At a recent KPMG Robotic Innovations event, Futurist and friend Gerd Leonhard delivered a keynote titled “The Digital Transformation of Business and Society: Challenges and Opportunities by 2020”. I highly recommend viewing the Video of his presentation. As Gerd describes, he is a Futurist focused on foresight and observations — not predicting the future. We are at a point in history where every company needs a Gerd Leonhard. For many of the reasons presented in the video, future thinking is rapidly growing in importance. As Gerd so rightly points out, we are still vastly under-estimating the sheer velocity of change.
With regard to future thinking, Gerd used my future scenario slide to describe both the exponential and combinatorial nature of future scenarios — not only do we need to think exponentially, but we also need to think in a combinatorial manner. Gerd mentioned Tesla as a company that really knows how to do this.
He then described our current pivot point of exponential change: a point in history where humanity will change more in the next twenty years than in the previous 300. With that as a backdrop, he encouraged the audience to look five years into the future and spend 3 to 5% of their time focused on foresight. He quoted Peter Drucker (“In times of change the greatest danger is to act with yesterday’s logic”) and stated that leaders must shift from a focus on what is, to a focus on what could be. Gerd added that “wait and see” means “wait and die” (love that by the way). He urged leaders to focus on 2020 and build a plan to participate in that future, emphasizing the question is no longer what-if, but what-when. We are entering an era where the impossible is doable, and the headline for that era is: exponential, convergent, combinatorial, and inter-dependent — words that should be a key part of the leadership lexicon going forward. Here are some snapshots from his presentation:
Because of exponential progression, it is difficult to imagine the world in 5 years, and although the industrial era was impactful, it will not compare to what lies ahead. The danger of vastly under-estimating the sheer velocity of change is real. For example, in just three months, the projection for the number of autonomous vehicles sold in 2035 went from 100 million to 1.5 billion
Six years ago Gerd advised a German auto company about the driverless car and the implications of a sharing economy — and they laughed. Think of what’s happened in just six years — can’t imagine anyone is laughing now. He witnessed something similar as a veteran of the music business where he tried to guide the industry through digital disruption; an industry that shifted from selling $20 CDs to making a fraction of a penny per play. Gerd’s experience in the music business is a lesson we should learn from: you can’t stop people who see value from extracting that value. Protectionist behavior did not work, as the industry lost 71% of their revenue in 12 years. Streaming music will be huge, but the winners are not traditional players. The winners are Spotify, Apple, Facebook, Google, etc. This scenario likely plays out across every industry, as new businesses are emerging, but traditional companies are not running them. Gerd stressed that we can’t let this happen across these other industries
Anything that can be automated will be automated: truck drivers and pilots go away, as robots don’t need unions. There is just too much to be gained not to automate. For example, 60% of the cost in the system could be eliminated by interconnecting logistics, possibly realized via a Logistics Internet as described by economist Jeremy Rifkin. But the drive towards automation will have unintended consequences and some science fiction scenarios could play out. Humanity and technology are indeed intertwining, but technology does not have ethics. A self-driving car would need ethics, as we make difficult decisions while driving all the time. How does a car decide to hit a frog versus swerving and hitting a mother and her child? Speaking of science fiction scenarios, Gerd predicts that when these things come together, humans and machines will have converged:
Gerd has been using the term “Hellven” to represent the two paths technology can take. Is it 90% heaven and 10% hell (unintended consequences), or can this equation flip? He asks the question: Where are we trying to go with this? He used the real example of Drones used to benefit society (heaven), but people buying guns to shoot them down (hell). As we pursue exponential technologies, we must do it in a way that avoids negative consequences. Will we allow humanity to move down a path where by 2030, we will all be human-machine hybrids? Will hacking drive chaos, as hackers gain control of a vehicle? A recent Jeep recall of 1.4 million jeeps underscores the possibility. A world of super intelligence requires super humanity — technology does not have ethics, but society depends on it. Is this Ray Kurzweil vision what we want?
Is society truly ready for human-machine hybrids, or even advancements like the driverless car that may be closer to realization? Gerd used a very effective Video to make the point
Followers of my Blog know I’m a big believer in the coming shift to value ecosystems. Gerd described this as a move away from Egosystems, where large companies are running large things, to interdependent Ecosystems. I’ve talked about the blurring of industry boundaries and the movement towards ecosystems. We may ultimately move away from the industry construct and end up with a handful of ecosystems like: mobility, shelter, resources, wellness, growth, money, maker, and comfort
Our kids will live to 90 or 100 as the default. We are gaining 8 hours of longevity per day — one third of a year per year. Genetic engineering is likely to eradicate disease, impacting longevity and global population. DNA editing is becoming a real possibility in the next 10 years, and at least 50 Silicon Valley companies are focused on ending aging and eliminating death. One such company is Human Longevity Inc., which was co-founded by Peter Diamandis of Singularity University. Gerd used a quote from Peter to help the audience understand the motivation: “Today there are six to seven trillion dollars a year spent on healthcare, half of which goes to people over the age of 65. In addition, people over the age of 65 hold something on the order of $60 trillion in wealth. And the question is what would people pay for an extra 10, 20, 30, 40 years of healthy life. It’s a huge opportunity”
Gerd described the growing need to focus on the right side of our brain. He believes that algorithms can only go so far. Our right brain characteristics cannot be replicated by an algorithm, making a human-algorithm combination — or humarithm as Gerd calls it — a better path. The right brain characteristics that grow in importance and drive future hiring profiles are:
Google is on the way to becoming the global operating system — an Artificial Intelligence enterprise. In the future, you won’t search, because as a digital assistant, Google will already know what you want. Gerd quotes Ray Kurzweil in saying that by 2027, the capacity of one computer will equal that of the human brain — at which point we shift from an artificial narrow intelligence, to an artificial general intelligence. In thinking about AI, Gerd flips the paradigm to IA or intelligent Assistant. For example, Schwab already has an intelligent portfolio. He indicated that every bank is investing in intelligent portfolios that deal with simple investments that robots can handle. This leads to a 50% replacement of financial advisors by robots and AI
This intelligent assistant race has just begun, as Siri, Google Now, Facebook MoneyPenny, and Amazon Echo vie for intelligent assistant positioning. Intelligent assistants could eliminate the need for actual assistants in five years, and creep into countless scenarios over time. Police departments are already capable of determining who is likely to commit a crime in the next month, and there are examples of police taking preventative measures. Augmentation adds another dimension, as an officer wearing glasses can identify you upon seeing you and have your records displayed in front of them. There are over 100 companies focused on augmentation, and a number of intelligent assistant examples surrounding IBM Watson; the most discussed being the effectiveness of doctor assistance. An intelligent assistant is the likely first role in the autonomous vehicle transition, as cars step in to provide a number of valuable services without completely taking over. There are countless Examples emerging
Gerd took two polls during his keynote. Here is the first: how do you feel about the rise of intelligent digital assistants? Answers 1 and 2 below received the lion share of the votes
Collectively, automation, robotics, intelligent assistants, and artificial intelligence will reframe business, commerce, culture, and society. This is perhaps the key take away from a discussion like this. We are at an inflection point where reframing begins to drive real structural change. How many leaders are ready for true structural change?
Gerd likes to refer to the 7-ations: Digitization, De-Materialization, Automation, Virtualization, Optimization, Augmentation, and Robotization. Consequences of the exponential and combinatorial growth of these seven include dependency, job displacement, and abundance. Whereas these seven are tools for dramatic cost reduction, they also lead to abundance. Examples are everywhere, from the 16 million songs available through Spotify, to the 3D printed cars that require only 50 parts. As supply exceeds demand in category after category, we reach abundance. As Gerd put it, in five years’ time, genome sequencing will be cheaper than flushing the toilet and abundant energy will be available by 2035 (2015 will be the first year that a major oil company will leave the oil business to enter the abundance of the renewable business). Other things to consider regarding abundance:
Efficiency and business improvement is a path not a destination. Gerd estimates that total efficiency will be reached in 5 to 10 years, creating value through productivity gains along the way. However, after total efficiency is met, value comes from purpose. Purpose-driven companies have an aspirational purpose that aims to transform the planet; referred to as a massive transformative purpose in a recent book on exponential organizations. When you consider the value that the millennial generation places on purpose, it is clear that successful organizations must excel at both technology and humanity. If we allow technology to trump humanity, business would have no purpose
In the first phase, the value lies in the automation itself (productivity, cost savings). In the second phase, the value lies in those things that cannot be automated. Anything that is human about your company cannot be automated: purpose, design, and brand become more powerful. Companies must invent new things that are only possible because of automation
Technological unemployment is real this time — and exponential. Gerd talked to a recent study by the Economist that describes how robotics and artificial intelligence will increasingly be used in place of humans to perform repetitive tasks. On the other side of the spectrum is a demand for better customer service and greater skills in innovation driven by globalization and falling barriers to market entry. Therefore, creativity and social intelligence will become crucial differentiators for many businesses; jobs will increasingly demand skills in creative problem-solving and constructive interaction with others
Gerd described a basic income guarantee that may be necessary if some of these unemployment scenarios play out. Something like this is already on the ballot in Switzerland, and it is not the first time this has been talked about:
In the world of automation, experience becomes extremely valuable — and you can’t, nor should attempt to — automate experiences. We clearly see an intense focus on customer experience, and we had a great discussion on the topic on an August 26th Game Changers broadcast. Innovation is critical to both the service economy and experience economy. Gerd used a visual to describe the progression of economic value:
Source: B. Joseph Pine II and James Gilmore: The Experience Economy
Gerd used a second poll to sense how people would feel about humans becoming artificially intelligent. Here again, the audience leaned towards the first two possible answers:
Gerd then summarized the session as follows:
The future is exponential, combinatorial, and interdependent: the sooner we can adjust our thinking (lateral) the better we will be at designing our future.
My take: Gerd hits on a key point. Leaders must think differently. There is very little in a leader’s collective experience that can guide them through the type of change ahead — it requires us all to think differently
When looking at AI, consider trying IA first (intelligent assistance / augmentation).
My take: These considerations allow us to create the future in a way that avoids unintended consequences. Technology as a supplement, not a replacement
Efficiency and cost reduction based on automation, AI/IA and Robotization are good stories but not the final destination: we need to go beyond the 7-ations and inevitable abundance to create new value that cannot be easily automated.
My take: Future thinking is critical for us to be effective here. We have to have a sense as to where all of this is heading, if we are to effectively create new sources of value
We won’t just need better algorithms — we also need stronger humarithms i.e. values, ethics, standards, principles and social contracts.
My take: Gerd is an evangelist for creating our future in a way that avoids hellish outcomes — and kudos to him for being that voice
“The best way to predict the future is to create it” (Alan Kay).
My Take: our context when we think about the future puts it years away, and that is just not the case anymore. What we think will take ten years is likely to happen in two. We can’t create the future if we don’t focus on it through an exponential lens
Having founded my startup a few years ago, I am familiar to why founders go through the pain & grit to build their own company. The statistics around startup survival rates show that the risk is high, but the potential reward both financially & emotionally is also significant.
In my case, risk was defined by the amount of money I invested in the venture plus the opportunity cost in case the startup goes nowhere. The later relates to the fact that I earned no salary at the beginning & that when I committed to that specific idea I was instantaneously saying “no” to many other opportunities and potential career advancements. The reward was two-fold too; the first one was the attractive financial outcome of a potential exit. The second one was the freedom to chase opportunities as they appear, doing what I want and how I want it.
Once I raised capital from investors, I basically traded reward for reduced risk. I started paying myself a small salary and anticipated that more resources would increase the success likelihood of the startup.
This pattern of weighing risk against rewards was crystal clear in my mind… until I joined the arena of corporate venture building. Directly during one of my first projects, I was tasked with the creation of a startup for a blue-chip corporate client. I was immediately puzzled by the reasoning behind this endeavor.
Ultimately corporate decisions are also guided by risk against reward: if they don’t take risks and innovate they might be left behind and, in some cases, join the once-great-now-extinct corporate hall of shame. That’s why they invest in research and development, spend hard earned cash in mergers and acquisitions and start innovation programs. But my interest was more at a micro level, meaning, which reasoning my corporate client follows to decide if and how to found a specific new venture?
Having thought about it a lot, I believe at micro level corporates weigh investment against control. Investment is the level of capital, manpower & political will provided by the corporate to propel the venture towards exit, break-even or strategic relevance. Control is the possibility to steer the venture towards the strategic goals the leadership team has in mind while defining the boundaries of what can & cannot be done.
In the startup case, the risk/reward is typically shared between the founders and external investors. In a corporate venture building case, the investment/control can be shared between the corporate, an empowered founder team and also external investors.
I am still in the middle of the corporate decision-making process but wanted to share with you the scenarios we are using to guide the discussions on how to structure the new venture. But before I do, I would like to mention that the considerations of investment vs. control takes place at three different stages of the venture’s existence:
• Incubation: develop & validate idea • Acceleration: validate business model incl. product, operations & customer acquisition (find the winning formula) • Growth: replicate the formula to grow exponentially
Based on that, three main scenarios are being considered to found the new venture.
Scenario 1: Control & Grow
Full investment & control during incubation & acceleration
Shared investment & control during the growth stage
Per definition, the incubation and acceleration stages are less capital intensive and is the moment when key strategic decisions that shape the future business are made. In these stages, the corporate is interested in maintaining the full control of the venture while absorbing the whole investment. Only when they enter the capital-intensive growth stage it becomes necessary to “share the burden” with other institutional or strategic investors. This scenario is suitable for ventures of high strategic value, especially the ones leveraging core assets and know-how of the corporate mothership.
Scenario 2: Spread the Bets
Lower investment & control during all stages
In this case, the corporate initiator empowers a founder team and joins the project almost like an external investor would do at Seed and Series A of a startup. They agree on a broad vision, provide the funding and retain a part of the shares with shareholder meetings in between to track progress. Beyond that, they let the founder team do their thing. External investors can join at any funding round to share the investment tickets. The corporate would have lower control and investment from the get-go and can increase their influence only when new funding rounds are required or via an acquisition offer. This scenario is suitable for ventures in which the corporate can function as the first client or use their network to manufacture, market or distribute the product or service.
Scenario 3: Build, operate & transfer
Lower investment & control during incubation & acceleration
Full investment & control during the growth stage
The venture is initially built by a founder team or external partners (often a consultancy). Only once they successfully finalized the incubation and acceleration stages, the corporate has the right or obligation to absorb the business. Differently than scenario 2, the corporate gains stronger control of the trajectory of the business during its initial stages by defining how a “transfer” event looks like. The investment necessary to put together a strong founder team is reduced by the reward of a pre-defined & short term exit event. The initial investment can be further reduced by the participation of Business Angels, also motivated by a clear path to exit and access to a new source of deal flow. This scenario is suitable for ventures closely linked to the core business of the corporate and where speed & excellence of execution is key.
There is obviously no right and wrong. Each scenario can make sense according to the end goal of the corporate. Furthermore, there are surely new scenarios and variations of the above. What is important in my opinion is to openly discuss which road to take. If the client can’t discern the alternatives and consequences, you will risk a “best of both worlds” mindset where expectations regarding investment & control don’t match. If that is the case, you will be up for a tough ride
Renting robots as temp labor? Not a new idea. But it’s certainly one that is gaining followers.
Rising labor shortages, tightly contested global markets, and growing interest in automation are tightening the screws on traditional business models. A broader spectrum of users are seeking flexible automation solutions. More suppliers are adopting new-age rental or lease options to satisfy the demand. Some are mature companies answering the call, others are startups blazing a path for the rest of the industry. Robotics as a Service (RaaS) is an emerging trend whose time has come.
Steel Collar Associates may have been ahead of its time when RIA spoke with its owner in 2013 about his “Humanoids for Hire” – aka Yaskawa dual-arm robots for rent. Already several years into his venture at the time, Bill Higgins was having little success contracting out his robo-employees. Back then, industry was barely warming up to the idea of cage-free robots rubbing elbows with their human coworkers. Now every major robot manufacturer has a collaborative robot on its roster. And a slew of startups have joined the fray.
Just like human-robot collaboration is helping democratize robotics, RaaS will help bring robots to the masses. And cobots aren’t the only robots for rent.
Whether you have a short-term need, want to try before you buy, forgo a capital expenditure, or lower your cost of entry to robotic automation, RaaS is worth a closer look. It’s robots on demand, when and where you want them.
Robots on Demand Out-of-the-box solutions like those offered by READY Robotics, which are easy to use and easy to deploy, are making RaaS a reality. Your next, or perhaps first, robotic solution may be a Johnny-on-the-spot – on wheels.
“The TaskMate is a ready-to-use, on-demand robot worker that is specifically designed to come out of its shipping crate ready to be deployed to the production line,” says READY Robotics CEO Ben Gibbs, noting that manufacturers without the time to undertake custom robot integration are looking for an out-of-the box automation solution. Rental options make the foray easier.
“Time is their most precious resource. They want something like the TaskMate that is essentially ready to go out of the box,” says Gibbs. “They may have to do a little fixturing or put together a parts presentation hopper. Besides that, it’s something they can deploy pretty quickly. We’re driving towards providing a solution that’s as easy to use as your personal computer.”
The system consists of a collaborative robot arm mounted on a stand with casters, so you can wheel it into position anywhere on the production floor. The ease of portability makes it ideal for high-mix, low-volume production where it can be quickly relocated to different manufacturing cells. Nicknamed the “Swiss Army Knife” of robots, the TaskMate performs a variety of automation tasks from machine tending to pick-and-place applications, to parts inspection.
The TaskMate comes in two varieties, the 5-kg payload R5 and 10-kg payload R10 (pictured). Both systems use robot arms from collaborative robot maker Universal Robots. The UR arm is equipped with a force sensor and a universal interface called the TEACHMATE that allows different robot grippers to be hot-swapped onto the end of the arm. Supported end effector brands include SCHUNK, Robotiq and Piab.
Contributing to the system’s ease of use is READY’s proprietary operating system, the FORGE/OS software. A simple flowchart interface (pictured) controls the robot arm, end-of-arm tooling and other peripherals. No coding is required.
For those tasks requiring a higher payload, reach, or cycle time than is capable with the power-and-force limiting cobot included with the TaskMate R5 and R10 systems, READY also offers its FORGE controller (formerly called the TaskMate Kit). Running the intuitive FORGE/OS software, the controller provides the same easy programming interface but is designed as a standalone system for ABB, FANUC, UR and Yaskawa robots.
“For example, if you plug the FORGE controller into a FANUC robot, you no longer have to program in Karel (the robot OEM’s proprietary programming language),” explains Gibbs. “On the teach pendant, you can use FORGE/OS to program the robot directly, so you have the same programming experience on the controller as you do on the TaskMate.
“We started primarily with smaller six degree-of-freedom robot arms, like the FANUC LR Mate and GP7 from Yaskawa,” continues Gibbs. “We have started to integrate some of the larger robots as well, like the FANUC M-710iC/50. Ultimately, we’re driving toward a ubiquitous programming experience regardless of what robot arm or robot manufacturer you’re using.”
In the Cloud A common element in the RaaS rental model is cloud robotics. READY offers customers the ability to remotely monitor the TaskMate or other robotic systems hooked up to the FORGE controller.
“We can set them up with alerts, so when the production cycle is completed or the robot enters an unexpected error state, they can receive an email notifying the floor manager or line operator to check the system,” says Gibbs.
You can also save and back up programs to the cloud, and deploy them from one robot to another. If an operator were to inadvertently lose a program, rather than rewrite it from scratch, you can just drop the backup version from the cloud onto the system and be up and running again in minutes.
The TaskMate systems and FORGE controller are available for both purchase and rental.
“We provide a menu to our customers of how they might want to consume our products and services,” says Gibbs. “That may be all the way from a traditional CapEx (capital expenditure) purchase if they want to buy one of our TaskMates upfront, to the other end of the spectrum where they can rent the system with no contract for however long or short of a duration they want.”
For an additional charge, READY can manage the entire asset for the customer.
“We set it up, we program it, and we remotely monitor it to make sure it’s maximizing its uptime. We can come in and tweak the program if it’s running into unexpected errors. All of the systems are equipped with cell modems, so they can update the software over the air. We handle all of the maintenance or it’s handled by our channel partners.”
No-Term Rental Gibbs says flexibility is the biggest advantage to their rental option. READY offers a 3-month trial rental. But customers are not required to keep it for that full term.
“We have a no-term rental. That’s even more appealing because it can come entirely out of your OpEx (operating expenditure) budget. Instead of going through a lengthy CapEx approval process, we’ve had some customers just run their corporate credit card, because the rental is below their approval level for an OpEx purchase. They can easily set up the system and use it for a few months. That alone provides them with a much stronger justification for moving forward with CapEx if they want, or just continue to expand their rental.
“At the end of the first month, if they decide that it’s not working out, just like any incompetent worker, they can fire it and send it back.”
If the customer chooses to continue renting, Gibbs says it’s more cost-effective to sign a contract. This reduces the risk for everyone, so there’s usually a financial incentive.
“The primary way we differentiate ourselves is that we offer that no-term rental with a fixed monthly fee, which allows these factories to capture the traditional value of automation. We don’t have a meter running that says you ran it 22 hours this day, so you owe us for 22 hours of work. We encourage them to run it as long as they want. The expectation is the longer you run it, the cheaper it should be.”
Flexibility for High-Mix, Low-Volume READY’s target customers range from small job shops to large multinationals and Fortune 500 companies.
“Attwood is a great example of the type of high-mix, low-volume production environment where the flexibility of the TaskMate really shines,” says Gibbs.
Attwood Marine in Lowell, Michigan, is one of the world’s largest producers of boat parts, accessories and supplies. If it’s on your boat, there’s a good chance this century-old company made it. They make thousands of different parts, but cater to a relatively small marine market. The challenges of high-mix, low-volume production in a highly competitive market had them looking for an automation solution.
The flexibility of the TaskMate to quickly deploy and redeploy depending on Attwood’s short- or long-term needs was a deciding factor. With only a couple hundred employees and no dedicated robotics programmer on staff, the customer appreciates the FORGE software’s ease of use. Plus the ability to rent the system plays to the seasonal nature of Attwood’s business and lowers the cost of their first foray into robotic automation.
Attwood has deployed the TaskMate R10 to a half-dozen cells on the production floor performing CNC machine tending, pick-and-place tasks like palletizing, loading/unloading conveyors and case packing, and even repetitive testing. You need to actuate a switch or pull a cord 250,000 times? That’s a job for flexible automation.
By deploying one robot system to multiple production cells, Attwood was able to spread their ROI across multiple product lines and realize up to a 30 percent reduction in overall manufacturing costs. Watch the TaskMate on the job at Attwood Marine.
Small to midsized businesses aren’t the only ones benefiting. Large multinationals like tools manufacturer Stanley Black & Decker use the TaskMate R10 for machine tending CNC lathes.
“Multinationals may have robot programmers on staff, but usually not enough of them,” says Gibbs. “Automation engineers are in high demand and very difficult to come by. Any technology that makes it faster and easier for people to set up robots is a tremendous value. Even with large multinationals, some like to be asset-light and do a rental, but everyone loves the ease of programming we offer through FORGE.”
Forged in the Lab READY’s portable plug-and-play solution is a technology spinoff from Professor Greg Hager’s research in human-machine collaborative systems at Johns Hopkins University. Gibbs, an alumnus, was working in the university’s technology ventures office helping researchers like Prof. Hager develop commercialization strategies for their new technologies. Hager, along with Gibbs, and fellow alum CTO Kelleher Guerin cofounded the startup in October 2015. Another cofounder, Drew Greenblatt, President of Marlin Steel Wire Products (an SME in the Know), offered up his nearby Baltimore, Maryland-based custom metal forms factory as a prototype test site for the TaskMate. The system was officially launched in July 2017.
Prof. Hager is now an advisor to the company. Distinguished robotics researcher, Henrik Christensen, is Chairman of the Board of Advisors. In December 2017, the startup secured $15 million in Series A funding led by Drive Capital.
READY maintains an office in Baltimore, while its headquarters is in Columbus, Ohio. They are a FANUC Authorized System Integrator. Gibbs says they are in the process of building a channel partner network of integrators and distributors to support future growth.
Pay As You Go Business models under the RaaS umbrella vary widely, and are evolving. Startups like Hirebotics and Kindred leverage cloud robotics more intensely to monitor robot uptime, collect data, and enhance performance using AI. They charge by the hour, or even by the second. You pay for only what you use. Each service model has its advantages.
Some RaaS advocates offer subscription-based models. Some took a page from the sharing economy. Think Airbnb, Lyft, TaskRabbit, Poshmark. Share an abode, a car or clothes. Skip the overhead, the infrastructure and the long-term commitment. Pay as you go for a robot on the run.
Mobile Robots for Hire Autonomous mobile robots (AMRs) are no strangers to the RaaS model, either. RIA members Aethon and Savioke lease their mobile robots for various applications in healthcare, hospitality and manufacturing. Startup inVia Robotics offers a subscription-based RaaS solution for its warehouse “Picker” robots.
We first explored the emergence of AMRs in the Always-On Supply Chain. It’s startling how much the logistics robot market has changed in just a couple of years. Since then, prototypes and beta deployments have turned into full product lines with significant investor funding. Major users like DHL, Walmart and Kroger, not to mention early adopter Amazon, are doubling down on their mobile fleets.
After triple-digit revenue growth in Europe, Mobile Industrial Robots (MiR) was just breaking onto the North American scene two years ago. Now, as they celebrate comparable growth on this side of the pond, MiR prepares to launch a new lease program in January.
MiR is another prodigy of Denmark’s booming robotics cluster. They join Danish cousin Universal Robots on the list of Teradyne’s smart robotics acquisitions. Odense must have the Midas touch.
Go Big or Go Home Responding to customer demands for larger payloads, MiR introduced its 500-kg mobile platform at Automatica in June. The MiR500 (pictured) comes with a pallet transport system that automatically lifts pallets off a rack and delivers them autonomously. Watch it in action on the production floor of this agricultural machine manufacturer.
“Everybody we deal with today is making a big push to eliminate forklift traffic from the inner aisleways of production lines,” says Ed Mullen, Vice President of Sales – Americas for MiR in Holbrook, New York. “That’s really driving the whole launch of the MiR500. We’ve gone through some epic growth here in my division.”
Mullen’s division is responsible for supporting MiR’s extensive distributor network in all markets between Canada and Brazil. Right now, the Americas account for about a third of the global business.
“We’re seeing applications in industrial automation, warehouses and distribution centers,” says Mullen. “Electronics, semiconductor and a lot of the tier automotive companies, like Faurecia, Visteon and Magna, have all invested in our platforms and are scaling the business. We see this being implemented across all industries, which is really adding to our excitement.”
Lease Options Although Mullen says they’ve seen tremendous success with the current buy model, MiR is trying to make it even easier to work with this emerging technology. That drove them to the RaaS model.
“We think a leasing option will allow companies that are still trying to understand the use cases for the technology to get in quicker, and then slowly scale the business up as they learn how to apply it and what the sweet spots are for autonomous mobile robots. The lease option is intended to reduce the cost of entry. Today it’s mainly the bigger multinationals that are buying, but we believe by providing options for lower entry points, this will make the use cases in the small-to-midsized companies come to light.”
He says a third-party company will handle all the leases. MiR’s distributor network will engage with the third-party company to put together lease programs for customers.
MiR has also implemented a Preferred System Integrator (PSI) program to augment the existing network of distribution partners. Two and a half years ago, it was mainly large companies investing in these mobile platforms. They were purchasing in volumes of one to five robots. Today, they’re seeing investments of 20, 30, or even more than 50 robots.
“When you get into these bigger deployments, it’s more critical to have companies that are equipped to handle them. Our distribution partners are set up as a sales channel. Although most of them have integration capabilities, they don’t want to invest in deploying hundreds of robots at one time. They rather hand that off to a company that’s able to properly support large-scale deployments.”
Over the last couple of years, MiR had been focused on bringing more efficiency to the manufacturing process; not necessarily replacing existing AGVs and forklifts.
“For example, you have a guy that gets paid a healthy salary to sit in front of a machine tool and use his skills to do a certain task. That’s what makes the company money. But when he has to get up and carry a tray of parts to the next phase in the production cycle, that’s inefficient. That’s what we’ve been focusing on, at least with our MiR100 and MiR200 (pictured).”
Technologies, an Indiana-based company specializing in custom plastic injection molding and mold tooling. The mobile robot loops the shop floor, autonomously transporting finished product from the presses to quality inspection. This frees up personnel for more high-value tasks and eliminates material flow bottlenecks.
“With the new MiR500, we’re going after heavier loads and palletizer loads. That’s replacing standard AGVs and forklifts. We’re also starting to see big conveyor companies like Simplimatic Automation and FlexLink move to a more flexible type of platform with autonomous mobile robots.
“Parallel to the hardware is our software. A key part of our company is the way we develop the software, the way we allow people to interface with the product. We’re continuously making it more intuitive and easier to use.”
MiR offers two software packages, the operating system that comes with the robot and the fleet management software that manages two or more robots. The latter is not a requirement, but Mullen says most companies are investing in it to get additional functionality when interfacing with their enterprise system. The newest fleet system is moving to a cloud-based option.
Hardware and software updates are all handled through MiR’s distribution channel and Mullen doesn’t think any of that will change under the lease option.
“The support model will stay the same. Our distributors are all trained on hardware updates, preventative maintenance and troubleshooting. I firmly believe the major component to our success today is our distribution model.”
Mullen says he’s looking forward to new products coming out in 2019. MiR is also hiring. They expect to double their employee count in the Americas and globally.
High-Tech, Short-Term Need It’s many of these feisty startups that we’re seeing adopt nontraditional models like RaaS. But stalwarts are coming on board, too.
Established in 1992, RobotWorx is part of SCOTT Technology Ltd., a century-old New Zealand-based company specializing in automated production, robotics and process machinery. RobotWorx joined the SCOTT family of international companies in 2014 and recently completed a rigorous audit process to become an RIA Certified Robot Integrator.
RobotWorx buys, reconditions and sells used robots, along with maintaining an inventory of new robotic systems and offering full robot integration and training services. Rentals are nothing new to them. They’ve been renting robots for several years, before it was a trend. But in response to the upswing in industry requests of late, RobotWorx rolled out a major push on their rental program this past spring.
“We’ve done a lot with the TV and film industry,” says Tom Fischer, Operations Manager for RobotWorx in Marion, Ohio. “If you’ve seen the latest AT&T commercial, there are blue and orange robots in it. We rented those out for a week.”
Dubbed “Bruce” and “Linda” on strips of tape along their outstretched arms, these brightly colored robots have a starring role in this AT&T Business commercial promoting Edge-to-Edge Intelligence? solutions. Fischer says companies in this industry usually select a particular size of robot, typically either a long-reach or large-payload material handling robot, like the Yaskawa Motoman long-reach robots in this AT&T commercial.
Ever wonder if the robots in commercials are just there for effect? It turns out, not always. Fischer says these are fully functioning robots. AT&T’s ad agency must have a robot wrangler off camera to keep Bruce and Linda in line. However, the other robots in the background are the result of TV magic.
“We basically just sent them the robots,” says Fischer. “They did what they wanted to do with them and then sent them back.”
For quick gigs like this commercial, or maybe a movie cameo or even a tradeshow display, rental robots make sense. But how do you know when it’s better to rent or buy?
“We’ll do a cost analysis with the customer,” says Fischer. “We have an ROI calculator on our website if they want to see what their long-term commitment capital investment would be. (Check out RIA’s Robot ROI Calculator). We also look at it from the standpoint that if they have a long-term contract with somebody, their return on investment is going to be a lot better with a purchase. If they think they’re only going to use the robot for six months, it doesn’t make sense for them to buy it.”
Rent-A-Cell RobotWorx rents robots by the week, month or year. A week is the minimum, but there’s no long-term commitment required. A rental includes a robot, the robot controller, teach pendant and end-of-arm tooling (EOAT). Robot brands available include ABB, FANUC, KUKA, Universal Robots, and Yaskawa Motoman.
They also rent entire ready-to-ship robot cells for welding or material handling. The most popular systems are the RWZero (pictured) and RW950 cells.
“The RWZero cell is very basic,” says Fischer. “You have a widget and you need 5,000 of them. Rent this cell and you have a production line instantly.”
The RW950 is more portable. Fisher calls it a “pallet platform.” The robot, controller, operator station and workpiece positioner all share a common base, which is basically a large steel structure that can be moved around with a forklift whenever needed. See the RW950 Welding Workcell in action.
“We’ve done a lot of the small weld cells,” he says. “We always have a couple on hand so we can supply those on demand. We’ve done larger material handling cells, as well.
“We have a third-party company that does the financing if you need it. A lot of people just end up paying it upfront. If they were to purchase the robot after they’ve rented it, we apply that towards the purchase as well.”
Fischer says 20 percent of the rental price is credited to the purchase if a customer decides to keep the robot. All the robots and robotic cells are up to date on maintenance before they leave the RobotWorx floor and shouldn’t require any major maintenance for at least a year. He says most customers end up buying the robot if their rental period exceeds a year.
Time is not always the deciding factor under the RaaS model. As robotic systems become easier to deploy and redeploy, the idea of robots as a service will gain more permanence as a long-term solution. In the future, robotics in our workplaces and homes will be as ubiquitous as the Internet. In the meantime, we’ll keep our eyes on RaaS as it gets ready for primetime
Many of you have seen the Gartner Hype Cycle curve. When a hot technology appears, it gets hyped and hyped until one day enough people become impatient, and sentiment turns against the technology. It then heads into what Gartner calls the Trough of Disillusionment. Eventually, the technology finds its role – often a major one – in the market.
The idea has always struck me as rather obvious (I described the curve to reporter colleagues on the tech beat at the Wall Street Journal years before I ever saw the Gartner chart), but Gartner popularized the notion, which is why it’s known as the Gartner Hype Cycle rather than, say, the Carroll Hype Cycle. Gartner is to be commended, because technologies can be plotted on the curve, and, drawing on history, their futures can be predicted with some confidence.
On the Carroll…er, Gartner Hype Cycle, the idea of technology-driven innovation in insurance seems to be heading into the Trough of Disillusionment (great name) among incumbents. A Lemonade or Trov hasn’t taken over the world. Big Tech is coming to insurance but not really here yet for most insurers. Industry executives seem to have read everything they care to about AI, blockchain, etc., and are starting to describe plans for small-bore improvements rather than truly innovative ones. Not total disillusionment, but headed in that direction.
Which brings me to the warning signs for 2019.
The slide into the Trough of Disillusionment creates real opportunities because prices of insurtechs will start to settle back toward reality. In any case, technologies keep maturing, no matter how we feel about them, so the day of reckoning in the market creeps closer all the time, and the slide toward disillusionment is the last opportunity for companies to position themselves before a host of technologies and startups will shake the insurance market.
If I’m right, 2019 may well be the last chance for insurance industry incumbents to start taking advantage of the opportunities presented by insurtech, or lose out to nimbler competitors. In that spirit, my colleagues and I at ITL pulled some thoughts together for incumbents on:
10 Signs You’re Headed for Trouble in 2019
You set up an innovation fund and think that means you’re innovative.
Your innovations focus on cutting expenses, to the exclusion of all else, and – worse – you reward executives based on those cuts.
You say your legacy IT systems are what is preventing you from innovating.
You say your defensive culture is preventing you from innovating.
You practice “innovation tourism,” going to Silicon Valley and assuming magic dust will wear off on you. (Related warning sign: You have a ping pong table and coffee bar and think they signify creativity.)
You have 6,000 ideas but can’t figure out how to turn one into a product.
You can’t name 20 insurtechs that operate in your strategic domain or adjacent ones.
You aren’t starting to move your operations into the cloud.
You don’t have significant diversity in your management team and board, in terms of gender, race, age and nationality.
You can’t quantify and measure how you’re doing on your innovation journey and hope you’re improving.
Bonus warning sign: You make television commercials criticizing innovative companies.
In “The Sun Also Rises,” a character is asked how he went bankrupt. “Two ways,” he says, “gradually, then suddenly.” We’re still in the “gradually” part of innovation driven by insurtech, but “suddenly” is coming. I suggest insurance industry incumbents view 2019 and warning signs like these as a last warning to get moving and avoid innovation bankruptcy.
Digital health innovation continues moving full-force in transforming the business of healthcare. For pharma and medtech companies in particular, this ongoing shift has pushed them to identify ways to create value for patients beyond the drugs themselves. From new partnerships between digital health and life science companies to revamped commercial models, collecting and extracting insights from data is at the core of these growth opportunities. But navigating the rapidly evolving terrain is no simple task.
To help these companies effectively incorporate and utilize digital health tools, Rock Health partner ZS Associates draws on over 30 years of industry expertise to guide them through the complex digital health landscape. We chatted with Principal Pete Masloski to discuss how he works with clients to help identify, develop, and commercialize digital health solutions within their core businesses—and where he sees patients benefiting the most as a result.
Note: This interview has been lightly edited for clarity.
Where does ZS see the promise of data- and analytics-enabled digital health tools leading to in the next five years, 10 years, and beyond?
Data and analytics will play a central role in the digital health industry’s growth over the next five to ten years. Startups are able to capture larger, novel sets of data in a way that large life science companies historically have not been able to. As a result, consumers will be better informed about their health choices; physicians will have more visibility into what treatment options work best for whom under what circumstances; health plans will have a better understanding of treatment choices; and pharmaceutical and medical device companies will be able to strategically determine which products and services to build.
We see personalized medicine, driven by genomics and targeted therapies, rapidly expanding over the next few years. Pharmaceutical discovery and development will also transition to become more digitally enabled. The ability to match patients with clinical trials and improve the patient experience will result in lower costs, faster completion, and more targeted therapies. The increase in real-world evidence will be used to demonstrate the efficacy of therapeutics and devices in different populations, which assures payers and providers that outcomes from studies can be replicated in the real world.
How is digital health helping life sciences companies innovate their commercial models? What is the role of data and analytics in these new models?
The pharmaceutical industry continues to face a number of challenges, including the increasingly competitive markets, growing biosimilar competition, and overall scrutiny on pricing. We’ve seen excitement around solutions that integrate drugs with meaningful outcomes and solutions that address gaps in care delivery and promote medication adherence.
Solving these problems creates new business model opportunities for the industry through fresh revenue sources and ways of structuring agreements with customers. For example, risk-based contracts with health plans, employers, or integrated delivery networks (IDNs) become more feasible when you can demonstrate increased likelihood of better outcomes for more patients. We see this coming to fruition when pharma companies integrate comprehensive digital adherence solutions focused on patient behavior change around a specific drug, as in Healthprize’s partnership with Boehringer Ingelheim. In medtech, digital health tools can both differentiate core products and create new profitable software or services businesses. Integrating data collection technology and connectivity into devices and adding software-enabled services can support a move from traditional equipment sales to pay-per-use. This allows customers to access the new equipment technology without paying a large sum up front—and ensures manufacturers will have a more predictable ongoing source of revenue.
That said, data and analytics remain at the core of these new models. In some cases, such as remote monitoring, the data itself is the heart of the solution; in others, the data collected helps establish effectiveness and value as a baseline for measuring impact. Digital ambulatory blood pressure monitors capture an individual’s complete blood pressure profile throughout the day, which provides a previously unavailable and reliable “baseline.” Because in-office only readings may be skewed by “white coat hypertension,” or stress-induced blood pressure readings, having a more comprehensive look at this data can lead to deeper understandings of user behaviors or conditions. Continuous blood pressure readings can help with diagnoses of stress-related drivers of blood pressure spikes, for example. These insights become the catalyst for life science companies’ new product offerings and go-to-market strategies.
What are some examples of how data sets gathered from partnerships with digital health companies can be leveraged to uncover new value for patients and address their unmet needs?
As digital health companies achieve a certain degree of scale, their expansive data sets become more valuable because of the insights that can be harnessed to improve outcomes and business decisions. Companies like 23andMe, for example, have focused on leveraging their data for research into targeted therapies. Flatiron Health is another great example of a startup that created a foundational platform (EMR) whose clinical data from diverse sources (e.g., laboratories, research repositories, and payer networks) became so highly valued in cancer therapy development that Roche acquired it earlier this year for close to $2B.
It’s exciting to think about the wide array of digital health solutions and the actionable insight that can be gleaned from them. One reason partnerships are important for the industry is few innovators who are collecting data have the capabilities and resources to fully capitalize on its use on their own. Pharma companies and startups must work together to achieve all of this at scale. Earlier this year, Fitbit announced a new partnership with Google to make the data collected from its devices available to doctors. Google’s API can directly link heart rate and fitness activity to the EMR, allowing doctors to easily review and analyze larger amounts of data. This increase in visibility provides physicians with more insight into how patients are doing in between visits, and therefore can also help with decision pathways.
Another example announced earlier this year is a partnership between Evidation Health and Tidepool, who are conducting a new research study, called the T1D Sleep Pilot, to study real-world data from Type 1 diabetics. With Evidation’s data platform and Tidepool’s device-agnostic consumer software, the goal is to better understand the dynamics of sleep and diabetes by studying data from glucose monitors, insulin pumps, and sleep and activity trackers. The data collected from sleep and activity trackers in particular allows us to better understand possible correlations between specific chronic conditions, like diabetes, and the impact of sleep—which in the past has been challenging to monitor. These additional insights provide a more comprehensive understanding of a patient’s condition and can lead to changes in treatment decisions—and ultimately, better outcomes.
How do you assess the quality and reliability of the data generated by digital health companies? What standards are you measuring them against?
Data quality management (DQM) is the way in which leading companies evaluate the quality and reliability of data sources. ISO 9000’s definition of quality is “the degree to which a set of inherent characteristics fulfills requirements.” At ZS, we have a very robust DQM methodology, and our definition goes beyond the basics to include both the accuracy and the value of the data. Factors such as accuracy and absence of errors, and fulfilling specifications (business rules, designs, etc.), are foundational, but in our experience it’s most important to also include an assessment of value, completeness, and lack of bias because often these factors can lead to misleading or inaccurate insights from analysis of that data.
However, it’s not easy assessing the value of a new data source, which presents an entirely different set of challenges. One very important one is the actual interpretation of the data that’s being collected. How do you know when someone is shaking their phone or Fitbit to inflate their steps, or how do you interpret that the device has been taken off and it’s not tracking activity? How do you account for that and go beyond the data to understand what is really happening? As we get more experience with IOT devices and algorithms get smarter, we will get better at interpreting what these devices are collecting and be more forgiving of underlying data quality.
What are the ethical implications or issues (such as data ownership, privacy, and bias) you’ve encountered thus far, or anticipate encountering in the near future?
The ethical stewardship and protection of personal health data are just as essential for the long-term sustainability of the digital health industry as the data itself. The key question is, how can the industry realize the full value from this data without crossing the line? Protecting personal data in an increasingly digitized world—where we’ve largely become apathetic to the ubiquitous “terms and conditions” agreements—is a non-negotiable. How digital health and life science companies collect, manage, and protect users’ information will remain a big concern.
There are also ethical issues around what the data that is captured is used for. Companies need to carefully establish how to appropriately leverage the data without crossing the line. For example, using de-identified data for research purposes with the goal of improving products or services is aligned with creating a better experience for the patient, as opposed to leveraging the data for targeted marketing purposes.
Companies also face the issue of potential biases that may emerge when introducing AI and machine learning into decision-making processes around treatment or access to care. Statistical models are only as good as the data that are used to train them. Companies introducing these models need to test datasets and their AI model outputs to ensure gaps are eliminated from training data, the algorithms don’t learn to introduce bias, and they establish a process for evaluating bias as the models continue to learn and evolve.
A lot of people are — understandably so — very confused when it comes to innovation methodologies, frameworks, and techniques. Questions like: “When should we use Design Thinking?”, “What is the purpose of a Design Sprint?”, “Is Lean Startup just for startups?”, “Where does Agile fit in?”, “What happens after the <some methodology> phase?” are all very common questions.
(How) does it all connect?
When browsing the Internet for answers, one notices quickly that others too are struggling to understand how it all works together.
Gartner (as well as numerous others) tried to visualise how methodologies like Design Thinking, Lean, Design Sprint and Agile flow nicely from one to the next. Most of these visualisations have a number of nicely coloured and connected circles, but for me they seem to miss the mark. The place where one methodology flows into the next is very debatable, because there are too many similar techniques and there is just too much overlap.
The innovation spectrum
It probably makes more sense to just look at Design Thinking, Lean, Design Sprint & Agile as a bunch of tools and techniques in one’s toolbox, rather than argue for one over the other, because they can all add value somewhere on the innovation spectrum.
Innovation initiatives can range from exploring an abstract problem space, to experimenting with a number of solutions, before continuously improving a very concrete solution in a specific market space.
An aspect which often seems to be omitted, is the business model maturity axis. For established products as well as adjacent ones (think McKinsey’s Horizon 1 and 2), the business models are often very well understood. For startups and disruptive innovations within an established business however, the business model will need to be validated through experiments.
Design Thinking really shines when we need to better understand the problem space and identify the early adopters. There are various flavors of design thinking, but they all sort of follow the double-diamond flow. Simplistically the first diamond starts by diverging and gathering lots of insights through talking to our target stakeholders, followed by converging through clustering these insights and identifying key pain-points, problems or jobs to be done. The second diamond starts by a diverging exercise to ideate a large number of potential solutions before prototyping and testing the most promising ideas. Design Thinking is mainly focussed on qualitative rather than quantitative insights.
The slight difference with Design Thinking is that the entrepreneur (or intrapreneur) often already has a good understanding of the problem space. Lean considers everything to be a hypothesis or assumption until validated …so even that good understanding of the problem space is just an assumption. Lean tends to starts by specifying your assumptions on a customer focussed (lean) canvas and then prioritizing and validating the assumptions according to highest risk for the entire product. The process to validate assumptions is creating an experiment (build), testing it (measure) and learn whether our assumption or hypothesis still stands. Lean uses qualitative insights early on but later forces you to define actionable quantitative data to measure how effective the solution addresses the problem and whether the growth strategy is on track. The “Get out of the building” phrase is often associated with Lean Startup, but the same principle of reaching out the customers obviously also counts for Design Thinking (… and Design Sprint … and Agile).
It appears that the Google Venture-style Design Sprint method could have its roots from a technique described in the Lean UX book. The key strength of a Design Sprint is to share insights, ideate, prototype and test a concept all in a 5-day sprint. Given the short timeframe, Design Sprints only focus on part of the solution, but it’s an excellent way to learn really quickly if you are on the right track or not.
Just like dealing with the uncertainty of our problem, solution and market assumptions, agile development is a great way to cope with uncertainty in product development. No need to specify every detail of a product up-front, because here too there are plenty of assumptions and uncertainty. Agile is a great way to build-measure-learn and validate assumptions whilst creating a Minimum Viable Product in Lean Startup parlance. We should define and prioritize a backlog of value to be delivered and work in short sprints, delivering and testing the value as part of each sprint.
Probably not really the answer you were looking for, but there is no clear rule on when to start where. There is also no obvious handover point because there is just too much overlap, and this significant overlap could be the explanation of why some people claim methodology <x> is better than <y>.
Anyhow, most innovation methodologies can add great value and it’s really up to the team to decide where to start and when to apply which methods and techniques. The common ground most can agree with, is to avoid falling in love with your own solution and listen to qualitative as well as quantitative customer feedback.
As designers, we want to work on problems that are intriguing and “game-changing”. All too often, we limit the “game-changing” category to a handful of consumer-facing mobile apps and social networks. The truth is: enterprise software gives designers a unique set of complex problems to solve. Enterprise platforms usually have a savvy set of users with very specific needs — needs that, when addressed, often affect a business’s bottom line.
One of my first projects as a product designer here at Instacart was to redesign elements of our inventory management tool for retailers (e.g. Kroger, Publix, Safeway, Costco, etc.). As I worked on the project more and more, I learned that Enterprise tools are full of gnarly complexity and often present opportunities to practice deep thought. As Jonathan, one of our current enterprise platform designers said —
The greater the complexity, the greater the opportunity to find elegance.
As we scoped the project we found that the existing product wasn’t enabling retailers to manage their inventories as concisely and efficiently as they could. We found retailer users were relying on customer support to help carry out smaller tasks. Our goal with the redesign was to build and deliver a better experience that would enable retailers to manage their inventory more easily and grow their business with Instacart.
The first step in redesigning was to understand the flow of the current product. We mapped out the journey of a partner going through the tool and spoke with the PMs to figure out what we could incorporate into the roadmap.
Once we had a good understanding of the lay of the land, engineering resources, and retailers’ needs, we got into the weeds. Here are a few improvements we made to the tool —
Present retailers with an actionable page on the get-go
Make it quick and easy to add, delete, and modify items
Establishing Overall Hierarchy
Our solution simplified a few things:
A search bar rests atop the product to help find and add items without having to be on this specific page. It pops up a modal that offers a search and add experience. This was visually prioritized since it’s the most common action taken by retailers
Decoupled search flow and “Add new product” flow to streamline the workflows
Pagination, which was originally on the top and bottom, is now pinned to the bottom of the page for easy navigation
We also rethought the information hierarchy on this page. In the example below, the retailer is in the “Beverages” aisle under the “Coffee” item category, which is on the top left. They are editing or adding the item “Eight O’Clock Coffee,” which is the page title. This title is bigger to anchor the user on the page and improve navigation throughout the platform
While it’s great that the older Item Details page was partitioned into sections, from an IA perspective, it offered challenges for two reasons:
The category grouping didn’t make sense to retailers
Retailers had to read the information vertically but digest it horizontally and vertically
To address this, we broke down the sections into what’s truly necessary. From there, we identified four main categories of information that the data fell under:
Images — This is first to encourage retailers to add product photos
Basic Info — Name, brand, size, and unit
Item description — Below the item description field, we offered the description seen on the original package (where the data was available) to help guide them as they wrote
Product attributes — help better categorize the product (e.g. Kosher)
Sources now pop up on the top right of the input fields so the editor knows who last made changes.
Seeking validation through numbers is always fantastic. We did a small beta launch of this product and saw an increase in weekly engagement and decrease in support requests.
I learned that designing enterprise products helps you extend yourself as a visual designer and deep product thinker. I approached this project as an opportunity to break down complex interactions and bring visual elegance to a product through thoughtful design. To this day, it remains one of my favorite projects at Instacart as it stretched my thinking and enhanced my visual design chops. Most importantly, it taught me to look at Enterprise tools in a new light; now when I look at them, I am able to appreciate the complexity within
Emotions are continually affecting our thought processes and decisions, below the level of our awareness. And the most common emotion of them all is the desire for pleasure and the avoidance of pain. Our thoughts almost inevitably revolve around this desire; we simply recoil from entertaining ideas that are unpleasant or painful to us. We imagine we are looking for the truth, or being realistic, when in fact we are holding on to ideas that bring a release from tension and soothe our egos, make us feel superior. This pleasure principle in thinking is the source of all of our mental biases. If you believe that you are somehow immune to any of the following biases, it is simply an example of the pleasure principle in action. Instead, it is best to search and see how they continually operate inside of you, as well as learn how to identify such irrationality in others.
These biases, by distorting reality, lead to the mistakes and ineffective decisions that plague our lives. Being aware of them, we can begin to counterbalance their effects.
1) Confirmation Bias
I look at the evidence and arrive at my decisions through more or less rational processes.
To hold an idea and convince ourselves we arrived at it rationally, we go in search of evidence to support our view. What could be more objective or scientific? But because of the pleasure principle and its unconscious influence, we manage to find that evidence that confirms what we want to believe. This is known as confirmation bias.
We can see this at work in people’s plans, particularly those with high stakes. A plan is designed to lead to a positive, desired objective. If people considered the possible negative and positive consequences equally, they might find it hard to take any action. Inevitably they veer towards information that confirms the desired positive result, the rosy scenario, without realizing it. We also see this at work when people are supposedly asking for advice. This is the bane of most consultants. In the end, people want to hear their own ideas and preferences confirmed by an expert opinion. They will interpret what you say in light of what they want to hear; and if your advice runs counter to their desires, they will find some way to dismiss your opinion, your so-called expertise. The more powerful the person, the more they are subject to this form of the confirmation bias.
When investigating confirmation bias in the world take a look at theories that seem a little too good to be true. Statistics and studies are trotted out to prove them, which are not very difficult to find, once you are convinced of the rightness of your argument. On the Internet, it is easy to find studies that support both sides of an argument. In general, you should never accept the validity of people’s ideas because they have supplied “evidence.” Instead, examine the evidence yourself in the cold light of day, with as much skepticism as you can muster. Your first impulse should always be to find the evidence that disconfirms your most cherished beliefs and those of others. That is true science.
2) Conviction Bias
I believe in this idea so strongly. It must be true.
We hold on to an idea that is secretly pleasing to us, but deep inside we might have some doubts as to its truth and so we go an extra mile to convince ourselves — to believe in it with great vehemence, and to loudly contradict anyone who challenges us. How can our idea not be true if it brings out of us such energy to defend it, we tell ourselves? This bias is revealed even more clearly in our relationship to leaders — if they express an opinion with heated words and gestures, colorful metaphors and entertaining anecdotes, and a deep well of conviction, it must mean they have examined the idea carefully and therefore express it with such certainty. Those on the other hand who express nuances, whose tone is more hesitant, reveal weakness and self-doubt. They are probably lying, or so we think. This bias makes us prone to salesmen and demagogues who display conviction as a way to convince and deceive. They know that people are hungry for entertainment, so they cloak their half-truths with dramatic effects.
3) Appearance Bias
I understand the people I deal with; I see them just as they are.
We do not see people as they are, but as they appear to us. And these appearances are usually misleading. First, people have trained themselves in social situations to present the front that is appropriate and that will be judged positively. They seem to be in favor of the noblest causes, always presenting themselves as hardworking and conscientious. We take these masks for reality. Second, we are prone to fall for the halo effect — when we see certain negative or positive qualities in a person (social awkwardness, intelligence), other positive or negative qualities are implied that fit with this. People who are good looking generally seem more trustworthy, particularly politicians. If a person is successful, we imagine they are probably also ethical, conscientious and deserving of their good fortune. This obscures the fact that many people who get ahead have done so by doing less than moral actions, which they cleverly disguise from view.
4) The Group Bias
My ideas are my own. I do not listen to the group. I am not a conformist.
We are social animals by nature. The feeling of isolation, of difference from the group, is depressing and terrifying. We experience tremendous relief to find others who think the same way as we do. In fact, we are motivated to take up ideas and opinions because they bring us this relief. We are unaware of this pull and so imagine we have come to certain ideas completely on our own. Look at people that support one party or the other, one ideology — a noticeable orthodoxy or correctness prevails, without anyone saying anything or applying overt pressure. If someone is on the right or the left, their opinions will almost always follow the same direction on dozens of issues, as if by magic, and yet few would ever admit this influence on their thought patterns.
5) The Blame Bias
I learn from my experience and mistakes.
Mistakes and failures elicit the need to explain. We want to learn the lesson and not repeat the experience. But in truth, we do not like to look too closely at what we did; our introspection is limited. Our natural response is to blame others, circumstances, or a momentary lapse of judgment. The reason for this bias is that it is often too painful to look at our mistakes. It calls into question our feelings of superiority. It pokes at our ego. We go through the motions, pretending to reflect on what we did. But with the passage of time, the pleasure principle rises and we forget what small part in the mistake we ascribed to ourselves. Desire and emotion will blind us yet again, and we will repeat exactly the same mistake and go through the same mild recriminating process, followed by forgetfulness, until we die. If people truly learned from their experience, we would find few mistakes in the world, and career paths that ascend ever upward.
6) Superiority Bias
I’m different. I’m more rational than others, more ethical as well.
Few would say this to people in conversation. It sounds arrogant. But in numerous opinion polls and studies, when asked to compare themselves to others, people generally express a variation of this. It’s the equivalent of an optical illusion — we cannot seem to see our faults and irrationalities, only those of others. So, for instance, we’ll easily believe that those in the other political party do not come to their opinions based on rational principles, but those on our side have done so. On the ethical front, few will ever admit that they have resorted to deception or manipulation in their work, or have been clever and strategic in their career advancement. Everything they’ve got, or so they think, comes from natural talent and hard work. But with other people, we are quick to ascribe to them all kinds of Machiavellian tactics. This allows us to justify whatever we do, no matter the results.
We feel a tremendous pull to imagine ourselves as rational, decent, and ethical. These are qualities highly promoted in the culture. To show signs otherwise is to risk great disapproval. If all of this were true — if people were rational and morally superior — the world would be suffused with goodness and peace. We know, however, the reality, and so some people, perhaps all of us, are merely deceiving ourselves. Rationality and ethical qualities must be achieved through awareness and effort. They do not come naturally. They come through a maturation process.
Recently in a risk management meeting, I watched a data scientist explain to a group of executives why convolutional neural networks were the algorithm of choice to help discover fraudulent transactions. The executives—all of whom agreed that the company needed to invest in artificial intelligence—seemed baffled by the need for so much detail. “How will we know if it’s working?” asked a senior director to the visible relief of his colleagues.
Although they believe AI’s value, many executives are still wondering about its adoption. The following five questions are boardroom staples:
1. “What’s the reporting structure for an AI team?”
Organizational issues are never far from the minds of executives looking to accelerate efficiencies and drive growth. And, while this question isn’t new, the answer might be.
Captivated by the idea of data scientists analyzing potentially competitively-differentiating data, managers often advocate formalizing a data science team as a corporate service. Others assume that AI will fall within an existing analytics or data center-of-excellence (COE).
AI positioning depends on incumbent practices. A retailer’s customer service department designated a group of AI experts to develop “follow the sun chatbots” that would serve the retailer’s increasingly global customer base. Conversely a regional bank considered AI more of an enterprise service, centralizing statisticians and machine learning developers into a separate team reporting to the CIO.
These decisions were vastly different, but they were both the right ones for their respective companies.
How unique (e.g., competitively differentiating) is the expected outcome? If the proposed AI effort is seen as strategic, it might be better to create team of subject matter experts and developers with its own budget, headcount, and skills so as not distract from or siphon resources from existing projects.
To what extent are internal skills available? If data scientists and AI developers are already clustered within a COE, it might be better to leave the team as-is, hiring additional experts as demand grows.
How important will it be to package and brand the results of an AI effort? If AI outcome is a new product or service, it might be better to create a dedicated team that can deliver the product and assume maintenance and enhancement duties as it continues to innovate.
2. “Should we launch our AI effort using some sort of solution, or will coding from scratch distinguish our offering?”
When people hear the term AI they conjure thoughts of smart Menlo Park hipsters stationed at standing desks wearing ear buds in their pierced ears and writing custom code late into the night. Indeed, some version of this scenario is how AI has taken shape in many companies.
Executives tend to romanticize AI development as an intense, heads-down enterprise, forgetting that development planning, market research, data knowledge, and training should also be part of the mix. Coding from scratch might actually prolong AI delivery, especially with the emerging crop of developer toolkits (Amazon Sagemaker and Google Cloud AI are two) that bundle open source routines, APIs, and notebooks into packaged frameworks.
These packages can accelerate productivity, carving weeks or even months off development schedules. Or they can exacerbate collaboration efforts.
Is time-to-delivery a success metric? In other words, is there lower tolerance for research or so-called “skunkworks” projects where timeframes and outcomes could be vague?
Is there a discrete budget for an AI project? This could make it easier to procure developer SDKs or other productivity tools.
How much research will developer toolboxes require? Depending on your company’s level of skill, in the time it takes to research, obtain approval for, procure, and learn an AI developer toolkit your team could have delivered important new functionality.
3. “Do we need a business case for AI?”
It’s all about perspective. AI might be positioned as edgy and disruptive with its own internal brand, signaling a fresh commitment to innovation. Or it could represent the evolution of analytics, the inevitable culmination of past efforts that laid the groundwork for AI.
I’ve noticed that AI projects are considered successful when they are deployed incrementally, when they further an agreed-upon goal, when they deliver something the competition hasn’t done yet, and when they support existing cultural norms.
Do other strategic projects require business cases? If they do, decide whether you want AI to be part of the standard cadre of successful strategic initiatives, or to stand on its own.
Are business cases generally required for capital expenditures? If so, would bucking the norm make you an innovative disruptor, or an obstinate rule-breaker?
How formal is the initiative approval process? The absence of a business case might signal a lack of rigor, jeopardizing funding.
What will be sacrificed if you don’t build a business case? Budget? Headcount? Visibility? Prestige?
4. “We’ve had an executive sponsor for nearly every high-profile project. What about AI?”
Incumbent norms once again matter here. But when it comes to AI the level of disruption is often directly proportional to the need for a sponsor.
A senior AI specialist at a health care network decided to take the time to discuss possible AI use cases (medication compliance, readmission reduction, and deep learning diagnostics) with executives “so that they’d know what they’d be in for.” More importantly she knew that the executives who expressed the most interest in the candidate AI undertakings would be the likeliest to promote her new project. “This is a company where you absolutely need someone powerful in your corner,” she explained.
Does the company’s funding model require an executive sponsor? Challenging that rule might cost you time, not to mention allies.
Have high-impact projects with no executive sponsor failed? You might not want your AI project to be the first.
Is the proposed AI effort specific to a line of business? In this case enlisting an executive sponsor familiar with the business problem AI is slated to solve can be an effective insurance policy.
5. “What practical advice do you have for teams just getting started?”
If you’re new to AI you’ll need to be careful about departing from norms, since this might attract undue attention and distract from promising outcomes. Remember Peter Drucker’s quote about culture eating strategy for breakfast? Going rogue is risky.
On the other hand, positioning AI as disruptive and evolutionary can do wonders for both the external brand as well as internal employee morale, assuring constituents that the company is committed to innovation, and considers emerging tech to be strategic.
Either way, the most important success measures for AI are setting accurate expectations, sharing them often, and addressing questions and concerns without delay.
Distribute a high-level delivery schedule. An unbounded research project is not enough. Be sure you’re building something—AI experts agree that execution matters—and be clear about the delivery plan.
Help colleagues envision the benefits. Does AI promise first mover advantage? Significant cost reductions? Brand awareness?
Explain enough to color in the goal. Building a convolutional neural network to diagnose skin lesions via image scans is a world away from using unsupervised learning to discover unanticipated correlations between customer segments. As one of my clients says, “Don’t let the vague in.”
These days AI has mojo. Companies are getting serious about it in a way they haven’t been before. And the more your executives understand about how it will be deployed—and why—the better the chances for delivering ongoing value.
Why Olam is Deploying Tech First, Then Thinking About CVC
“We have realized that some companies have gone down the wrong path by adopting the approach of inventing the problem. They find a technology that’s exciting and try to force-fit that technology for a problem that they don’t have. This is why we want to be very deliberate about the problems first, and then come to technology.”
“I’ll give you an example of blockchain. There’s so much hype about blockchain around the world. And in our industry, there are a few companies that have done some pilots. But we have not gone down that route, because we have not seen a tangible, scalable use case that could give us significant benefits for adopting blockchain.”
If one company could benefit from the efficiencies new technology can bring, it’s Olam, with a complex supply chain that grows, sources, processes, manufactures, transports, trades and markets 47 different agrifood products across 70 countries. These include commodities like coffee, cotton, cocoa, and palm oil that are farmed by over 4 million farmers globally, most of which are smallholders in developing countries.
But the third largest agribusiness in the world has been noticeably absent from the agrifood corporate venture capital scene in recent years, instead opting mostly to build its own technology solutions in-house. (It did deploy Phytech’s FitBit for crops in Australia in 2016 as an outside example.)
For traceability, and perhaps an alternative to blockchain-enabled technology, there’s Olam AtSource, with a digital dashboard that provides Olam customers with access to rich data, advanced foot-printing, and granular traceability. Olam hopes AtSource will help its customers “meet multiple social and environmental targets thereby increasing resilience in supply chains.”
Olam has also developed and deployed the Olam Farmer Information System (OFIS), a smallholder farm data collection platform providing smallholders with management tools and Olam customers with information about the provenance of products.
“OFIS solves the information issue by providing a revolutionary tech innovation for collecting and analyzing first mile data,” Brayn-Smith told AgFunderNews when OFIS launched in 2017. “We are able to register thousands of smallholders, GPS map their farms and local infrastructure, collect all types of farm gate level data such as the age of trees, and record every training intervention.”
This product is a clear example of a “transformational technology” that solves a problem for Olam and also gives the business efficiencies that could impact the bottom line, according to Sundararajan.
And Olam has built on top of OFIS to transact directly with cocoa farmers in Indonesia where Olam is publishing prices to around 30,000 farmers and buying cocoa directly from them.
“Before technology was available, it was almost impossible for any company to buy directly from the farmers, just because of the sheer volume and number of farmers. But, with technology, you have a far better reach, which will allow us to directly communicate with them,” Sundararajan tells AgFunderNews.
“Now the farmer can just accept a price and type in that he wants to supply it, and we arrange the complete logistics to pick up the cocoa from the farmer,” he says adding that the company’s country heads in other parts of the world are keen to launch this service in their markets. The company is starting next in Peru, then Guatemala, Colombia, Cote d’Ivoire, Ghana, and Nigeria.
Olam as Disruptor
While Olam deployed OFIS to solve for a problem, it also gives the company the opportunity to be disruptive in the markets it serves, according to Sundararajan.
As well as looking for transformational ways to solve specific problems, Olam also looks at “any ideas we have that will give Olam an opportunity to disrupt our own industry. So, we end up being a disrupter and not be at the risk of being disrupted by a new player,” he says.
“This fundamental shift in terms of Olam getting an opportunity to directly interact and transact with farmers is a starting point of disruption for us. This is a very complex point, which will bring into play several technologies for us to be able to successfully scale it.”
Going down this route, Sundararajan says Olam could end up providing farmers with new services and creating “separate streams of revenue that has nothing to do with what we were doing five or 10 years back.”
In this vein, Olam is working on deploying a technology to detect moisture — and therefore quality — in its commodities. The company is also looking at financial tools for its farmers.
“Looking at our business model, we believe that we have a few very good opportunities at the first mile of the supply chain and the last mile of the supply chain to change the way we compete,” says Sundararajan. “We believe that since we have control of the supply chain end-to-end, we can use technology to differentiate our service to customers in a way that our competitors will find difficult to replicate.”
Informal Startup Interactions
Olam does interact with startups on a selective basis, and Sundararajan’s participation in Rethink’s Singapore conference, as well as a hackathon it took part in with Fujitsu in Australia last year, are two examples. Sundararajan said he is considering an idea like The Unilever Foundry, but the company has yet to create a formal process or framework for these interactions. And the same goes for corporate venture capital.
“We believe that our digital journey has to mature much more, where we should demonstrate success within, by implementing the solutions that we’re developing, before even considering investing in venture capital. We believe that we have a very good strategy and a suite of products, stretching across from farm to the factories, to digitize our operations, whether it is a digital buying model, or whether it is spot factories in terms of predictive maintenance or increasing yield or it’s drone imagery from our own plantations, and productivity apps for employees.”