Why Cortana doesn’t work at work

Microsoft is betting that Cortana will bring AI to the workplace. Here’s why that won’t happen.

Cortana is an intelligent agent  that is supposed to act as a personal assistant. You can interact with her (notice I said “her”? More on that in a minute) via voice or text, on mobile devices or on desktop computers. Given that Microsoft’s mobile market share has fallen below 1%, it’s pretty much a certainty that most people would interact with Cortana in their offices.

We know that most Windows 10 computers are in workplaces, so there’s a very strong likelihood that people will talk to Cortana in an office. This is very different place than where people might interact with Siri on their phones, or Alexa in their homes. Siri and Alexa t are called upon in private, controlled places (in fact, just 3% of iOS users report using Siri in public).

Let’s walk through that interaction of Cortana as a member of a workplace.

Microsoft encourages you to command Cortana by saying, “Hey Cortana…” and then giving her a command. A typical office scenario might be, “I wonder if I should book a vacation for the first week of August. Hmm. I’ll ask Cortana.”

This is how Cortana is supposed to work:

User: Hey Cortana, should I book a vacation for the first week of August?

Cortana: Let me check your calendar. Looks like you have a meeting on Monday, August 1st. Should I move it for you?

User: Yes, that’d be great.

Cortana: Okay, I’ve moved that meeting to Monday August 8th. Would you like to see some vacation suggestions?

User: Yes, please!

This is exactly how it plays out on a demo video one Microsoft’s site.

But let’s face it: there are a lot of contextually dependent reasons why this is completely unrealistic. Leaving aside Cortana’s technical limitations for the moment (and there are many), let’s take a look at what a real office and real user might look like.

Most offices are either open concept without even the suggestion of walls. As many as 70% of us work in open concept offices. As anyone who’s worked in such an office can tell you, hearing a neighbor on the phone can be excruciatingly annoying or excruciatingly awkward, depending on your neighbor’s TMI quotient.

cortana

So there’s a good chance that everyone in the user’s office will hear this idealized scenario.  There are two clear disincentives against this happening. First, Cortana will make more “boundary work” for office workers. The mere act of trying to keep your private life private at work is turns out to be, well, work. Recent research  has found that keeping work and life private actually causes cognitive overload. If people use Cortana as intended, she is poised to make that much worse.

Second, Cortana demands office workers treat their workplaces as if they were kings and queens, instead of pawns and rooks. Voice interactions require workers to own their workspace, something that we know they do not do. Typical workers share their workspaces with others, and because we are apt social animals, we tend to comply with unwritten rules of workplace etiquette. Bosses’ calendars take precedence over workers’ calendars. Bosses talk more than workers. Men talk more than women. In other words, people with power talk out loud more than people with less power.

Which brings me to the fact that Cortana is a woman. Is it any coincidence that most intelligent agents today are anthropomorphized as women? One of the most striking changes in the twentieth century workplace was the almost total elimination of support staff, which were typically women. Only the most senior executives have assistants nowadays, and other mid-level white collar workers are on their own for scheduling  and administrative work.

cortana 02

Let’s not forget that Cortana is actually based on a supportive AI character in a video game. Cortana provides these workers with a sense that they can indeed recoup the times of Mad Men and have a compliant, supportive, and self-abnegating assistant who has no needs of her own. Practically, this promises white-collar workers with a huge productivity boost, but the symbolic nature of this is even more interesting. When white-collar workers have a virtual assistant, they have re-claimed a sense of hierarchy, of control, and power (even if it is completely imaginary).

And this is why Cortana will not work in the workplace. Today’s typical office worker does not have power enough to command the space around her, and bark orders to anyone out loud, even if just to an intelligent agent. This office worker has been stripped of her ability to occupy a rung on the ladder higher than admin or support staff, because there is no admin or support staff. This typical office worker is embedded in a physical space that reflects this lack of hierarchical position — she has no command over it.

Scholars of gender and technology have described some ill-advised approaches to gender equality as “add women and stir.” The same applies to Cortana and other intelligent agents. You cannot “add Cortana and stir” and expect to see productivity improvements that somehow negate the existing organizational and physical structures of contemporary workplaces.

 

Making hard choices

Over the holidays, I had a chance to reflect and consider my future. I found myself unsatisfied with my daily work. Somewhat serendipitously, I stumbled across a TED Talk from philosopher Ruth Chang. Chang helped me decide to leave Microsoft and take a job at Amazon. I start on Monday, January 25.

Dr. Chang’s research focuses on the nature of hard choices. She argues that hard choices are hard because we tend to try to resolve them by using quantitative techniques. For example, adding up all the pros and cons of each choice theoretically should indicate the superior choice. But it doesn’t, in practice, because human experience is not quantitative.

Instead, Chang tells us, we should see competing choices as “on par” or “in the same neighborhood” as each other. From there, how do you decide? Chang urges us, in all existentialist glory, to embrace a choice for what it represents. Choose a job that declares who you are, she says. Move to the country because you declare you are “for the country.” Make a decision that symbolizes the self you want to be. And embrace that hard choice!

So I have done exactly that. I am choosing to be urban (Amazon is a 40-minute walk from my home). I’m choosing to be a little bit chaotic. I’m choosing crazy unknowns (I have no idea what I’ll be working on). I’m choosing to jump with both feet.

I was not unhappy at Microsoft; I just wasn’t growing. So above all, I am declaring myself in favor of growth. I am choosing new.

DO you have a hard choice? Chang tells us to welcome it:

“Let us not resolve to work harder at being the selves we already are. Instead, let’s resolve to make ourselves into the selves we can commit to being.”

Qualitative research and innovation

Qualitative research is not generally considered “real” research, and this has terrible implications for innovation. Companies’ thirst for operational effectiveness begs for quantitative data. But quantitative data does not and cannot form strategy. Qual data are a key ingredient to strategy, or the development of new and differentiated products.

Many people are familiar with Michael Porter’s famous paper, “What is strategy?”  Porter famously argued that many companies mistake operational effectiveness for corporate strategy. Operational effectiveness, according to Porter, is about quality, productivity, and speed. It is about doing the same thing as others, but doing it better.

Strategy, by contrast, is about being different. It is about doing entirely different activities to deliver value to customers. Other authors have called this the “blue ocean” or finding a place in the market that is calm, unoccupied, and yours for the taking.  A “red ocean” is full of competitors, doing exactly the same thing as you are, and demanding ever higher performance.

porter

Framed this way, it is clear that operational effectiveness relies heavily on quantitative data. How efficient are we? How do we stack up against the competition? How good are our products? How fast do we make them?

Operational effectiveness simply begs for quantitative data, and now that we have access to petabytes of passively collected data relating to productivity, quality, and speed, it is easier than ever to be operationally effective.

Or it should be. We know that quantitative data requires a great deal of cleaning, massaging, and managing, not to mention analysis, to make it useful for operational effectiveness.

The shift to data-driven operations has demanded a great deal of companies’ attention, mostly because data collection and analysis is not as easy as most think it to be.

But let us not mistake this for strategy.

There is nothing inherent to benchmarking performance that lends itself to strategic advantage. Quantitative data does not reveal how or in what ways customers are making their own workarounds. Quantitative data shows us how many products meet a particular standard, how many products are produced or sold, or how fast a company makes them. It can tell you the average satisfaction a customer may have, but it cannot reveal any of the detail behind that satisfaction.

In their insightful Harvard Business Review article, “An Anthropologist Walks Into A Bar,” Christian Madsbjerg and Mikkel Rasmussen argue that qualitative research gives companies the ability to bridge the “complexity gap,” or what a study of 1500 CEOs revealed as their main challenge. Why do customers do what they do? You must do qualitative research to find out. And, by extension, you must do qualitative research to innovate.

mads

 

Qualitative research is explicitly about revealing detail. Qualitative research shows how people are using products, or how these products sit and gather dust in the corner of the kitchen. Qualitative research, particularly field-based research like ethnography, offers that path to delivering truly different products.

Companies that do ethnography regularly uncover entirely new or different ways to deliver value to customers. Oftentimes, this is done unsystematically. Skillful product and brand managers know that observing everyday life can reveal the how and the why of product failure.

Nike is a great example of a company that is in touch with culture. Its marketers are largely acknowledged to be among the best in the world. Its product innovation is continual, and its brand equity is unparalleled. Even their lab-based researchers like the fabulously named Gordon Valiant are active members of the running community.  His lab-based practice is complemented by regular participation in running events, where he comes into contact with other runners.

Valiant conducts in-lab studies systematically, but observes human behavior in an ad hoc way. Imagine the advantage to companies that do this research systematically. Imagine having a steady stream of insight into real people and why they do what they do. Imagine having thick description of painful workaround work, and regular replenishment of unmet customer needs.

That can only come from systematic, regular, and rigorous qualitative research.

Compare that to a company tirelessly benchmarks its quality, productivity, and speed. The company with qualitative insight into human behavior will have almost limitless potential to do things differently, to deliver new products or services, to find entirely unexamined oceans of product innovations.

Yet we spend almost nothing on qualitative research.  Esomar, the international market research association, estimates that in 2013, corporations spent $6.6B USD on qualitative research, worldwide. The vast majority of money spent on qualitative research is on focus groups, but Esomar estimates almost $1.6B is spent on the more interpretive methods of in-depth interviewing and ethnography. This amount is dwarfed by the $32.4B USD spent on quantitative market research.

It could be argued that many companies need to start with operational effectiveness. Fair enough. But no company can survive on quality, productivity, and speed alone. It is too competitive a marketplace. Qualitative research, therefore, is a cheap way to guide the company toward different product offerings, and ultimately, toward innovation.

 

Design researchers must think fast and slow

Research into brain science some surprising insights for guiding research practice. These findings suggest that the scientific method constrains our natural creativity.
Too often, researchers take their cue from the scientific method. While this method undoubtedly changed the world and our knowledge of it, it is antithetical to the creative needs of a well-rounded researcher. It is especially problematic for design research, which requires creative solutions to existing problems.

Design researchers should embrace less structure and more openness at the early stages of product design, and rigor and structure in the mature stages of product sales. As sales drop off and the product loses its natural match to the culture, design researchers should once again embrace openness in their research approaches.

research phases

Generally, we think of research as the focused, systematic collection of data, over time, in keeping with a given framework or theory. In this view, research is intended to confirm or deny given hypotheses, and incrementally improve our knowledge about a given topic.

We know from the book Thinking Fast and Slow, however, that this research approach only serves one type of thinking. Thinking Fast and Slow author Daniel Kahneman tells us that “Type 2” or “slow thinking” is a disciplined, focused, kind of thought that roughly matches the deductive reasoning of the scientific method and other traditional forms of research. It is structured and deliberate, requiring the cerebral cortex.

But Type 1 or “fast thinking” is less structured, more instinctual, and involves the more reptilian parts of the brain. At first glance, fast thinking appears to be undisciplined or even lazy – the antithesis of the scientific method. But fast thinking produces creative and intuitive leaps that are impossible with the iterative, deductive, and controlled manner of slow thinking.

Design research both thinking fast, and thinking slow. Thinking fast entails creating novel combinations, unusual interpretations, or unique syntheses. Thinking slow entails systematic evaluation and the structured contribution to a body of knowledge.

Gifted researchers engage in both thinking fast, and thinking slow. As sociologist C. Wright Mills describes, a researcher must have her “files,” which is a set of unstructured, messy, and without order:

 

mills

C. Wright Mills

…You will notice that no one project ever dominates [the files], or sets the master categories in which it is arranged. In fact, the use of the file encourages expansion of the categories which you use in your thinking. And the way in which these categories change, some being dropped and others being added – is an index of your intellectual progress and breadth. Eventually, the files will come to be arranged according to several large projects, having many sub-projects that change from year to year. [1, p. 3]

 

Anthropologist Branislaw Malinowski echoes this messy disorder when he describes what will eventually become his masterwork The Argonauts of the South Pacific:

 

malinowskiBranislaw Malinowski I estimate that my future publication will be voluminous, roughly three volumes of 500 pages each at 500 words per page. It will take me about two years to get the [manuscript] ready and see it through the press. My material is now a chaotic mass of notes. To work it out and put it into the right theoretical frame is perhaps the most difficult, exacting, and important stage of research. To work it out efficiently I must give it all my time. [2, p. 582]

Malinowski recognizes the “chaotic mass of notes” must be whipped into shape to become a manuscript, but he must first grapple with the disorder. This is precisely what psychotherapist Rollo May describes as the “creative encounter,” or the unstructured time an artist (or researcher) spends with her subject of study.

 

may

Rollo May

The first thing we notice in a creative act is that it is an encounter. Artists encounter the landscape they propose to paint – they look at it, observe it from this angle and that. They are, as we say, absorbed in itOr scientists confront their experiment, the laboratory task, in a similar situation of encounter.  [3, p. 39] P. 39

 

Consider also the “commonplace book,” or the kind of notebook great thinkers like John Locke and Charles Darwin used to organize their thoughts. As innovation author Stephen Johnson tells us, early modern readers did not read sequentially, but jumped around, setting the stage making creative connections.

johnson

Steven Johnson

The tradition of the commonplace book contains a central tension between order and chaos, between the desire for methodical arrangement, and the desire for surprising new links of association….Each re-reading of the commonplace book becomes a new kind of revelation. [4, pp. 109–110]

 

In other words, researchers who allow themselves to read out of order, or to collect without regard for structure, are able to make creative, intuitive leaps. But researchers who fail to methodically manage their knowledge fail to close the loop of production. Researchers need to think fast and to think slow. They need to think broadly and think narrowly. Type 1 and Type 2 thinking translates into 3 kinds of research: exploratory (thinking fast), evaluative (thinking fast and thinking slow), and experimental (thinking slow).

 

Frequently, social scientists in particular focus on “rigor” as the solution to good research. But rigor without creativity adds little to our collective knowledge.  As Heideggerian scholar Carol Steiner argues, this “fore-structure” – or predetermined way of looking at the world – stops us from conducting innovative research and producing innovative things. Instead, innovative researchers, she found, are open to “Being,” or the ability to have experiences, people, and objects reveal themselves to them.

steinerCarol J. Steiner The innovators I studied seemed sometimes to be attuned to that old understanding of the relationship between Being and people…Losing faith in the scientific method has allowed them to understand themselves as other than knowledge-makers. Consequently, they often project an openness that allows them a different world to shine through for them, the public world. [5, p. 594]

 

In other words, researchers in particular must struggle against the “fore-structure” or their extensive theoretical and methodological training which interferes with receptivity. As Rollo May argues, being receptive does not mean lacking in rigor.

Rollo May The receptivity of the artist [or researcher] must never be confused with passivity. Receptivity is the artist holding him or herself alive and open to hear what being may speak. Such receptivity requires a nimbleness, a fine-honed sensitivity in order to let one’s self be the vehicle of whatever vision may emerge. [3, p. 80]

 

Rigor must be introduced later in the process – after the researcher becomes open to a vision, after the researcher grapples with the complexities of the data and their incongruence. Rigor often comes after a period of unconscious processing of the data.  Taking walks, playing, napping, and engaging in unstructured activity have all been shown to allow synthetic ideas to emerge.

 

Researchers should therefore use the scientific method with caution. Be aware of when you need rigor, and when you need creativity.

 

 

 

 

References

[1]      C. W. Mills, The Sociological Imagination. New York: Oxford University Press, 1959.

[2]      M. W. Young, Malinowksi: Odyssey of an Anthropologist. New Haven, CT: Yale University Press, 2004.

[3]      R. May, The Courage to Create. New York: WW Norton, 1994.

[4]      S. Johnson, Where Good Ideas Come From: The Natural History of Innovation. New York, NY: Riverhead Books, 2010.

[5]      C. Steiner, “Constructive Science and Technology Studies: On the Path to Being?,” Soc. Stud. Sci., vol. 29, no. 4, pp. 583–616, 1999.