The Algorithmic Leader
How to Be Smart When Machines Are Smarter Than You
Page Two, 2019
Algorithms give leaders the freedom to “reimagine” their enterprises.
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Futurist consultant Mike Walsh warns business leaders that the algorithmic age is coming, ready or not. An “algorithmic leader” lets machines do what they do best: find connections in the data to provide insight into customer needs. Leaders then focus on connecting platforms, people and partners. Machines will augment the work humans already do, and leaders will be designers of exceptional experiences to their customers. Algorithmic leaders connect work to their people’s identity and purpose.
- Algorithmic leaders adapt their skills to the complexities of the machine age.
- The artificial intelligence (AI) future will focus on how human behavior and identity interact with algorithms.
- Increase your impact exponentially, not incrementally.
- Artificial intelligence is “augmented intelligence.”
- New technology “elevates” old jobs instead of “decimating” them.
- Algorithms create new ethical challenges for leaders.
- Humans make AI for humans. Resist standardizing human behavior with algorithms.
- The “algorithmic inequality trap” is a potential workplace disruptor.
Algorithmic leaders adapt their skills to the complexities of the machine age.
Algorithms are taking over the world, building the “bridge between computation and real-world challenges.” Information about the world today finds its expression in data that reside in almost all human interactions. Leaders who came of age in the analog era may worry whether their skills remain relevant. Ecosystems are rapidly displacing familiar hierarchies. Effective algorithmic leaders understand what generates true value. The best data, for example, are not deciding factors. What matters is the capacity to “connect people, partners and platforms.”
“Being smart when machines are smarter than you requires you to become something new.”
Two French philosophers, Gilles Deleuze and Félix Guattari, argued for discarding the tree as an analogy for the growth and dispersal of knowledge. They favored the “rhizome,” a tangled underground mass that forms a vast web, with nodes that send shoots to the surface. It has multiple “entry points,” and stores nutrients in its nodes to efficiently distribute energy. To be a “leader in the rhizome,” reject outmoded structures like the tree. Algorithms facilitate the rhizome model for business because their broad applications distribute themselves through the web. Algorithmic leaders let their teams self-organize, and don’t worry about being right all the time. They understand that algorithms are only as good as the data that feed into them. Algorithms enable leaders and employees opportunities to apply their “human” skills more effectively.
The artificial intelligence (AI) future will focus on how human behavior and identity interact with algorithms.
The Greek mathematician Euclid was among the first to calculate algorithms, which are essentially step-by-step instructions in which the output of one step provides the input for the next. Computers have used algorithms for decades, but in the last 10 years, deep learning systems exponentially increased algorithmic speed and power. Over time, algorithms can write their own code, adapt, learn from their mistakes and improve. While they may be “smarter” than humans when seeking patterns, making connections and sometimes making decisions, they cannot interpret results in a meaningful way. Humans will apply insights to “create experiences, transform organizations and reinvent the world.”
“The journey to becoming an algorithmic leader is fundamentally one of personal accountability.”
Consider how machines interact with humans, and the impact they have on human behavior and identity. Project yourself into the future, and work backward. Look at today’s children to see what the future holds. Children born after 2007 have known smartphones all their lives. In the algorithmic age, people’s lives will enmesh in networks. The algorithmic leader must consider users’ intentions on the basis of their behavior, their interactions with platforms and services, and their identity (or, how the experience affects them emotionally). These three elements are cyclical, and reinforce each other. Users should stop noticing truly successful algorithms as leaders apply algorithms to optimize user experiences.
Increase your impact exponentially, not incrementally.
Twenty-first-century businesses that rely on data and algorithms operate in a “winner-take-all” universe. Algorithms work best at scale. Amazon, for example, understood that by offering a wider selection of products at low prices, they could attract more customers, and the more customers they had, the greater the range of selections they could offer.
“Too often, digital transformation is just digital incrementalism.”
Data comprise your most important asset. It can take years to build up proprietary data sets. Centralize and organize your data. Consider your data’s availability, as well as how you acquire and label your data and exercise their governance. With data as your platform, you can launch “disruptive ideas.” For example, the start-up Haven Life used extensive data from its parent company, MassMutual, to better assess new applicants’ risk profiles. It required no time-consuming human effort, only an algorithm correlating between past and future customers. Companies exist to “reduce or eliminate transaction costs,” so will companies still need employees? Blockchain exemplifies how large distributive databases can move beyond simply buying and trading to negotiate “smart contracts” that effectively cut out the human intermediary.
Artificial intelligence is “augmented intelligence.”
Algorithmic leaders must think computationally. They must take a problem and break it down into constituent parts, creating a structured and iterative methodology. This separates strategy from execution and brings together people from disparate backgrounds – such as a Google engineer and an astronomer, who together built a neural network to locate and catalog exoplanets. No human could possibly examine all that data, let alone make connections as machine learning can. Such collaborations will become more the norm as companies seek more diverse perspectives.
“Automation is not only an opportunity to elevate your teams; it is also an invitation to profoundly reimagine what you do.”
AI is augmented intelligence because deep learning systems take over the more arduous work, leaving people time to do “human” activities such as brainstorming, relationship building and developing a vision. But the machines require humans to correctly frame the problem the machine is tasked to solve. Leaders should avoid “algorithm aversion” and trust them to perform as well as, or likely much better than, they would without human intervention. In space exploration, for instance, the new “space barons” Elon Musk and Jeff Bezos want to automate everything, while NASA prefers to allow its astronauts to override capabilities. Cost may prove the deciding factor. Automation is cheaper. As algorithms increasingly infiltrate daily activities, people will be less averse.
New technology “elevates” old jobs instead of “decimating” them.
Pundits and the public worry that more automation means fewer jobs. Algorithmic leaders can’t afford to resist automation, because their competitors will put them out of business. However, history indicates that when new technology appears, it elevates human skills, rather than decimates them. In the Industrial Revolution, for example, many feared the power loom would put people out of work. Instead, increased output made cloth cheaper. More people could buy it, thus driving increased production. More recently, bank tellers feared becoming obsolete when ATMs appeared, but now more bank tellers are at work, because they expanded their skill sets to deliver better customer experiences. They found a “new job inside the old job.”
“People who argue that robots will take away all our jobs assume that there is a simple relationship between automation and employment.”
With algorithms taking on more routine, repetitive jobs, people need to work on their “human” skills. Lifelong education will become more the norm, as people train for new jobs to replace those that algorithms automated. This will be a challenge because AI’s impact is still evolving, and quickly. Leaders have an opportunity to rethink their teams and how they coordinate themselves, and can “profoundly reimagine” what their company actually does. For instance, Nike wanted to automate custom shoe production, and hired Flex, an engineering firm, to invent a process that could laser-cut anything in any material. Now, Nike makes custom shoes on the factory floor from a digital file.
Algorithms create new ethical challenges for leaders.
Facebook’s ethical integrity met a difficult challenge when Cambridge Analytica, a UK statistics firm, built profiles of 87 million users for political purposes. Facebook made it easy by allowing third-party access to user information, a treasure trove for the Republican Party to target its messaging in the 2016 election. People didn’t have to express their political views: Algorithms collated browsing activity with voting preferences. The Obama campaign leveraged data it collected from visitors to its sites only after participants gave permission. Users understand they sacrifice privacy in exchange for access to applications. However, boundaries are necessary. Facebook may not have broken any laws, but it did not act in the “best interests” of its users.
“At Facebook, the product is the consumer. Facebook sells consumers to advertisers.”
In addition to vigilance regarding privacy, leaders must monitor bias in their systems. People create algorithms, and imbue them with their biases, consciously or unconsciously. More alarming, a bias can do real harm when replicated across vast networks. Leaders must know the “ingredients” that comprise their data sets. Because algorithms tend to generalize by looking for patterns, they overlook outliers. Designers must “think deeply” about people, engineers must know ethics, and leaders must anticipate worst outcomes. Otherwise, machine learning systems could unfairly target Black men as potential criminals, for example. Machine learning works because it “optimizes” data, such as GPS for navigating traffic. However, poor design creates flawed optimization. Leaders must avoid abstraction, and remain focused on people. When automating a process, you must know how the “pieces fit together.”
Humans make AI for humans. Resist standardizing human behavior with algorithms.
Humans are complex. For decades, standardization in manufacturing gave people few choices, to save on costs. Now, with bespoke solutions, products and services, companies use algorithms to anticipate individual preferences. They must design products with “an actual human in mind.” In their retail space, for example, Apple embraced a “town square,” in which shoppers could “hang out” while waiting for service. Nordstrom may forego selling merchandise, and use their outlets for human-to-human advice from stylists and tailors. Online, “trust and safety teams” at YouTube manually reviewed millions of videos in 2017 to flag inappropriate content, thus training its algorithm to catch violators. Netflix has “taggers” who watch content and assign abstract concepts to help viewers make choices.
“Whether it be health, finance, security or transportation, for an algorithmic society to really work, it requires scale.”
Algorithms can make accessible services that usually prove too expensive for average people. Pefin, for example, an AI financial adviser, custom-tailors financial plans. People conceive the problem they want to solve, and algorithms assist. Selling itself won’t disappear because people sell themselves. Algorithms will give leaders time to do what they do best, “brokering relationships.”
The “algorithmic inequality trap” is a potential workplace disruptor.
When atomized, projects become just a “virtual assembly line.” People working on them may not understand or see the bigger picture, which helps them feel their work is relevant. They lose accountability and sense of ownership. They need to see results. When leaders consider digital transformation, they must keep purpose in mind.
The algorithmic inequality trap may divide people between those who design algorithms and those who work for the algorithm. Many companies like Uber, TaskRabbit and Instacart, for example, employ transient workers to do menial work, and use outdated regulations as loopholes to avoid taxes and paying benefits. Worse, some companies use algorithms as compliance enforcement, monitoring workers’ every move, and calculating costs down to the nanosecond, to maximize efficiency. This can lead to employees feeling like robots.
Using algorithms for time management might optimize the bottom line, but can ruin employee relations. For example, Forever 21 used Kronos, a workforce optimization platform, to relegate hundreds of full-time workers to part-time status, thus eliminating their benefits. Leaders should ask: How would they feel if algorithms restructured their livelihoods without their input?
Algorithms are tools. They signal a turning point in how people do business, and give leaders the opportunity to reimagine themselves and what they do. Algorithms reinforce the need for leaders to be personally accountable, and to guide their workforce through this transition.
About the Author
A pioneer in digital space since the 1990s, Mike Walsh founded and ran Jupiter Research – one of the first research agencies to track the early adoption of e-commerce and digital business models by online consumers – in the Asia Pacific.
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