# data[].attributes.attachment_link.description
The beginnings of a fundamental transformation in the way we create, communicate and consume fashion are already taking shape.

# data[].attributes.headline
Biotechnology - sustainable fabrics creation 

# data[].attributes.subject
Biotechnology - sustainable fabrics creation 

# data[].attributes.attachment_link.readable_text
The fashion industry is seeing the beginnings of a biotechnological revolution: using biology to make clothing to make textiles better and more sustainable.
Biotechnology already plays an important role in the textile industry. Enzymes are used routinely to wash and bleach textiles, to give jeans a denim look or to prevent wool from shrinking. A new wave of technology could take this a step further. In a not-so-distant future, our clothes will be made and dyed by living microbes, ditching many of the chemical processes that make fashion one of the most polluting industries in the world.

Many players around the world are already disrupting the fashion industry with biofabrication. I talked to four companies in Europe leading the biological revolution in the fashion industry to find out more about how their technology is changing the way we make clothes.

Growing spider silk with bacteria

Spider silk is known for being a strong, flexible and lightweight material, but it is not possible to farm spiders at an industrial scale. Based in Germany, AMSilk uses genetically engineered bacteria to get around that problem. Inside fermentation bioreactors, the bacteria produce spider silk protein, which is then spun into fibers, creating a brand new material with unique properties.

"In textiles, we haven't seen a truly new material in decades," CEO Jens Klein told me. AMSilk just celebrated the launch of the first product made with this spider silk - a luxury watch strap . The company is working in several other products using the spider silk fibers, including biodegradable sneakers for Adidas.

"In terms of design, you can tune the material properties from a molecular level, granting you significantly more control over the final product than is possible with traditional materials," said Klein. From a sustainability perspective, the material doesn't need any animal or petroleum input.

Nurturing the body with algae

Not only is the fashion industry responsible for 20% of water pollution in the world. The chemicals used to make and dye fabrics are often toxic, harming and killing thousands of workers. These chemicals can also leach to the skin of the wearer.

Algalife aims to reach mass production this year, with the first products focused on home and sportswear. In the future, Krebs envisions being able to customize the specific compounds produced by algae, such as proteins or vitamins. "It's a win-win solution for all stakeholders: manufacturers, workers, and customers."

Growing clothes from fungi

"Factories are closing due to environmental issues, plastic pollution is a big problem and there is only so much land we can use for cotton growth, instead of food. The fashion industry needs to change and is looking for new innovations," said Aniela Hoitink, founder of the Dutch firm NEFFA.

Hoitink has created a fabric that is grown out of mycelia - the roots of mushrooms. The fungi are grown in discs that are then stuck together to create custom clothes without seams. "As we skip steps of spinning yarn, weaving cloth, cutting patterns, and sewing garments, we are not only reducing waste throughout the production phase, but also resources like water, farmland, and transport Hoitink told me.

NEFFA has already created several prototypes of dresses, jackets and purses created using this method. "Technologies to fabricate textiles need an huge update," said Hoitink. "A tree has a new set of leaves every year. Nature has its own consumptive behaviour and yet it manages to keep the planet healthy. What can we learn from that? Biomimicry and biology should play a bigger role in our future."

Dyeing clothes with bacteria

Chieza is now working with several brands to develop together the best strategies to incorporate this dyeing technology to their individual needs. She expects to have the first products launched in the next couple of years.

The future of fashion

There is still a lot of work needed until biofabrication becomes mainstream in the fashion industry and replaces traditional methods to produce and dye fabric. One of the big challenges is transforming prototype biological processes into industrial-scale production, which will be key to make these products widely available and affordable.

Another challenge is to determine the real impact that these new processes actually have on the environment. "What needs to be measured, and nobody has done this yet because scale-up is still in progress, is a full life cycle analysis," said Chieza. "We have to remember that fermentation is tied to carbon, which currently comes from sugar. We have to look at sustainability in terms of the input."

It will take time. After all, it took decades since nylon was developed in the 1930s until it became as ubiquitous as it is today. But the future of fashion is already here, and biology is the future.
Images via AMSilk, Algalife, NEFFA, and Faber Futures

# data[].attributes.attachment_link.title
Biotechnology Is Changing How We Make Clothes

# data[].attributes.attachment_link.description
The fashion industry is experiencing a revolution: using biology to make clothing to make textiles better and more sustainable.

# data[].attributes.attachment_link.readable_text
The natural world is a constant source of inspiration for textile and fashion designers. The results can be joyous, colourful and highly desirable, yet their instant appeal makes it all too easy to forget the damage the fashion industry inflicts on nature. Today the industry's processes and practices often harm environments, ecosystems and human communities around the globe.

The modern linear system of manufacturing and the promotion of fast, disposable fashion puts pressure on non-renewable and threatened resources, especially water, and produces huge quantities of waste, most of which goes to landfill or for incineration. The industry's methods and materials can also be highly polluting. Its use of fossil fuels generates carbon dioxide that contributes to global warming by trapping heat in the atmosphere. Many dyeing and finishing processes rely on toxic chemicals that affect the health of workers and leach out into rivers and ground water systems degrading the land, compromising drinking water and killing fish. Synthetic fibres in the clothes we wear every day affect our oceans, rivers and marine life. When these clothes are washed tiny plastic microfibers too small to be trapped in filters enter not only the water system but also the food chain.

The textile and fashion industry has always caused pollution but it increased dramatically in countries like Britain as the industry grew in scale during the 19th century. During this period, manufacturers utilised new technologies and faster manufacturing methods, particularly steam power, and retailers developed more sophisticated marketing and sales techniques. At the same time, the audience for fashion grew as the population increased, the middle class expanded, disposable incomes grew and prices fell. As the volume of manufacturing and consumption rose, so did air and water pollution.

Newspaper reports from the 19th century describe the foul conditions we now see in some developing countries. In 1833 the Manchester Times vividly evoked the stench and danger of the 'Black Ditch' at Pendleton which overflowed to flood the adjacent footpath with over a metre of mud contaminated with "waste dye stuff and filth" making it impassable and a public nuisance. The British government introduced legislation to control pollution from the 1860s but it was often flawed. Although government and industry recognised the link between poor health, environmental damage and pollution, these risks tended to be accepted as a necessary price of industrial development and manufacturers were reluctant to spend more than strictly necessary on pollution controls.

Manufacturing methods and the widespread use of toxic chemicals can also be hazardous. The new aniline dyes, developed in the second half of the 19th century, created bright, low cost colours but caused skin problems and, in some cases, an increased risk of cancer for those who worked with them. Some wearers suffered from sickness, swelling and rashes. The felt hatting industry was dangerous because most workshops used mercuric nitrate to speed up the felting process. Mercury can be absorbed through the lungs, skin and stomach and it stays in the body. Mercury poisoning affects the neuro-motor system, causing long-term tremors, anxiety and irritation. Our collection of hats that have been created using mercury are stored in labelled, sealed bags to contain any remaining toxic contaminants.

The environmental damage and occupational health hazards that were once common in Britain have now shifted elsewhere. The modern textile and fashion industry is global and most mass-manufacturing is outsourced to countries with low labour costs which often have weak labour and environmental laws.

The use of toxic chemicals remains a serious issue. In 2011 the environmental charity Greenpeace called the industry to account. Its Detox campaign challenges big clothing brands to take responsibility for the environmental impact of their supply chains and to commit to achieving zero discharges of hazardous chemicals by 2020. Only 80 companies signed up and progress has been made but they represent just 15% of the global industry.

What can we do to change this?

Today designers, scientists, business, government and campaigners are coming together to develop a range of high and low-tech solutions, tested and experimental, which together have the potential to create a cleaner, greener, less wasteful industry. Instead of the take, make, dispose model of the linear system of manufacturing, research is focussing on circular systems, which take, make and re-make.

Other approaches include reducing waste by designing products to be more durable and offering consumers the option of customisation; using sustainable materials like linen grown in Western Europe more widely; investing in the development of commercially viable methods of recycling blended materials, and converting waste from non-textiles sources like food into materials suitable for clothing and accessories; reducing carbon emissions by employing energy sources other than fossil fuels, using local centres of production and utilizing more energy efficient methods; reducing water consumption and the use of toxins in dyeing and finishing processes by developing new technologies;anddeveloping bio-fabricated materials and alternative processes based in synthetic biology as an alternative to synthetic materials made in the petro-chemical industry.

As wearers, we can also make a difference by choosing and looking after our clothes more carefully, wearing them longer, having smaller wardrobes, re-cycling the clothes we no longer want and thinking about alternatives to buying such as swapping or renting outfits.

Find out more about the Fashioned From Nature exhibition.

# data[].attributes.attachment_link.title
Pollution: the dark side of fashion · V&A

# data[].attributes.attachment_link.description
The natural world is a constant source of inspiration for textile and fashion designers. The results can be joyous, colourful and highly desirable, yet their instant appeal makes it all too easy to forget the damage the fashion industry inflicts on nature. Today the industry's processes and practices often harm environments, ecosystems and human communities around the globe.

# data[].attributes.attachment_link.readable_text
The 'new normal' for beauty: how will the industry adapt to a post-coronavirus world?

There's no denying that the coronavirus has brought about some challenging times for businesses. As brands prepare to come out the other side, we're ready to explore what a post- lockdown world could mean for the future of beauty. As an industry well known for innovative in-store experiences, how will beauty brands invest in their consumer journeys in a way which holds them accountable, both online and offline? The in-store beauty experience The prominence of the in-store beauty experience has become a key way to re-engage with consumers in recent years, taking a step back from the digital autonomy of...

# data[].attributes.attachment_link.title
How blockchain will impact luxury retail in the next 10-20 years

# data[].attributes.attachment_link.description
There's no denying that the coronavirus has brought about some challenging times for businesses. As brands prepare to come out the other side, we're ready to explore what a post- lockdown world could mean for the future of beauty.

# data[].attributes.headline
Blockchain is changing the fashion industry (luxury sector)

# data[].attributes.subject
Blockchain is changing the fashion industry (luxury sector)

# data[].attributes.attachment_link.readable_text
Blockchain is one of the fastest-growing sectors in tech, and the fashion industry is taking note.

Investment in blockchain startups has increased at a compound annual growth rate of 90 per cent over the past five years, according to Nathan Pacer, co-founder and chief research officer of research firm Venture Scanner. "At its core, blockchain is all about proof of exclusivity and anti-counterfeiting - areas that are important to fashion. Blockchain is still a relatively young technology, but it is showing signs of traction."

"In fashion, authenticity is important, limited edition is important, being the first to wear or discover something is important - and it's impossible to prove the authenticity of a digital thing without blockchain," adds Roham Gharegozlou, CEO of Vancouver-based Dapper Labs, which created the $140,000 CryptoKitty.

Here are six ways luxury companies are already using blockchain.

Track diamonds even after they've been cut

This month, San Francisco jeweller Brilliant Earth will introduce blockchain-integrated diamonds through a partnership with London-based Everledger, which has created a unique digital record of more than 2.2 million diamonds.

By registering a diamond's unique markings, Everledger tracks and permanently records every step in a gem's lifecycle. This includes location origin, rough carat weight, customer ownership and videos of the rough diamond, all accessible through Brilliant Earth's website. Brilliant Earth's co-CEO Beth Gerstein says that this appeals directly to millennial and Gen Z customers who want to purchase socially conscious products and engage with cutting-edge technology.

Brands who already use Everledger's platform have observed "material" increases in both the value of the diamond customers are willing to buy online and how quickly they buy them, says Everledger head of commercials Evgeny Gokhberg.

Prove authenticity

Similarly, Paris-based non-profit Arianee has developed a blockchain-based protocol that creates a digital identity for valuable goods, such as bags, sneakers and watches. Advisors include execs from Richemont International and Balenciaga. Its first projects include verifying vintage timepieces for a leading watchmaker, which will debut 1 June, as well as a proof-of-concept for a high-end sneaker brand.

The digital identity is like a passport that assures the authenticity of both new and vintage goods and records information such as transfers of ownership or when the item is serviced. Depending on the brand, the passport can be linked to the item through serial number, chip or QR code, accessible through an app.

As this process becomes standard, it will be challenging to sell fakes at scale, explains Arianee co-founder and CEO Pierre Nicolas Hurstel. "You can always create a fake, but if you are not able to generate the digital certificate, no second-hand platform will list it," he says. In future, the Arianee protocol can add new features, such as the ability to declare an item lost or stolen, which could make it difficult to cross borders with stolen goods.

Communicate with customers after a sale

Arianee's platform also allows the digital passport to act as a customer service channel. The brand and the customer can communicate while allowing the owner to remain anonymous - even if the object changes hands. In this way, a brand could share information or the customer could submit a customer service request, which would be documented on the item's record.

To participate in the Arianee consortium, a brand must apply to become a member. Costs depend on the size of the company; the price of issuing an individual certificate is 10 cents (USD), and customer messages are 15 cents each.

Record and share supply chain information

Seattle-based Fuchsia uses blockchain platform Provenance to share details about the workers who hand-make the brand's shoes in Pakistan. Six months after adding the information to its e-commerce product pages, the company saw a 31 per cent increase in online conversions and a 45 per cent boost in engagement.

Additional Provenance clients include knitwear designer Martine Jarlgaard, whose smart labels trace garment timelines back to the alpaca farm, and vegan accessories brand Mashu, which shares details on its bags' Greek origins via in-store QR tags.

Provenance clients tend to be fast-growing, direct-to-consumer brands, and they often have sustainability and transparency built into their DNA, says Provenance founder Jessi Baker. "As that happens more, it will be vital that [supply chain information] is calibrated, stored and measured in a way that everyone can trust, and the use of blockchain tech to be that single source of truth will be vital if brands are starting to compete on impact," she says.

Evaluate worker welfare

In April, Levi Strauss & Co began testing a worker well-being survey that records factory employees' answers anonymously on a blockchain platform, starting with a Mexico vendor that employs 4,000. In a post on the company's blog, Levi Strauss Foundation 's Kim Almeida said that because some employees might be hesitant to share personal health details and speak out about direct managers, the anonymity of blockchain is "critical".

The company is already seeing benefits in terms of how quickly it can collect feedback and share results, which show on screens on the factory floor. The results from one survey were processed within 48 hours of its completion, and three other sets of responses were processed in less than a week.

"This is the first time ever in my 30 years of researching work and health that the workers were the first to see the results of the survey and not management," says Eileen McNeely, director of SHINE at the Harvard T.H. Chan School of Public Health, which worked on the project. "This is groundbreaking transparency."

Transfer ownership of digital clothing

On 11 May, Dapper Labs auctioned off a one-of-a-kind digital garment at New York's Ethereal Summit, designed by Dutch digital fashion studio The Fabricant. The winning bid was $9,500, and the winner will have the option to be digitally fitted in the garment. (Last year's auction - the Cryptokitty - garnered $140,000.)

Ownership of the garment is transferred through blockchain, making the digital design impossible to counterfeit. "Blockchain allows us to create digital one-offs," says The Fabricant founder Kerry Murphy. "It's a way to create uniqueness and desirability."

"The further I delve into blockchain, the crazier it gets," he continues. "When the internet first started, we also couldn't foresee all the things it would supply us. Then you see it adding value to people's lives, and it isn't an abstract concept anymore."
A previous version of this article stated that Levi Strauss & Co began testing its worker well-being survey in January. The article has since been updated. 14 May, 2019.
To become a Vogue Business Member and receive the Sustainability Edit newsletter, click here.
Comments, questions or feedback? Email us at feedback@voguebusiness.com.

# data[].attributes.attachment_link.description
Blockchain is one of the fastest-growing sectors in tech, and the fashion industry is taking note. Investment in blockchain startups has increased at a compound annual growth rate of 90 per cent over the past five years, according to Nathan Pacer, co-founder and chief research officer of research firm Venture Scanner.

# data[].attributes.attachment_link.readable_text
When Satoshi Nakamoto, whose true identity is still unknown, released the whitepaper Bitcoin: A Peer to Peer Electronic Cash System in 2008 that described a "purely peer-to-peer version of electronic cash" known as Bitcoin, blockchain technology made its public debut. Blockchain, the technology that runs Bitcoin, has developed over the last decade into one of today's biggest ground-breaking technologies with potential to impact every industry from financial to manufacturing to educational institutions. Here's a brief history of blockchain technology and some thoughts about where it might go in the future.
Bitcoin Beginnings
You can't discuss the history of blockchain technology without first starting with a discussion about Bitcoin. Shortly after Nakamoto's whitepaper was released, Bitcoin was offered up to the open source community in 2009. Blockchain provided the answer to digital trust because it records important information in a public space and doesn't allow anyone to remove it. It's transparent, time-stamped and decentralized.

"Blockchain is to Bitcoin, what the internet is to email. A big electronic system, on top of which you can build applications. Currency is just one," Sally Davies, FT Technology reporter.
Blockchain Separates from Bitcoin
Even today, there are many who believe Bitcoin and blockchain are one and the same, even though they are not. Those who started to realize around 2014 that blockchain could be used for more than cryptocurrency started to invest in and explore how blockchain could alter many different kinds of operations. At its core, blockchain is an open, decentralized ledger that records transactions between two parties in a permanent way without needing third-party authentication. This creates an extremely efficient process and one people predict will dramatically reduce the cost of transactions.

When entrepreneurs understood the power of blockchain, there was a surge of investment and discovery to see how blockchain could impact supply chains, healthcare, insurance, transportation, voting, contract management and more. Nearly 15% of financial institutions are currently using blockchain technology.
Ethereum Rises: Smart Contracts
Vitalik Buterin, co-founder of Ethereum and Bitcoin magazine, was also an initial contributor to the Bitcoin codebase, but became frustrated around 2013 with its programming limitations and pushed for a malleable blockchain. Met with resistance from the Bitcoin community, Buterin set out to build the second public blockchain called Ethereum. The largest difference between the two is that Ethereum can record other assets such as loans or contracts, not just currency. Ethereum launched in 2015 and can be used to build "smart contracts"-those that can automatically process based on a set of criteria established in the Ethereum blockchain. This technology has attracted the attention of corporations such as Microsoft, BBVA and UBS who are intrigued by the potential of the smart contract functionality to save time and money.
Transition to Proof of Stake
Currently, blockchain operates on the proof of work concept where an expensive computer calculation or "mining" is done in order to create a block (or a new set of trustless transactions). Currently, when you initiate a transaction, it is bundled into a block. Then miners verify the transactions are legitimate within that block by solving a proof-of-work problem-a very difficult mathematical problem that takes an extraordinary amount of computing power to solve. The first miner to solve the problem gets a reward and then the verified transaction is stored on the blockchain. Ethereum developers are interested in changing to a new consensus system called proof of stake.

Proof of stake has the same goal as proof of work-to validate transactions and achieve consensus in the chain-and it uses an algorithm but with a different process. With proof of stake, the creator of a new block "is chosen in a deterministic way, depending on its wealth, also defined as a stake. " Since in a proof of stake system, there is no block reward, but the miners, known as forgers, get the transaction fees. Proponents of this shift, including Ethereum co-founder Buterin, like proof of stake for the energy and cost savings realized to get to a distributed form of consensus.
Blockchain Scaling on the Horizon
Since currently, every computer in a blockchain network processes every transaction, it can be very slow. A blockchain scaling solution would determine how many computers are necessary to validate every transaction in a way that doesn't compromise security.

Today, Bitcoin is just one of the several hundred applications that use blockchain technology. It's been an impressive decade of transformation for blockchain technology and it will be intriguing to see where the next decade takes us.

... Read More

# data[].attributes.attachment_link.title
A Very Brief History Of Blockchain Technology Everyone Should Read

# data[].attributes.attachment_link.description
Blockchain technology has only existed for the last 10 years, but its influence and potential are extraordinary. From its Bitcoin beginnings to the future of blockchain scaling, we share a brief history of blockchain technology and where it might be headed in the not too distant future.

# data[].attributes.body
decentralised distributed ledger that records the provenance of a digital asset

# data[].attributes.attachment_link.title
Blockchain: What It Is, How It Works, Why It Matters | Built In

# data[].attributes.attachment_link.description
Blockchain technology is a decentralized, distributed ledger that stores the record of ownership of digital assets.

# data[].attributes.attachment_link.readable_text
There seems to be an odd disconnect when it comes to the public's embracing of artificial intelligence. Ask marketers and they'll tell you they love the technology. They visualize a rosy future where life is good thanks to AI innovations. Leaders of global enterprise also seem equally enamored by AI. They can't wait to deploy groundbreaking programs that will render impeccable insights on their customer's every want and need.

But then there's the consumer group itself. AI's ultimate target demographic. This is the massive group that's poised to reap the most benefits from an AI-powered customer experience, and yet they're also the group that expresses cautious, mixed feelings about adopting this new technology into daily life.

In order to glean a better understanding of how consumers assess the current AI landscape, and what they think about its future prospects, my agency, Blue Fountain Media, surveyed more than 1,000 people in the US between the ages of 18-65 for their authentic feelings about AI.

As marketers, these survey findings hold key insights into how we might shape our communications with consumers, as AI becomes a larger part of our tool belt.

To summarize, while there still appears to be a great deal of consumer trepidation about AI, inroads are being made -- so there's no reason to ring the death knell on the technology quite yet. But what can we learn from understanding the areas of success -- and the areas where there are still misgivings -- when it comes to the consumer's relationship with AI?

Summary findings
Many consumers aren't sure what AI is or does
While those of us in the business world are invested in knowing what AI is and how it can be leveraged, some 43% of survey respondents reported they weren't exactly sure what AI is or how it's being currently employed. In fact, some 7% indicated that they don't know and don't care what AI is. And when you look at the stat below, it seems that even those who are knowledgeable of AI's capabilities don't have the best feelings about it.

Marketers can help educate the public about the benefits of AI by featuring it as a part of the story whenever they introduce consumers to an AI-powered program or campaign. It's time to give AI such much-needed credit for improving the customer experience and raise consumer awareness about the benefits.
Consumers are wary of an AI-powered future
It's telling that only 26% of respondents reported feeling "great" about Artificial Intelligence. A majority of respondents (60%) had a lukewarm acceptance of AI, allowing for its ultimate future potential but also noting that we need to be careful of how it's employed. In what might be a sign that the tide is turning on public opinion, only a relatively small percentage of respondents (6%) said they feel "bad" about AI or that the technology presents an imminent threat to mankind.

Drilling into the apprehension about AI a little more, 34% cited they were concerned an AI program might inadvertently injure or kill someone. A slightly smaller percentage of respondents (32%) worried that AI would eventually replace all of our jobs which would drastically affect the consumer economy.

Despite these fears, a majority of respondents either felt positive or neutral about the pace at which AI is rolling out. Some 38% said they feel comfortable with how quickly AI is being incorporated into their lives, while 26% said they feel neutral because they haven't noticed one way or the other.

Once again, as marketers, we can make our jobs a lot easier if we run some interference against these negative perceptions. The sooner consumers get comfortable with AI integrating into their lives, the sooner they will trust and embrace our messages.
Where are consumers embracing AI?
When it comes to enjoying the fruits of the AI consumer experience, people are looking at their smart home devices which currently represent AI's most visible and personally impactful use. About 39% of survey respondents cited smart home devices, such as voice assistants like Alexa, Nest thermostats, and Ring doorbells as their favorite use of AI. The other area of AI consumer acceptance (18%) said AI has its largest impact on GPS and related navigational tours.

Having a voice assistant strategy is a must-have for marketers today. Not only have they embraced these devices, but they're also increasingly relying on them for search and shopping. With a sound voice strategy, the brand or clients you represent will have a better shot of being discovered and used.
Consumers demand a human touch
One interesting survey finding reveals a deep-seated concern of the part of consumers that a human element remains an element of the AI equation. Overwhelmingly, 87% report that they would trust the diagnosis of a human doctor over an AI one. When it comes to customer service, jaded no doubt by years of telephone-tree loops and robocalls, 41% said they wanted their service issues resolved by with a human agent.

People will always play a big role in AI. Developing it, enhancing it and correcting it. It's important to let consumers know that AI doesn't spell the end of human interaction - and we can get that message across by including the human touch in our campaigns.
Conclusion
The promise of AI for business is tremendous, but consumers still have concerns that brands must be aware of as they manage AI rollouts. The Blue Fountain Media survey suggests businesses use AI to augment experiences and customer service, without replacing human touchpoints altogether. Awareness of consumer sensitivities around the technology can help brands avoid missteps.
Brian Byeris vice president, GM of Blue Fountain Media, a PacteraEDGE Company. He has 20 years of operations, technology, marketing, management and business analysis expertise. He manages the New York City agency's activities relating to the creation of professional content and commerce websites and facilitates the growth of the company's technology partnerships. If you enjoyed this article, sign up for SmartBrief's free email: ANA Data & Analytics SmartBrief. SmartBrief offers more than 250 industry-focused newsletters.

# data[].attributes.attachment_link.title
What do consumers think about artificial intelligence?

# data[].attributes.attachment_link.description
There seems to be an odd disconnect when it comes to the public's embracing of artificial intelligence. Ask marketers and they'll tell you they love the technology. They visualize a rosy future where life is good thanks to AI innovations. Leaders of global enterprise also seem equally enamored by AI.

# data[].attributes.attachment_link.readable_text
Fashion retailers know about the value of dedicated followers. Today, these brands may need to get on-trend themselves, for the artificial intelligence revolution sweeping through so many sectors of commerce has the potential to utterly transform their businesses.

The advance of AI - computer systems that can learn for themselves and carry out tasks that were once the preserve of humans - has partly been enabled by increased processing power and innovation in the development of algorithms. But, even more importantly, the pools of data that AI depends upon to do its learning and make smarter decisions are larger and more diverse than ever before.

The fashion sector, with its extraordinary depth of consumer and market insight, has access to some of the biggest data sets in all business. And, while some fashion executives are uncomfortable with the suggestion that an industry built on creativity and imagination can be digitalized or automated, this could be said to miss the point. The promise of AI for fashion retailers that can marry creativity with digital innovation has a powerful competitive edge.

We need to approach AI not just as artificial intelligence, but augmented intelligence, implying that AI can help augment the human thinking process by creatively stripping away the more mundane tasks that can often be automated, elevating our designers, merchants, marketeers etc. to focus on higher value decision-making that can move the needle. Thus the question we need to ask is not "Can AI become my fashion designer?" The more appropriate question is "What aspects of my fashion designer's tasks, if augmented via AI, can help them do more creative work?"
Powering productivity and creativity
Some AI-enabled gains will be found in the processes that drive the fashion sector, with smart technologies powering greater speed, lower cost and increased flexibility at each stage of the supply chain. AI has the potential to drive improvements in areas including forecasting, capacity planning and merchandising, as well as in production automation and delivery. Consumers will be the ultimate beneficiaries, enjoying improved product availability and accelerated fulfillment.

Yet AI can do more than simply help fashion businesses to work more efficiently - valuable though these gains will be. It can also support the creative process itself.

The fashion industry's digital leaders are already employing AI to augment their design skills and enhance their product development processes. AI tools enable companies to sift through consumer data to understand, in far richer detail, which product features their customers are likely to prefer.

Take the e-commerce giant Amazon. It is on the verge of launching the first clothing designer powered by AI , courtesy of a powerful algorithm it has developed to analyze images and to replicate popular styles. Crunching the data, the AI can develop new designs that appeal to customers on the basis of previous preferences.

Elsewhere, the Indian designers Shane and Falguni Peacock are using an AI tool to analyze more than 600,000 images of couture and international fashion runway shows. They expect the project to enable them to map out a future for Bollywood fashion based on the long-term trends observed in the past.

The fashion start-up Stitch Fix provides another example of what is possible. Unlike traditional designers, its team does not begin the creative process of developing new ideas with mood boards. Instead, they depend on the algorithms built into their AI tools. These tools crunch the profiles completed by clients then wade through 30 million combinations of clothing in order to handpick items they expect to fit the customer's tastes, needs and budget. Consumers keep what they like and send everything else back.
Connecting with the customer
There will be many more examples of these applications of AI in the fashion industry to come. Indeed, technology has the power to redefine how fashion businesses engage and interact with their customers.

The industry's digital leaders are already making gains, securing greater customer insight from their rich datasets and pursuing opportunities such as product recommendations, dynamic pricing and improved customer relationship management. In the case of the latter, Tommy Hilfiger has built a chatbot to interact with potential customers. The bot, an automated computer program that manages conversations with live people based on cues they provide in their questions, offers answers and content tailored to their interests.

Nor is the potential of AI confined to transforming the online customer experience: the technology can power better offline engagement, including in the physical store. Some fashion businesses are using sensors that deliver data to their AI tools via the Internet of Things, enabling them to work out how to improve the in-store experience.

Farfetch's Store of the Future is a good example of what is possible. It features tools such as automatic customer recognition as the shopper enters the store, RFID-enabled customer racks to boost inventory management and support purchasing, and digital mirrors that allow customers to compare combinations of different sizes, colors and styles. Using AI, the company has been able to integrate many of the experiences routinely offered to customers online when they're actually in the store.

AI can also play a key role in ensuring retailers have sufficient reserves of the most in-demand items. AI-powered tools for demand forecasting are making substantial inroads into a traditional bugbear of the industry. In the best cases, such tools are enabling retailers to reduce forecasting errors by up to 50 percent, while simultaneously reducing inventory by 20 percent to 50 percent.
Conclusion: AI as a strategic imperative
With so many use cases, fashion businesses may need to think about their priorities for AI, focusing on the areas with the greatest potential to add value given their individual business strategy.

The "people question" also presents a strategic challenge. With some research suggesting AI could replace up to 30 percent of the work carried out by fashion designers, how will companies manage their workforces and introduce new tools without alienating employees?

The secret will be to harness a value that has long driven success in the fashion industry: collaboration. In practice, AI tools are likely to support and augment much of the work currently done by designers, rather than replacing creatives altogether. And while some tasks may disappear, AI will also create new opportunities.

For companies working together with design - and the rest of the business - AI offers exciting new possibilities throughout the fashion industry. Think of it as fundamentally disrupting your style - not a passing fad.
Jill Standish is senior managing director and head of Accenture's retail practice and Vish Ganapathy is managing director and retail technology lead at Accenture.

# data[].attributes.attachment_link.title
Think Tank: How AI Can Power the Future of Fashion

# data[].attributes.attachment_link.description
Fashion retailers know about the value of dedicated followers. Today, these brands may need to get on-trend themselves, for the artificial intelligence revolution sweeping through so many sectors of commerce has the potential to utterly transform their businesses.

# data[].attributes.attachment_link.readable_text
In his first guest post for WhichPLM, Aaron Chi, Digital Marketing Manager for Chain of Demand, explores the current landscape of AI for fashion, and the benefits we're already realising. Chain of Demand has built an AI-driven solution that helps fashion retailers track and predict sales demand more quickly and accurately than ever before, thereby reducing markdowns and improving cash flow.
The world of fashion changes.

It is fast-moving and the people within - from designers to buyers - all need to make sure they look ahead to catch the latest changing trends.

The way we find and buy items has shifted dramatically. In the last few years, no other industry has apparently taken more of a hit than retail.

Customization - Just as how Netflix, YouTube or Amazon has the ability to suggest similar shows or products, AI in the fashion industry can do just the same. With e-commerce becoming a central way that people shop, there is more data about the consumer being tracked than ever before. Many successful fashion websites are able to keep a log of the browsing patterns of their customers, and in turn suggest similar items based on color, style, and design.

Improved customer service & communication - Calling and emailing, while still prevalent amongst many businesses, are slowly becoming a thing of the past. In the last few years, the emergence of chat-bot technology has significantly increased conversion and communication between the customer and fashion brands. From IBM Watson to Hubspot Chatbot, there are a multitude of solutions that help track leads, answer questions, and even give product recommendations.

Better buying and planning - For many fashion buyers and planners, a difficulty that is commonly faced is knowing exactly which material to buy for designers. Ordering too much of one thing might be detrimental to the business, especially if that product does not sell as well as they hoped. Yet, with advanced predictive analytics, buyers can effectively learn from customer behavior, and in turn plan with greater accuracy. By knowing which items are best and worst sellers, you can buy and plan with precision.

Automating operations - One of the greatest benefits in using artificial intelligence is the ability to automate mundane tasks. A majority of company working time is used for menial data entry, calculations, and other efforts that can easily be run by artificial intelligence alone. This is a huge asset to fashion brands, as they can assess margins with greater accuracy and organization using AI, requiring less man-power. This frees up many retailers to focus on more important matters like strategy and execution.

Managing inventory - A huge problem that retailers face is over-ordering and losing out on profit due to the unsold product. It has always been quite tricky to keep business moving, yet not hold too much that you are left with wasted items. Today, companies like Chain of Demand use machine learning algorithms to make more accurate predictions and choices. While demand forecasting techniques have been around for years, traditional methods only used historical sales data. The majority of calculative work needed to be done by humans and with limitations when the data sets go over 10,000. Yet, with machine learning, you can reduce forecasting errors by up to 50%, and are not restricted to how many data points and sources are used, thereby making more precise predictions.

Reduced returns - Apparel returns take approximately 3x longer to inspect than other verticals. This makes operational costs quite high. However, with the help of AI, retailers can help customers to make more informed purchase decisions (as described above). This increase in customization and improved communication prevents dissatisfaction, and in turn reduces the return rate. Retailers lose about $642.6 billion every year from preventable returns, which is why the use of AI would be incredible.

Improved product discovery - With the help of computer vision - an extension of machine learning - shoppers are now able to upload a photo of a product that they desire and get instant feedback on whether that item exists in the specified stores. There are also solutions that help scan the photo to give recommendations of where they can buy said product and at what retailer. One great example of this is Google Lens, which allows mobile users to snap a photo of a product, then find and buy similar styles right from their smartphone. In the same way, Pinterest's Lens uses AI technology to search for visually similar pins across their database.

Of the list of emerging technologies (AI, blockchain, IoT, 3D printing, and AR/VR), artificial intelligence has been at the forefront of disruption for every industry. Perhaps one of the most prominent ways fashion and technology have been integrated is the ability to turn a diverse set of data into engaging, enriching information.

Some of the biggest names in the fashion industry - from H&M to Tommy Hilfiger - are now investing in algorithms that help suggest styles to their customers. A plethora of AI-based start-ups are also helping one end to the other - from retailers to consumers - take the guessing out of the game.

Based on a report by Tractica, the global AI software market is expected to grow massively in the next few years, with revenues increasing from around 9.5 billion to 118.6 billion USD by 2025. Also, according to a study by Juniper Research, global retail spending on AI will grow to $7.3 billion per year by 2022. This is up by 5 billion, from an estimated $2 billion in 2018. Moreover, recent research has found that in the next five years, retailers will also be heavily investing in other related technologies like blockchain and the Internet of Things.

There is no denying that artificial intelligence has been dramatically shifting the way business is done. From the use of predictive analytics in business to computer vision in identifying product attributes, there are a myriad ways AI has been benefiting the field of fashion. Some of the ways down below illustrate just how powerful applying AI for fashion is.

Even though AI has been around for several years now, the technologies that are currently being applied to fashion is still in its early days. While there is still a long way to go until in-store artificial intelligence is fully realized, the following companies are just a few examples of those that are leading the way.

Alibaba

Since 2018, the Chinese retail and technology multinational, Alibaba Group, announced the idea of "new retail." It was the idea that they would revolutionize the way we shop completely, with the notion of e-commerce and data boom changing our consumer behaviors. It was during this year that they opened their first " FashionAI " store as a means to simplify the shopping experience for customers.

The key features for the store include intelligent garment tags, smart mirrors, and omnichannel integration. For the first, every product in the store contain special RFID (radio-frequency identification) tags and Bluetooth chips which carried specialized information within them. This can help give greater information on what items (based on color and size) are being sold the most and other deeper insights about the customers. In addition, the garments also contain what are known as gyro-sensors, which help to determine how an item is being touched or moved.

Smart mirrors (the second feature) are said to be located on every sales and changing room floor. Not only will they have touch screens that relay information on whether or not a person is inside, they will also help give information about the item the customer has brought into the store. These mirrors can also suggest other items that may go well with their item, which allows customers to fully customize and try on a multitude of items from the mirror alone, without having to carry every single piece of clothing they desire.

Finally, omnichannel integration will allow FashionAI to be connected to the Mobile Taobao app, which also contains a virtual wardrobe allowing customers to view the clothes they tried on. This way, consumers are able to continue shopping for styles they were recommended or that fitted in the store.

Amazon

With Amazon being the tech powerhouse that it is, growing into a titan through its recommendation systems, it is not a surprise to see that they are also involved in using AI for fashion. Amazon has developed an algorithm that is capable of designing clothes through the analysis of many images. It copies the styles and applies it to a new item from scratch.

It also has what is known as Amazon's Echo Look, an Alexa fashion assistant that keeps track of what is in your wardrobe, and in turn gives recommendations on what to include in your outfit. Using machine intelligence, the technology here can help you stylize better.

H&M

Although the company is one of thousands that have been affected by the retail apocalypse, H&M continues to hold on and thrive by investing in the big data boom. One interesting way they are doing so is through the analysis of receipts and loyalty card data. This information helps them customize and tailor the merchandise for each specific store by analyzing the returns, receipts and loyalty card data to tailor the merchandise for each store.

The retailer has also started to offer personalized recommendations for online shoppers, with plans to bring over similar technology into its bricks-and-mortar stores with the implementation of RFID.

Moreover, with Google, they have also created Coded Couture, a technology that allows people to create one of a kind designs based on their lifestyle. As they write on their website, Coded Couture is where, " Art and code work together. Simply install our app on your phone which translates a week of your life into a one-of-a-kind design. Creating a dress with your unique personal story to it. "

Tommy Hilfiger

IBM has teamed up with Tommy Hilfiger and the Fashion Institute of Technology (FIT) Infor Design and Tech Lab for a project called 'Reimagine Retail' - a way to help give retailers an edge by equipping them with skills in AI design.

Students of FIT were given access to IBM's AI research, from computer vision to natural language processing, and used the plethora of data to design better personalized outfits. They found that the use of AI allowed design teams to reduce lead times and generally increase their creativity. The ability to analyze and learn from thousands of images and videos quickly using the power of AI gave the designers greater insight into creating the right product, for the right person.

ASOS

The company is another retailer that integrated AI into its system, where they introduced a new tool to enhance the shopping experience. This new technology helped consumers determine the right size, allowing them to make greater informed purchases. The technology, known as ' Fit Assistant ' combines a platform that goes through a database of garment information and purchase histories to correctly adjust the right fit.

There is no doubt that AI is revolutionizing the fashion industry in ways no one would've imagined. In this rapid change retailers, manufacturers, designers, and managers alike are learning to benefit greatly by harnessing the underlying power of AI: machine learning.

Companies like Chain of Demand are some of the several start-ups that are standing at the forefront of this disruption. Through the use of AI and big data, fashion retailers are able to use such solutions to predict best and worst sellers with better accuracy than ever before. Not only does this open a field of opportunity for growth in profit, it also leads the way to greater sustainability by reducing inventory waste.

# data[].attributes.attachment_link.title
The Future of Fashion: How AI is Changing the Fashion Retail Industry - WhichPLM

# data[].attributes.attachment_link.description
In his first guest post for WhichPLM, Aaron Chi, Digital Marketing Manager for Chain of Demand, explores the current landscape of AI for fashion, and the benefits we're already realising. Chain of Demand has built an AI-driven solution that helps fashion retailers track and predict sales demand more quickly and accurately than ever before, thereby reducing ...

# data[].attributes.attachment_link.readable_text
Can Machines Think?

In the first half of the 20 th century, science fiction familiarized the world with the concept of artificially intelligent robots. It began with the "heartless" Tin man from the Wizard of Oz and continued with the humanoid robot that impersonated Maria in Metropolis. By the 1950s, we had a generation of scientists, mathematicians, and philosophers with the concept of artificial intelligence (or AI) culturally assimilated in their minds. One such person was Alan Turing, a young British polymath who explored the mathematical possibility of artificial intelligence. Turing suggested that humans use available information as well as reason in order to solve problems and make decisions, so why can't machines do the same thing? This was the logical framework of his 1950 paper, Computing Machinery and Intelligence in which he discussed how to build intelligent machines and how to test their intelligence.

Making the Pursuit Possible

Unfortunately, talk is cheap. What stopped Turing from getting to work right then and there? First, computers needed to fundamentally change. Before 1949 computers lacked a key prerequisite for intelligence: they couldn't store commands, only execute them. In other words, computers could be told what to do but couldn't remember what they did. Second, computing was extremely expensive. In the early 1950s, the cost of leasing a computer ran up to $200,000 a month. Only prestigious universities and big technology companies could afford to dillydally in these uncharted waters. A proof of concept as well as advocacy from high profile people were needed to persuade funding sources that machine intelligence was worth pursuing.

The Conference that Started it All

Five years later, the proof of concept was initialized through Allen Newell, Cliff Shaw, and Herbert Simon's, Logic Theorist. The Logic Theorist was a program designed to mimic the problem solving skills of a human and was funded by Research and Development (RAND) Corporation. It's considered by many to be the first artificial intelligence program and was presented at the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI) hosted by John McCarthy and Marvin Minsky in 1956. In this historic conference, McCarthy, imagining a great collaborative effort, brought together top researchers from various fields for an open ended discussion on artificial intelligence, the term which he coined at the very event. Sadly, the conference fell short of McCarthy's expectations; people came and went as they pleased, and there was failure to agree on standard methods for the field. Despite this, everyone whole-heartedly aligned with the sentiment that AI was achievable. The significance of this event cannot be undermined as it catalyzed the next twenty years of AI research.

Roller Coaster of Success and Setbacks

From 1957 to 1974, AI flourished. Computers could store more information and became faster, cheaper, and more accessible. Machine learning algorithms also improved and people got better at knowing which algorithm to apply to their problem. Early demonstrations such as Newell and Simon's General Problem Solver and Joseph Weizenbaum's showed promise toward the goals of problem solving and the interpretation of spoken language respectively. These successes, as well as the advocacy of leading researchers (namely the attendees of the DSRPAI) convinced government agencies such as the Defense Advanced Research Projects Agency (DARPA) to fund AI research at several institutions. The government was particularly interested in a machine that could transcribe and translate spoken language as well as high throughput data processing. Optimism was high and expectations were even higher. In 1970 Marvin Minsky told Life Magazine, "from three to eight years we will have a machine with the general intelligence of an average human being." However, while the basic proof of principle was there, there was still a long way to go before the end goals of natural language processing, abstract thinking, and self-recognition could be achieved.

Breaching the initial fog of AI revealed a mountain of obstacles. The biggest was the lack of computational power to do anything substantial: computers simply couldn't store enough information or process it fast enough. In order to communicate, for example, one needs to know the meanings of many words and understand them in many combinations. Hans Moravec, a doctoral student of McCarthy at the time, stated that "computers were still millions of times too weak to exhibit intelligence." As patience dwindled so did the funding, and research came to a slow roll for ten years.

In the 1980's, AI was reignited by two sources: an expansion of the algorithmic toolkit, and a boost of funds. John Hopfield and David Rumelhart popularized "deep learning" techniques which allowed computers to learn using experience. On the other hand Edward Feigenbaum introduced expert systems which mimicked the decision making process of a human expert. The program would ask an expert in a field how to respond in a given situation, and once this was learned for virtually every situation, non-experts could receive advice from that program. Expert systems were widely used in industries. The Japanese government heavily funded expert systems and other AI related endeavors as part of their Fifth Generation Computer Project (FGCP). From 1982-1990, they invested $400 million dollars with the goals of revolutionizing computer processing, implementing logic programming, and improving artificial intelligence. Unfortunately, most of the ambitious goals were not met. However, it could be argued that the indirect effects of the FGCP inspired a talented young generation of engineers and scientists. Regardless, funding of the FGCP ceased, and AI fell out of the limelight.

Ironically, in the absence of government funding and public hype, AI thrived. During the 1990s and 2000s, many of the landmark goals of artificial intelligence had been achieved. In 1997, reigning world chess champion and grand master Gary Kasparov was defeated by IBM's Deep Blue, a chess playing computer program. This highly publicized match was the first time a reigning world chess champion loss to a computer and served as a huge step towards an artificially intelligent decision making program. In the same year, speech recognition software, developed by Dragon Systems, was implemented on Windows. This was another great step forward but in the direction of the spoken language interpretation endeavor. It seemed that there wasn't a problem machines couldn't handle. Even human emotion was fair game as evidenced by Kismet, a robot developed by Cynthia Breazeal that could recognize and display emotions.

Time Heals all Wounds

We haven't gotten any smarter about how we are coding artificial intelligence, so what changed? It turns out, the fundamental limit of computer storage that was holding us back 30 years ago was no longer a problem. Moore's Law, which estimates that the memory and speed of computers doubles every year, had finally caught up and in many cases, surpassed our needs. This is precisely how Deep Blue was able to defeat Gary Kasparov in 1997, and how Google's was able to defeat Chinese Go champion, Ke Jie, only a few months ago. It offers a bit of an explanation to the roller coaster of AI research; we saturate the capabilities of AI to the level of our current computational power (computer storage and processing speed), and then wait for Moore's Law to catch up again.

Artificial Intelligence is Everywhere

We now live in the age of " big data," an age in which we have the capacity to collect huge sums of information too cumbersome for a person to process. The application of artificial intelligence in this regard has already been quite fruitful in several industries such as technology, banking, marketing, and entertainment. We've seen that even if algorithms don't improve much, big data and massive computing simply allow artificial intelligence to learn through brute force. There may be evidence that Moore's law is slowing down a tad, but the increase in data certainly hasn't lost any momentum. Breakthroughs in computer science, mathematics, or neuroscience all serve as potential outs through the ceiling of Moore's Law.

The Future

So what is in store for the future? In the immediate future, AI language is looking like the next big thing. In fact, it's already underway. I can't remember the last time I called a company and directly spoke with a human. These days, machines are even calling me! One could imagine interacting with an expert system in a fluid conversation, or having a conversation in two different languages being translated in real time. We can also expect to see driverless cars on the road in the next twenty years (and that is conservative). In the long term, the goal is general intelligence, that is a machine that surpasses human cognitive abilities in all tasks. This is along the lines of the sentient robot we are used to seeing in movies. To me, it seems inconceivable that this would be accomplished in the next 50 years. Even if the capability is there, the ethical questions would serve as a strong barrier against fruition. When that time comes (but better even before the time comes), we will need to have a serious conversation about machine policy and ethics (ironically both fundamentally human subjects), but for now, we'll allow AI to steadily improve and run amok in society.
Rockwell Anyoha is a graduate student in the department of molecular biology with a background in physics and genetics. His current project employs the use of machine learning to model animal behavior. In his free time, Rockwell enjoys playing soccer and debating mundane topics.
This article is part of a Special Edition on Artificial Intelligence.

For more information:

Brief Timeline of AI
https://www.livescience.com/47544-history-of-a-i-artificial-intelligence-infographic.html
Complete Historical Overview
http://courses.cs.washington.edu/courses/csep590/06au/projects/history-ai.pdf
Dartmouth Summer Research Project on Artificial Intelligence
https://www.aaai.org/ojs/index.php/aimagazine/article/view/1904/1802
Future of AI
https://www.technologyreview.com/s/602830/the-future-of-artificial-intelligence-and-cybernetics/
Discussion on Future Ethical Challenges Facing AI
http://www.bbc.com/future/story/20170307-the-ethical-challenge-facing-artificial-intelligence
Detailed Review of Ethics of AI
https://intelligence.org/files/EthicsofAI.pdf

# data[].attributes.attachment_link.title
The History of Artificial Intelligence - Science in the News

# data[].attributes.attachment_link.description
by Rockwell Anyoha Can Machines Think? In the first half of the 20th century, science fiction familiarized the world with the concept of artificially intelligent robots. It began with the "heartless" Tin man from the Wizard of Oz and continued with the humanoid robot that impersonated Maria in Metropolis.

# data[].attributes.headline
Business Problems that can be solved with the use of AI

# data[].attributes.subject
Business Problems that can be solved with the use of AI

# data[].attributes.attachment_link.readable_text
6 min read

Opinions expressed by Entrepreneur contributors are their own.

Although every business is different, even those in completely separate industries face some of the same long-standing problems. In recent years, artificial intelligence has become the technology that's well-positioned to solve many of these business challenges.

Let's look at five key challenges businesses face and how AI-powered solutions from specific companies are addressing those obstacles.

1. Fraud

Handling more digital and mobile transactions gives customers what they want. However, it may also give criminals what they want - that is, an opportunity to grab sensitive personal and financial data. With stronger consumer expectations about transaction speed, companies are struggling to meet demand while ensuring each transaction is scanned for potential fraud.
Related: How Self-Learning Software Is Already a Huge Part of Your Life
AI has become the only technology solution fast enough to help companies process such speedy transactions. For example, companies like Sift Science and Feedzai leverage AI and machine learning algorithms to sort and assess data in a matter of seconds. As a result, these companies have vastly decreased fraud, spammers and a wide range of financial crimes. Other companies such as PoshMark, Door Dash and others have been able to reduce fraudulent transactions, chargebacks and customer spamming.

2. Customer support

Thanks to the immediacy that accompanies the digital marketplace, the customer experience has become a vital part of every company's success. Today's companies might deliver faster transactions, but they still struggle with round-the-clock customer support.

AI is stepping in to help companies offer responsive customer support across multiple channels, even without a human being to handle customer inquiries. For example, Agara is helping B2C companies adopt AI-enabled support for an enhanced customer experience. Customers enjoy having a human-like, real-time voice that can quickly answer their questions with informed responses.
Related: How AI Will Change The Way We Work In 2020
AI is the only solution that can react to customer queries as they speak while simultaneously traversing a company's complex software grid to offer tips and assistance to operators in real-time.

Similarly, companies like Verint Next IT deliver intelligent virtual assistants (IVAs) and enterprise chatbots. Verint's approach is different because it offers a virtual agent to talk to the clients, while Agara keeps the operators but gives them AI tools. This type of technology often lead to faster, more effective resolutions for customers, which in turn builds brand reputation and customer loyalty.

3. Personalization

While customers might like the convenience of shopping online and with their mobile devices, they still want brands to see them as individuals and provide personalized interactions. With a much larger customer base and without the connection of face-to-face, in-store transactions, companies are struggling with how to personalize each experience.

Amazon was one of the first to use AI to create personalized recommendations based on past orders. That feature was just the beginning of what AI-powered solutions are now able to do. For example, Persado uses AI to personalize marketing messages based on the technology's continual learning processes to assess formatting, word positioning, word choices and more.

Dynamic Yield takes it one step further by using AI to determine how personalization can be added throughout the customer journey. This means studying, processing and segmenting for behavioral messaging, targeting and retargeting and recommendations. Case studies show that the use of AI to improve personalization has yielded increased conversions and revenue.

4. Data analysis

The increase in data is beneficial, but it's still a challenge to structure and usefully mine all this information. Although AI has become a major part of data analysis in the last decade, organizing that data is still a complex undertaking.

DataRobot uses AI to help AI. Implementing a technology it invented known as automated machine learning (AutoML), the company has figured out how to automate part of the process of developing machine learning and AI applications, including those for data analysis. Data and software engineers, as well as analytics experts, can quickly build effective data analysis models to improve their AI-powered data analysis processes.
Related: Here's What You Can Do Now to Stay Employed When They Finally Have a Robot for Your Job
Similarly, H2o.ai created an open-source platform to improve AI's ability to analyze data in a transparent, accurate and trustworthy way. The company's platform has assisted the financial, insurance, healthcare, manufacturing and marketing industries, among others, to improve how they leverage AI for data analysis and business decisions.

5. Productivity

Companies that want to get the most out of their workforce and processes focus on working smarter for enhanced productivity. Once again, AI can provide a better solution.

Appnomic calls itself a "self-healing" enterprise and takes a proactive approach to solve the challenge of continuity of business applications. They use AI for predicting and preventing IT issues before they turn into problems that impact productivity. The company has applied its solution to a wide range of industries, from financial to retail to manufacturing.

Without AI's prediction capabilities, businesses would need to both fix the problem as well as any damage that's been made. AI keeps the IT department from firefighting and helps them do their jobs better.

Many industries, such as insurance, financial services and healthcare, suffer from legacy processes that can decrease productivity in the digital age. That's the main issue that AI-driven Vidado plans to fix. Using AI, the company can help these industries speed up their digital transformations by transforming paper processes into automated digital processes. Greater efficiency means increased productivity and reduced expenses.

AI to the rescue

Thanks to these AI-enabled solutions, age-old business challenges are finally being addressed in an effective way. In the process, organizations can satisfy customers, secure transactions, improve audience and customer interactions, better manage data and become more productive.

It's important to note that AI is not a silver bullet. Appnomic, for instance, detects IT issues with AI, but relies on automated scripts to solve them (or alert an operator). It's the art of knowing when and how to use AI that makes these solutions useful.
Related: 2020: The Year Of Artificial Intelligence For Your Business

# data[].attributes.attachment_link.title
How AI Solutions Are Solving 5 Long-Standing Business Challenges

# data[].attributes.attachment_link.description
6 min read Opinions expressed by Entrepreneur contributors are their own. Although every business is different, even those in completely separate industries face some of the same long-standing problems. In recent years, artificial intelligence has become the technology that's well-positioned to solve many of these business challenges.

# data[].attributes.attachment_link.readable_text
We use cookies to track usage and preferences

I understand Privacy policy

An uncertain future?

As with many new and emerging technologies, Artificial Intelligence (AI) is still, to some extent, an enigma. And while it can be hard to separate the myths from the facts, there should be no doubt that AI is already beginning to have a big impact on our daily lives.

From internet-connected voice assistants that help us with our shopping lists, to hidden algorithms that make decisions about what price to charge us for our hotel room, the use of AI has lots of implications, both for consumers and the consumer organisations that seek to protect them.

Last month, we attended and spoke at a Euroconsumers event in Brussels, Belgium, which set out to explore the opportunities and challenges presented by the emergence AI, and what potential it has for the future as machine learning continues to evolve and expand its capabilities.

Below are some of the key learnings from the event -

1. Laws vs Codes

The speed at which technologies such as AI grow and develop makes effective regulation incredibly difficult. By the time legislators have reacted to a problem and passed the necessary laws to protect against this problem, there is a good chance that the tech giants who dominate the AI industry are already taking their algorithms in a different direction.

As a result, there are conflicting opinions as to whether AI should be governed by binding law, or whether certain areas can be left to a code of ethics and self-regulation. At the Euroconsumers event, Paul Nemitz, Principal Advisor to DG Justice at the European Commission, suggested that AI should be developed in accordance to with GDPR principles and consumer law, and the right to ' meaningful information' and to ' object to automated-decision making ' must be monitored and enforced by regulators.

Whilst a code of ethics could help to ensure that killer robots don't have a place outside of Sci-Fi movies, it is unlikely that ethics alone will be enough to fully protect consumers from the negative impacts that AI could have on their rights. This is why directives such as GDPR will still be an important form of protection both in Europe and beyond.

2. There are lots of issues yet to be solved...

Artificial intelligence throws up a number of a challenging issues for consumer protection across the globe; lack of transparency in terms of how algorithms are used to shape the services & prices we receive online, human bias and discrimination that is reflected in technology, invasive AI that undermines our privacy. AI can even be a fatal safety hazard when it comes to driverless cars.

How we resolve these issues is unclear, but the consensus at the event was that a combination of approaches would be necessary, including greater transparency, stronger enforcement and more corporate accountability. Collaboration will also be key, with consumer organisations, civil society, business and policy-makers working together to develop answers to the questions raised by AI.

3. ...but consumers organisations can use AI to their advantage

As a general-purpose technology, the potential uses for AI are almost endless, and although we often tend to focus on the negative things associated with AI, consumers organisations can use algorithms to their advantage.

Miguel Lage, Market Analyst at our member DECO Proteste delivered an engaging talk on how consumer organisations could use chatbots to provide useful information to consumers about their rights and the services available to them. BEUC also mentioned an exciting new project they are working on with the University of Florence, which would use AI to trawl through privacy policies and send alerts when it spots GDPR breaches.

Understanding and using new technologies will be an essential step for consumer organisations if they are to evolve and continue to serve consumers in an increasingly ubiquitous digital world.

4. Voice-activated technology to give consumers a voice

Away from the highly publicised battle between Amazon and Google to win the right to place their voice assistant in smart homes across the globe, little attention is given to how AI could be harnessed to change the lives of marginalised consumers in developing countries.

In India, voice-activated technology is a rapidly expanding market, with over 150 million Indians likely to use voice assistants by 2019. Language, however, is a key barrier - 90% of the voice assistants currently available in India only support English. For the millions of Indians who speak languages such as Hindi, Bengali or Tamil as their first language, this is a huge barrier. That is why Indian start-ups such as Liv.Ai, Vokal and Reverie Language Technologies are making strides to create AI technology that can serve consumers in some of the country's many regional languages, a move which could enable consumers in poorer, rural communities to benefit from the power of voice assistants.

As our Head of Digital Advocacy Liz Coll mentioned in her panel at Euroconsumers' event, AI can also be a powerful tool for financial inclusion. In African countries such as Chad, Niger and Burkina Faso where levels of illiteracy high, voice activated mobile technology can be used to access essential banking services such as basic payments or even creating a bank account for the first time.

Affordable access to digital products and services, however, will be essential if the true potential of AI is to be realised for consumers in developing countries across the globe.

5. The consumer movement must be ready to adapt

A running theme throughout the Euroconsumers event, especially in the closing remarks from Marco Pierani, Altroconsumo and Henrique Lian, Proteste, was the necessity for consumer organisations to embrace the opportunities brought about by digital technology.

In a world where leading tech companies are racing ahead of the curve, our global consumer movement is in a unique place to serve as the ' enzyme that rebalances the digital ecosystem '. The challenges of artificial intelligence can be countered by a collective intelligence, which requires knowledge sharing and cross-sector collaboration between consumer organisations, businesses, governments and civil society.

By working together, we can ensure that we create an environment where emerging technologies are built with consumer safety, privacy and security in mind. Where digital products and services are as inclusive and affordable as they are innovative. AI is therefore a challenge that consumer organisations can embrace, but only if they are ready to adapt.

# data[].attributes.attachment_link.title
AI for Consumers: Five things we learnt at the Euroconsumers event on Artificial Intelligence

# data[].attributes.attachment_link.description
As with many new and emerging technologies, Artificial Intelligence (AI) is still, to some extent, an enigma. And while it can be hard to separate the myths from the facts, there should be no doubt that AI is already beginning to have a big impact on our daily lives.

# data[].attributes.attachment_link.readable_text
Department stores are increasingly using data to help predict the rise and decline of future trends.

Fashion houses like Marni and Miu Miu use data analytics to identify opportunities and weaknesses in their collections.

While data-driven tools have improved trend forecasting, human interpretation remains essential.

When Detroit-based luxury goods brand Shinola began working on its new Vinton watch, the team designed with a woman in mind, but testing the product through analytics platform MakerSights, which correlates consumer feedback with historical sales data, revealed the style appealed to all genders. As a result, the brand deepened its buy-in on those by about 70 per cent. "You never design by data, but the data, but the data provides a compass as you're navigating a hunch," says Shinola CEO Tom Lewand.

In other words, Shinola already had a great vision - and the data enhanced it.

Using data to validate intuition

MakerSights is among a new class of data-driven analytics platforms that combine factors such as search queries, social media activity, e-commerce sell-throughs and consumer feedback to provide clues into what is most likely to become a trend.

Debenhams in-house forecaster Angela Baidoo utilises insights from retail analytics platform Edited, which analyses the online inventories of fashion brands in 130 countries by using natural language processing (to identify text) and computer vision (to understand images). Baidoo might already anticipate a revival of '90s silhouettes, but the data tells her what brands have started to offer them, whether they are selling out and what colours are likely to be a hit.

This information helps Baidoo predict when a style is ready for the mass market. For instance, she'd been tracking the "innerwear as outerwear" trend since 2016, and advised Debenhams to buy in for Autumn/Winter 2019. But once she saw that Topshop and Zara were already selling out of the styles - combined with their reappearance on the Resort and Spring/Summer 2019 runways - the company then decided to bring several innerwear-inspired pieces forward for SS19.

The limits of data

"Data is exceptionally useful to detect when a rising trend is about to hit mass adoption and to anticipate the decline of a trend," says Andrea Bell, head of insight at trend forecasting agency WGSN, which Baidoo also uses. What it can't do is invent a trend.

It's important to "break your own news", says Ed Burstell, senior vice president of product innovation at Neiman Marcus. "Look at Gucci - it is the biggest phenomenon of the last couple of years, and it certainly wasn't waiting around to see if it would be a trend. It put its flag in the sand and said, 'This is it.'"

Burstell depends on data-driven marketing and prediction agencies such as The Future Laboratory and J. Walter Thompson Intelligence, as well as cultural influencers and financial analysts, to track trends. "If you're going to people who will advise you, go to someone who is a genius," he says, citing The Phluid Project founder and expert on Gen Z Rob Smith.

The opportunity for luxury

There's a pervasive belief that high-end fashion houses eschew trend-tracking in favour of creative vision. A designer at a Parisian luxury label tells Vogue Business that many fashion houses do what they feel is right - and the market follows. "[Louis] Vuitton might use some sort of analytics for bags, but anybody who tries to tell Nicolas Ghesquière how to do his collection wouldn't have a job anymore," the designer says. Moreover, designers who consult data probably wouldn't admit it.

But some are starting to acknowledge its uses.

Marni uses Edited to help build out product ranges, price its collections, and identify opportunities and weaknesses. Former Miu Miu accessories designer and illustrator Sofia Antonielli says that many companies are already using data tools - and usage is expanding.

When Antonielli was at Miu Miu, she consulted iCoolHunt's NextAtlas software, which uses artificial intelligence to analyse the social media behaviour of more than 300,000 influencers and their followers. The tech looks at the same types of elements that forecasters consult to identify social shifts like gender fluidity, apocalyptic mindsets and veganism. The difference is that the inputs come from platforms including Instagram, Tumblr, Twitter and Pinterest, which are more agile and less subjective than traditional forecasting.

The human factor

While data-driven tools have improved trend forecasting, human interpretation remains essential. Baidoo emphasises the importance of sources like books and art when evaluating trends. "We are in an age where social media can call you out. If you say, 'I love this design from Celine', Diet Prada might call out the fact that it's an indigenous costume from the Arctic," she says.

She uses a recent presentation from Google Trends, which showed a set of striped dresses that Baidoo compares to catalogue images, as an example. "Google Trends can't create that highbrow element. It just isn't there yet."

6 trend-tracking tools to know

1. 1

Spate: Currently in private beta, it uses online behavioural signals to predict trends in food, beauty and fashion.

2. 2

MakerSights: Correlates consumer feedback with historical sales data to inform product development. (Subscription-based.)

3. 3

Google Trends: Shows historical search interest on any keyword, sorting by date, category and geography. (Free.)

4. 4

Edited: Provides real-time retail data, such as what is being stocked and what is sold, from brands around the world. (Subscription-based.)

5. 5

NextAtlas: Detects emerging trends from social media influencers. (Subscriptions start from €599 per month.)

6. 6

Pinterest: Advertisers can access insights about what customers are interested in based on their Pinterest behaviour. (In addition to a free annual trend report.)

Correction: A previous version of this article misquoted Angela Baidoo as saying "Antarctica" instead of "the Arctic." The article has since been updated. (7 February 2019)
To receive the Vogue Business Technology Edit, sign up here. Comments, questions or feedback? Email us at feedback@voguebusiness.com. More from this author: Europe is still a bargain for luxury shoppers With Zyper investment, VCs are betting on "influencer fatigue" What Google Search can reveal about trends

# data[].attributes.attachment_link.title
Analytics are reshaping fashion's old-school instincts

# data[].attributes.attachment_link.description
Department stores are increasingly using data to help predict the rise and decline of future trends. Fashion houses like Marni and Miu Miu use data analytics to identify opportunities and weaknesses in their collections. While data-driven tools have improved trend forecasting, human interpretation remains essential.

# data[].attributes.headline
Augmented reality, digital clothing and blockchain will continue to develop, while rental, resale and influencer marketing will grow.

# data[].attributes.subject
Augmented reality, digital clothing and blockchain will continue to develop, while rental, resale and influencer marketing will grow.

# data[].attributes.attachment_link.readable_text
The best of 2019's tech experiments will mature, with smart brands building on only those that provide meaningful returns.

Augmented reality, digital clothing and blockchain will continue to develop, while rental, resale and influencer marketing will grow.

Investors will look to B2B startups that provide "behind the scenes" technologies for customer service, logistics and more as new areas for growth.

2019 saw fashion brands and retailers testing new technologies and developing new business models. 2020 will see them identifying, and scaling, practical use-cases.

Last year's fashion-tech investments followed shifting trends in customer behaviour. Developments in data and AI led to new programs from the likes of Kering and Yoox Net-a-Porter aimed to better deliver what customers want, including smarter omnichannel shopping journeys, advanced clienteling and data-informed product development and merchandising. An increasing appetite for newness that doesn't create waste led to a slew of rental and resale models: Urban Outfitters Inc. launched rental service Nuuly; luxury brands like Burberry inked official partnerships with The RealReal and other resale sites. Pushing that trend forward, brands like Louis Vuitton and Farfetch experimented with digital designs through the world of gaming.

This year, brands will continue to eschew big, tech-centric launches in favour of incremental tests, and they will build on what worked best in 2019's year of experimentation. Based on momentum from many of last year's emerging themes, here are the technologies and trends expected to make headlines in the year ahead.

Influencer marketing could benefit from a data squeeze. As data literacy matures, so does consumer and governmental awareness of data privacy. California's new Consumer Privacy Act builds on the EU's General Data Protection Regulation, which gives people more control over the usage of their data, and Apple is making it harder for third parties to gather data. Thus, Forrester predicts that "2020 will be a bad year for third-party data and a good year for marketers who take consumer privacy seriously."

Brands will have to limit reliance on aggregated third-party data to target customers, Forrester says, which might result in decreasing data-informed personalisation efforts. Instead, brands should seek to build authentic connections through influencer marketing, especially as Instagram builds tools to help brands create targeted influencer campaigns based on ideal audience.

Blockchain use-cases will mature and scale. Last year confirmed that the potential of blockchain is becoming apparent to fashion retailers: Farfetch joined the Libra Association, LVMH linked up with Microsoft on Aura and Ba&sh is offering resale through blockchain-enabled purchases. This year, expect Farfetch and LVMH's blockchain strategies to accelerate, which will likely include providing a unique digital identity to items, plus more pilots and products that use blockchain to help authenticate luxury goods like bags and sneakers or anonymously transfer ownership of pre-owned goods. This year could potentially even see the development of luxury payment through cryptocurrency.

Rental and resale will continue their proliferation. In 2019, Burberry, Harvey Nichols, Selfridges, Farfetch and Ba&sh saw business opportunities in customers' growing interest in consigning, while startups like Caastle, Reflaunt, The Restory, LePrix and Eon grew to help both brands and customers participate in circular business models. The RealReal's continued rise encapsulated the surge of the model: The company's Q3 revenue increased 55 per cent to $80.5 million, and average order value climbed to $438 from $418. This year, expect a major fashion player to make an even more significant step toward fashion's version of "reduce, reuse, recycle" through in-house resale, rental or the use of deadstock fabrics to make new designs. Last November, for example, Kering's chief client and digital officer Grégory Boutté acknowledged that rental and resale were on his radar. Menswear and streetwear wear will also see more options for rental and resale with startups like Seasons and Laced.

A luxury brand will offer digital clothes. Last year introduced the concept of clothing that exists only digitally: Gucci let customers "try on" Ace sneakers through augmented reality, Louis Vuitton designed "skins" for League of Legends characters and Drest sold digitised versions of Farfetch inventory. While selling digital clothing at luxury prices is yet to come, it's an intriguing method for a brand to experiment with technology, engage with customers and cater to both Instagram culture and sustainability.

Already, some brands are using digital designs to gauge market appetite: In addition to Tommy Hilfiger's digital showrooms, San Francisco brand Taylor Stitch invites customers to pre-order digital designs before they go into production. This programme has been profitable, says Matt Field, co-founder of product development platform Maker/Sights, which works with the brand. Field anticipates seeing bigger brands adopting "flavours of that approach" - by "dropping" digital designs on Instagram before the most popular design is produced, for instance. Already, Scandinavian brand Carlings has created and sold a T-shirt whose designs can be changed digitally using Instagram AR filters.

5G will power augmented reality and live video shopping. The processing power of smartphones and their networks has limited effective augmented-reality projects, including shop-adjacent uses in fashion. As the fifth generation of wireless technology, 5G promises faster downloads and less latency. Combined with the influx of AR developer tools from mainstream platforms like Apple and Facebook, 5G could facilitate the technology's presence on social media in 2020. For example, customers will be able to "try on" designs on social platforms before checking out in-app.

5G will also allow new streaming media formats with higher-definition graphics, sound and interactive technologies, says eMarketer principal analyst Yoram Wurmser, suggesting that livestream video shopping, already generating $4.4 billion in sales in China as of 2018, will become more popular in Western regions. Early players include H&M-owned Monki and ShopShops, which connects Chinese shoppers with US boutiques. Expect platforms like Facebook, Instagram and YouTube to jump in with potential new features that facilitate in-app live shopping.

VCs will reward profitability and practicality. Consider it the Softbank effect: the venture funding climate has grown more conservative, with investors prioritising stable growth over bloated scale. In the year ahead, investors will look for companies that can demonstrate customer loyalty and diverse customer acquisition strategies beyond Facebook - a strategy that applies to existing consumer brand startups as well. Building customer loyalty and competing with the technology of Amazon while achieving profitability is a lot to accomplish at once, which is why 2020 will require brands to invest in the less sexy categories of innovation, including fulfilment technology and e-commerce experience.

The B2B companies that facilitate these capabilities behind the scenes are in a position to step into the spotlight this year. Forrester anticipates the rise of "agent whisperer" technologies that help customer service agents more intuitively and accurately respond to customer inquiries.

EMarketer's Lipsman says that brands will find refuge in "Retail as a Service" (RaaS) platforms that deliver off-the-shelf, on-demand services that "fill the innovation gap with back-end technology and infrastructure". He points to Shopify's new fulfilment services, Microsoft's white-label "store-of-the-future" technology and Neighborhood Goods's flexible pop-up format as clear leaders.

After 2019's experiments, 2020 will be a time to stick to the technologies that help brands with staying power. "2020 could become the year that companies convert on the potential energy built up in 2019 to move from prudent investments to breakout growth," says Forrester senior vice president of research Sharyn Leaver. "The most successful will be those that properly invested in their foundation - and have the innovation skills to move quickly."
To receive the Vogue Business newsletter, sign up here. Comments, questions or feedback? Email us at feedback@voguebusiness.com. More from this author: Can innovation be taught? VC Sonya Brown on how to stand out as a DTC startup How fashion startups get accepted into tech accelerators

# data[].attributes.attachment_link.description
The best of 2019's tech experiments will mature, with smart brands building on only those that provide meaningful returns. Augmented reality, digital clothing and blockchain will continue to develop, while rental, resale and influencer marketing will grow. Investors will look to B2B startups that provide "behind the scenes" technologies for customer service, logistics and more as new areas for growth.

# data[].attributes.headline
The future of fashion? Everyone’s going to buy less and think more - Alexa Chung, designer

# data[].attributes.subject
The future of fashion? Everyone’s going to buy less and think more - Alexa Chung, designer

# data[].attributes.attachment_link.description
Bazaar’s fashion director Avril Mair weighs in