AI and Human Future

Expert View: The Human Promise of the AI Revolution
By Kai-Fu Lee
He is the Chairman and CEO of Sinovation Ventures and the former president of Google China.

Artificial intelligence is a technology that sparks the human imagination. What will our future look like as we come to share the earth with intelligent machines? Our minds gravitate to extremes, to the sharply contrasting visions that have captured public attention and divided much of the technological community. As a longtime AI researcher and venture capitalist in China and the U.S., I’ve observed these two camps across continents and over many decades.

Utopians believe that once AI far surpasses human intelligence, it will provide us with near-magical tools for alleviating suffering and realizing human potential. In this vision, super-intelligent AI systems will so deeply understand the universe that they will act as omnipotent oracles, answering humanity’s most vexing questions and conjuring brilliant solutions to problems such as disease and climate change.

But not everyone is so optimistic. The best-known member of the dystopian camp is the technology entrepreneur Elon Musk, who has called super-intelligent AI systems “the biggest risk we face as a civilization,” comparing their creation to “summoning the demon.” This group warns that when humans create self-improving AI programs whose intellect dwarfs our own, we will lose the ability to understand or control them.

Which vision to accept? I’d say neither. They simply aren’t possible based on the technology we have today or any breakthroughs that might be around the corner. Both scenarios would require “artificial general intelligence”—that is, AI systems that can handle the incredible diversity of tasks done by the human brain. Making this jump would require several fundamental scientific breakthroughs, each of which may take many decades, if not centuries.

The real battles that lie ahead will lack the apocalyptic drama of Hollywood blockbusters, but they will disrupt the structure of our economic and political systems all the same. Looming before us in the coming decades is an AI-driven crisis of jobs, inequality and meaning. The new technology will wipe out a huge portion of work as we’ve known it, dramatically widening the wealth gap and posing a challenge to the human dignity of us all.

This unprecedented disruption requires no new scientific breakthroughs in AI, just the application of existing technology to new problems. It will hit many white-collar professionals just as hard as it hits blue-collar factory workers.

Despite these immense challenges, I remain hopeful. If handled with care and foresight, this AI crisis could present an opportunity for us to redirect our energy as a society to more human pursuits: to taking care of each other and our communities. To have any chance of forging that future, we must first understand the economic gauntlet that we are about to pass through.

Many techno-optimists and historians would argue that productivity gains from new technology almost always produce benefits throughout the economy, creating more jobs and prosperity than before. But not all inventions are created equal. Some changes replace one kind of labor (the calculator), and some disrupt a whole industry (the cotton gin). Then there are technological changes on a grander scale. These don’t merely affect one task or one industry but drive changes across hundreds of them. In the past three centuries, we’ve only really seen three such inventions: the steam engine, electrification and information technology.

”Han,” a humanoid robot developed by Hanson Robotics.

”Han,” a humanoid robot developed by Hanson Robotics. PHOTO: ISAAC LAWRENCE/GETTY IMAGES

Looking at this smaller data set, we have a mixed bag of economic impacts. The steam engine and electrification created more jobs than they destroyed, in part by breaking down the work of one craftsman into simpler tasks done by dozens of factory workers. But information technology (and the associated automation of factories) is often cited by economists as a prime culprit in the loss of U.S. factory jobs and widening income inequality.

The AI revolution will be of the magnitude of the Industrial Revolution—but probably larger and definitely faster. Where the steam engine only took over physical labor, AI can perform both intellectual and physical labor. And where the Industrial Revolution took centuries to spread beyond Europe and the U.S., AI applications are already being adopted simultaneously all across the world.

AI’s main advantage over humans lies in its ability to detect incredibly subtle patterns within large quantities of data and to learn from them. While a human mortgage officer will look at only a few relatively crude measures when deciding whether to grant you a loan (your credit score, income, and age), an AI algorithm will learn from thousands of lesser variables (what web browser you use, how often you buy groceries, etc.). Taken alone, the predictive power of each of these is minuscule but added together, they yield a far more accurate prediction than the most discerning people are capable of.00:19 / 12:29

For cognitive tasks, this ability to learn means that computers are no longer limited to simply carrying out a rote set of instructions written by humans. Instead, they can continuously learn from new data and perform better than their human programmers. For physical tasks, robots are no longer limited to repeating one set of actions (automation) but instead can chart new paths based on the visual and sensor data they take in (autonomy).

Together, this allows AI to take over countless tasks across society: driving a car, diagnosing a disease or providing customer support. AI’s superhuman performance of these tasks will lead to massive increases in productivity. According to a June 2017 study by the consulting firm PwC, AI’s advance will generate $15.7 trillion in additional wealth for the world by 2030. This is great news for those with access to large amounts of capital and data. It’s very bad news for anyone who earns their living doing soon-to-be-replaced jobs.

There are, however, limits to the abilities of today’s AI, and those limits hint at a hopeful path forward. While AI is great at optimizing for a highly narrow objective, it is unable to choose its own goals or to think creatively. And while AI is superhuman in the coldblooded world of numbers and data, it lacks social skills or empathy—the ability to make another person feel understood and cared for. Analogously, in the world of robotics, AI is able to handle many crude tasks like stocking goods or driving cars, but it lacks the delicate dexterity needed to care for an elderly person or infant.

What does that mean for workers who fear being replaced? Jobs that are asocial and repetitive, such as fast-food preparers or insurance adjusters, are likely to be taken over in their entirety. For jobs that are repetitive but social, such as bartenders and doctors, many of the core tasks will be done by AI, but there remains an interactive component that people will continue to perform. The jobs that will be safe, at least for now, are those well beyond the reach of AI’s capabilities in terms of creativity, strategy and sociability, from social workers to CEOs.

Even where AI doesn’t destroy jobs outright, however, it will exacerbate inequality. AI is inherently monopolistic: A company with more data and better algorithms will gain ever more users and data. This self-reinforcing cycle will lead to winner-take-all markets, with one company making massive profits while its rivals languish.

A similar consolidation will occur across professions. The jobs that will remain relatively insulated from AI fall on opposite ends of the income spectrum. CEOs, home care nurses, attorneys and hairstylists are all in “safe” professions, but the people in some of these professions will be swimming in the riches of the AI revolution while others compete against a vast pool of desperate fellow workers.

We can’t know the precise shape and speed of AI’s impact on jobs, but the broader picture is clear. This will not be the normal churn of capitalism’s creative destruction, a process that inevitably arrives at a new equilibrium of more jobs, higher wages and better quality of life for all. Many of the free market’s self-correcting mechanisms will break down in an AI economy. The 21st century may bring a new caste system, split into a plutocratic AI elite and the powerless struggling masses.

Recent history has shown us just how fragile our political institutions and social fabric can be in the face of disruptive change. If we allow AI economics to run their natural course, the geopolitical tumult of recent years will look like child’s play.

On a personal and psychological level, the wounds could be even deeper. Society has trained most of us to tie our personal worth to the pursuit of work and success. In the coming years, people will watch algorithms and robots easily outmaneuver them at tasks they’ve spent a lifetime mastering. I fear that this will lead to a crushing feeling of futility and obsolescence. At worst, it will lead people to question their own worth and what it means to be human.

So what can be done?

This grim vision is shared by many technologists in Silicon Valley, and it has sent them casting about for solutions. As the architects and profiteers of the AI age, they feel a mix of genuine social responsibility and fear of being targeted when the pitchforks come out. In their rush for a quick fix, many of the techno-elite have seized on the idea of a universal basic income: an unconditional, government-provided cash stipend to allow every citizen to meet their basic needs.

I can see the appeal. UBI is exactly what Silicon Valley entrepreneurs love: an elegant technical solution to tangled social problems. UBI can be the magic wand that lets them wish away the messy complexities of human psychology and get back to building the technologies that “make the world a better place,” while making them rich. It’s an approach that maps well onto how they tend to view society: as a collection of “users” rather than as citizens, customers and human beings.

We can do better. Some form of guaranteed income may indeed be necessary, but if we allow such support to be the endgame, we will miss the opportunity presented by this transformative technology. Instead of simply falling back on an economic painkiller like a universal basic income, we should use the economic bounty generated by AI to double down on what separates us from machines: human empathy and love.

Such a revolution in how we relate to work will require a rethink from all corners of society. In the private sector, instead of simply viewing AI as a means for cost-cutting through automation, businesses can create new jobs by seeking out symbiosis between AI optimizations and the human touch. This will be especially powerful in areas such as health care and education, where AI can produce crucial insights but only humans can deliver them with care and compassion.

Beyond the private sector, governments across the world need to start thinking now about how to use the riches generated by AI to rewrite the social contract and reorient our economies to promoting human flourishing.

“Pepper,” a robot manufactured by SoftBank Robotics, is designed to interact with human beings.

“Pepper,” a robot manufactured by SoftBank Robotics, is designed to interact with human beings. PHOTO: GETTY IMAGES

At the center of this vision, I would suggest, there needs to be what I call the Social Investment Stipend, a respectable government salary for those who devote their time to three categories of activities: care work, community service and education. These activities would form the pillars of a new social contract, rewarding socially beneficial activities just as we now reward economically productive activities. The idea is simple: to inject more ambition, pride and dignity into work focused on enhancing our communities.

Care work could include parenting or home schooling of young children, assisting aging parents or helping a friend with mental or physical disabilities live life to the full. Service work would focus on much of the current work of nonprofit and volunteer groups: leading after-school programs, guiding tours at parks or collecting oral histories from elders in our communities. Supported education activities could range from professional training for the jobs of the AI age to taking classes that turn a hobby into a career.

The participation requirements of the stipend wouldn’t be designed to dictate the lives of citizens. There would be a wide enough range of choices for all workers who have been displaced by AI. The more people-oriented could opt for care work, the ambitious could enroll in high-tech training, and others could take up community-service work.

By requiring some social contribution to receive the stipend, we would foster a public philosophy far different from the laissez-faire individualism of universal basic income. Providing a stipend in exchange for participation in community-building activities carries a clear message: Collective effort from people across society allowed us to reach this point of economic abundance, and now we must use that abundance to recommit ourselves to one another and to our humanity.

Many difficult questions remain to be answered, of course, before we could consider implementing such a sweeping and idealistic policy. The urgency to create, and the ability to pay for, a far-reaching Social Investment Stipend will depend on the pace and nature of AI’s economic impact. But the humanistic values it embodies can serve as a guide while we navigate the treacherous waters that lie ahead. We may yet be able to harness the full potential of both machines that think and humans who love.

—This essay is adapted from Dr. Lee’s new book, “AI Superpowers: China, Silicon Valley and the New World Order,” which will be published by Houghton Mifflin Harcourt on Sept. 25. He is the Chairman and CEO of Sinovation Ventures and the former president of Google China.

Original Publication link

the-next-stage-of-human-machine-collaboration

The next stage of human-machine collaboration

Some AI-based services and tasks today are relatively trivial – such as a song recommendation on a streaming music platform.

However, AI is playing an expanding role in other areas with far greater human impact. Imagine you’re a doctor using AI-enabled sensors to examine a patient, and the system comes up with a diagnosis demanding urgent invasive treatment.

In situations such as this, an AI-driven decision on its own is not enough. We also need to know the reasons and rationale behind it. In other words, the AI has to “explain” itself, by opening up its reasoning to human scrutiny.

The transition to Explainable AI is already underway, and within three years, we expect it to dominate the AI landscape for businesses.

Explainable AI systems will play this pivotal role through their ability to:

Explainable AI, ready for takeoff

The transition to Explainable AI is already underway, and within three years, we expect it to dominate the AI landscape for businesses. It will empower humans to take corrective actions, if needed, based on the explanations machines give them. But how will it do this?

There are three ways of manifesting and conveying the reasoning behind AI decisions made by machines:

  1. Using data from the machine learning – using comparisons with other examples to justify the decisions

2. Using the model itself – explanations mimic the learning model by abstracting it through rules or combining it with semantics

3. Hybrid approach combining both data and model – offers metadata and feature-level explanations.”The future of AI lies in enabling people to collaborate with machines to solve complex problems. Like any efficient collaboration, this requires good communication, trust and understanding.” 

– FREDDY LECUE, Explainable AI Research Lead, Accenture Labs

Two use cases for Explainable AI

No. 1 – Detecting abnormal travel expenses
Most existing systems for reporting travel expenses apply pre-defined views, such as time period, service or employee group. While these systems aim to detect abnormal expenses systematically, they usually fail to explain why the claims singled out are judged to be abnormal.

To address this lack of visibility into the context of abnormal travel expense claims, Accenture Labs designed and built a travel expenses system incorporating Explainable AI. By combining knowledge graph and machine learning technologies, the system delivers insight to explain any abnormal claims in real-time.

No. 2 – Project risk management
Most large companies manage hundreds, if not thousands, of projects every year across multiple vendors, clients and partners. A company’s expectations are often out of line with the original estimates because of the complexity and risks inherent in the critical contracts.

This means decision-makers need systems that not only predict the risk tier of each contract or project, but also give them an actionable explanation of these predictions. To address the challenges, Accenture Labs applied Explainable AI and developed a five-stage process to explain the risk tier of projects and contracts.

Measuring effectiveness

Eight measures can be applied to assess its value and effectiveness. These measures capture the elements that people need in an explanation, but cannot necessarily all be achieved. While explainable AI will use and expose techniques that address these questions, we—as humans—should still expect a trade-off between value and effectiveness.

Comprehensibility

How much effort is needed for a human to interpret it?

Succinctness

How concise is it?

Actionability

How actionable is the explanation? What can we do with it?

Reusability

Could it be interpreted/reused by another AI system?

Accuracy

How accurate is the explanation?

Completeness

Does the “explanation” explain the decision completely, or only partially?

A technology revolution with people at its heart

Explanation is fundamental to human reasoning, guiding our actions, influencing our interactions with others and driving efforts to expand our knowledge. AI promises to help us identify dangerous industrial sites, warn us of impending machine failures, recommend medical treatments, and take countless other decisions.

The promise of these systems won’t be realized unless we understand, trust and act on the recommendations they make. To make this possible, high-quality explanations are essential.

Source: Accenture Lab AI report 2018

Accenture Labs, in a new report, details how we can meet the need for more information by giving AI applications the ability to explain to humans not just what decisions they made, but also why they made them.

Unlocking IoT

Unlocking Opportunities in the Internet of Things with Darren Jackson from Bain

Copyright is Bain & Company

Enterprise customers remain bullish on IoT. However, expectations have certainly been tempered. Solutions are taking a bit longer to implement and achieve the ROI that they expected. That said, we still expect to see significant growth in the market.

In 2021, across hardware, software and services, we expect the markets to achieve about $520 billion of revenue. And that’s 2x what we saw just in 2017. We are seeing some changes in the market. For example, cloud service providers have achieved a more prominent, influential role.

CSPs lower the barriers to adoption, enabling companies to scale quicker. Yet their offer is not tied with specific industry vertical and it’s a bit broad, enabling opportunities for others. In order to capitalize on these opportunities, companies need to focus on fewer verticals.

We’re still seeing people focus on four, five, six verticals, which we would say is too many. We would say two to three at best. Once you focus on those verticals, you achieve deep expertise and a competitive advantage. As vendors gain this competitive advantage, they can partner to create true end-to-end solutions, which we know customers are demanding. Understanding customer pain points is the first step. Addressing them in an end-to-end fashion will enable companies to succeed in the Internet of Things.

Europeans Extend Their Lead in the Industrial Internet of Things

Looking ahead at the investment plans of industrial customers, Europeans are poised to hold a narrowing lead, with more than twice as many extensive implementations in 2020 as their US counterparts. Looking almost 10 years ahead—an imprecise endeavor, but one that can shed light on aspirations—the investment plans of executives in both regions could result in a more competitive position, with the US expecting to see similar levels of POCs and implementations as the Europeans.

The key challenge for both regions remains addressing cybersecurity concerns, not only by the vendors but also for customers, who will need to ramp up their security investments significantly to benefit fully from IoT technologies. Bain research finds that customers would buy more IoT devices, and pay more for them (about 22% more on average), if their security concerns were addressed. Becoming a leader in security remains a powerful opportunity for European IoT champions.

IoT providers in both regions could also speed their progress in reducing barriers by focusing their investments on fewer industries, which would allow them to develop greater expertise and deliver more comprehensive end-to-end solutions to their customers. The learning curve effects from POCs will allow vendors to overcome the implementation concerns of their customers by offering more packaged solutions that can scale more easily.

Partnerships with industrial vendors are especially important, as they provide specific domain knowledge and system integration capabilities. Without these partnerships, IoT vendors may continue to struggle to sell solutions that integrate smoothly with customers’ businesses and processes.

Leading industrial companies are moving quickly past the proof stage, and now, it’s all about scaling. Over the next two to three years, clear winners will emerge, as the benefits of early investment kick in and their POCs scale up to operational levels. Companies that have put off investment will lose ground to competitors that are learning how to derive value from the Internet of Things and becoming more data-driven every day—skills that will form the basis of competition in a world of extreme automation and artificial intelligence.

Bain & Company Official Report Link

European Union Regulations on Cryptocurrency

The European Banking Authority (EBA) and the European Securities and Markets Authority (ESMA) have called for an EU-wide approach to cryptocurrency and initial coin offerings (ICO) regulation in order to protect investor.

The EBA published the results of its assessment of EU laws and how they relate to cryptocurrencies earlier this week. The report found that cryptoassets typically fall outside the scope of EU financial laws since specific services relating to crypto custodian wallet provision and crypto trading platforms do not constitute regulated activities. According to the EBA, these factors give rise to potential issues, including regarding consumer protection, operational resilience, market integrity and the level playing field.

The EBA calls on the European Commission (EC) to carry out a comprehensive cost/benefit analysis to determine what, if any, action is required at the EU level at this stage to address these issues, specifically with regard to the opportunities and risks presented by crypto activities and new technologies that may entail the use of cryptoassets.

“The EBA also advises the European Commission to have regard to the latest recommendations and any further standards or guidance issued by the Financial Action Task Force (FATF) and to take steps where possible to promote consistency in the accounting treatment of crypto-assets.” the EBA said.

Separately, ESMA published its “Advice to the European Union (EU) Institutions” on ICOs and cryptocurrencies. ESMA said that while it does not believe that cryptocurrency currently raises financial stability issues, it is concerned about the risks it poses to investor protection and market integrity. ESMA identifies the most significant risks as fraud, cyber-attacks, money laundering, and market manipulation.

“A key consideration for regulators is the legal status of crypto-assets, as this determines whether financial services rules are likely to apply, and if so which, and hence the level of protection to investors,” ESMA said. “Because the range of crypto-assets are diverse and many have hybrid features, ESMA believes that there is not a ‘one size fits all’ solution when it comes to legal qualification.”

The regulator noted that some EU member states have or are considering some bespoke rules at the national level for all or a subset of those cryptoassets that do not qualify as MiFID financial instruments. While ESMA understands the intention to bring to the topic both a protective and supportive approach, ESMA is concerned that this does not provide for a level playing field across the EU. ESMA believes that an EU-wide approach is relevant, also considering the cross-border nature of cryptocurrencies.

Blockchain and Insurance Industry

While the banking sector’s quest for modernization was a major early driver behind the growth in enterprise blockchain technology, the insurance industry has not traditionally had such a healthy appetite for change. That is, until now.

Over the past couple of years, insurers have migrated away from their conservative image, leveraging several emerging technologies, including blockchain, to re-think their current business models.  One of the most significant technologies leading this digital transformation is blockchain which is streamlining back-office processes and systems. Heading into 2019, insurers are accelerating their deployment of the most innovative use-cases of enterprise blockchain technology yet.

Laying the back-office building blocks

Insurance companies face a complex web of challenges in today’s market. Regulatory demands are increasing; fraudulent claims are commonplace and the flow of data is ever increasing. Meanwhile, as digital technology permeates the financial services industry more broadly, customers expect a greater level of innovation than ever before.

Despite the growing demand for tailored products and services, insurers recognized that for transformation to be sustainable, it must begin in the back office. Legacy systems combined with patchwork solutions have perpetuated a closed-off insurance information environment with data silos and resulting operational inefficiencies. Building customer-facing digital solutions on these crumbling foundations would have disastrous consequences.

That is why, over the past two years, insurers have been hard at work behind the scenes deploying cutting-edge enterprise blockchain platforms to overhaul and modernize their back offices. Integrating even just the foundational technology can have a huge impact on a company’s transparency, stability and efficiency.

By taking the first step of moving its transactions onto a shared ledger, an insurer can potentially eliminate fraudulent and duplicate claims by logging each transaction in a decentralized repository. Instantly, an insurance company is able to verify the authenticity of a customer, policy or claim. This is a simple premise but a huge step forward for the industry.

In addition, with the rise of the Internet of Things (IoT) and connected devices, blockchain provides an efficient and secure way to manage, share and leverage an ever-growing amount of data. Purpose-built enterprise blockchain platforms like Corda overcome the challenges of traditional public blockchains by ensuring sensitive data is only shared with parties that have a need to see it in each instance.

The potential efficiency gains for both the insurer and the insured are dramatic. Consider, for example, a re-insurer, insurer and broker consolidating their policy data and storing it on a blockchain – the underwriting and application process could be reduced from weeks or even months to near real-time, with no burden on each entity having to gather, reconcile and submit documents.

These core benefits of blockchain technology are now being realized across the global insurance industry, with forward-thinking initiatives such as the RiskBlock Alliance and B3i leveraging the power of collaboration to drive adoption and deployment.

By moving to a model in which disparate parties such as insurers, reinsurers and brokers can share and store policy information in a cryptographically secure way, the industry has laid the foundations for the next phase of blockchain-enabled innovation.

A convergence of technologies

Insurers are acutely aware of the need to evolve in order to stay competitive, and streamlining market operations with blockchain technology is freeing up precious capital and resources previously spent on auditing and administrative costs.

Newly created roles such as Chief Digital Officer and Chief Innovation Officer are now commonplace across the industry, with firms vying to increase their market share by developing solutions that meet customers’ demands for innovation while increasing efficiency and profitability. Once data has been migrated to a blockchain platform, the potential to apply other technologies such as artificial intelligence (AI) to utilize this immutable, real-time information is vast.

Dynamic pricing is an example of an emerging blockchain-enabled innovation that benefits both the insurer and the customer, with broad-ranging potential across health insurance, car insurance, property insurance and beyond.

Taking the case of shipping insurance, advances in technologies such as AI and telematics enable insurers to access detailed, real-time information about a ship’s location, age and condition. This means, if a ship enters pirate waters, its location data would automatically be updated on the blockchain and the insurer can make the necessary adjustments to its risk profile and policy pricing. The same applies in the converse scenario – for example if a ship is young, in good condition and doesn’t stray from safe waters.

Now consider that the ship is transporting refrigerated cargo, which is also insured. How does an insurer know whether a temperature spike is taking place in a crate at sea a thousand miles from its destination that could potentially destroy the cargo? Thanks to telematics, sensors in the cargo containers can communicate accurate information about temperature, humidity and atmosphere.

This information can be updated in a smart contract on a blockchain platform in real-time, enabling an automatic pay-out to the customer if the cargo is spoiled by high or low temperatures. This saves the insurance company time and money while providing the customer with a better experience.

Dynamic pricing also has huge potential in the health insurance space. Health insurers require a vast amount of information about a customer’s medical history and lifestyle in order to piece together a policy, and provision of false or inaccurate information is commonplace. Blockchain enables insurers to accumulate data from multiple verified sources with updates occurring in real-time, allowing them to carry out more frequent risk assessments and customize pricing accordingly.

Usage-based insurance (UBI) is another innovation currently reshaping the car insurance industry. Many cars now come equipped with connected features or advanced driver-assisted systems (ADAS), which is having a profound impact on the way auto insurers handle policies.

Traditionally, car insurance policies have been based on driver characteristics like age, personal information and accident history. With UBI, insurers are able to incorporate driving behavior data such as speed and hard braking that is updated in real-time on the blockchain. In addition, telematics technology in the car can measure the time a driver spends on the road each day, opening up opportunities for pay-as-you-drive insurance policies that incorporate this data into a smart contract.

A digital future

These developments would be innovative in any sector, but when you consider the processes underpinning the insurance industry have remained largely unchanged for hundreds of years, the evolution is even more dramatic.

By harnessing the attributes of blockchain to tackle back-office challenges head-on, insurers have made the necessary investment to position themselves to take advantage of the myriad of opportunities and further efficiencies that blockchain – and its convergence with other new technologies – will deliver over the coming years.

2019 will undoubtedly see the insurance industry enter the next stage of its digital transformation, and we are proud to play an ongoing role in fostering this innovation with the launch of the first R3 Corda InsureTech Challenge.

Original Article by By: Ryan Rugg, Global Head Of Insurance, R3

Industrial Internet of Things Market Trends

The Industrial Internet of Things market (let’s simply say the Internet of Things without the Consumer Internet of Things part) is growing fast as digital transformation in many industries is accelerating in an Industry 4.0 reality.

With strong alliances between leading Industrial Internet of Things (IIoT) players, proven IIoT applications (and use cases) that inspire companies across several industries and a clear focus on the improvement of operational efficiency, the IIoT market is expected to grow with a double-digit compound annual growth rate (CAGR) until at least 2021.

Manufacturing is a major segment of the Industrial Internet of Things market.

According to data from Dell, the benefits reported by manufacturing executives who deployed Industrial Internet of Things applications are clear:

  • 53 percent of manufacturing executives utilizing IIoT reported an improvement in business innovation.
  • 50 percent of manufacturing leaders increased their competitive edge.
  • 50 percent also says to have reduced total cost of ownership (TCO).

It isn’t too late to attach intelligent IoT gateways to existing industrial assets in the combination of IT and OT, what the IIoT is about, and let them become key providers of insights using IIoT data and analytics, Dell says.

And of course, the other way around, these insights can be used to innovate and optimize what you do with those assets, even if they weren’t designed to gather data. In the end, Caterpillar, the often-mentioned success case in the Industrial Internet of Things was relatively late as well and just look where they are now.

The manufacturing segment is the largest segment in the Industrial Internet of Things market and Industry 4.0 is seen as the main enabler of this big increase.

The major growth segments in the Industrial Internet of Things market until 2021


The opportunities of the Industrial Internet of Things as seen in a 2016 infographic by Visual Capitalist – view source and full infographic

Back to the overall Industrial Internet of Things market. According to research by Industry ARC, released end June 2016, the Industrial Internet of Things market is poised to grow to a whopping $123.89 Billion by 2021. That’s a big chunk of the Internet of Things market overall.

We mentioned the manufacturing industry earlier for a reason as it will be the Industrial Internet of Things market segment that will be generating the highest revenue in the forecast period and for IndustryARC Industry 4.0 is key in it.

The highest growth rate nevertheless is for another industry: the healthcare or medical devices IIoT segment which is expected to grow at a CAGR of 59.8 percent.

Next come utilities, more specifically the energy sector, with amongst others smart grid penetration and an expected CAGR of 39.7 percent.

Looking at the data from a regional perspective, the European region was the leader in the IIoT market in 2015 (there is enormous focus on Industry 4.0 in Europe) and the market is further expected to grow at a CAGR of 22.2 percent until 2021. Among the driving industry segments in Europe are manufacturing and energy but also transport.

The Industrial Internet of Things market is booming but challenges remain

While the Internet of Things in many areas still needs a lot of work and catching up (certainly in the Consumer Internet of Things space), the Industrial Internet of Things is clearly here and the next few years will lead to big investments and many mature projects in the intersection of IT, OT, processes, people and Industry 4.0.

This of course doesn’t mean that there aren’t challenges to tackle in IIoT as well. The previously mentioned data from Dell show that, while 33 percent of manufacturers who use IIoT report improved asset utilization, 49 percent achieved improved process performance and 36 percent reported lower machine and asset downtime, there are three key barriers blocking the adoption of the Industrial Internet of Things:

The three main challenges regarding IIoT adoption – according to Dell.

  • Resource constraints rank first with 48 percent reporting budgetary constraints.
  • Security comes second (and it is of course THE topic since the end of the Summer of 2016 as you know), with 34 percent reporting security and compliance concerns.
  • Last but not least comes uncertain ROI with 27 percent reporting unclear financial benefits and difficulty calculating ROI.

Top image: Shutterstock – Copyright: a-image – All other images are the property of their respective mentioned owners.

Source: I-Scoop
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The Internet of Things (IoT) in 2018 and beyond

The Internet of Things (IoT) in 2018 and beyond – towards monetization, tangible value and an increasing focus on IoT in a holistic context of transformation, strategy, multiple complementary technologies, and larger deployments.
While for many organizations IoT is still new and most investments happen in IIoT, the narrative changes.

Today it’s increasingly about value instead of potential, about the combination of IoT, AI and other related technologies to derive insights, decisions and revenues from sensor data and about IoT monetization, as scalable, IoT-enabled projects become part of less limited business objectives and digital transformation projects with a focus on services and applications.

Although a majority of organizations overall are only beginning, there is a clear shift from the age-old focus on the number of connected devices towards this broader vision in which business goals, people and value take center stage.

On top of a shift in terms of thinking about and understanding IoT there is a shift in the actual usage and deployment of it among leading organizations.

The essential definition of the Internet of Things still holds: IoT is a network of connected devices with 1) unique identifiers in the form of an IP address which 2) have embedded technologies or are equipped with technologies that enable them to sense, gather data and communicate about the environment in which they reside and/or themselves.

However, in the broader scope and amid the mentioned shifts the definition of IoT doesn’t matter that much if it isn’t put into a larger perspective.

Still, now is not the time for definitions and semantics anymore. Now it’s time for business, value, monetization, evolutions and IoT in action. And if we want definitions, to broaden them in order to finally encompass the why instead of just the essential what of IoT, would make more than sense.

An IoT 2018 business reality check

Although there are more devices active, more large scale IoT projects and a growing de facto usage of IoT, with the mentioned shifts in mind, there is still quite some work for both suppliers and organizations in reality.

  • While the narrative finally starts to shift towards value and business, many organizations still don’t know what IoT is or can mean for them. You rarely talk about IoT with a business executive, you talk about it with CIOs and people who already kind of know it, leverage it (which could be the CIO but also many others, depending on project) or are doing their homework. How do you explain IoT to those who take the big business decisions and do they even need to know what it is? Go at a random IoT event, you mainly see people who are involved in the industry or already look at the possibilities and have a decision-making or advisory role but far less people who sign off the project on a management, let alone board level.
  • Related with the previous point: IoT decisions are taken on a business level. Yet, they are not IoT decisions, they are business decisions in which IoT has a crucial role. This is a challenge for the market: on one hand you need to explain what IoT is and does but on the other hand it’s key to talk about business whereby the Internet of Things is part of bigger picture of many related technologies (once more, depending on project and scope) and an enabler, not a goal (as no technological reality ever is).
  • While IoT is highly transformational in nature (more below), digital transformation is not a matter of IoT or any technology alone. In some industries there isn’t a real case for IoT projects. In others it’s about pure tactical goals. Cost cutting, data (which is acquired and used for some insights or simple goals but often remains underutilized or even unused as has always been the case with data), automation, you name it. It’s time to go beyond selling the benefits of IoT and focus on the industries and use cases where advanced analytics and IoT really make a difference. On the other hand, it’s also important to realize that in some industries there isn’t a case for it yet and that de facto strategic and tactical goals are far higher than transformational ones. Leading project partners know how to help organizations do far more with data.
  • A large portion of companies simply isn’t ready for IoT because there are issues that should be long solved. The way in which you get the most out of your data as just mentioned is one example. Although there is IoT network coverage pretty much everywhere, there is a range of platforms offering plenty of ways to get IoT projects rolling and there are ample frameworks such as the Industry 4.0 vision for industrial markets, reality shows ample challenges in a far broader perspective. If you visit an average factory you often see they’re still dealing with paper-based processes, legacy systems and so forth. IoT deployments will continue to happen at different speeds because of the fact that in some use cases and industries there is far more value and readiness than in others.
  • Many organizations who want to start with IoT projects don’t know how. It is one of the reasons why system integrators, who know the business of their customers inside out and are their long trusted partners, often take the lead in projects where IoT is involved (as is the fact that, as previously mentioned, some organizations want to leverage the Internet of Things but have to solve other challenges first in order to be ready). The end-to-end vision and full understanding of the markets and technologies, as well as the hurdles, is key. Many companies simply don’t know where to start, where to end and how to get from start to end. Why to start is something else. Given the fact that IoT is increasingly embraced by small and medium enterprises or for very specific use cases there certainly is a market for more vertical and SME-oriented trusted partners as well.

IoT 2018 trends and evolutions

Across this overview we’ve already mentioned (the roots) of many evolutions in IoT for 2018. In fact, all the blue colored boxes contain one each.

As always several of these evolutions are hidden in the evolutions as we see them in the here and now, in real-life projects, in vendor initiatives that respond to a clear and present need, in 2017 research and so forth, yet there are also changes that might be somewhat ‘unexpected’.

As we’ve been emphasizing the importance of business, value, outcomes, real-life challenges and solutions, projects and so forth throughout this article it wouldn’t make much sense to only focus on technology trends for 2018.

Most of them were already in the make:

  • yes, blockchain and IoT becomes an important combination on various levels;
  • indeed, it’s clear that artificial intelligence will be more important in IoT;
  • true, LPWAN will continue to grow given new sensors, technologies and higher reach in combination with the nature of many Internet of Things projects;
  • and, for sure, there is a growing evolution towards the edge (with edge computing and fog computing and AI and IoT across other areas as described below),where projects demand it.

As IoT technology moves to the edge, business (re)centralizes

The movement towards to the edge by the way is nothing surprising and, just as the increasing role of blockchain, among others, fits in the overall context of decentralization and movements towards to the edge which we’ve been emphasizing in so many areas, from document capture and security to information management and intelligence in the field since years.

However, make no mistake: we are mainly talking about technologies and applications here. It’s not because technologies, some business processes and applications move towards the edge that all business aspects do. Well on the contrary: we see a trend towards a full centralization of decision power and of previously decentralized ways of working in many organizations, just as several companies start to get some workloads out of the cloud again (also a token of maturity) as organizations look more at what matters in detail.

IoT spending 2018 – approaching the $800 billion mark

According to IDC, total spending on IoT in 2018 will reach $772.5 billion in 2018. It is slightly less than was expected in previous forecasts of the firm but the main trends and evolutions haven’t changed.

There are various possible reasons why these forecasts get revised of course. Moreover, there are some evolutions which here and there could lead to a slower than expected investment. These don’t even have to be related with the usage of IoT as such. The increasing attention for changing data protection and privacy rules, for instance, are on the mind as risks for ample organizations. Moreover, these new personal data and privacy rules require budgets. And these budgets need to come from somewhere.

Source: I-Scoop
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