Yeoman, CEO of Concirrus
We highlighted three key drivers at the start of the year:
Ability to dynamically innovate will become the most important competitive advantage.
Optionality and degrees of freedom will be key.
Economies of skill and digital capabilities will matter more than economies of scale.
The move toward partnership built on the use of open platforms and APIs seen in fintech is now prevalent in insurance.
“We are getting, through our partnerships, access to the latest technology, a deeper understanding of the end customers and a closer engagement with them, and this enables us to continue to be able to better design insurance products to meet the evolving needs and expectations of the public.” Munich Re Digital Partners
Key trends to look out for in 2018
Established tech players in the insurance space becoming more active in acquiring or partnering with emerging solutions to augment their business models
Tech giants accelerating pace of innovation, with Chinese taking a particularly active role in AI applications
Acceleration of the trend from analogue to digital and digital to AI
Shift in focus to results rather than hype and to later-stage business models that can drive real impact
Valuation corrections with down rounds, consolidation and failures becoming more common as the sector matures
Continued growth of the digital MGA
Emergence of developing-market champions
Increasing focus on how innovation can be driven across all parts of the value chain and across product lines, including commercial lines
Insurers continuing to adapt their business models to improve their ability to partner effectively with startups — winners will start to emerge
“As we enter 2018, I think that we’ll see a compression of the value chain as the capital markets move ever closer to the risk itself and business models that syndicate the risk with the customer — active risk management is the new buzzword.” – Andrew Yeoman, CEO Concirrus
We’re excited to be at the heart of what will be an unprecedented period of change for the insurance industry.
A quick thank you to our partners and all those who have helped and supported us during 2017. We look forward to working and collaborating with you in 2018.
Jeff Heepke knows where to plant corn on his 4,500-acre farm in Illinois because of artificial intelligence (AI). He uses a smartphone app called Climate Basic, which divides Heepke’s farmland (and, in fact, the entire continental U.S.) into plots that are 10 meters square. The app draws on local temperature and erosion records, expected precipitation, soil quality and other agricultural data to determine how to maximize yields for each plot. If a rainy cold front is expected to pass by, Heepke knows which areas to avoid watering or irrigating that afternoon. As the U.S. Department of Agriculture noted, this use of artificial intelligence across the industry has produced the largest crops in the country’s history.
Climate Corp., the Silicon Valley–based developer of Climate Basic, also offers a more advanced AI app that operates autonomously. If a storm hits a region, or a drought occurs, it lowers local yield numbers. Farmers who have bought insurance to supplement their government coverage get a check; no questions asked, no paper filing necessary. The insurance companies and farmers both benefit from having a much less labor-intensive, more streamlined and less expensive automated claims process.
Monsanto paid nearly $1 billion to buy Climate Corp. in 2013, giving the company’s models added legitimacy. Since then, Monsanto has continued to upgrade the AI models, integrating data from farm equipment and sensors planted in the fields so that they improve their accuracy and insight as more data is fed into them. One result is a better understanding of climate change and its effects — for example, the northward migration of arable land for corn, or the increasing frequency of severe storms.
Applications like this are typical of the new wave of artificial intelligence in business. AI is generating new approaches to business models, operations and the deployment of people that are likely to fundamentally change the way business operates. And if it can transform an earthbound industry like agriculture, how long will it be before your company is affected?
An Unavoidable Opportunity
Many business leaders are keenly aware of the potential value of artificial intelligence but are not yet poised to take advantage of it. In PwC’s 2017 Digital IQ survey of senior executives worldwide, 54% of the respondents said they were making substantial investments in AI today. But only 20% said their organizations had the skills necessary to succeed with this technology (see “Winning with Digital Confidence,” by Chris Curran and Tom Puthiyamadam).
Reports on artificial intelligence tend to portray it as either a servant, making all technology more responsive, or an overlord, eliminating jobs and destroying privacy. But for business decision makers, AI is primarily an enabler of productivity. It will eliminate jobs, to be sure, but it will also fundamentally change work processes and might create jobs in the long run. The nature of decision making, collaboration, creative art and scientific research will all be affected; so will enterprise structures. Technological systems, including potentially your products and services, as well as your office and factory equipment, will respond to people (and one another) in ways that feel as if they are coming to life.
In their book Artificial Intelligence: A Modern Approach (Pearson, 1995), Stuart Russell and Peter Norvig define AI as “the designing and building of intelligent agents that receive percepts from the environment and take actions that affect that environment.” The most critical difference between AI and general-purpose software is in the phrase “take actions.” AI enables machines to respond on their own to signals from the world at large, signals that programmers do not directly control and therefore can’t anticipate.
The fastest-growing category of AI is machine learning, or the ability of software to improve its own activity by analyzing interactions with the world at large (see “The Road to Deep Learning,” below). This technology, which has been a continual force in the history of computing since the 1940s, has grown dramatically in sophistication during the last few years.
This may be the first moment in AI’s history when a majority of experts agree the technology has practical value. From its conceptual beginnings in the 1950s, led by legendary computer scientists such as Marvin Minsky and John McCarthy, its future viability has been the subject of fierce debate. As recently as 2000, the most proficient AI system was roughly comparable, in complexity, to the brain of a worm. Then, as high-bandwidth networking, cloud computing, and high-powered graphics-enabled microprocessors emerged, researchers began building multilayered neural networks — still extremely slow and limited in comparison with natural brains, but useful in practical ways.
The best-known AI triumphs — in which software systems beat expert human players in Jeopardy, chess, Go, poker and soccer — differ from most day-to-day business applications. These games have prescribed rules and well-defined outcomes; every game ends in a win, loss or tie. The games are also closed-loop systems: They affect only the players, not outsiders. The software can be trained through multiple failures with no serious risks. You can’t say the same of an autonomous vehicle crash, a factory failure or a mistranslation.
There are currently two main schools of thought on how to develop the inference capabilities necessary for AI programs to navigate through the complexities of everyday life. In both, programs learn from experience — that is, the responses and reactions they get influence the way the programs act thereafter. The first approach uses conditional instructions (also known as heuristics) to accomplish this. For instance, an AI bot would interpret the emotions in a conversation by following a program that instructed it to start by checking for emotions that were evident in the recent past.
The second approach is known as machine learning. The machine is taught, using specific examples, to make inferences about the world around it. It then builds its understanding through this inference-making ability, without following specific instructions to do so. The Google search engine’s “next-word completion” feature is a good example of machine learning. Type in the word artificial, and several suggestions for the next word will appear, perhaps intelligence, selection and insemination. No one has programmed the search engine to seek those complements. Google chose the strategy of looking for the three words most frequently typed after artificial. With huge amounts of data available, machine learning can provide uncanny accuracy about patterns of behavior.
The type of machine learning called deep learning has become increasingly important. A deep learning system is a multilayered neural network that learns representations of the world and stores them as a nested hierarchy of concepts many layers deep. For example, when processing thousands of images, it recognizes objects based on a hierarchy of simpler building blocks: straight lines and curved lines at the basic level, then eyes, mouths and noses, and then faces, and then specific facial features. Besides image recognition, deep learning appears to be a promising way to approach complex challenges such as speech comprehension, human-machine conversation, language translation and vehicle navigation (see Exhibit A).
Though it is the closest machine to a human brain, a deep learning neural network is not suitable for all problems. It requires multiple processors with enormous computing power, far beyond conventional IT architecture; it will learn only by processing enormous amounts of data; and its decision processes are not transparent.
News aggregation software, for example, had long relied on rudimentary AI to curate articles based on people’s requests. Then it evolved to analyze behavior, tracking the way people clicked on articles and the time they spent reading, and adjusting the selections accordingly. Next it aggregated individual users’ behavior with the larger population, particularly those who had similar media habits. Now it is incorporating broader data about the way readers’ interests change over time, to anticipate what people are likely to want to see next, even if they have never clicked on that topic before. Tomorrow’s AI aggregators will be able to detect and counter “fake news” by scanning for inconsistencies and routing people to alternative perspectives.
AI applications in daily use include all smartphone digital assistants, email programs that sort entries by importance, voice recognition systems, image recognition apps such as Facebook Picture Search, digital assistants such as Amazon Echo and Google Home and much of the emerging Industrial Internet. Some AI apps are targeted at minor frustrations — DoNotPay, an online legal bot, has reversed thousands of parking tickets — and others, such as connected car and language translation technologies, represent fundamental shifts in the way people live. A growing number are aimed at improving human behavior; for instance, GM’s 2016 Chevrolet Malibu feeds data from sensors into a backseat driver–like guidance system for teenagers at the wheel.
Despite all this activity, the market for AI is still small. Market research firm Tractica estimated 2016 revenues at just $644 million. But it expects hockey stick-style growth, reaching $15 billion by 2022 and accelerating thereafter. In late 2016, there were about 1,500 AI-related startups in the U.S. alone, and total funding in 2016 reached a record $5 billion. Google, Facebook, Microsoft, Salesforce.com and other tech companies are snapping up AI software companies, and large, established companies are recruiting deep learning talent and, like Monsanto, buying AI companies specializing in their markets. To make the most of this technology in your enterprise, consider the three main ways that businesses can or will use AI:
Assisted intelligence, now widely available, improves what people and organizations are already doing.
Augmented intelligence, emerging today, enables organizations and people to do things they couldn’t otherwise do.
Autonomous intelligence, being developed for the future, creates and deploys machines that act on their own.
Many companies will make investments in all three during the next few years, drawing from a wide variety of applications (see Exhibit 1). They complement one another but require different types of investment, different staffing considerations and different business models.
Assisted intelligence amplifies the value of existing activity. For example, Google’s Gmail sorts incoming email into “Primary,” “Social” and “Promotion” default tabs. The algorithm, trained with data from millions of other users’ emails, makes people more efficient without changing the way they use email or altering the value it provides.
Assisted intelligence tends to involve clearly defined, rules-based, repeatable tasks. These include automated assembly lines and other uses of physical robots; robotic process automation, in which software-based agents simulate the online activities of a human being; and back-office functions such as billing, finance and regulatory compliance. This form of AI can be used to verify and cross-check data — for example, when paper checks are read and verified by a bank’s ATM. Assisted intelligence has already become common in some enterprise software processes. In “opportunity to order” (basic sales) and “order to cash” (receiving and processing customer orders), the software offers guidance and direction that was formerly available only from people.
The Oscar W. Larson Co. used assisted intelligence to improve its field service operations. This is a 70-plus-year-old family-owned general contractor, which, among other services to the oil and gas industry, provides maintenance and repair for point-of-sales systems and fuel dispensers at gas stations. One costly and irritating problem is “truck rerolls”: service calls that have to be rescheduled because the technician lacks the tools, parts or expertise for a particular issue. After analyzing data on service calls, the AI software showed how to reduce truck rerolls by 20%, a rate that should continue to improve as the software learns to recognize more patterns.
Assisted intelligence apps often involve computer models of complex realities that allow businesses to test decisions with less risk. For example, one auto manufacturer has developed a simulation of consumer behavior, incorporating data about the types of trips people make, the ways those affect supply and demand for motor vehicles and the variations in those patterns for different city topologies, marketing approaches and vehicle price ranges. The model spells out more than 200,000 variations for the automaker to consider and simulates the potential success of any tested variation, thus assisting in the design of car launches. As the automaker introduces cars and the simulator incorporates the data on outcomes from each launch, the model’s predictions will become ever more accurate.
AI-based packages of this sort are available on more and more enterprise software platforms. Success with assisted intelligence should lead to improvements in conventional business metrics such as labor productivity, revenues or margins per employee and average time to completion for processes. Much of the cost involved is in the staff you hire, who must be skilled at marshaling and interpreting data. To evaluate where to deploy assisted intelligence, consider two questions: What products or services could you easily make more marketable if they were more automatically responsive to your customers? Which of your current processes and practices, including your decision-making practices, would be more powerful with more intelligence?
Augmented intelligence software lends new capability to human activity, permitting enterprises to do things they couldn’t do before. Unlike assisted intelligence, it fundamentally alters the nature of the task, and business models change accordingly.
For example, Netflix uses machine learning algorithms to do something media has never done before: suggest choices customers would probably not have found themselves, based not just on the customer’s patterns of behavior but on those of the audience at large. A Netflix user, unlike a cable TV pay-per-view customer, can easily switch from one premium video to another without penalty, after just a few minutes. This gives consumers more control over their time. They use it to choose videos more tailored to the way they feel at any given moment. Every time that happens, the system records that observation and adjusts its recommendation list — and it enables Netflix to tailor its next round of videos to user preferences more accurately. This leads to reduced costs and higher profits per movie, and a more enthusiastic audience, which then enables more investments in personalization (and AI). Left outside this virtuous circle are conventional advertising and television networks. No wonder other video channels, such as HBO and Amazon, as well as recorded music channels such as Spotify, have moved to similar models.
Over time, as algorithms grow more sophisticated, the symbiotic relationship between human and AI will further change entertainment industry practices. The unit of viewing decision will probably become the scene, not the story; algorithms will link scenes to audience emotions. A consumer might ask to see only scenes where a Meryl Streep character is falling in love, or to trace a particular type of swordplay from one action movie to another. Data accumulating from these choices will further refine the ability of the entertainment industry to spark people’s emotions, satisfy their curiosity and gain their loyalty.
Another current use of augmented intelligence is in legal research. Though most cases are searchable online, finding relevant precedents still requires many hours of sifting through past opinions. Luminance, a startup specializing in legal research, can run through thousands of cases in a very short time, providing inferences about their relevance to a current proceeding. Systems like these don’t yet replace human legal research. But they dramatically reduce the rote work conducted by associate attorneys, a job rated as the least satisfying in the U.S. Similar applications are emerging for other types of data sifting, including financial audits, interpreting regulations, finding patterns in epidemiological data and (as noted above) farming.
To develop applications like these, you’ll need to marshal your own imagination to look for products, services or processes that would not be possible at all without AI. For example, an AI system can track a wide number of product features, warranty costs, repeat purchase rates and more general purchasing metrics, bringing only unusual or noteworthy correlations to your attention. Are a high number of repairs associated with a particular region, material or line of products? Could you use this information to redesign your products, avoid recalls or spark innovation in some way?
The success of an augmented intelligence effort depends on whether it has enabled your company to do new things. To assess this capability, track your margins, innovation cycles, customer experience and revenue growth as potential proxies. Also watch your impact on disruption: Are your new innovations doing to some part of the business ecosystem what, say, ride-hailing services are doing to conventional taxi companies?
You won’t find many off-the-shelf applications for augmented intelligence. They involve advanced forms of machine learning and natural language processing, plus specialized interfaces tailored to your company and industry. However, you can build bespoke augmented intelligence applications on cloud-based enterprise platforms, most of which allow modifications in open source code. Given the unstructured nature of your most critical decision processes, an augmented intelligence application would require voluminous historical data from your own company, along with data from the rest of your industry and related fields (such as demographics). This will help the system distinguish external factors, such as competition and economic conditions, from the impact of your own decisions.
The greatest change from augmented intelligence may be felt by senior decision makers, as the new models often give them new alternatives to consider that don’t match their past experience or gut feelings. They should be open to those alternatives, but also skeptical. AI systems are not infallible; just like any human guide, they must show consistency, explain their decisions and counter biases, or they will lose their value.
Very few autonomous intelligence systems — systems that make decisions without direct human involvement or oversight — are in widespread use today. Early examples include automated trading in the stock market (about 75% of Nasdaq trading is conducted autonomously) and facial recognition. In some circumstances, algorithms are better than people at identifying other people. Other early examples include robots that dispose of bombs, gather deep-sea data, maintain space stations and perform other tasks inherently unsafe for people.
The most eagerly anticipated forms of autonomous intelligence — self-driving cars and full-fledged language translation programs — are not yet ready for general use. The closest autonomous service so far is Tencent’s messaging and social media platform WeChat, which has close to 800 million daily active users, most of them in China. The program, which was designed primarily for use on smartphones, offers relatively sophisticated voice recognition, Chinese-to-English language translation, facial recognition (including suggestions of celebrities who look like the person holding the phone) and virtual bot friends that can play guessing games. Notwithstanding their cleverness and their pioneering use of natural language processing, these are still niche applications, and still very limited by technology. Some of the most popular AI apps, for example, are small, menu- and rule-driven programs, which conduct fairly rudimentary conversations around a limited group of options.
Despite the lead time required to bring the technology further along, any business prepared to base a strategy on advanced digital technology should be thinking seriously about autonomous intelligence now. The Internet of Things will generate vast amounts of information, more than humans can reasonably interpret. In commercial aircraft, for example, so much flight data is gathered that engineers can’t process it all; thus, Boeing has announced a $7.5 million partnership with Carnegie Mellon University, along with other efforts to develop AI systems that can, for example, predict when airplanes will need maintenance. Autonomous intelligence’s greatest challenge may not be technological at all — it may be companies’ ability to build in enough transparency for people to trust these systems to act in their best interest.
As you contemplate the introduction of artificial intelligence, articulate what mix of the three approaches works best for you.
Are you primarily interested in upgrading your existing processes, reducing costs and improving productivity? If so, then start with assisted intelligence, probably with a small group of services from a cloud-based provider.
Do you seek to build your business around something new — responsive and self-driven products, or services and experiences that incorporate AI? Then pursue an augmented intelligence approach, probably with more complex AI applications resident on the cloud.
Are you developing a genuinely new technology? Most companies will be better off primarily using someone else’s AI platforms, but, if you can justify building your own, you may become one of the leaders in your market.
The transition among these forms of AI is not clean-cut; they sit on a continuum. In developing their own AI strategy, many companies begin somewhere between assisted and augmented, while expecting to move toward autonomous eventually (see Exhibit 2).
Though investments in AI may seem expensive now, the costs will decline over the next 10 years as the software becomes more commoditized. “As this technology continues to mature,” writes Daniel Eckert, a managing director in emerging technology services for PwC US, “we should see the price adhere toward a utility model and flatten out. We expect a tiered pricing model to be introduced: a free (or freemium model) for simple activities, and a premium model for discrete, business-differentiating services.”
AI is often sold on the premise that it will replace human labor at lower cost — and the effect on employment could be devastating, though no one knows for sure. Carl Benedikt Frey and Michael Osborne of Oxford University’s engineering school have calculated that AI will put 47% of the jobs in the U.S. at risk; a 2016 Forrester research report estimated it at 6%, at least by 2025. On the other hand, Baidu Research head (and deep learning pioneer) Andrew Ng recently said, “AI is the new electricity,” meaning that it will be found everywhere and create jobs that weren’t imaginable before its appearance.
At the same time that AI threatens the loss of an almost unimaginable number of jobs, it is also a hungry, unsatisfied employer. The lack of capable talent — people skilled in deep learning technology and analytics — may well turn out to be the biggest obstacle for large companies. The greatest opportunities may thus be for independent businesspeople, including farmers like Jeff Heepke, who no longer need scale to compete with large companies, because AI has leveled the playing field.
It is still too early to say which types of companies will be the most successful in this area — and we don’t yet have an AI model to predict it for us. In the end, we cannot even say for sure that the companies that enter the field first will be the most successful. The dominant players will be those that, like Climate Corp., Oscar W. Larson, Netflix and many other companies large and small, have taken AI to heart as a way to become far more capable, in a far more relevant way, than they otherwise would ever be.
Recently, I chaired the 4th annual Asia Insurance CIO Technology summit in Jakarta, Indonesia. The experience brought me into contact with an entirely different set of insurers and insurance technology players. I was rewarded with a fresh view on the challenges and opportunities of insurance during an era of disruptive innovation, as well as a new perspective on how Asian insurers are creating and launching products, defining new channels and new models to out-innovate the competition.
I should state at the outset that Asian insurers aren’t doing everything differently than North American and European insurers. It is a global era. In many ways, their competitive issues are similar. We are all having the same conversations. As I considered the similarities, however, it made the small differences stand out. Just as Asia is hours ahead of the Western world throughout the day, I had the strange feeling that I was listening to the ends of conversations that are only beginning in other parts of the world. Because populations, cultures, use of digital technology and the nature of businesses vary, I thought I would share a short list of insights from my eavesdropping in an effort to shed light on how disruption is being embraced elsewhere and how it could ripple through the industry. I’ll center my thoughts on models, mandates and marketing.
Everyone is discussing models. Business models. Technology models. Distribution models. Transaction models. There is good reason. It’s a model v. model world, and Asia-Pacific insurers know that the model is the center of a business. For the outer layer to be responsive, the business model can’t be a slow-moving leviathan. Disruption has the disturbing tendency to render perfectly good models obsolete. Creating a responsive, obsolescent-proof business model is of great interest to Asian insurers, which are responding to radically different consumer expectations and competitive models than in prior decades.
Traditional insurers at the conference (as well as challengers) are aggressively rethinking the insurance business model. Some believe that insurance will be run more in an open ecosystem, becoming more fragmented and niche-focused, building on the micro concept. If an insurer can embed products in other business models/industries, especially those with high-frequency transactions, then they capture the opportunity for both a new distribution channel and a new product. New Distribution Channel + New Product = New Market Opportunity.
These are areas where insurers can see quantum leaps in growth, yet they are also the areas where insurers are most susceptible to start-ups beating them to the punch.
Three clear mandates stood out above all others for Asian insurers – the role of CIOs, the necessity of new cyber security solutions and a new, enterprise-wide look at analytics.
For CIOs, the clarion call was for a rapid advancement and widening of scope for their role within the insurance organization. CIOs must become change agents and grow in influence. They must be active in technology review and adoption, more collaborative with CMOs regarding digital platforms and data sharing and more effective at translating business vision into system and process transformation.
Cybersecurity is a never-ending mandate that also seems to never have the perfect solution. It was universally agreed-upon that today’s security measures have the frustrating trait of being mostly temporary solutions. Blockchain technology (currently in use by Bitcoin, among others) was discussed as a more permanent solution for many security issues. Blockchain use makes transaction fraud nearly impossible. Verification of transaction authenticity is instant and can be performed by any trusted source, from any trusted location.
On a broader note, however, it was conceded that security is no longer just an IT issue, but it is a board-level, organization-wide imperative because security concerns the full enterprise. Boards must fund and address cybersecurity across three aspects: confidentiality, availability and integrity.
Enterprise-wide analytics was another organizational mandate. Some Asian insurers are moving toward using end-to-end analytics solutions that cross the enterprise in an effort to gain a single client view and execute a targeted pipeline, with unified campaigns and advertising. Analytics will also give them risk- and assessment-based pricing, improved predictability for loss prevention and better management of claims trends, recovery and services.
Insurers are rapidly moving from product-driven to customer-driven strategies and from traditional distribution channels (such as agents) to an array of channels based on customer choice. At the same time that Asian insurers are looking at relevant business models, they are diving deeply into how marketing tactics may completely shift from a central hub to a decentralized “micro” model. The industry spark has been a short list of both established insurers and start-ups that are capturing new business through new marketing methods, new partnerships and new market spaces.
ZhongAn, for example, is selling return insurance for anything bought on Alibaba. Huatai Life is promoting unit-linked policies on JD.com and selling A&H insurance via a WeChat app. PICC Life has found a distribution partner in Qunar.com, an online travel information provider. These examples require a completely different, high-volume, interaction-based, data-rich, small-issue marketing plan. That kind of marketing will prove to be of great value to insurers that have added flexible, transaction-capable core insurance systems…that are cloud-based to scale rapidly.
Aggregators are now commonplace in insurance, and Asian insurers are looking at how this channel will affect their business, as well as how to use aggregators as a tool for competitive advantage. GoBear, currently selling in Singapore and Thailand, was given as a prime example of how aggregators represent the future of insurance shopping. GoBear isn’t just an aggregator. It is an innovator, revamping the concept of insurance relationships. GoBear Matchmaker, for example, will allow a prospect to pick insurance but also allow the insurer to pick prospects/clients. GoBear Groups will leverage groups/crowd sourcing.
What do these M’s add up to?
Insurance business models, mandates and marketing are all ripe for inspection and change. In some ways, Asian insurers are in a better position for these ground-shaking industry changes because so many of them recognize the stakes involved and the cultural shift required to thrive. Asian populations and culture are ready to embrace technology solutions to meet consumer demands. As all insurers globally address their models, mandates and marketing, it will be fascinating and educational to see how quickly the different markets adapt and are emerging as innovative leaders and how these regional innovations will influence other regions as they turn into global solutions.
One thing was clear to me in my time in Jakarta – Asian insurers are optimistic, active and excited about the road ahead.