One way to trace human history is to follow the evolution of work.
First came the artisan, who labored over one pair of shoes at a time, basically an ad hoc process. Next came the Industrial Revolution, with its standardized parts and repeatable processes, vastly improving productivity but at the expense of variety. More recently, the norm has been adaptable processes, in which the same people and equipment can be adapted to provide more variety. But the adaptations often come slowly and are fraught with both process design and execution risk.
Now there is a new game in town: intelligent processes, which have been made possible by the explosion of digital technologies, and which are set to reinvent much of the way that businesses are run—in as soon as the next five years.
Intelligent processes create a virtuous cycle of constant improvement fed by continuous feedback. An intelligent process is studded with sensors that monitor every move and feed those observations into sophisticated models that allow people and software to make real-time adjustments and decisions. Digital technologies make it possible to identify opportunities for adaptation, analyze the trade-offs and then adapt faster and more efficiently.
By introducing the ability to continuously sense internal operations and external market conditions and to analyze variations quickly, digital capabilities allow intelligent processes to identify opportunities for improvement. And once an opportunity for improvement is found, other digital technologies, such as intelligent tools, advanced collaboration technologies and adaptive robotics, execute changes (even relatively complex ones) quickly.
Intelligent processes make it possible to take advantage of fluctuations in the price of raw materials or spikes in the demand for specific products or services—and then respond in real time or, at a minimum, at a fraction of what it took even adaptable processes to do. By combining the ability to detect and analyze quickly with the ability to respond just as fast, intelligent processes are able to adapt and self-evolve.
Significantly, intelligent processes help companies break free from traditional approaches to the organization of work. In fact, over the next five years, Accenture anticipates that managers will have the opportunity to greatly expand the use of three critical work-design options.
First, they will be able to incorporate experiment-driven rapid iteration, in which people and technologies interact in new ways to accelerate the evolution of products. Second, they will be able to take advantage of recombination—the ability to shift work between boundaries, especially between humans and machines—to make work processes even more flexible. And finally, they will be able to pursue edge-centricity by pushing decision making away from corporate headquarters to the far corners of the enterprise—hastening the circulation of knowledge to the edges of the enterprise, around the perimeter and, ultimately, from the edge back to the core.
Fast and flexible. No, that’s not the title of a popular Hollywood action movie franchise. Instead, it’s at the core of an increasingly popular work-design option. What we call rapid iteration is a fail-fast, experiment-driven approach that requires managers to rethink many tasks that, in the past, have followed a more predictable pattern.
Large retailers have been among the leaders in this practice, often testing prices and adjusting them rapidly to take account of changing market conditions. Today, some are using their vast amounts of stored data to make and fine-tune offers in real time, catering to “the nonstop customer.”
In product development, auto companies and aircraft manufacturers have enthusiastically embraced the use of “nondestructive testing”—for example, computer-based simulations of crashes and other stress conditions— to replace the extraordinarily expensive and labor-intensive practice of building physical prototypes and destroying them to get data.
But iterative automobile design has also evolved rapidly in recent years. The focus is no longer solely on using simulated prototypes to predict a vehicle’s durability. Increasingly, carmakers are competing on their engineers’ ability to customize software components that are not only functional but also mirror rapidly changing consumer tastes driven by their experience with smartphones and tablet computers.
To this point, crowdsourcing initiatives are quickly changing the tempo and flow of the work done by design engineers. Take, for example, Audi’s Virtual Lab. This online network, which automatically evaluated R&D prototypes based on crowdsourced responses from customers, was used to co-create Audi’s software-based infotainment system. Customers were able to design their ideal in-car multimedia system based on how much money they were willing to spend, creating a simulated purchasing decision that mimicked what happens at a car dealership.
This is where rapid iteration came into play: Audi’s automated system intelligently adapted to consumer responses, using rapid data analysis (machine learning) to continually refine the questions it asked customers based on their demographic profiles, on their real-time responses and on existing virtual prototypes (developed by Audi’s R&D team). The system then employed a “closest match analysis” to the prototypes already developed by Audi engineers. Ultimately, the system helped the engineers identify and distinguish between “must-have” and “nice-to-have” features based on customer demand, which then improved the next round of simulated prototyping.
The carmaker’s iterative engagement with customers paid off: Audi recently won awards for its infotainment systems, including being named Connected Car of the Year in 2012 and 2013.
Audi’s product development teams continue to explore new ways to involve customers in early-stage product development, including refining hardware prototypes. At a Milan furniture show in 2012, the company used thermal-imaging technology to collect data from nearly 1,500 people who tested out its R18 Ultra Chair. The data and customer feedback were fed into a proprietary algorithm and used to guide further iterations of the Ultra Chair. The net effect on design engineering is more data from a wider array of sources knitted together in faster and richer design cycles.
Carmakers aren’t the only ones that see the value of rapid iteration. Pharmaceutical companies have turned to “combinatorial chemistry”—an iterative process of drug discovery that quickly synthesizes compounds and then tests and refines the drugs based on customer data. Pfizer, for instance, is investing heavily in combinatorial chemistry as part of its effort to develop medicines for people with neurological disorders, such as Alzheimer’s and epilepsy.
Retail banks, public-sector service agencies and educational institutions are all experimenting with rapid iteration in an effort to better align themselves with customer needs, drive costs out of their development processes and gather valuable data that can accelerate their own evolution.
How do you feel about managing robots? The prospect isn’t as far-fetched as it sounds and could be coming soon to a job near you, thanks to process recombination—reallocating work processes between people and intelligent tools or even robots. In other words, “teaming with machines” may become the norm.
Robotic devices have been transforming physical work since the 1960s. But only in recent years have robots moved beyond simple replication of human activity (such as welding or steadily placing microscopic parts) to include intensive interaction with—and learning from—human beings. The next five years promise dramatic changes in how work is designed to capture even more of their potential.
Advances in machine vision and software controls for robots are behind two approaches to work design that are growing in popularity and impact. The first is relatively familiar: Humans use remote control robots to “project” themselves into toxic or dangerous situations—bomb disposal, for example. Increasingly, another sort of guided robot is showing up in medical and even educational applications. With telerobotics, doctors can “visit” patients by maneuvering a robot equipped with a camera and video screen through hospital corridors. Homebound sick children can still attend classes through similar devices.
The intelligent warehouse
The second approach is more novel: Small, flexible robots interact with human workers by sensing and adapting to their shared environment in real time. This requires a more intensive exchange of information between people and machines. Sound too sci-fi for you? It’s simpler than that. Consider, for example, what’s behind your ability to receive mixed packages of books, clothes and vitamins from Amazon.com so cheaply and rapidly.
In 2012, Amazon, looking to overhaul its vast distribution facilities, acquired robot maker Kiva Systems for $775 million. For some observers, the high price and Kiva’s low profile made this a head-scratching decision. But Kiva’s robots have allowed Amazon to reshape a work process that had been arduous for humans alone: unpacking goods, storing them, “picking” them to ship for individual orders and then bundling them into boxes for actual shipping. Taking care of these tasks, a warehouse worker can walk between seven and 15 miles per shift.
Here’s where the robots come in: A central computer system controls an army of robots that do all the heavy lifting of retrieving and bringing the stock to a workstation, where a “pick worker” (a person, not a robot) puts the correct item into a box. The design increases the speed and accuracy of order fulfillment, and frees up some workers to focus on process improvement for customers.
Warehouse operations benefit from this intelligent process, as software commands are continuously improved. Kiva’s robots are designed to interact autonomously in the warehouse environment, traveling the most efficient path and even knowing when to recharge themselves. The contributions of mobile, flexible robots, at Amazon and elsewhere, are only likely to increase over time.
Swiss robotics maker ABB, in response to customer input, developed the ABB Dual Arm robot (formerly known as FRIDA, for “Friendly Robot for Industrial Dual-Arm”). A team from MIT created an algorithm for this concept robot, which would enable it to learn an individual’s preference for a certain task and then adapt so that it could help complete the task. As a test case, the researchers looked at spar assembly, the process of building the main structural element of an aircraft’s wing; in the future, the Dual Arm (which is not a commercial product as of this writing) may be programmed to help with spar assembly.
Boston-based Rethink Robotics has gone even further. Its next-generation robot, the Baxter, has a variety of sensors that effectively allows it to see with its hands. What’s more, it not only interacts with humans—it can learn from them. If Baxter is shown part of a specific task, the robot can figure out how to perform the rest of that task.
Such human-robot collaboration is just the beginning. When humans and robots have time to learn from one another, the results get better. A recent study on human-robot cross-training—in which a human and a robot perform a task together but frequently switch roles—found that productivity improved in both humans and robots when each side learned how the other works. Rodney Brooks, cofounder of iRobot—maker of robots for everything from military operations to the Roomba vacuum cleaner—suggests that robots have huge potential to revitalize and sustain small manufacturers. This can be seen in Rethink Robotics’ Baxter, which can be up-and-running in an hour—a profound improvement over the 18 months it can take to integrate an older industrial robot. Sounds pretty manageable.
Centralized corporate managers now command terabytes of data. As masters of the data, they should control decision making—right?
Well, yes, up to a point. But technology that gathers localized data can empower local decision making in a process we call edge-centricity. With edge-centricity, information and decision-making authority are pushed out to the most customer-facing points in the organization, where the information can be put to the best practical use.
A case in point is convenience grocer 7-Eleven. The company’s retail information system—a technology that collects data from point-of-sale terminals and transmits it in real time to a data repository—has brought about a reimagined work process of inventory management on a store-by-store basis. By embracing an edge-centric mindset, the company has given local managers considerable leeway in deciding what to stock—shifting crucial responsibilities from senior executives to store managers in thousands of locations.
Using analytics software, the system sifts the data for clues about customer demand, pricing and possible product innovations. Managers receive daily, weekly and monthly sales figures. For fresh-food items, managers base their daily orders on that day’s sales from the previous week, taking into account other factors.
Take, for example, one simple but unusual key to the retail system’s value: Since retail sales depend on the weather, the company provides weather forecasts to help managers gauge demand. As, say, a hurricane develops, 7-Eleven managers are able to assess conditions and adjust inventories to meet changing demand.
Humans remain very much in the loop. In fact, through this technology, managers and employees are now operating in analytical roles—monitoring daily activity in an effort to harness and capitalize on data as it surfaces. They are also responsible for noticing and documenting such variations as shifts in the weather, local or neighborhood social events, political demonstrations and the like.
The process pushes responsibilities to those who truly understand the clientele, using technology that relays crucial information at a rapid pace. In the past, it was nearly impossible for a convenience chain to operate with a localized mindset, yet digital innovations have enabled 7-Eleven to return to its initial formula for success—pinpointing local consumer preferences through groundwork instead of taking a one-size-fits-all approach.
The benefits of edge-centric work design aren’t limited to improving consumer choices. Bill Ruh, vice president at General Electric Co.’s global software and analytics center, told us how the company is building edge-centricity into industrial operations. For example, power plant operators need to know immediately if they can ramp up turbines during times of peak demand without causing breakdowns or damaging the machinery. Right now, they have to get someone to model the situation for them—and that can take days.
Similarly, GE Transportation is designing technology to improve railroad operations. Using sensors to track some 250 different variables, operators can constantly monitor equipment, determine schedules and plan for locomotive servicing. This technology also helps push decision making to the edge. Train operators can work in close contact with a help desk to monitor changes and make quick decisions about routes, schedules and maintenance.
What are the major implications of intelligent processes and the attendant work-design options for the skills of managers and workers?
First, managers and workers alike need to adopt an experimental mindset and skills. A firehose of data won’t put out a fire if managers don’t know how to direct it. Managers and workers will need to get more comfortable using data to design experiments that lead to meaningful results.
They will also have to live by rules that appear exotic now—along the lines of Facebook’s admonitions to “move fast and break things” and “done is better than perfect.”
They will need to reward experimentation and foster a culture that encourages resilience in the face of inevitable failures. Companies have a steep learning curve ahead of them.
Second, managers will have to recognize that their real value-added contribution will increasingly take the form of judgment rather than knowledge creation. Knowledge work won’t disappear completely. But much of what is currently referred to as knowledge work—the formulation of plans, completion of forms and coordination of data files—will soon be done by software guided by algorithms. What remains is judgment work: balancing opposing views and stakes, crafting a plan of action and making decisions. But judgment requires insight drawn from experience, and experience often involves a form of experimentation.
Third, managers and professionals (whether they are in engineering, medicine, marketing, business strategy or operations) will need to get accustomed to taking advice from machines. No one disputes the value of contextual knowledge and human judgment, but it is a limited perspective—being able to see only what’s out your own window—that has most often prevented managers from seeing and exploiting opportunities for great gain.
Finally, managers need to understand that the pursuit of intelligent processes is a choice. They can choose conventional approaches, but if they do, they shouldn’t expect the powerful results that can come from intelligent processes. Capturing the benefits of new technology will not be automatic.
Paypal to allow users to buy, hold and sell four cryptocurrencies
“The shift to digital forms of currencies is inevitable, bringing with it clear advantages in terms of financial inclusion and access; efficiency, speed and resilience of the payments system; and the ability for governments to disburse funds to citizens quickly,” said Dan Schulman, president and CEO, PayPal.“Our global reach, digital payments expertise, two-sided network, and rigorous security and compliance controls provide us with the opportunity, and the responsibility, to help facilitate the understanding, redemption and interoperability of these new instruments of exchange. We are eager to work with central banks and regulators around the world to offer our support, and to meaningfully contribute to shaping the role that digital currencies will play in the future of global finance and commerce.”
This is great news for crypto but I’m told it shouldn’t have been entirely unexpected In June, there was a report that Paypal was working on direct crypto sales.
Nokia awarded contract to build 4G network on the moon
Nokia has been awarded a contract to establish a 4G network on the moon. The contract is one of several that NASA is awarding to companies as it plans a return to the moon.
The $14.1 million contract was given to Nokia’s US subsidiary and is a small part of the $370 million total awarded to companies such as SpaceX. The cellular service will allow astronauts, rovers, lunar landers, and habitats to communicate with one another according to Jim Reuter, the Associate Administrator for NASA’s Space.
The 4G network that Nokia will build will be miles superior to the form of communication that was used during the early missions to the moon.
This is not Nokia’s first attempt to launch an LTE network on the moon. It planned to do so in 2018 in collaboration with PTScientists, a German space firm, and Vodafone UK to launch an LTE network at the site of the Apollo 17 landing but the plan never came to fruition.
Stripe acquires Nigeria’s Paystack for $200M+ to expand into the African continent
When Stripe announced earlier this year that it had picked up another $600 million in funding, it said one big reason for the funding was to expand its API-based payments services into more geographies. Today the company is coming good on that plan in the form of some M&A.
Stripe is acquiring Paystack, a startup out of Lagos, Nigeria that, like Stripe, provides a quick way to integrate payments services into an online or offline transaction by way of an API. (We and others have referred to it in the past as “the Stripe of Africa.”)
Paystack currently has around 60,000 customers, including small businesses, larger corporates, fintechs, educational institutions and online betting companies, and the plan will be for it to continue operating independently, the companies said.
Terms of the deal are not being disclosed, but sources close to it confirm that it’s over $200 million. That makes this the biggest startup acquisition to date to come out of Nigeria, as well as Stripe’s biggest acquisition to date anywhere. (Sendwave, acquired by WorldRemit in a $500 million deal in August, is based out of Kenya.)
It’s also a notable shift in Stripe’s strategy as it continues to mature: Typically, it has only acquired smaller companies to expand its technology stack, rather than its global footprint.
The deal underscores two interesting points about Stripe, now valued at $36 billion and regularly tipped as an IPO candidate. (Note: It has never commented on those plans up to now.) First is how it is doubling down on geographic expansion: Even before this news, it had added 17 countries to its platform in the last 18 months, along with progressive feature expansion. And second is how Stripe is putting a bet on the emerging markets of Africa specifically in the future of its own growth.
“There is enormous opportunity,” said Patrick Collison, Stripe’s co-founder and CEO, in an interview with TechCrunch. “In absolute numbers, Africa may be smaller right now than other regions, but online commerce will grow about 30% every year. And even with wider global declines, online shoppers are growing twice as fast. Stripe thinks on a longer time horizon than others because we are an infrastructure company. We are thinking of what the world will look like in 2040-2050.”
For Paystack, the deal will give the company a lot more fuel (that is, investment) to build out further in Nigeria and expand to other markets, CEO Shola Akinlade said in an interview.
“Paystack was not for sale when Stripe approached us,” said Akinlade, who co-founded the company with Ezra Olubi (who is the CTO). “For us, it’s about the mission. I’m driven by the mission to accelerate payments on the continent, and I am convinced that Stripe will help us get there faster. It is a very natural move.”
Paystack had been on Stripe’s radar for some time prior to acquiring it. Like its U.S. counterpart, the Nigerian startup went through Y Combinator — that was in 2016, and it was actually the first-ever startup out of Nigeria to get into the world-famous incubator. Then, in 2018, Stripe led an $8 million funding round for Paystack, with others participating, including Visa and Tencent. (And for the record, Akinlade said that Visa and Tencent had not approached it for acquisition. Both have been regular investors in startups on the continent.)
In the last several years, Stripe has made a number of investments into startups building technology or businesses in areas where Stripe has yet to move. This year, those investments have included backing an investment in universal checkout service Fast, and backing the Philippines-based payment platform PayMongo.
Collison said that while acquiring Paystack after investing in it was a big move for the company, people also shouldn’t read too much into it in terms of Stripe’s bigger acquisition policy.
“When we invest in startups we’re not trying to tie them up with complicated strategic investments,” Collison said. “We try to understand the broader ecosystem, and keep our eyes pointed outwards and see where we can help.”
That is to say, there are no plans to acquire other regional companies or other operations simply to expand Stripe’s footprint, with the interest in Paystack being about how well they’d built the company, not just where they are located.
“A lot of companies have been, let’s say, heavily influenced by Stripe,” Collison said, raising his eyebrows a little. “But with Paystack, clearly they’ve put a lot of original thinking into how to do things better. There are some details of Stripe that we consider mistakes, but we can see that Paystack ‘gets it,’ it’s clear from the site and from the product sensibilities, and that has nothing to do with them being in Africa or African.”
Stripe, with its business firmly in the world of digital transactions, already has a strong line in the detection and prevention of fraud and other financial crimes. It has developed an extensive platform of fraud protection tools, but even with that, incidents can slip through the cracks. Just last month, Stripe was ordered to pay $120,000 in a case in Massachusetts after failing to protect users in a $15 million cryptocurrency scam.
Now, bringing on a business from Nigeria could give the company a different kind of risk exposure. Nigeria is the biggest economy in Africa, but it is also one of the more corrupt on the continent, according to research from Transparency International.
And related to that, it also has a very contentious approach to law and order. Nigeria has been embroiled in protests in the last week with demonstrators calling for the disbanding of the country’s Special Anti-Robbery Squad, after multiple accusations of brutality, including extrajudicial killings, extortion and torture. In fact, Stripe and Paystack postponed the original announcement in part because of the current situation in the country.
But while those troubles continue to be worked through (and hopefully eventually resolved, by way of government reform in response to demonstrators’ demands), Paystack’s acquisition is a notable foil to those themes. It points to how talented people in the region are identifying problems in the market and building technology to help fix them, as a way of improving how people can transact, and in turn, economic outcomes more generally.
The company got its start back when Akinlade, for fun (!) built a quick way of integrating a card transaction into a web page, and it was the simplicity of how it worked that spurred him and his co-founder to think of how to develop that into something others could use. That became the germination of the idea that eventually landed them at YC and in the scope of Stripe.
“We’re still very early in the Paystack payments ecosystem, which is super broken,” said Akinlade. The company today provides a payments API, and it makes revenue every time a transaction is made using it. He wouldn’t talk about what else is on Paystack’s radar, but when you consider Stripe’s own product trajectory as a template, there is a wide range of accounting, fraud, card, cash advance and other services to meet business needs that could be built around that to expand the business. “Most of what we will be building in Africa has not been built yet.”
Last month, at Disrupt, we interviewed another successful entrepreneur in the country, Tunde Kehinde, who wisely noted that more exits of promising startups — either by going public or getting acquired — will help lift up the whole ecosystem. In that regard, Stripe’s move is a vote of confidence not just for the potential of the region, but for those putting in the efforts to build tech and continue improving outcomes for everyone.
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