Do you ever think about tomorrow? Many retailers don’t. They’re too concerned with what’s happening in the moment. They’re too wrapped up in managing their daily business operations or maintaining profit margins.

Don’t get me wrong – those things are important. But tomorrow matters more than they know.

With game-changing technologies like the Internet of Things (IoT), virtual reality, and machine learning reshaping the retail landscape, tomorrow can no longer be ignored. If your company wants to stay ahead of the competition – both now and in the future – you need to begin experimenting with these innovations today.

Beer, there, and everywhere: Create an immersive customer experience

Imagine you’re a Brooklyn-based brewery. You craft the most delicious beer anyone’s ever tasted, and Brooklynites are absolutely gaga over your product. But how do you spread the word? How can you make people in Seattle or San Francisco thirst for your beverage?

Virtual reality and IoT tools can help you create a more immersive customer experience – one that gives people an in-depth view into your brewery – so folks across the country can get excited about sampling your suds.

By setting up a 360-degree video camera and implementing virtual reality capabilities, you can invite people all over the world to tour your facility. They can visit the tasting room, check out the outdoor patio, and watch the kettles work their magic in the production area.

IoT sensors, meanwhile, can provide prospective customers with insight around your brewing processes. Attached to the brew kettles, these sensors enable you to share real-time data about each batch of beer, from when the hops reach a boil to when fermentation is complete.

If viewers like what they see, they can order a case of your beer online.

Creating an immersive customer experience, where people get a glance behind the curtain to see how your company operates and how your product is made, is a surefire recipe for retail success.

A passion for fashion: Predict trends so your customers are always dressed to kill

Instagram, the popular image-sharing app, has a global community of more than 800 million users. These users share upwards of 95 million photos and videos per day.

If a woman from the United States is traveling to Tokyo for an upcoming vacation and wants to make sure she looks fashionable while visiting Japan’s capital city, where can she turn?

Instagram, of course.

With a simple keyword search for “fashion” and “Tokyo,” this woman could be knee-deep in results highlighting the top trends from this chic metropolitan hotspot. Now, with a better idea of what the locals are wearing, she can pick up a few new outfits before her trip, and she won’t feel so out of place in her American attire when she visits.

Retailers, particularly fashion brands, can benefit from how consumers are using apps like Instagram. By analyzing what people are wearing in photos taken in fashion meccas like London, Paris, Tokyo, Milan, or New York, your business can have its finger firmly on the pulse.

Pairing your analysis with machine learning capabilities can enable your retailer to detect and predict the hottest fashion trends. This will help your designers tailor the clothing they create to what’s happening – or what will be happening – in the market.

If more people are wearing floral-print miniskirts, you can design matching leggings. If more people are dressing in denim, you can ramp up production on jean jackets.

Staying up to date on the latest fashion trends can keep your retailer at the top of its game. Predicting the next big thing in fashion using machine learning? That will have your business declaring “game over” to all your competitors.

Not your grandma’s kitchen: Increase customer convenience through greater connectivity

Connected products are invading our homes. We have smart TVs in our living rooms. We have showerheads equipped with Bluetooth speakers in our bathrooms. We have lights that brighten or dim based on our sleeping schedules in our bedrooms.

In the kitchen, though, things are getting really intelligent. From precision cookers that alert you when dinner’s ready to coffee makers you can operate with your smartphone, kitchen appliances are creating a whole new level of convenience for customers.

With a smart refrigerator, customers can create shopping lists using a touch screen on the door. IoT capabilities enable people to add or remove items from their lists using a mobile device. Customers can even submit their grocery orders to a nearby store through their smart fridge, a convenient click-and-collect shopping scenario.

Augmented reality, meanwhile, allows people to peek inside their refrigerators without even opening them. If a woman at work wants to see if she has enough milk for a bowl of cereal tomorrow, she can check using a tablet or smartphone.

Retailers and consumer products companies can leverage this technology to deliver a more engaging product experience. The packaging of a stick of butter, for instance, might have a code on it. When a man peers into his refrigerator using his smartphone, he could click on the code and find out the product’s expiration date. Or perhaps he can learn a few new recipes he could bake using the butter.

By creating a hassle-free shopping experience and enhancing how your buyers engage with your products, you can increase sales and earn your customers’ loyalty.

Home sweet home: Modernize retail like real-estate agents have revolutionized homebuying

Think of how the realty business has changed over the past 25 years. In the early ‘90s, prospective homebuyers had to schedule an appointment with a Realtor or attend an open house to see a home they liked.

In the mid-2000s, house hunting went online, with sites like Trulia and Zillow springing up. Today, homebuyers can snap a photo of an on-the-market house they like using a mobile app and see pictures of the home’s interior, learn the price, find out the square footage, and discover how many bathrooms it has.

Retailers should strive to modernize their industry like the realty business has revolutionized homebuying. Barcode scanning and sensor tracking are just a couple technologies that could help.

If a customer is walking through the aisles of your store, you could offer them the opportunity to scan a tag on a shirt with their mobile device and instantly give them access to outfit ideas or show them accessories that match the top.

Sensors, meanwhile, could track where a shopper is in a store, allowing your retailer to send timely and relevant offers based on their location.

Adding value to your customer experience is the name of the game in retail. And there’s no better way to create a more valuable in-store customer experience than with the latest technology.

Innovation experimentation: Forge your path to a brighter future with revolutionary tech tools

Innovations like IoT, virtual reality, and machine learning are shaping what retail’s future will look like.

Your company’s success – both today and tomorrow – will depend on your willingness to embrace these technologies and experiment with new ways to engage and satisfy your customers.

Join us at the National Retail Forum’s 2018 conference and EXPO at the Jacob K. Javits Convention Center in New York City on January 14–16 to learn how the SAP Leonardo digital innovation system can help your organization bring these exciting technologies to life.

 

We have been talking about artificial intelligence (AI) for quite a while now but, so far, it has failed to really make its mark. It is showing a great deal of potential but has not yet lived up to the hype. Anyone who uses Siri or Alexa quickly discovers the limitations when they try to step out of a strict set of rules.

However, there is no denying that it is only a matter of time before AI starts to make a big difference in multiple industries and in a number of areas.

Healthcare

Healthcare is arguably one of the industries likely to see the biggest growth in the use and application of AI in the next few years, and this is backed up by the huge amount of investment in this industry. There are a number of areas in the healthcare industry where AI is already gaining a lot of ground.

One of the things the healthcare industry has in abundance is data. Governments and healthcare organizations have billions of data records going back many decades, and mining this data to gain an insight can be a big challenge. AI is already used to mine and analyze this data to spot subtle patterns in the progression, diagnosis, and treatment of many medical conditions.

The diagnosis of a medical condition or disease is not as black and white as you may think. A diagnosis is often made by piecing together a number of indications and observations until the balance of probability is sufficiently in favor of a diagnosis. I was recently surprised to hear that very few blood tests for common diseases are 100% accurate. Almost all have a few percentage points of error.

Healthcare professionals rely on years of experience and prebuilt algorithms to recognize the signs of illness and make a diagnosis, but they can often miss things. For example, doctors may look at an x-ray that, to most of us, looks normal, but they will see a subtle shadow that can indicate an illness. How subtle can that shadow be before it is missed?

The BBC reported on new research using AI to outperform experienced cardiologists in spotting early signs of heart disease. The report states that even the best doctors get it wrong about 20% of the time. They rely on their experience to help them spot telltale signs of disease, whether that is a pattern in a scan or shadows on an x-ray. Researchers fed the system data on 1,000 patients, including their scan results, and information about whether they later had heart problems. Using machine learning, the AI system was able to more accurately spot signs of heart disease in the scans than experienced doctors. This research is still in the early stages, but it is a good illustration of the potential of what AI can bring to this industry.

As this and similar systems are fed more and more historical data, they will get better and better at spotting patterns and signs of disease. I can see AI being used more and more for medical diagnosis across the healthcare industry.

Automotive

We all know about Tesla and Elon Musk’s claims about the capability of its autopilot feature. We hear that the company is very close to being able to navigate autonomously from coast to coast in the United States. I have witnessed autopilot firsthand and, on a motorway, it was very impressive. However, that was a motorway with fairly straight lanes and nice, clear road markings. Navigating through the center of London or, even harder, on a single-track country lane with hedges on either side, is an entirely different proposition. In my opinion, we are a very long way from AI that is powerful and experienced enough to safely navigate a very complex journey on its own.

Car manufacturers are now looking at embedding AI services such as Amazon Alexa into their vehicles to allow passengers to control technology in the car through natural language voice commands. At this year’s CES show in Las Vegas, Mercedes-Benz demonstrated its new AI-powered in-car personal assistant, and in 2017, BMW announced it would start integrating Alexa into selected BMW and Mini vehicles in 2018. Kia also recently announced it will soon embed Google Assistant into its infotainment systems.

Undoubtedly, we will see increasingly powerful AI within new cars over the next few years, used for both navigation and as in-car virtual assistants. But I think it will be many years (even decades) before we have cars that use AI to be truly autonomous.

Cybersecurity

One of the more worrying trends I expect to see over the next few years is the use of AI in cyberattacks. This has been happening for quite a while now in a relatively basic form. For years, the Internet has been awash with bots that are constantly poking at web servers looking for vulnerabilities. As soon as they find a vulnerability, they will report back to their owner or automatically exploit the vulnerability. These bots, however, are fairly unintelligent and will not learn or automatically adapt their behavior based on what they find.

This is where I see AI being exploited and weaponized. Rather than simply poke at servers looking for holes, AI-powered bots will have the ability to learn, adapt, and evade cybersecurity. It is often said that humans are the weakest link when it comes to cybersecurity. AI tools are already being developed that can learn what phishing techniques are most effective and automatically create phishing campaigns that are better than those created by humans. This technique was tested by two data scientists from security company ZeroFox in 2016. They built an AI tool that would use machine learning to determine what phishing techniques gained the best results and adapt the emails based on this learning. In tests, the AI tool significantly outperformed a human.

This view that AI will be increasingly weaponized is shared by the industry. During the Black Hat USA 2017 conference in July last year, 62% of surveyed attendees agreed there is a high possibility that AI could be used by hackers for offensive purposes.

While AI is expected to be weaponized over the next few years for offensive cyberattacks, it is also expected it will be used defensively within the cybersecurity industry. One of the key roles of any cybersecurity system is to recognize threats and protect against them. This is usually done by recognizing threat signatures that match a predefined list. AI and machine learning can identify malicious behavior that does not necessarily have a known signature and then defend against that behavior. While this approach is still at a very early stage, I expect it to become more prevalent, especially as the offensive use of AI becomes more widespread.

E-commerce and customer service

Retail is one of the fastest-moving industries in the world, and e-commerce retail is even faster. Competition is often fierce, and this drives innovation within the industry.

Chances are you have already experienced AI in e-commerce, but you may not have noticed. Every time Amazon recommends a product to you, this is driven by AI. A very complex set of algorithms is used to determine what you are likely to purchase based on your demographic profile, your purchasing history, and what other products you have viewed. Amazon generates vast quantities of data, and this data can be used by AI to generate highly targeted recommendations.

You may also have used a live chat tool, either on a website or on a platform such as Facebook, to communicate with a brand. There is a good chance that, at least once, you have been speaking to an AI-powered bot that is feeding you a preset range of replies based on your comments.

Customer service is the perfect area for automation using AI. Most customer service queries follow a very similar pattern, such as “where is my order?” or “can I change the delivery address?” Customer service agents will normally have a script to follow based on the query, and the majority of queries will fit into a small set of scenarios.

For example, if a customer calls to ask when an order will be delivered, the customer service agent will probably ask the customer for authentication, maybe with an order number and postcode, then search for that order within internal systems to find its status and delivery date. The customer may then ask to change it, and the agent may do that.

This process could very easily be automated, as it does not really take any initiative from the agent, who is following a standard and scripted process. By automating processes like this, humans can be freed up to deal with the more complex queries that AI would struggle to handle.

It will be interesting to see how AI will be used in e-commerce over the next few years. I predict that AI will have its biggest impact in customer service, but also in user personalization to provide more targeted recommendations and experience to users.

Virtual personal assistants

There is currently a fierce battle taking place between Apple (HomePod), Google (Google Home), and Amazon (Echo) over home virtual assistant devices. Right now, Amazon seems to be winning with the Echo powered by Alexa. The skills of these assistants are fairly basic at the moment and are mainly limited to choosing music, answering a few questions, and controlling home automation devices.

I expect to see big advances in the capability of these devices over the next few years, especially with some of the biggest and most innovative companies in the world behind them. I predict they’ll be integrated with more consumer devices and home automation systems, and also for their AI to improve significantly. The software that powers these devices is already being integrated into products such as cars, Sonos, and even an LG fridge. I predict that this trend will start to accelerate in the next 12 months.

In summary, it seems that the development and use of AI are accelerating, and AI is likely to become much more prevalent across many industries over the next few years. I have picked a few industries but, in reality, AI is likely to have an impact across almost all industries to some degree.

However, I do think that we are a very long way from AI that can handle situations outside of a clear set of predefined scenarios. My car is not likely to be driving me all the way to work anytime soon, and you will still need to vacuum your stairs for years to come.

Learn how AI is already transforming industries in An AI Shares My Office.

This article originally appeared on The Future of Customer Experience and Commerce.

 

Artificial intelligence (AI) and machine learning are poised to revolutionize the way utilities produce, transmit, and consume energy by powering the modern smart grid. Machine learning is an application of AI in which machines are given access to data and, based on this data, “learn” without being explicitly programmed. Machine learning may still be in the early stages of utility industry development and implementation, but its potential is enormous, according to Harvard University. Key machine learning benefits include more reliable energy, greater consumer choice and engagement, asset optimization, service restoration, outage management, and increased cybersecurity. Utilities that take steps now to modernize their infrastructure and adopt machine learning will gain a competitive advantage.

The need for the smart grid

In 2003, an overloaded transmission line in Northern Ohio sagged and hit a tree, causing the line to fail and shut down. Normally, this shutdown would have tripped an alarm at FirstEnergy Corporation, the Ohio-based utility company responsible for the line. The alarm system malfunctioned, however, and over subsequent hours, additional lines began to sag and fail. This seemingly mundane failure had a cascading and catastrophic effect, leaving 50 million people in the northeastern United States and southeastern Canada without power for two days, according to Scientific American. The massive blackout cost an estimated $6 billion and led to an official inquiry. The finding: the blackout was the result of human error and faulty equipment. Could machine learning have identified the elevated line fault risk before it ever happened and prevented the blackout?

This is one question that utility companies and machine learning experts are now trying to answer. Nearly 15 years after that 2003 blackout, the American power grid remains a vast network of more than 5,800 power plants and 2.7 million miles of power lines. The average power plant is over 30 years old and the average transformer is more than 40 years old.

In an effort to update and modernize America’s utility grid, the U.S. Department of Energy has invested $4.5 billion in smart grid infrastructure. This includes installing over 15 million smart meters to monitor energy usage and alert utilities to local blackouts. Artificial intelligence will be the “brain” for this smart grid.

Machine learning and the utility industry: Key benefits

The sheer volume of data collected by smart sensors can be overwhelming, making real-time analysis and action difficult. AI is solving this problem by becoming the “brain” of the future smart grid. Advances in deep learning algorithms are now making it possible for AI to instantly analyze real-time data. AI is able to spot patterns and anomalies in datasets, allowing utilities to make on-the-spot decisions about how to best allocate energy resources. These deep learning algorithms are revolutionizing both the demand and supply side for the energy economy in the following ways.

1. Improve distributed generation management. The current system is not constructed to accommodate energy source diversification. For example, the rise of distributed generation is complicating supply and demand forces. When private users generate their own electricity from renewable sources such as wind and solar, utility companies can absorb the excess energy into the grid. This complicates supply and demand, however. AI can help utilities realize the next-generation grid through enhanced distributed resource management that automatically flows power through the grid to deliver more reliable energy and greater customer choice.

2. Asset optimization. Utilities are developing algorithms based on industry intelligence that will predict the probability of failure. These algorithms take into account industry-wide early failure rates for equipment, creating a richer understanding of premature failure risks for enhanced asset maintenance, workflow, and portfolio management.

3. Outage management. Utilities are using analytics-validating models to predict and identify outages. Machine learning and device automation allow for better resource management, reducing downtime and improving reliability. Self-healing grids can automatically detect and address vulnerabilities, reducing the likelihood of outages.

4. Customer engagement. Utilities are mining data with the aid of machine learning to understand customer behavior and service needs. Using this data, utilities can provide faster and more intuitive interactive customer service via voice response, personalization, and service matching.

Learn how to innovate at scale by incorporating individual innovations back to the core business to drive tangible business value by reading Accelerating Digital Transformation in Utilities.

 

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In 2013, several UK supermarket chains discovered that products they were selling as beef were actually made at least partly—and in some cases, entirely—from horsemeat. The resulting uproar led to a series of product recalls, prompted stricter food testing, and spurred the European food industry to take a closer look at how unlabeled or mislabeled ingredients were finding their way into the food chain.

By 2020, a scandal like this will be eminently preventable.

The separation between bovine and equine will become immutable with Internet of Things (IoT) sensors, which will track the provenance and identity of every animal from stall to store, adding the data to a blockchain that anyone can check but no one can alter.

Food processing companies will be able to use that blockchain to confirm and label the contents of their products accordingly—down to the specific farms and animals represented in every individual package. That level of detail may be too much information for shoppers, but they will at least be able to trust that their meatballs come from the appropriate species.

The Spine of Digitalization

Keeping food safer and more traceable is just the beginning, however. Improvements in the supply chain, which have been incremental for decades despite billions of dollars of technology investments, are about to go exponential. Emerging technologies are converging to transform the supply chain from tactical to strategic, from an easily replicable commodity to a new source of competitive differentiation.

You may already be thinking about how to take advantage of blockchain technology, which makes data and transactions immutable, transparent, and verifiable (see “What Is Blockchain and How Does It Work?”). That will be a powerful tool to boost supply chain speed and efficiency—always a worthy goal, but hardly a disruptive one.

However, if you think of blockchain as the spine of digitalization and technologies such as AI, the IoT, 3D printing, autonomous vehicles, and drones as the limbs, you have a powerful supply chain body that can leapfrog ahead of its competition.

Hints of the Future

In the mid-1990s, when the World Wide Web was in its infancy, we had no idea that the internet would become so large and pervasive, nor that we’d find a way to carry it all in our pockets on small slabs of glass.

But we could tell that it had vast potential.

Today, with the combination of emerging technologies that promise to turbocharge digital transformation, we’re just beginning to see how we might turn the supply chain into a source of competitive advantage (see “What’s the Magic Combination?”).

Amazon, for example, is becoming as much a logistics company as a retailer. Its ordering and delivery systems are so streamlined that its customers can launch and complete a same-day transaction with a push of a single IP-enabled button or a word to its ever-attentive AI device, Alexa. And this level of experimentation and innovation is bubbling up across industries.

Consider manufacturing, where the IoT is transforming automation inside already highly automated factories. Machine-to-machine communication is enabling robots to set up, provision, and unload equipment quickly and accurately with minimal human intervention. Meanwhile, sensors across the factory floor are already capable of gathering such information as how often each machine needs maintenance or how much raw material to order given current production trends.

Once they harvest enough data, businesses will be able to feed it through machine learning algorithms to identify trends that forecast future outcomes. At that point, the supply chain will start to become both automated and predictive. We’ll begin to see business models that include proactively scheduling maintenance, replacing parts just before they’re likely to break, and automatically ordering materials and initiating customer shipments.

Italian train operator Trenitalia, for example, has put IoT sensors on its locomotives and passenger cars and is using analytics and in-memory computing to gauge the health of its trains in real time, according to an article in Computer Weekly. “It is now possible to affordably collect huge amounts of data from hundreds of sensors in a single train, analyse that data in real time and detect problems before they actually happen,” Trenitalia’s CIO Danilo Gismondi told Computer Weekly.

Blockchain allows all the critical steps of the supply chain to go electronic and become irrefutably verifiable by all the critical parties within minutes: the seller and buyer, banks, logistics carriers, and import and export officials.

The project, which is scheduled to be completed in 2018, will change Trenitalia’s business model, allowing it to schedule more trips and make each one more profitable. The railway company will be able to better plan parts inventories and determine which lines are consistently performing poorly and need upgrades. The new system will save €100 million a year, according to ARC Advisory Group.

New business models continue to evolve as 3D printers become more sophisticated and affordable, making it possible to move the end of the supply chain closer to the customer. Companies can design parts and products in materials ranging from carbon fiber to chocolate and then print those items in their warehouse, at a conveniently located third-party vendor, or even on the client’s premises.

In addition to minimizing their shipping expenses and reducing fulfillment time, companies will be able to offer more personalized or customized items affordably in small quantities. For example, clothing retailer Ministry of Supply recently installed a 3D printer at its Boston store that enables it to make an article of clothing to a customer’s specifications in under 90 minutes, according to an article in Forbes.

This kind of highly distributed manufacturing has potential across many industries. It could even create a market for secure manufacturing for highly regulated sectors, allowing a manufacturer to transmit encrypted templates to printers in tightly protected locations, for example.

Meanwhile, organizations are investigating ways of using blockchain technology to authenticate, track and trace, automate, and otherwise manage transactions and interactions, both internally and within their vendor and customer networks. The ability to collect data, record it on the blockchain for immediate verification, and make that trustworthy data available for any application delivers indisputable value in any business context. The supply chain will be no exception.

Blockchain Is the Change Driver

The supply chain is configured as we know it today because it’s impossible to create a contract that accounts for every possible contingency. Consider cross-border financial transfers, which are so complex and must meet so many regulations that they require a tremendous number of intermediaries to plug the gaps: lawyers, accountants, customer service reps, warehouse operators, bankers, and more. By reducing that complexity, blockchain technology makes intermediaries less necessary—a transformation that is revolutionary even when measured only in cost savings.

“If you’re selling 100 items a minute, 24 hours a day, reducing the cost of the supply chain by just $1 per item saves you more than $52.5 million a year,” notes Dirk Lonser, SAP go-to-market leader at DXC Technology, an IT services company. “By replacing manual processes and multiple peer-to-peer connections through fax or e-mail with a single medium where everyone can exchange verified information instantaneously, blockchain will boost profit margins exponentially without raising prices or even increasing individual productivity.”

But the potential for blockchain extends far beyond cost cutting and streamlining, says Irfan Khan, CEO of supply chain management consulting and systems integration firm Bristlecone, a Mahindra Group company. It will give companies ways to differentiate.

“Blockchain will let enterprises more accurately trace faulty parts or products from end users back to factories for recalls,” Khan says. “It will streamline supplier onboarding, contracting, and management by creating an integrated platform that the company’s entire network can access in real time. It will give vendors secure, transparent visibility into inventory 24×7. And at a time when counterfeiting is a real concern in multiple industries, it will make it easy for both retailers and customers to check product authenticity.”

Blockchain allows all the critical steps of the supply chain to go electronic and become irrefutably verifiable by all the critical parties within minutes: the seller and buyer, banks, logistics carriers, and import and export officials. Although the key parts of the process remain the same as in today’s analog supply chain, performing them electronically with blockchain technology shortens each stage from hours or days to seconds while eliminating reams of wasteful paperwork. With goods moving that quickly, companies have ample room for designing new business models around manufacturing, service, and delivery.

Challenges on the Path to Adoption

For all this to work, however, the data on the blockchain must be correct from the beginning. The pills, produce, or parts on the delivery truck need to be the same as the items listed on the manifest at the loading dock. Every use case assumes that the data is accurate—and that will only happen when everything that’s manufactured is smart, connected, and able to self-verify automatically with the help of machine learning tuned to detect errors and potential fraud.

Companies are already seeing the possibilities of applying this bundle of emerging technologies to the supply chain. IDC projects that by 2021, at least 25% of Forbes Global 2000 (G2000) companies will use blockchain services as a foundation for digital trust at scale; 30% of top global manufacturers and retailers will do so by 2020. IDC also predicts that by 2020, up to 10% of pilot and production blockchain-distributed ledgers will incorporate data from IoT sensors.

Despite IDC’s optimism, though, the biggest barrier to adoption is the early stage level of enterprise use cases, particularly around blockchain. Currently, the sole significant enterprise blockchain production system is the virtual currency Bitcoin, which has unfortunately been tainted by its associations with speculation, dubious financial transactions, and the so-called dark web.

The technology is still in a sufficiently early stage that there’s significant uncertainty about its ability to handle the massive amounts of data a global enterprise supply chain generates daily. Never mind that it’s completely unregulated, with no global standard. There’s also a critical global shortage of experts who can explain emerging technologies like blockchain, the IoT, and machine learning to nontechnology industries and educate organizations in how the technologies can improve their supply chain processes. Finally, there is concern about how blockchain’s complex algorithms gobble computing power—and electricity (see “Blockchain Blackouts”).

“We don’t know yet what the market will adopt. In a decade, it might be status quo or best practice, or it could be the next Betamax, a great technology for which there was no demand,” Lonser says. “Even highly regulated industries that need greater transparency in the entire supply chain are moving fairly slowly.”

Blockchain will require acceptance by a critical mass of companies, governments, and other organizations before it displaces paper documentation. It’s a chicken-and-egg issue: multiple companies need to adopt these technologies at the same time so they can build a blockchain to exchange information, yet getting multiple companies to do anything simultaneously is a challenge. Some early initiatives are already underway, though:

• A London-based startup called Everledger is using blockchain and IoT technology to track the provenance, ownership, and lifecycles of valuable assets. The company began by tracking diamonds from mine to jewelry using roughly 200 different characteristics, with a goal of stopping both the demand for and the supply of “conflict diamonds”—diamonds mined in war zones and sold to finance insurgencies. It has since expanded to cover wine, artwork, and other high-value items to prevent fraud and verify authenticity.
• In September 2017, SAP announced the creation of its SAP Leonardo Blockchain Co-Innovation program, a group of 27 enterprise customers interested in co-innovating around blockchain and creating business buy-in. The diverse group of participants includes management and technology services companies Capgemini and Deloitte, cosmetics company Natura Cosméticos S.A., and Moog Inc., a manufacturer of precision motion control systems.
• Two of Europe’s largest shipping ports—Rotterdam and Antwerp—are working on blockchain projects to streamline interaction with port customers. The Antwerp terminal authority says eliminating paperwork could cut the costs of container transport by as much as 50%.
• The Chinese online shopping behemoth Alibaba is experimenting with blockchain to verify the authenticity of food products and catch counterfeits before they endanger people’s health and lives.
• Technology and transportation executives have teamed up to create the Blockchain in Transport Alliance (BiTA), a forum for developing blockchain standards and education for the freight industry.

It’s likely that the first blockchain-based enterprise supply chain use case will emerge in the next year among companies that see it as an opportunity to bolster their legal compliance and improve business processes. Once that happens, expect others to follow.

Customers Will Expect Change

It’s only a matter of time before the supply chain becomes a competitive driver. The question for today’s enterprises is how to prepare for the shift. Customers are going to expect constant, granular visibility into their transactions and faster, more customized service every step of the way. Organizations will need to be ready to meet those expectations.

If organizations have manual business processes that could never be automated before, now is the time to see if it’s possible. Organizations that have made initial investments in emerging technologies are looking at how their pilot projects are paying off and where they might extend to the supply chain. They are starting to think creatively about how to combine technologies to offer a product, service, or business model not possible before.

A manufacturer will load a self-driving truck with a 3D printer capable of creating a customer’s ordered item en route to delivering it. A vendor will capture the market for a socially responsible product by allowing its customers to track the product’s production and verify that none of its subcontractors use slave labor. And a supermarket chain will win over customers by persuading them that their choice of supermarket is also a choice between being certain of what’s in their food and simply hoping that what’s on the label matches what’s inside.

At that point, a smart supply chain won’t just be a competitive edge. It will become a competitive necessity