“An Uber for Excel”: Reflections on AI, Augmentation, and Platforms

Steven Adler
Sep 1, 2018 · 11 min read

Even two years ago this ride may have posed a challenge — ride-share coverage in Westchester, summer 2016, was spotty at best — but on this night there was little difficulty: The driver arrived at my relative’s home outside New York City, pulled up directions via Waze, and we were off to New Jersey.

As we drove and made light conversation — accompanied by my listening to Presidential-candidate (and fellow Brown alum) Andrew Yang’s The War on Normal People on Audible— I had opportunity to reflect on the “new economy” in which we find ourselves.

Well worth a read (or listen) — and not just because Andrew went to Brown.

Though the driver had never been down to this part of New Jersey, nor had I, we continued our ride with turn-by-turn precision until the hotel came into sight, aided by a host of technologies that go beyond the skills formerly required of a driver. In this manner, the ride exemplified many of the boons — but also the challenges — of a forthcoming labor displacement; it also helped to stimulate my thoughts about transformations we may soon see in other forms of work.

Technology as a new skill paradigm

A common broad-strokes refrain about AI’s impact on the job market runs as follows:

  • Many human jobs will be displaced as AI becomes increasingly capable of automating the tasks a particular job requires.
  • Historically, these automated jobs, which now require fewer people, have been replaced or supplanted by new categories of jobs, including jobs difficult to envision at the outset (e.g., social media managers, search engine optimizers).
  • Though this is the historical pattern, there is not guarantee that this job-replacement must continue forever, and there is disagreement about what percentage of jobs will become automated and at what pace.

While I agree with most tenets of the above, this summary leaves unstated what I believe to be a major shift in the economy: AI and similar technologies are increasingly automating the tasks of a job, yes, but they are also transforming the skills required of many jobs, beyond merely mimicking the human tasks.

I’ll confess to having Googled “weird jobs that exist today” — and the search results didn’t disappoint.

Consider the example of a driver: In the past, the core skills of a taxi driver may have been safe, attentive driving; knowledge of a city to efficiently pick up and route passengers; and work-ethic to grind out a full week’s work, even if activity seemed slow or conditions were suboptimal. Certainly these skills remain important today, but there is also more emphasis on literacy with technology, data, and apps/interfaces as an enabling skill:

  • Surge-maps provided by ride-share companies and 3rd-parties help drivers to determine the peak hours and locations in which to drive
  • Apps such as Waze help drivers navigate efficiently in real-time, even in locales with which they are entirely unfamiliar
  • Detailed electronic records allow more-granular means of analyzing one’s financials and spotting trends in income, types of passengers, etc.
Waze offers many features to its drivers — not all of which productivity-enhancing.

A driver who ‘works for’ Uber and is quite good at actually driving — but who struggles to utilize these tools — will likely not earn their maximum income; the core facets of driving are no longer enough to succeed.

Certainly part of what we’re seeing here is technology and comfort with it being valued in the driving market (e.g., enough know-how to install Waze, configure it to one’s liking, etc.), but what we’re also seeing is a democratization in expertise, driven by technological augmentation.

Whereas before a driver could build an advantage by by really learning an area’s routes, patterns, and idiosyncrasies, this data is now readily available without the upfront investment. Drivers and their expertise have become more interchangeable, fed by the platforms that call upon on-demand workers and augment them with the data of thousands of other drivers’ experiences. (This point about augmentation is perhaps best made in Tim O’Reilly’s WTF? What’s the Future and Why It’s Up to Us, with the example of Uber and Lyft drivers being able to see ‘hailing’ passengers, even blocks away and in awful weather conditions. How can a normal worker compete?)

What does this look like for traditional office work?

Building upon the lessons of drivers, augmentation, and Uber, we can extend the example to consider how similar types of technology may impact office work. In particular, we ought to recognize that the labor impacts of AI extend beyond automation, in which software can wholly replicate a human’s job or a significant portion of it; we should also be attuned to how AI may reduce the complexity or barriers-to-entry of human jobs in the interim, largely through technological augmentation (prior to wholesale automation).

For the purpose of this example, I’m going to discuss where my mind goes when I think of in-demand, relatively high-skilled office work: Microsoft Excel. As a former McKinsey consultant, I’m a bit too acquainted with Excel and its utility across many different jobs — business analytics, financial analysis, performance reporting, general data organization, and so on. For professionals across all sorts of jobs, Excel is routinely one of the top tools of the trade — so what would it mean for Excel skills to be “Uber”-ified?

Ah, what a pretty spreadsheet — with color-coding of some kind, too. Though I’m not envious of the user trying to operate Excel on a Mac …

Let’s start by addressing the traditional “Uber for X” conception: One of my realizations in reflecting on the new economy is that, generally, when people discuss “an Uber for X”, this description leaves a bit to be desired. Often this is in reference to a “push button for service” model for an industry, particularly one that cuts out middle-men and in which regular people monetize their own assets through micro-transactions — but I think this overlooks significant aspects of what would make for successful “Uber”-ification. We can step through some aspects of these transformations and extend them to consider the impact on office work.

Reliable on-demand labor is certainly one aspect of successful “Uber”-ification, but for this to succeed at-scale the work products must either be sufficiently commoditized or the labor sufficiently skilled and curated to overcome concerns of platform users. While platforms for office workers, such as Upwork and Catalent, allow for easier sourcing of freelancers than previously existed, today they are still plagued by quality risks and transaction friction. How might office work evolve to meet this market demand, in advance of high levels of automation?

  • Many types of office work today are not commoditized, and therefore platforms care deeply about matching the right talent to the right problems. For instance, many sites are implementing AI-powered features, such as highlighting promising candidates for a gig based on a combination of prior reviews, listed skills, and other criteria.
  • While this can help certain workers to receive new opportunities, it also erodes stability for workers who had previously built up a niche: Similar to an Uber driven being ‘hailed’ by a passenger out of vision’s range (who perhaps did not know the hot pickup spots), these discovery features may reduce barriers-to-entry of freelance office work, at the cost of existing frontrunners. Workers may no longer need pretty websites or successful SEO to attract attention; they can focus on their core skills, albeit with less of a competitive moat once they do get a foothold going. In this way, a good Excel freelancer may then double down on their Excel skills because their auxiliary skills are now less important.
  • To further extend the analogy to core software, imagine a world in which the operating tools, such as Excel, became capable of assessing a particular user’s skills. If Excel could detect and certify skill at particular analyses and work patterns, it could route workers to jobs suited for their skills, such as if Uber automatically matched chattier drivers with passengers who prefer small-talk. In the ideal version for productivity, this enhanced Excel may even be able to guide and instruct workers on analyses they otherwise could not take on. In the same way that Uber’s rating system and GPS navigation help a passenger know what they are getting, and help drivers perform at more consistent levels, these changes to office work would certainly reduce the need (and premium) for a worker’s expertise while stopping short of full automation. Workers would perform at a higher, more-reliable level than when not connected to the technological network.
Upwork does a good job of measuring the skills needed and deployed across its platform — but even more powerful would be skill assessments built into the underlying software.

Democratized skill and efficiency are other major (and related) factors that influence both the amount of people who can perform a given job, and also each individual’s productivity at that job. An increase in either of these factors drives down the demand for any individual worker, and in turn should depress the average wage for a job. (Economics digression: As work becomes sufficiently cheap, one might expect to see an offsetting increase in the total volume of work demanded from firms, as labor for their projects has become cheaper; the net impact on total labor spending is ambiguous depending on what economists call price elasticity of demand for labor at certain wage-levels … But like I said, I digress.)

  • In the context of Excel and office work, democratizing skill and efficiency may entail making use of ‘simpler’ software defaults, shortcuts, and interfaces, some of which powered by AI. For instance, perhaps software users could describe analyses in natural language and have the software produce more-complex outputs like Pivot Tables, which today only a subset of users can create. Similarly, Excel could come pre-loaded with adaptive templates that auto-fit column widths and use other presentation-friendly tactics for which McKinsey analysts currently memorize keyboard shortcuts (ALT+h+o+i is a real crowd-pleaser).
  • One might also imagine democratization through easier access to add-on software or external research, such as CapIQ for pulling in financials, ideally with simple walk-throughs to guide even an Excel rookie. At McKinsey, we at times augmented our core client service teams with a Research & Insights team, who were experts in market research reports and proprietary data sources. We invested quite heavily in these teams, but if access to these systems becomes less-specialized or built native into software, soon workers without the McKinsey infrastructure will be able to participate in large efficiency gains.
There, you’re basically an Excel wizard now.

Finally, integration of work across systems is another large driver of efficiency, though often may not be classified as automation depending on the nature of these integrations. For instance, Uber’s incorporation of GPS directions into their app and their automatic reporting of finances from a particular ride certainly aid a driver’s productivity, but it isn’t clear to me that this is what anyone means by automation streamlining work. Regardless, this subarea is fairly interesting for possible productivity gains in office work, in part because of how much time workers spend working across disparate systems (email, Excel, PPT, ERP systems, etc.) and doing work that largely serves to transfer knowledge from one location to another.

  • One ‘low-hanging’ but surprisingly difficult problem to crack is to easily port materials between Excel and PowerPoint — the most common presentation mode for the analyses one creates within Excel. (At many consulting firms, the business purchases for third-party software, such as ThinkCell, to help resolve the tediousness of moving data from one system to another.) If this were no longer such a bottleneck, how much more time could each office worker devote to the true knowledge work of their job? Yet we likely do not need full-scale automation (at least I hope not) to achieve this goal.
  • Similarly, in a future with enhanced productivity tools, a worker might be able to cross-off an item on Asana or Trello and be instantaneously provided a draft status update, as well as make changes in any linked documents in one fell swoop. Of course, AI/automation is not yet at that level (one can dream), and human judgment continues to be important in deciding how to present information and in what contexts. But in the not-too-far future, we may have technology that reduces the communication load of knowledge work and allows each worker to operate more productively — as well as requires new fluency with technologies and skills unneeded in certain jobs today.

Wrapping up — what this means for workers today

I’ve written a lot of words above, but what does it actually mean? I believe there are two important insights any knowledge worker ought to carry in mind:

  • AI automation of tasks is part of the coming disruption to jobs, but it is only one piece of the equation. A young worker choosing among prospective careers should be attuned not only to how automatable their job seems today — I’ll plug the McKinsey Global Institute’s excellent research on this front — but also to how technology might change particular careers and the skills required in advance of full-scale automation.
  • Given the uncertainty about future careers and skill-sets, the most important traits to cultivate are an ability to learn and a desire to be curious and self-driven in charting one’s path. It is very difficult, if possible at all, to sit here in 2018 and know exactly how any career path will play out — and that’s where adaptability and agility come in. Regrettably, many of these ‘traits’ and opportunities will come cloaked in privilege, as it is much easier to be agile and throw yourself into a new field if you can sustain a new months without income to retrain — a luxury many do not have. But for those who can find time to invest in these traits, I would expect pretty good outcomes (or at least more enjoyment of learning overall).
An example chart from MGI’s automation research; the full report is well worth a read.

In addition to these insights for workers, I believe there continues to be much to ponder for society about how it ought to engage with platforms and other sources of labor augmentation. Certainly we have seen platforms offer enhanced (or new forms of) productivity, and alongside them often consumer surplus — such as my relief at being able to take the Uber ride mentioned at the top article. But alongside these gains we have also seen the erosion of many workers’ economic sources of distinction, including in areas in which workers may have striven for several years or dedicated significant capital to building out these abilities.

I am hopeful society will consider how to balance these interests against one another, rather than take a wholesale view of automation/productivity as purely a gift or a curse. Ultimately the choice is with all of us, both as consumers and constituents, to support the services we wish to see in the world. This responsibility means that the onus is on to keep an eye on the nuances of the labor and technology discussion and to check media narratives, whether overly frothy or bleakly pessimistic, when they fail to capture a full picture of reality.

Steven Adler is a former strategy consultant focused across AI, technology, and ethics.

If you want to follow along with Steven’s projects and writings, make sure to follow this Medium account. Learn more on LinkedIn.

Steven Adler

Written by

I work at the intersection of AI, ethics, and business strategy; thoughts are my own. www.linkedin.com/in/sjgadler

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