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.

 

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.

 

Microsoft’s Future Productivity Vision

At long last, I am able to share our work on the future of productivity. Video storytelling is a way of envisioning the future without the constraints of actually building all the prototypes. These conceptual prototypes cover a few important themes. Look for:

  • The end of the full-time job
  • Inter-generational care work, via distance
  • Online reputation management
  • Networked production of several enterprises and individuals
  • Just-in-time work spaces and places
  • Mobile productivity and seamless integration with traditional computing systems
  • Visualizing production through social network analysi

 

Productivity: it ain’t about being faster

If you tell people you build productivity technology, they often think of assembly lines, conveyor belts, and stopwatches. Productivity means building things faster, right? Wrong. That view is so 20th century. Today, the real problem workers have is finding collaborative spaces to share information. Right now, our productivity tools often make even more work. Instead, we should build tools that emulate face-to-face interactions instead of assembly lines.

 

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Doing things faster was indeed a problem for early industrial times. Production was slow, inconsistent, and riddled with errors. Engineer F. W. Taylor’s “scientific management” emerged as a solution to this problem. Taylor, a probable obsessive compulsive, devoted his life to finding “one best way” to do everything. He also gave managers everywhere an excuse to control workers more tightly.

But today, we already have high-quality production. Systems like six sigma and lean production have standardized and stripped down production processes to the leanest, and most consistent elements. Innovation isn’t about “being faster,” but helping workers collaborate and share.

Why does collaboration matter more than ever?

Today’s products are complex. Rarely can only a single discipline design, build, and market a product. You need designers, engineers, and marketers to be truly successful. But this means they have to coordinate schedules, share information, and share their expertise. They need tools to store information, to build trust, to smooth cultural divides, and to protect heads-down time.

The Collaboration Penalty

Collaboration means working together, but also it ironically makes more work.

  1. Managing workflow: Who will do what, and when? This is especially difficult in heterogeneous, disparate, or physically distributed teams. Typical tasks include scheduling and task allocation.
  1. Creating shared information spaces: Creating, sharing, distributing, maintaining, and finding shared artifacts. Typical tasks include sharing via email or dropbox.
  1. Moving work products between collaborative spaces and individual spaces: Removing artifacts from shared spaces to complete an individual task, and replacing them into shared spaces. Typical tasks include checking in or out documents or code.

These three large buckets can overlap. For example, allocating tasks in a co-located team may mean simply writing down assignments on a white board. But in teams distributed by time or space will need to create a shared, digital artifact that summarizes these task allocations.

The irony is that as teams collaborate more, they create ever more shared digital artifacts, which increases the need for shared information spaces, and increases the cognitive load of evaluating whether a work product is ready to share.

collab

 

Strategies for the Collaboration Penalty

Our typical approaches for dealing with the collab penalty are no longer working. We have tried structured ontologies, or taxonomies. But it’s always so much faster to just talk with a person directly. That doesn’t scale. Informal ways are more powerful, and make less work.

Formal Ways to Pay Collaboration Penalty Informal Ways to Pay Collaboration Penalty
Standardization of procedures or inputsFormalized roles or responsibilitiesConceptual schema, such as taxonomies, ontologies, or other standard concepts “Bodywork” or physical proximityInformal communicationHigh-fidelity shared objects, like posters, prototypes, and whiteboardsIM, Skype, or real-time tech communication

Instead we should use things like handwritten notes — in digital form — to give more fidelity to our messages.

Human practices are far swifter and culturally adept than most technologies. This is the primary reason why work teams choose face-to-face strategies, even if they seem duplicative; informal strategies are higher in fidelity than structures or standardization.