Insurers are full of economy-speak these days. We have the gig economy, the digital economy, the data economy and the sharing economy. There is the economy of one, the economy of the many, the service economy and, of course, the experience economy. These concepts are all real and vital considerations for insurers, yet most deal with the implications of external impact, asking, “How will the world affect our business?”
In one striking case, however, we are faced with an alternative question: How will our operations affect our world? We are in the midst of the digital age race where survival and winning will require rapid adaptability and innovation. The digital age represents a seismic shift in the insurance industry, pushing a sometimes slow-to-adapt industry by challenging the traditional business models and assumptions of the past 30-50 years. The business models of the past will not meet the needs or expectations of the future for digital insurance. So insurers will be drawing upon the strengths of a new type of economy that will provide internal energy to the organization and competitive drive to the industry.
This economy is the platform economy.
Cloud platforms are the future because they are the core of revolutionized business models. They are proven. They are intelligent. They combine sought-after technologies. Best of all, they fit an industry that has been trying to become consumer-centric.
Of course, there is an issue. The cloud-based, digital-ready platforms within the platform economy are easiest to plant in uncultivated environments. Most established insurers are in the thick of modernization of a different type and scale. When faced with the options, many will choose digital answers that are painted over modernized frameworks. At the same time, they will be flirting with the idea that a real platform shift may represent a hyper-jump into insurance’s agile future.
The Rise of the Platform Economy
In our new thought leadership, Cloud Business Platform: The Path to Digital Insurance 2.0, we note that the use of big data, artificial intelligence and cloud computing is changing the nature of work and the structure of the economy. Companies such as Apple, Amazon, Netflix, Facebook, Google, Salesforce and Uber are creating online structures that enable a wide range of activities. They have opened the doors to radical changes in how we work, socialize, create value in the economy and compete for profits. This is why a digital platform economy is emerging.
Cloud business platforms represent a new era of impact and industry upheaval. A cloud business platform is one that can run key business applications and services to match the reality and requirements of the current business environment. That environment is characterized by constant disruption, heavy competition and growing market demands. Insurtech entrants are embarking upon business and technology initiatives that exploit untapped markets and address under- or un-met needs. Incumbents with outdated technologies are at a huge disadvantage because they are unable to respond with the flexibility, agility and speed that has become the hallmark of companies that are digital natives.
With investments in this market subset being tracked at just under $16 billion since 2010, insurers need to immediately take notice. Successful companies across all industries leverage technologies such as mobile, social and cloud to make better decisions, automate processes, strengthen their connection with customers/partners/channels and pursue innovation. They do all of this at an increasingly rapid pace, positioning them as “digital first” companies. The acceleration in the uptake of digital technologies and cloud foundations is a crucial first step to entering into the platform world and the shift to a new era of insurance we call Digital Insurance 2.0.
The implication from all this is that the digital age economy is powered by the platform revolution.
Digital Insurance 2.0
Traditional insurers must have digital daydreams now and then. What if we could have started like Amazon instead of like a traditional insurer? What if we had a digital native architecture like Netflix? Why couldn’t we have turned an app into a multi-billion-dollar business as Uber did? Google was disruptive because its framework and model were created to meet the future head on. How do we do what they have done while we are shackled to the constraints of insurance? The advantages these companies enjoy compared to the challenges faced by insurers can make digitalization of insurance seem like an impossible task. The reality is, however, that insurers now have every opportunity for freedom within traditional insurer constraints utilizing a Digital Insurance 2.0 framework.
What are the attributes of Digital Insurance 2.0? In every aspect, digital platforms are driving toward business models with fewer barriers and greater data access with improved flow. Digital insurance platforms share these traits:
Maximized effectiveness across the entire customer journey with deeper, personalized engagement;
Process digitization that improves operational efficiencies and customer experience;
The ingestion and use of digital data-driven insights for better decision-making and to actively identify customer needs;
The ability to rapidly roll out new products and capabilities while expanding into new markets or geographies; and
Quick adaptation to rapid changes.
The crucial technology underpinning digital insurance platforms is cloud-based. The idea that a 10-year old technology like cloud computing could provide new opportunities for insurers seems far-fetched.
Cloud platforms, however, have become the option of choice for Greenfield or startup operations that are offering digitally-enabled traditional insurance products — like Lemonade, Slice and TROV. Cloud platforms are the basis of a new generation of core systems based on a micro-services architecture that is needed for innovative new insurance products like on-demand and micro-insurance offerings.
Shifting from Products to Platforms
Since the beginning of automation, the insurance industry has seen fundamental design, architecture and technology shifts in insurance core software solutions. First, we had the monolithic solutions running on the mainframe from the 1960s to early 2000s. This was followed with the best of breed components in early 2000s for policy, billing and claims based on J2EE and service-oriented architecture — but with each still using different business, data and technology architectures. Next, beginning in the early 2010s, came the loosely coupled “suites,” inclusive of the policy, billing and claims components but with a consistent and common business, data and technology architecture.
Yet, through these transitions, they maintained a product-focused business architecture view, emphasizing policy and billing and claims capabilities and with implementation primarily on-premise or in a private hosted environment, often a “pseudo cloud environment.”
Today’s digital shift will require cloud-based platforms that provide a great promise to address new challenges and opportunities that enable insurers to disrupt their markets before they are disrupted. This requires a new thinking of our solutions… one that makes the transition from products to platforms and is underpinned by three key attributes: ecosystem-friendly, centered on customer experience and enabled by cloud computing.
Unfortunately, too many insurers are taking a page from their old business transformation playbooks and are expecting it to work in today’s digital age. They are forging a new path by “paving the old cow paths,” which is simply creating greater complexity while moving in a direction that will not serve them well in the future. Instead, insurers need to look outside their companies to a new cadre of digital leaders and imagine the art of the possible. What can insurers do now that they could not do before because of technology, customer and market boundary changes? Today’s emerging new competitors are answering these questions ahead of traditional insurers, positioning themselves as the new generation of market leaders in a time of significant disruption and change.
Fundamentally, to succeed in the digital age, an insurer’s strategy must focus on the following attributes:
Customer experience and engagement is priority No. 1 (People)
Business innovation is mandatory (Technology)
Ecosystems extend value (Market Boundaries)
Speed-to-value is the differentiator
For an effective digital transformation, it is important that core, data and digital capabilities are broken out into micro-services. They are then integrated back into the platform to provide a digital experience. Innovative, “digital-first” companies like Google, Amazon, Salesforce, Workday, Uber, Airbnb and Netflix have successfully used this architecture and technology that is disrupting industries. In the case of insurance, digital experiences are enabled by cloud economies of scale — an advantage that many digital-first companies do not have.
Why is this important? Because it will allow insurance companies to more rapidly position themselves in the digital era of Insurance 2.0 and enable them to:
Accelerate digital transformation to become digital era market leaders;
Accelerate innovation with new business models and products;
Accelerate ecosystem opportunities and value; and
Avert disruption or extinction by new competition within and outside the industry.
At the heart of this disruption is a shift from Insurance 1.0 to Digital Insurance 2.0 and a growing gap where innovative insurtech or existing insurers are taking advantage of a new generation of buyers with new needs and expectations and are capturing the opportunity to be the next market leaders in the digital age.
The path to a cloud business platform will evolve differently for each insurer undertaking it. Being open to operationalize around the cloud platform’s promise as a new business model paradigm acknowledges the role innovation will continue to play as insurers encounter future insurance ecosystems. The time for plans, preparation and execution is now — recognizing that the gap is widening and the timeframe to respond is closing.
Will established insurers suffer at the hands of tech-savvy, culture-savvy competition, or will they turn their digital daydreams into dynamic realities?
In a rapidly changing insurance market, new competitors do not play by the traditional rules. Insurers need to be a part of rewriting the rules, because there is less risk when you write the new rules.
Despite a generally soft market for traditional P&C products, the fact that so many industries and the businesses within them are being reshaped by technology is creating opportunities (and more challenges). Consider insurers with personal and commercial auto. Pundits are predicting a rapid decline in personal auto premiums and questioning the viability of both personal and commercial auto due to the emergence of autonomous technologies and driverless vehicles, as well as the increasing use of alternative options (ride-sharing, public transportation, etc.).
Finding alternative growth strategies is “top of mind” for CEOs. Opportunities can be captured from the change within commercial and specialty insurance. New risks, new markets, new customers and the demand for new products and services may fill the gaps for those who are prepared.
New technologies, demographics, behaviors and more will fuel the growth of new businesses and industries over the next 10 years. Commercial and specialty insurance provides a critical role to these businesses and the economy — protecting them from failure by assuming the risks inherent in their transformation.
Industry statistics for the “traditional” commercial marketplace don’t yet reflect the potential growth from these new markets. The Insurance Information Institute expects overall personal and commercial exposures to increase between 4% and 4.5% in 2017 but cautioned that continued soft rates in commercial lines could cause overall P&C premium growth to lag behind economic growth.
But a diverse group of customers will increasingly create narrow segments that will demand niche, personalized products and services. Many do not fit neatly within pre-defined categories of risk and products for insurance, creating opportunities for new products and services.
Small and medium businesses are at the forefront of this change and at the center of business creation, business transformation and growth in the economy.
By 2020, more than 60% of small businesses in the U.S. will be owned by millennials and Gen Xers — two groups that prefer to do as much as possible digitally. Furthermore, their views, behaviors and expectations are different than those of previous generations and will be influenced by their personal digital experiences.
The sharing/gig/on-demand economy is an example of the significant digitally enabled changes in people’s behaviors and expectations that are redefining the nature of work, business models and risk profiles.
The rapid emergence of technologies and the explosion of data are combining to create a magnified impact. Technology and data are making it easier and more profitable to reach, underwrite and service commercial and specialty market segments. In particular, insurers can narrow and specialize various segments into new niches. In addition, the combination of technology and data is disrupting other industries, changing existing business models and creating businesses and risks that need new types of insurance.
New products can be deployed on demand, and industry boundaries are blurring. Traditional insurance or new forms of insurance may be embedded in the purchase of products and services.
Insurtech is re-shaping this new digital world and disrupting the traditional insurance value chain for commercial and specialty insurance, leading to specialty protection for a new era of business. Consider insurtech startups like Embroker, Next Insurance, Ask Kodiak, CoverWallet, Splice and others. Not being left behind, traditional insurers are creating innovative business models for commercial and specialty insurance, like Berkshire Hathaway with biBERK for direct to small business owners; Hiscox, which offers small business insurance (SBI) products directly from its website; or American Family, which invested in AssureStart, now part of Homesite, a direct writer of SBI.
The Domino Effect
We all likely played with dominoes in our childhood, setting them up in a row and seeing how we could orchestrate a chain reaction. Now, as adults, we are seeing and playing with dominoes at a much higher level. Every business has been or likely will be affected by a domino effect.
What is different in today’s business era, as opposed to even a decade ago, is that disruption in one industry has a much broader ripple effect that disrupts the risk landscape of multiple other industries and creates additional risks. We are compelled to watch the chains created from inside and outside of insurance. Recognizing that this domino effect occurs is critical to developing appropriate new product plans that align to these shifts.
Just consider the following disrupted industries and then think about the disrupters and their casualties: taxis and ridesharing (Lyft, Uber), movie rentals (Blockbuster) and streaming video (NetFlix), traditional retail (Sears and Macy’s) and online retail, enterprise systems (Siebel, Oracle) and cloud platforms (Salesforce and Workday), and book stores (Borders) and Amazon. Consider the continuing impact of Amazon, with the announcement about acquiring Whole Foods and the significant drop in stock prices for traditional grocers. Many analysts noted that this is a game changer with massive innovative opportunities.
The transportation industry is at the front end of a massive domino-toppling event. A report from RethinkX, The Disruption of Transportation and the Collapse of the Internal-Combustion Vehicle and Oil Industries, says that by 2030 (within 10 years of regulatory approval of autonomous vehicles (AVs)), 95% of U.S. passenger miles traveled will be served by on-demand autonomous electric vehicles owned by fleets, not individuals, in a new business model called “transportation-as-a-service” (TaaS). The TaaS disruption will have enormous implications across the automotive industry, but also many other industries, including public transportation, oil, auto repair shops and gas stations. The result is that not just one industry could be disrupted … many could be affected by just one domino … autonomous vehicles. Auto insurance is in this chain of disruption.
And commercial insurance, because it is used by all businesses to provide risk protection, is also in the chain of all those businesses affected – a decline in number of businesses, decline in risk products needed and decline in revenue. The domino effect will decimate traditional business, product and revenue models, while creating growth opportunities for those bold enough to begin preparing for it today with different risk products.
Transformation + Creativity = Opportunity
Opportunity in insurance starts with transformation. New technologies will be enablers on the path to innovative ideas. As the new age of insurance unfolds, insurers must recommit to their business transformation journey and avoid falling into an operational trap or resorting to traditional thinking. In this changing insurance market, new competitors don’t play by the rules of the past. Insurers need to be a part of rewriting the rules for the future, because there is less risk when you write the new rules. One of those rules is diversification. Diversification is about building new products, exploring new markets and taking new risks. The cost of ignoring this can be brutal. Insurers that can see the change and opportunity for commercial and specialty lines will set themselves apart from those that do not.
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.
Creating businesses is the challenge of the day for large organizations. After years of cost-cutting and downsizing, companies have realized they can’t shrink their way to success.
In a world where what’s possible is advancing at breakneck speeds, social behavior, technology and global economy are driving forces for change. Established brands have realized they can’t stay relevant, differentiate themselves or gain a competitive advantage by tweaking aging product portfolios, buying out rivals or expanding to developing nations.
Innovation is crucial now more than ever, so companies must become Janus-like — looking in two directions at once, with one face focused on the old that still accounts for the bulk of their revenue and the other seeking out the new.
Innovation brings the hope of new value and the fear of the unknown. It is often born at the fringes of an organization’s established divisions and, at times, it exists in the spaces between. The truth is that innovation is a messy business. The high levels of uncertainty associated with new ventures need adaptive organizational structures to succeed. A company’s operating, financial and governance models are seldom the same as existing businesses. In fact, most new business models are not fully defined in the beginning; they become clearer as new strategies are tried, customer needs are understood and anticipated and new applications are developed to facilitate new experiences. This uncertainty results in half-baked superficial changes that happen at the edge because it is easiest there, that require minimal organizational effort and that get the most visibility. Launching innovation labs, incubators or venture units requires a few bodies on the ground in a trendy office — even if they don’t produce much tangible value after the post-launch media hype wears off.
Crossing the threshold to innovate is imperative, but transitions from the current tried-and-tested state to the new state with unfamiliar rules and values is daunting for most people. It takes clarity of vision to create momentum and inspire others. Above all, it’s a balancing act between the old and the new cultures that are often placed in conflict with one another if the company takes an either or approach to corporate entrepreneurship.
Even when a breakthrough innovation is ready to be implemented, delivery becomes impossible in this corporate environment. Most leaders find there’s a fine line between corporate entrepreneurship and insubordination.
I get asked by CEOs and heads of departments how we solve these problems. How do we make a real impact with consensus and harmony? I suggest a new approach is called for, one that blends these cultures to avoid extreme behavior and creates equilibrium in areas of strategy, operations and organization. We have only to look at any successful enterprise such as Apple, Uber or Netflix, and we’ll find innovation at its core. These companies are bold about taking risks, driving change for the better and doing it at scale through human-centered design. This understanding and building a collaborative culture to actively seek out solutions to challenging problems and identifying relevant strategies continues to expand the realm of the possible.
More than a half century ago, Ted Levitt transformed the strategic marketing agenda by asking a seemingly simple question. In his classic Harvard Business Review article “Marketing Myopia,” Levitt declared that truly effective executives needed the courage, creativity and self-discipline to answer, “What business are we really in?”
Were railroads, he asked, in the railroad business or the transportation business? Are oil companies in the oil business or hydrocarbon or energy business? The distinctions aren’t subtle, Levitt argued, and they subverted how companies saw their futures. Marketing myopia blinded firms to both disruptive threats and innovation opportunities.
Levitt’s provocative question remains both potent and perceptive for marketers today. But my research in human capital investment and “network effects” suggests that it, too, needs a little visionary help. Increasingly, successful market leaders and innovators – the Amazons, Apples, Googles, Facebooks, Netflixs and Ubers– also ask, “Who do we want our customers to become?”
That question is as mission-critical for insurance and financial services innovators as for Silicon Valley startups. The digitally disruptive influence of platforms, algorithms and analytics comes not just from how they transform internal enterprise economics but from their combined abilities to transform customers and clients, as well. Successful innovators transform their customers.
The essential insight: Innovation isn’t just an investment in product enhancement or better customer experience; innovation is an investment in your customer’s future value. Simply put, innovation is an investment in the human capital, capabilities, competencies and creativity of one’s customers and clients.
This is as true for professional services and business-to-business industries as for consumer products and services companies.
History gives great credence to this “human capital” model of innovation. Henry Ford didn’t just facilitate “mass production,” he enabled the human capital of “driving.” George Eastman didn’t just create cheap cameras and films; Kodak created photographers. Sam Walton’s Walmart successfully deployed scale, satellite and supply chain superiority that transformed “typical” shoppers into higher-volume, one-stop, everyday-low-pricing customers.
Similarly, Steve Jobs didn’t merely “reinvent” personal computing and mobile telephony; he reinvented how people physically touched, stroked and talked to their devices. Google’s core technology breakthrough may appear to be “search,” but the success of the company’s algorithms and business model is contingent upon creating more than a billion smart “searchers” worldwide.
The essential economic takeaway is that sustainable innovation success doesn’t revolve simply around what innovations “do”; it builds on what they invite customers to become. Simply put, making customers better makes better customers.
Successful companies have a “vision of the customer future” that matters every bit as much as their products and services road maps.
Insurance, fintech and insurtech industries should be no different. The same digital innovation and transformation dynamics apply. That means financial services firms must go beyond the “faster, better, cheaper” innovation ethos to ask how their innovations will profitably transform customer behaviors, capabilities and expectations.
In other words, it’s not enough to answer Levitt’s question by declaring, “We’re in the auto/property/life insurance business.” The challenge comes from determining how insurance companies want their new products, innovative services and novel user experiences to transform their customers. How can insurance companies invest in their customers in ways that make them more valuable? Who are they asking their customers to become?
So when insurers innovate in ways that give customers and prospects new capabilities — like Progressive’s price-comparison tools and Snapshot vehicle-usage plug-ins or Allstate’s mobile-phone-enabled QuickFoto claims submission option — they’re not just solving problems but asking customers to engage in ways they never had before.
Who are these companies asking their customers to become? People who will comparison shop; allow themselves to be monitored in exchange for better prices and better service; collaboratively gather digital data to review and expedite claims. These are but the first generation of innovation investments that suggest tomorrow’s customers will do much more.
This is of a piece with how a Jeff Bezos, Steve Jobs, Mark Zuckerberg or Reed Hastings innovates to make their customers — not just their products — more valuable.
Today’s Web 2.0 “network effects” business model — where a service becomes more valuable the more people use it — are superb examples of how smart companies recognize that their own futures depend on how ingeniously they invest in the future capabilities of their customers. Their continuous innovation is contingent on their customers’ continuous improvement. Call it “customer kaizen.”
How rigorously and ruthlessly fintech, insurtech and insurance companies champion this innovation ethos will prove crucial to their success. Being in “the blockchain business” is radically and fundamentally different than asking who we want our blockchain users to become.
Giving better, faster and cheaper advice on risk management via digital devices is different than fundamentally transforming how customers perceive and manage risk. It’s the difference between “transactional innovation” and innovation based on more sustainable relationships of mutual gain.
The insurance industry needs to transform its innovation mindset. Start thinking how innovations make customers and clients more valuable. If your innovations aren’t explicit, measurable investments in your customers’ futures, then you are taking a myopic view of your own.
Today’s strategic marketing and innovation challenge is how best to align “What business are we in?” with “Who do we want our customers to become?”