Harvard Professor Steven Pinker on Why We Refuse to See the Bright Side, Even Though We Should

Pinker is a professor of psychology at Harvard and the author of 10 books

According to the latest data, people are living longer and becoming healthier, better fed, richer, smarter, safer, more connected–and, at the same time, ever gloomier about the state of the world. As the political scientist John Mueller once summed up the history of the West, “People seem simply to have taken the remarkable economic improvement in stride and have deftly found new concerns to get upset about.” How can we explain pessimism in a world of progress?

It’s not that people are naturally glum. On the contrary, they tend to see their lives through rosetinted glasses: they say they are happy, their schools are good, their neighborhoods are safe and that they are less likely than the average person to become the victim of an accident, a disease, a layoff or crime.

But when people are asked about their countries, they switch from Pollyanna to Eeyore: everyone else is miserable, they insist, and the world is going to hell in a handcart.

This disconnect originates in the nature of news. News is about what happens, not what doesn’t happen, so it features sudden and upsetting events like fires, plant closings, rampage shootings and shark attacks. Most positive developments are not camera-friendly, and they aren’t built in a day. You never see a headline about a country that is not at war, or a city that has not been attacked by terrorists–or the fact that since yesterday, 180,000 people have escaped extreme poverty.

The bad habits of media in turn bring out the worst in human cognition. Our intuitions about risk are driven not by statistics but by images and stories. People rank tornadoes (which kill dozens of Americans a year) as more dangerous than asthma (which kills thousands), presumably because tornadoes make for better television. It’s easy to see how this cognitive bias–stoked by the news policy “If it bleeds, it leads”–could make people conclude the worst about where the world is heading.

Irrational pessimism is also driven by a morbid interest in what can go wrong–and there are always more ways for things to go wrong than to go right. This creates a market for experts to remind us of things that can go wrong that we may have overlooked. Biblical prophets, oped pundits, social critics, dystopian filmmakers and tabloid psychics know they can achieve instant gravitas by warning of an imminent doomsday. Those who point out that the world is getting better–even hardheaded analysts who are just reading out the data–may be dismissed as starry-eyed naïfs.

Psychologists have identified other reasons we are nostalgic about the past and jaundiced about the present. Time heals most wounds: the negative coloring of bad experiences fades with the passing of years. Also, we are liable to confuse the heavier burdens of maturity with a world that has lost its innocence, and the inevitable decline in our faculties with a decline in the times. As the columnist Franklin Pierce Adams pointed out, “Nothing is more responsible for the good old days than a bad memory.”

The cure for these biases is numeracy: basing our sense of the world not on bleeding headlines or gory images but on measures of human flourishing such as longevity, literacy, prosperity and peace. Numbers, after all, aggregate the good and the bad, the things that happen and the things that don’t. A quantitative mind-set, despite its nerdy aura, is not just a smarter way to understand the world but the morally enlightened one. It treats every human life as equal, rather than privileging the people who are closest to us or most photogenic. And it holds out the hope that we might identify the causes of our problems and thereby implement the measures that are most likely to solve them.

Source and author: Pinker is a professor of psychology at Harvard and the author of 10 books

This appears in the January 15, 2018 issue of TIME.

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More Skills? By John McGonagle

In Time, Harvard Professor Steven Pinker recently wrote about how the media impact human cognition (don’t yawn). Specifically, he points out that people tend to “see their lives through rose-colored glasses”. Yet, when discussing others (people, countries, etc.), they see “everyone is miserable…and the world is going to hell in a handcart.”

He attributes these “biases”, his word not mine, to the “bad habits” of the media as well as our own “morbid interest in what can go wrong”. The cure? Numeracy – literacy about numbers. That skill, he contends, helps to develop and maintain a quantitative mindset, which is not just “smarter” but “more enlightened:”.

Does this mean that quantitative skills should be added to the list that CI analysts, collectors, and even end-users should consider as “must haves” and not just “useful”?

I vote yes.

Source: John McGonagle, Proactive Intelligence

 

Early Warning, by John McGonagle

I am a proponent of the “early warning” use of competitive intelligence. However, any early warning program requires an inclusive, rather than selective, definition of competitors and what constitutes potential competitors.

Let’s look at a recent event from the perspective of the health insurance market.

“Two mega-health insurance mergers terminated” [1].

Would/could early warning pick that up? Almost certainly. Why? Because these transactions involved the 4 dominant players in the group health insurance market. Any/every health insurer should have been watching all 4 of these firms and should have been on top of the legal and financial drivers which might enable/hinder these transactions, probably without much additional research.

What about these two other recent events impacting the same market?

 “CVS Health [owners of the pharmacy company] to Acquire Aetna; Combination to Provide Consumers with a Better Experience, Reduced Costs and Improved Access to Health Care Experts in Homes and Communities Across the Country”[2].

“Amazon, Berkshire Hathaway, JPMorgan Chase to tackle employee health care costs, delivery”[3].

I doubt that any early warning mechanisms in this market  picked these up, for any one or more of several reasons (disclosure, before coming to CI, I previously worked in the health insurance industry):

It is unlikely that most firms in this market have heavily invested in early warning activities because their regulatory structure establish high barriers to entry. Firms regard this as virtually eliminating the entry of new or expansion of existing competitors, at least without some notice from monitoring filings with state regulators.

People move relatively freely from one competitor firm to another, producing an underlying sense that “we sort of know what is probably going on”. That undermines the effort to advance early warning, at least by limiting its focus to the “usual suspects”.

The focus of the industry has been on monitoring the political and regulatory activities at the state and federal level, inducing and supporting a short-term vision of structure and marketing. Short-term stress does not work well with long-term early warning.

The same focus would preclude any early warning system from considering the likelihood that non-insurance firms could/would take steps that might fundamentally change the health insurance industry.

The CI focus in the industry is most often on new products and their marketing, technology acquisitions, and changes in relationships with brokers and agents. Did you see the concept of structural change or existential threat? I didn’t.

Lesson? Come to an early warning system with as few preconceptions as possible. Recall the analogy of the lookout at sea: the lookout surveys all directions (not just on either side), constantly (not only periodically), and for anything that could become a potential threat (not merely any anticipated and identified threat).

[1] https://www.bizjournals.com/milwaukee/news/2017/02/14/two-mega-health-insurance-mergers-terminated.html.

[2] https://cvshealth.com/newsroom/press-releases/cvs-health-acquire-aetna-combination-provide-consumers-better-experience.

[3] https://www.usatoday.com/story/money/americasmarkets/2018/01/30/amazon-berkshire-hathaway-jpmorgan-chase-tackle-employee-health-care-costs-delivery/1077866001/. This event reportedly caused an immediate 5% drop in the stock prices of major health insurers.

Source: John McGonagle, Proactive Intelligence

 

Revista Inteligência Competitiva – Chamada para publicação

A Revista Inteligência Competitiva recebe artigos, relatos técnicos, de experiências, de pesquisas, entrevistas, resenhas e estudos de casos em regime de fluxo contínuo, ou seja, os materiais são avaliados à medida que são recebidos.

A submissão é realizada pelo seguinte link

Nós agradecemos muito a colaboração de todos e contamos com os autores que vêm contribuindo com pesquisas voltadas à Inteligência Competitiva.

Prof. Dr. Alfredo Passos
Editor Chefe

Competitive Intelligence: Why You Shouldn’t Be Afraid of Artificial Intelligence by Lili Cheng

Alexander the friendly robot visits the Indoor Park to interact with children by telling classic fairy tales, singing and dancing at Westfield London on August 10, 2016 in London, England.

Alexander the friendly robot visits the Indoor Park to interact with children by telling classic fairy tales, singing and dancing at Westfield London on August 10, 2016 in London, England.
Jeff Spicer—Getty Images

 

Artificial intelligence is one of the hottest, least understood and most debated technological breakthroughs in modern times. In many ways, the magic of AI is that it’s not something you can see or touch. You may not even realize you are using it today. When your Nest thermostat knows how to set the right temperature at home or when your phone automatically corrects your grammar or when a Tesla car navigates a road autonomously–that’s AI at work.

For most of our lives, people have had to adapt to technology. To find a file on a computer, we input a command on a keyboard attached to one particular machine. To make a phone call, we tap an assortment of numbers on a keypad. To get a piece of information, we type a specific set of keywords into a search engine.

AI is turning that dynamic on its head by creating technologies that adapt to us rather than the other way around–new ways of interacting with computers that won’t seem like computing at all.

Computer scientists have been working on AI technologies for decades, and we’re now seeing that work bear fruit. Recent breakthroughs, based on computers’ ability to understand speech and language, and have vision, have given rise to our technology “alter ego”–a personal guide that knows your habits and communication preferences, and helps you schedule your time, motivate your team to do their best work, or be, say, a better parent. Those same achievements have divided leading voices inside the world of technology about the potential pitfalls that may accompany this progress.

Core to the work I do on conversational AI is how we model language–not only inspired by technical advances, but also by insight from our best and brightest thinkers on the way people use words. To do so, we revisit ideas in books, such as Steven Pinker’s The Stuff of Thought, that give us closer looks at the complexity of human language, which combines logical rules with the unpredictability of human passion.

Humanity’s most important moments are often those risky interactions where emotion comes into play–like a date or a business negotiation–and people use vague, ambiguous language to take social risks. AI that understands language needs to combine the logical and unpredictable ways people interact. This likely means AI needs to recognize when people are more effective on their own–when to get out of the way, when not to help, when not to record, when not to interrupt or distract.

The advances that AI is bringing to our world have been a half-century in the making. But AI’s time is now. Because of the vast amounts of data in our world, only the almost limitless computing power of the cloud can make sense of it. AI can truly help solve some of the world’s most vexing problems, from improving day-to-day communication to energy, climate, health care, transportation and more. The real magic of AI, in the end, won’t be magic at all. It will be technology that adapts to people. This will be profoundly transformational for humans and for humanity.

Source: TIME, January 4, 2018 , Cheng is a corporate vice president of Microsoft AI & Research

Competitive Intelligence: Artificial Intelligence for the Real World, by Thomas H. Davenport and Rajeev Ronanki

In 2013, the MD Anderson Cancer Center launched a “moon shot” project: diagnose and recommend treatment plans for certain forms of cancer using IBM’s Watson cognitive system.

But in 2017, the project was put on hold after costs topped $62 million—and the system had yet to be used on patients. At the same time, the cancer center’s IT group was experimenting with using cognitive technologies to do much less ambitious jobs, such as making hotel and restaurant recommendations for patients’ families, determining which patients needed help paying bills, and addressing staff IT problems. The results of these projects have been much more promising: The new systems have contributed to increased patient satisfaction, improved financial performance, and a decline in time spent on tedious data entry by the hospital’s care managers.

Despite the setback on the moon shot, MD Anderson remains committed to using cognitive technology—that is, next-generation artificial intelligence—to enhance cancer treatment, and is currently developing a variety of new projects at its center of competency for cognitive computing.

The contrast between the two approaches is relevant to anyone planning AI initiatives. Our survey of 250 executives who are familiar with their companies’ use of cognitive technology shows that three-quarters of them believe that AI will substantially transform their companies within three years.

However, our study of 152 projects in almost as many companies also reveals that highly ambitious moon shots are less likely to be successful than “low-hanging fruit” projects that enhance business processes.

This shouldn’t be surprising—such has been the case with the great majority of new technologies that companies have adopted in the past. But the hype surrounding artificial intelligence has been especially powerful, and some organizations have been seduced by it.

In this article, we’ll look at the various categories of AI being employed and provide a framework for how companies should begin to build up their cognitive capabilities in the next several years to achieve their business objectives.

Three Types of AI

It is useful for companies to look at AI through the lens of business capabilities rather than technologies. Broadly speaking, AI can support three important business needs: automating business processes, gaining insight through data analysis, and engaging with customers and employees.

Process automation.

Of the 152 projects we studied, the most common type was the automation of digital and physical tasks—typically back-office administrative and financial activities—using robotic process automation technologies.

RPA is more advanced than earlier business-process automation tools, because the “robots” (that is, code on a server) act like a human inputting and consuming information from multiple IT systems. Tasks include:

  • transferring data from e-mail and call center systems into systems of record—for example, updating customer files with address changes or service additions;
  • replacing lost credit or ATM cards, reaching into multiple systems to update records and handle customer communications;
  • reconciling failures to charge for services across billing systems by extracting information from multiple document types; and
  • “reading” legal and contractual documents to extract provisions using natural language processing.

RPA is the least expensive and easiest to implement of the cognitive technologies we’ll discuss here, and typically brings a quick and high return on investment. (It’s also the least “smart” in the sense that these applications aren’t programmed to learn and improve, though developers are slowly adding more intelligence and learning capability.) It is particularly well suited to working across multiple back-end systems.

At NASA, cost pressures led the agency to launch four RPA pilots in accounts payable and receivable, IT spending, and human resources—all managed by a shared services center. The four projects worked well—in the HR application, for example, 86% of transactions were completed without human intervention—and are being rolled out across the organization. NASA is now implementing more RPA bots, some with higher levels of intelligence. As Jim Walker, project leader for the shared services organization notes, “So far it’s not rocket science.”

One might imagine that robotic process automation would quickly put people out of work. But across the 71 RPA projects we reviewed (47% of the total), replacing administrative employees was neither the primary objective nor a common outcome. Only a few projects led to reductions in head count, and in most cases, the tasks in question had already been shifted to outsourced workers. As technology improves, robotic automation projects are likely to lead to some job losses in the future, particularly in the offshore business-process outsourcing industry. If you can outsource a task, you can probably automate it.

Cognitive insight.

The second most common type of project in our study (38% of the total) used algorithms to detect patterns in vast volumes of data and interpret their meaning. Think of it as “analytics on steroids.” These machine-learning applications are being used to:

  • predict what a particular customer is likely to buy;
  • identify credit fraud in real time and detect insurance claims fraud;
  • analyze warranty data to identify safety or quality problems in automobiles and other manufactured products;
  • automate personalized targeting of digital ads; and
  • provide insurers with more-accurate and detailed actuarial modeling.

Cognitive insights provided by machine learning differ from those available from traditional analytics in three ways: They are usually much more data-intensive and detailed, the models typically are trained on some part of the data set, and the models get better—that is, their ability to use new data to make predictions or put things into categories improves over time.

Versions of machine learning (deep learning, in particular, which attempts to mimic the activity in the human brain in order to recognize patterns) can perform feats such as recognizing images and speech.

Machine learning can also make available new data for better analytics. While the activity of data curation has historically been quite labor-intensive, now machine learning can identify probabilistic matches—data that is likely to be associated with the same person or company but that appears in slightly different formats—across databases.

GE has used this technology to integrate supplier data and has saved $80 million in its first year by eliminating redundancies and negotiating contracts that were previously managed at the business unit level.

Similarly, a large bank used this technology to extract data on terms from supplier contracts and match it with invoice numbers, identifying tens of millions of dollars in products and services not supplied.

Deloitte’s audit practice is using cognitive insight to extract terms from contracts, which enables an audit to address a much higher proportion of documents, often 100%, without human auditors’ having to painstakingly read through them.

Cognitive insight applications are typically used to improve performance on jobs only machines can do—tasks such as programmatic ad buying that involve such high-speed data crunching and automation that they’ve long been beyond human ability—so they’re not generally a threat to human jobs.

Cognitive engagement.

Projects that engage employees and customers using natural language processing chatbots, intelligent agents, and machine learning were the least common type in our study (accounting for 16% of the total). This category includes:

  • intelligent agents that offer 24/7 customer service addressing a broad and growing array of issues from password requests to technical support questions—all in the customer’s natural language;
  • internal sites for answering employee questions on topics including IT, employee benefits, and HR policy;
  • product and service recommendation systems for retailers that increase personalization, engagement, and sales—typically including rich language or images; and
  • health treatment recommendation systems that help providers create customized care plans that take into account individual patients’ health status and previous treatments.

The companies in our study tended to use cognitive engagement technologies more to interact with employees than with customers. That may change as firms become more comfortable turning customer interactions over to machines.

Vanguard, for example, is piloting an intelligent agent that helps its customer service staff answer frequently asked questions. The plan is to eventually allow customers to engage with the cognitive agent directly, rather than with the human customer-service agents.

SEBank, in Sweden, and the medical technology giant Becton, Dickinson, in the United States, are using the lifelike intelligent-agent avatar Amelia to serve as an internal employee help desk for IT support. SEBank has recently made Amelia available to customers on a limited basis in order to test its performance and customer response.

R1801H_DAVENPORT_BENEFITS.png

Companies tend to take a conservative approach to customer-facing cognitive engagement technologies largely because of their immaturity. Facebook, for example, found that its Messenger chatbots couldn’t answer 70% of customer requests without human intervention. As a result, Facebook and several other firms are restricting bot-based interfaces to certain topic domains or conversation types.

Our research suggests that cognitive engagement apps are not currently threatening customer service or sales rep jobs. In most of the projects we studied, the goal was not to reduce head count but to handle growing numbers of employee and customer interactions without adding staff.

Some organizations were planning to hand over routine communications to machines, while transitioning customer-support personnel to more-complex activities such as handling customer issues that escalate, conducting extended unstructured dialogues, or reaching out to customers before they call in with problems.

As companies become more familiar with cognitive tools, they are experimenting with projects that combine elements from all three categories to reap the benefits of AI. An Italian insurer, for example, developed a “cognitive help desk” within its IT organization. The system engages with employees using deep-learning technology (part of the cognitive insights category) to search frequently asked questions and answers, previously resolved cases, and documentation to come up with solutions to employees’ problems. It uses a smart-routing capability (business process automation) to forward the most complex problems to human representatives, and it uses natural language processing to support user requests in Italian.

Despite their rapidly expanding experience with cognitive tools, however, companies face significant obstacles in development and implementation. On the basis of our research, we’ve developed a four-step framework for integrating AI technologies that can help companies achieve their objectives, whether the projects are moon shoots or business-process enhancements.

1. Understanding The Technologies

Before embarking on an AI initiative, companies must understand which technologies perform what types of tasks, and the strengths and limitations of each. Rule-based expert systems and robotic process automation, for example, are transparent in how they do their work, but neither is capable of learning and improving.

Deep learning, on the other hand, is great at learning from large volumes of labeled data, but it’s almost impossible to understand how it creates the models it does. This “black box” issue can be problematic in highly regulated industries such as financial services, in which regulators insist on knowing why decisions are made in a certain way.

We encountered several organizations that wasted time and money pursuing the wrong technology for the job at hand. But if they’re armed with a good understanding of the different technologies, companies are better positioned to determine which might best address specific needs, which vendors to work with, and how quickly a system can be implemented. Acquiring this understanding requires ongoing research and education, usually within IT or an innovation group.

R1801H_DAVENPORT_CHALLENGES.png

In particular, companies will need to leverage the capabilities of key employees, such as data scientists, who have the statistical and big-data skills necessary to learn the nuts and bolts of these technologies. A main success factor is your people’s willingness to learn. Some will leap at the opportunity, while others will want to stick with tools they’re familiar with. Strive to have a high percentage of the former.

If you don’t have data science or analytics capabilities in-house, you’ll probably have to build an ecosystem of external service providers in the near term. If you expect to be implementing longer-term AI projects, you will want to recruit expert in-house talent. Either way, having the right capabilities is essential to progress.

Given the scarcity of cognitive technology talent, most organizations should establish a pool of resources—perhaps in a centralized function such as IT or strategy—and make experts available to high-priority projects throughout the organization. As needs and talent proliferate, it may make sense to dedicate groups to particular business functions or units, but even then a central coordinating function can be useful in managing projects and careers.

2. Creating a Portfolio of Projects

The next step in launching an AI program is to systematically evaluate needs and capabilities and then develop a prioritized portfolio of projects. In the companies we studied, this was usually done in workshops or through small consulting engagements. We recommend that companies conduct assessments in three broad areas.

Identifying the opportunities.

The first assessment determines which areas of the business could benefit most from cognitive applications. Typically, they are parts of the company where “knowledge”—insight derived from data analysis or a collection of texts—is at a premium but for some reason is not available.

  • Bottlenecks. In some cases, the lack of cognitive insights is caused by a bottleneck in the flow of information; knowledge exists in the organization, but it is not optimally distributed. That’s often the case in health care, for example, where knowledge tends to be siloed within practices, departments, or academic medical centers.
  • Scaling challenges. In other cases, knowledge exists, but the process for using it takes too long or is expensive to scale. Such is often the case with knowledge developed by financial advisers. That’s why many investment and wealth management firms now offer AI-supported “robo-advice” capabilities that provide clients with cost-effective guidance for routine financial issues.
  • In the pharmaceutical industry, Pfizer is tackling the scaling problem by using IBM’s Watson to accelerate the laborious process of drug-discovery research in immuno-oncology, an emerging approach to cancer treatment that uses the body’s immune system to help fight cancer. Immuno-oncology drugs can take up to 12 years to bring to market. By combining a sweeping literature review with Pfizer’s own data, such as lab reports, Watson is helping researchers to surface relationships and find hidden patterns that should speed the identification of new drug targets, combination therapies for study, and patient selection strategies for this new class of drugs.
  • Inadequate firepower. Finally, a company may collect more data than its existing human or computer firepower can adequately analyze and apply. For example, a company may have massive amounts of data on consumers’ digital behavior but lack insight about what it means or how it can be strategically applied. To address this, companies are using machine learning to support tasks such as programmatic buying of personalized digital ads or, in the case of Cisco Systems and IBM, to create tens of thousands of “propensity models” for determining which customers are likely to buy which products.

Determining the use cases.

The second area of assessment evaluates the use cases in which cognitive applications would generate substantial value and contribute to business success. Start by asking key questions such as: How critical to your overall strategy is addressing the targeted problem? How difficult would it be to implement the proposed AI solution—both technically and organizationally? Would the benefits from launching the application be worth the effort? Next, prioritize the use cases according to which offer the most short- and long-term value, and which might ultimately be integrated into a broader platform or suite of cognitive capabilities to create competitive advantage.

Selecting the technology.

The third area to assess examines whether the AI tools being considered for each use case are truly up to the task. Chatbots and intelligent agents, for example, may frustrate some companies because most of them can’t yet match human problem solving beyond simple scripted cases (though they are improving rapidly). Other technologies, like robotic process automation that can streamline simple processes such as invoicing, may in fact slow down more-complex production systems. And while deep learning visual recognition systems can recognize images in photos and videos, they require lots of labeled data and may be unable to make sense of a complex visual field.

In time, cognitive technologies will transform how companies do business. Today, however, it’s wiser to take incremental steps with the currently available technology while planning for transformational change in the not-too-distant future. You may ultimately want to turn customer interactions over to bots, for example, but for now it’s probably more feasible—and sensible—to automate your internal IT help desk as a step toward the ultimate goal.

3. Launching Pilots

Because the gap between current and desired AI capabilities is not always obvious, companies should create pilot projects for cognitive applications before rolling them out across the entire enterprise.

Proof-of-concept pilots are particularly suited to initiatives that have high potential business value or allow the organization to test different technologies at the same time. Take special care to avoid “injections” of projects by senior executives who have been influenced by technology vendors. Just because executives and boards of directors may feel pressure to “do something cognitive” doesn’t mean you should bypass the rigorous piloting process. Injected projects often fail, which can significantly set back the organization’s AI program.

If your firm plans to launch several pilots, consider creating a cognitive center of excellence or similar structure to manage them. This approach helps build the needed technology skills and capabilities within the organization, while also helping to move small pilots into broader applications that will have a greater impact. Pfizer has more than 60 projects across the company that employ some form of cognitive technology; many are pilots, and some are now in production.

At Becton, Dickinson, a “global automation” function within the IT organization oversees a number of cognitive technology pilots that use intelligent digital agents and RPA (some work is done in partnership with the company’s Global Shared Services organization). The global automation group uses end-to-end process maps to guide implementation and identify automation opportunities. The group also uses graphical “heat maps” that indicate the organizational activities most amenable to AI interventions. The company has successfully implemented intelligent agents in IT support processes, but as yet is not ready to support large-scale enterprise processes, like order-to-cash. The health insurer Anthem has developed a similar centralized AI function that it calls the Cognitive Capability Office.

Business-process redesign.

As cognitive technology projects are developed, think through how workflows might be redesigned, focusing specifically on the division of labor between humans and the AI. In some cognitive projects, 80% of decisions will be made by machines and 20% will be made by humans; others will have the opposite ratio. Systematic redesign of workflows is necessary to ensure that humans and machines augment each other’s strengths and compensate for weaknesses.

The investment firm Vanguard, for example, has a new “Personal Advisor Services” (PAS) offering, which combines automated investment advice with guidance from human advisers. In the new system, cognitive technology is used to perform many of the traditional tasks of investment advising, including constructing a customized portfolio, rebalancing portfolios over time, tax loss harvesting, and tax-efficient investment selection.

Vanguard’s human advisers serve as “investing coaches,” tasked with answering investor questions, encouraging healthy financial behaviors, and being, in Vanguard’s words, “emotional circuit breakers” to keep investors on plan. Advisers are encouraged to learn about behavioral finance to perform these roles effectively. The PAS approach has quickly gathered more than $80 billion in assets under management, costs are lower than those for purely human-based advising, and customer satisfaction is high.

Vanguard understood the importance of work redesign when implementing PAS, but many companies simply “pave the cow path” by automating existing work processes, particularly when using RPA technology. By automating established workflows, companies can quickly implement projects and achieve ROI—but they forgo the opportunity to take full advantage of AI capabilities and substantively improve the process.

Cognitive work redesign efforts often benefit from applying design-thinking principles: understanding customer or end-user needs, involving employees whose work will be restructured, treating designs as experimental “first drafts,” considering multiple alternatives, and explicitly considering cognitive technology capabilities in the design process. Most cognitive projects are also suited to iterative, agile approaches to development.

4. Scaling Up

Many organizations have successfully launched cognitive pilots, but they haven’t had as much success rolling them out organization-wide. To achieve their goals, companies need detailed plans for scaling up, which requires collaboration between technology experts and owners of the business process being automated. Because cognitive technologies typically support individual tasks rather than entire processes, scale-up almost always requires integration with existing systems and processes. Indeed, in our survey, executives reported that such integration was the greatest challenge they faced in AI initiatives.

Companies should begin the scaling-up process by considering whether the required integration is even possible or feasible. If the application depends on special technology that is difficult to source, for example, that will limit scale-up. Make sure your business process owners discuss scaling considerations with the IT organization before or during the pilot phase: An end run around IT is unlikely to be successful, even for relatively simple technologies like RPA.

The health insurer Anthem, for example, is taking on the development of cognitive technologies as part of a major modernization of its existing systems. Rather than bolting new cognitive apps onto legacy technology, Anthem is using a holistic approach that maximizes the value being generated by the cognitive applications, reduces the overall cost of development and integration, and creates a halo effect on legacy systems. The company is also redesigning processes at the same time to, as CIO Tom Miller puts it, “use cognitive to move us to the next level.”

In scaling up, companies may face substantial change-management challenges. At one U.S. apparel retail chain, for example, the pilot project at a small subset of stores used machine learning for online product recommendations, predictions for optimal inventory and rapid replenishment models, and—most difficult of all—merchandising. Buyers, used to ordering product on the basis of their intuition, felt threatened and made comments like “If you’re going to trust this, what do you need me for?” After the pilot, the buyers went as a group to the chief merchandising officer and requested that the program be killed. The executive pointed out that the results were positive and warranted expanding the project. He assured the buyers that, freed of certain merchandising tasks, they could take on more high-value work that humans can still do better than machines, such as understanding younger customers’ desires and determining apparel manufacturers’ future plans. At the same time, he acknowledged that the merchandisers needed to be educated about a new way of working.

If scale-up is to achieve the desired results, firms must also focus on improving productivity. Many, for example, plan to grow their way into productivity—adding customers and transactions without adding staff. Companies that cite head count reduction as the primary justification for the AI investment should ideally plan to realize that goal over time through attrition or from the elimination of outsourcing.

The Future Cognitive Company

Our survey and interviews suggest that managers experienced with cognitive technology are bullish on its prospects. Although the early successes are relatively modest, we anticipate that these technologies will eventually transform work. We believe that companies that are adopting AI in moderation now—and have aggressive implementation plans for the future—will find themselves as well positioned to reap benefits as those that embraced analytics early on.

Through the application of AI, information-intensive domains such as marketing, health care, financial services, education, and professional services could become simultaneously more valuable and less expensive to society. Business drudgery in every industry and function—overseeing routine transactions, repeatedly answering the same questions, and extracting data from endless documents—could become the province of machines, freeing up human workers to be more productive and creative. Cognitive technologies are also a catalyst for making other data-intensive technologies succeed, including autonomous vehicles, the Internet of Things, and mobile and multichannel consumer technologies.

The great fear about cognitive technologies is that they will put masses of people out of work. Of course, some job loss is likely as smart machines take over certain tasks traditionally done by humans. However, we believe that most workers have little to fear at this point. Cognitive systems perform tasks, not entire jobs. The human job losses we’ve seen were primarily due to attrition of workers who were not replaced or through automation of outsourced work. Most cognitive tasks currently being performed augment human activity, perform a narrow task within a much broader job, or do work that wasn’t done by humans in the first place, such as big-data analytics.

Most managers with whom we discuss the issue of job loss are committed to an augmentation strategy—that is, integrating human and machine work, rather than replacing humans entirely. In our survey, only 22% of executives indicated that they considered reducing head count as a primary benefit of AI.

We believe that every large company should be exploring cognitive technologies. There will be some bumps in the road, and there is no room for complacency on issues of workforce displacement and the ethics of smart machines. But with the right planning and development, cognitive technology could usher in a golden age of productivity, work satisfaction, and prosperity.

A version of this article appeared in the January–February 2018 issue (pp.108–116) of Harvard Business Review.

Thomas H. Davenport is the President’s Distinguished Professor in Management and Information Technology at Babson College, a research fellow at the MIT Initiative on the Digital Economy, and a senior adviser at Deloitte Analytics. Author of over a dozen management books, his latest is Only Humans Need Apply: Winners and Losers in the Age of Smart Machines


Rajeev Ronanki is a principal at Deloitte Consulting, where he leads the cognitive computing and health care innovation practices. Some of the companies mentioned in this article are Deloitte clients.

Source: Harvard Business Review, FROM THE JANUARY–FEBRUARY 2018 ISSUE. 

Why a Data and Analytics Strategy Today Gives Marketers an Advantage Tomorrow by Matt Lawson and Shuba Srinivasan

The content on this page was commissioned by our sponsor, Google Analytics 360 Suite. The MIT SMR editorial staff was not involved in the selection, writing, or editing of the content on this page.

A Google perspective by Matt Lawson, Director, Performance Ads Marketing, Google

Intelligent and mobile technologies have dramatically altered the customer journey, all but erasing the line between digital and offline consumer behavior. Whether tapping on their smartphone, speaking to a home device, or interacting with their car’s connectivity systems, consumers continuously engage with a wide array of digital systems when they want to learn something, do something, or buy something. It doesn’t matter if it’s a high-end purchase like a large-screen TV or a more mundane sundry like deodorant—it’s becoming second nature for consumers to steal moments throughout the day to answer any question that comes to mind, using an ever-increasing variety of devices.

The more personalized and relevant the results, the more meaningful these engagements become. Therein lies the new marketing opportunity for brands: building a data strategy to collect and analyze the trails of data consumers create, uncover insights, and take action to boost the value of these engagements.

The expanding array of channels and devices makes it more challenging—but also more important than ever—for marketers to gain a complete understanding of their audience and generate actionable insights. Brands that build a foundation of data and analytics and use advanced technology to deliver engaging experiences throughout the customer journey will have the advantage going forward.

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Optimizing the Data Opportunity

Marketers of all stripes are well aware of the vast opportunity. Indeed, nearly 90 percent of senior business-to-consumer (B2C) marketing executives surveyed for a June 2017 study conducted by Econsultancy in partnership with Google said that understanding user journeys across channels and devices is critical to marketing success.1 However, the gap is widening between “mainstream” marketers and those who have reoriented their marketing and advertising approaches to reflect the need for more refined, integrated data strategies, according to the study.

The study found that leaders (defined as those whose marketing results significantly exceeded their business goals in 2016) are 50 percent more likely to have a clear understanding of customer journeys across channels and devices. They are also twice as likely to routinely take action based on analytical insights. Rather than focusing on clicks and conversions, these organizations are working to create a more complete understanding of customers across devices and channels—and capturing the long-term value of those relationships.

Tying together the many aspects of the customer journey requires breaking down the organizational silos that have developed in marketing and advertising over the years. No longer can marketers operate as a collection of disparate teams focused on search media, TV buying, performance marketing, brand and store buying. These groups need to become integrated, collaborative teams that are aligned behind common goals. They must work together to create a holistic view of customer behavior and brand performance that can be shared by all. In fact, 86 percent of all respondents in the Econsultancy study said eliminating silos is critical to expanding the use of data and analytics in decision-making.

Integrating the Technology Stack

Leading marketers are also updating their technology systems to support increased organizational alignment and revenue growth, according to the study. In addition, they are 52 percent more likely than the mainstream to have fully integrated the marketing and advertising technology stack. 

Matt Lawson
Shuba Srinivasan

Left: Matt Lawson, Director, Performance Ads Marketing, Google; Right: Shuba Srinivasan, Adele and Norman Barron Professor of Management, Boston University Questrom School of Business

 

A unified marketing and advertising technology stack enables companies to not only identify valuable customer segments but also deliver customized experiences to them. Marketers with fully integrated solutions are 45 percent more likely to use audience-level data to personalize the customer experience, according to the study, and 60 percent more likely to optimize experiences in real time, using analytics. The Econsultancy research also shows they are able to accurately attribute business value to their marketing and advertising efforts to evaluate how channels work together and better allocate their investments.

Getting there takes time, commitment and continual refinement. For example, Google has “store visits” technology that can inform brands when a consumer clicks an ad and then enters a store. Retailers can extrapolate what this data means for their business performance by, for example, running “hold-out tests”: investing heavily in media in some markets while going dark in others, to gauge the incremental impact on results such as revenue growth or average order volumes. Based on that, they can arrive at a proxy business value for store visits and test it over time as assumptions change.

A Digital Foundation for the Future

The following key elements can help organizations foster a fully integrated, data-driven marketing function.

Executive buy-in. Unequivocal support from the top levels of the business are vital for a customer-centric, data-driven transformation to succeed.

Data-savvy marketers. The marketing function needs people with not only data skills but also an understanding of the potential to optimize real-time customer interactions based on data insights. Ideal marketing professionals are proficient in the application of analytics, naturally curious, and inclined toward ongoing optimization.

Cross-functional collaboration. Teams that work together will establish new metrics and build new benchmarks that will deliver the insight into how media impacts business goals and drives real-time decision-making.

A learning culture. Marketing transformation takes time and experimentation. Organizations need to commit to ongoing testing to deliver better experiences that drive business growth.

Technology investment. It’s crucial to consolidate data to not only visualize the customer as he or she moves across channels but also connect those insights back to enterprise data, analyze and segment it, and apply those insights to meaningful and profitable actions. Modern technology is increasingly either a growth accelerator or a business inhibitor.

For organizations that make the investment in a holistic data and analytics approach, the role of marketing in the business will expand. Rather than functioning solely as an acquisition vehicle, marketing will become an engine of growth, driving effective upselling, cross-selling and customer retention. When marketing leaders develop a data and analytics strategy to better understand customer journeys, invest in a unified technology platform, and collaborate on achieving shared business goals, they can offer tremendous value in the form of actionable insights that will benefit not only customers but also the business. 

Scholar Perspective

Messages and Metrics That Match the Customer Journey

A scholar perspective by Shuba Srinivasan, Adele and Norman Barron Professor of Management at Boston University Questrom School of Business

Increasing demands for accountability have created a sense of urgency for marketers to determine the most effective metrics for both driving growth and demonstrating marketing’s value. But mobility has complicated those efforts. We live in an always-on world. That’s an enormous challenge for marketing organizations, but one with a huge upside if they can turn data into insight.

On the one hand, multichannel access to searching and shopping results in conversion friction—a straightforward, sequential customer journey no longer exists. On the other hand, consumers can search and shop 24/7/365, generating enormous amounts of customer data that can empower marketers to better serve their customers. According to a study published in the Marketing Science journal, multichannel shoppers are more profitable than single-channel customers. This fortifies the case for marketers to master their messages and metrics for the omnichannel world. Smart brands are trying to figure out how to target the right customer with the right message at the right time.

An enormous untapped opportunity exists in mobile ad platforms. Mary Meeker’s most recent Internet Trends report notes that U.S. consumers spend 28 percent of their time on mobile devices, but that mobile accounts for just 21 percent of advertising dollars. That leaves a $16 billion opportunity sitting on the table.

More importantly, however, consumers are embracing mobile as a complement to, not a replacement for, other channels. We’ve found that a multichannel marketing approach compounds the financial impact of the marketing spend. When brands simultaneously invest across channels, they see a significant increase in results—one plus one equals much more than two. This points to the need for better coordination among the often independent entities that handle paid search, online display and TV/print/radio advertising.

More from Google

Built for the enterprise, the Google Analytics 360 Suite is a powerful marketing analytics solution. It helps you get a handle on all your marketing data and find insights you can use to improve customer experiences.

Metrics That Matter

The best way to engage with customers depends on where they are in their decision-making process. We recently studied the financial impact of online display ads and paid search, analyzing the results of more than 1,600 companies over a five-year period. Online display ads, which are typically shown to consumers in the awareness phase of the customer journey, are initiated by the brand and cast a wider net. Paid search, meanwhile, is typically delivered to consumers in the consideration and purchase phases, is initiated by the consumer and addresses a narrower audience. Based on our research, we’ve found that online display and paid search advertising each exhibit significantly positive effects on business performance and firm value. We also found that online display advertising has distinct long-term value, while the differential benefit of paid search accrues in the short term.

The marketing metrics that matter vary throughout the customer journey, too. In the early stages, important metrics might include the length of time a customer spends in the store or how often they visit the website. At the purchase stage, it would make more sense to measure revenue per user, conversion rates and acquisition costs. Post-purchase, metrics such as retention, lifetime customer value and loyalty serve as proxies for future financial impact.

Gearing Up for a New Approach

Determining how to best engage with customers and measure the effectiveness of those efforts is something each marketing organization must determine for itself through experimentation. This requires new skills, mindsets and processes, including:

Analytics expertise. Today’s marketing organizations require professionals with the quantitative expertise to analyze data and account for causality, along with the business and domain knowledge to put all the pieces together.

Willingness to learn. Figuring out the right approaches and metrics demands a willingness to experiment and conduct A/B testing to determine what works. A commitment must also come from the very top for data-driven marketing decision-making.

Customer empathy. Although quantitative skills are critical, so is understanding of the customer. Without a clear idea of a customer’s goals and motivations throughout the customer journey, all the numbers are meaningless.

Mobile and omnichannel customer behaviors are here to stay—and it’s vital for marketing organizations to adapt their own behaviors to maximize success.

About the Authors: Matt Lawson is the director of performance ads marketing at Google. Shuba Srinivasan is the Adele and Norman Barron Professor of Management at Boston University Questrom School of Business.

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References (1)

1. Econsultancy/Google. “Customer Experience Is Written in Data,” May 2017. We surveyed 677 marketing and measurement executives at companies with over $250 million in revenues, primarily in North America. Total respondents included 199 “leading marketers,” who reported that marketing significantly exceeded top business goals in 2016, and 478 “mainstream marketers.” https://goo.gl/uxd9mx