The Robotic Process Automation market will reach $443 million this year

Have we ever got so excited about a market that isn’t even yet past the half-billion dollar spend level? Are we getting over excited about solutions because of their potential before they are fully tried and tested in reality? Let’s get to the realities of RPA by examining the size and five-year forecast for software and related services expenditure:

The global market for RPA Software and Services reached $271 million in 2016 and is expected to grow to $1.2 billion by 2021 at a compound annual growth rate of 36%. The direct services market includes implementation and consulting services focused on building RPA capabilities within an organization. It does not include wider operational services like BPO, which may include RPA becoming increasingly embedded in its delivery.

RPA describes a software development toolkit that allows non-engineers to quickly create software robots (known commonly as “bots”) to automate rules-driven business processes. At the core, an RPA system imitates human interventions that interact with internal IT systems. It is a non-invasive application that requires minimum integration with the existing IT setup; delivering productivity by replacing human effort to complete the task. Any company which has labor-intensive processes, where people are performing high-volume, highly transactional process functions, will boost their capabilities and save money and time with robotic process automation. Similarly, RPA offers enough advantage to companies which operate with very few people or shortage of labor. Both situations offer a welcome opportunity to save on cost as well as streamline the resource allocation by deploying automation.

The bottom-line: RPA provides the building blocks for digitizing rudimentary processes in the digital underbelly, but the broader market for intelligent process automation is more than 10x the size

Stay tuned for our broader forecast for the global Intelligent Process Automation market, which is in the final stages of its fine-tuning, as the expenditure enterprises and service providers are making their internal teams to learn how to automate business processes intelligently, the internal training and development, pilot projects and trial implementations, is so much larger than simply software licences and third party professional services to work the software effectively.

Net-net, we have to be realistic about the value RPA brings to enterprises today, versus its potential for the future. RPA’s value for most of today’s early adopters lies in the digitizing of rudimentary manual processes. It’s a starting point for designing the underbelly that enables a digital OneOffice environment:

Digital effectiveness is all about organizations enjoying real-time process flows forged through the elimination of manual process break-points and intelligent linking of data patterns across the front and back offices. RPA is a critical building block in facilitating this journey, but ultimately it’s the whole OneOffice, not the sum of the parts, that matters for true real-time effectiveness. This is about one integrated organization unit, where teams function autonomously across front, middle and back office functions and processes to promote real-time data flows and rapid decision making, based on meeting defined outcomes. In the future… front, middle and back offices will cease to exist, as they will be, simply, OneOffice, and RPA has a critical role to play supporting the building blocks. However, the market is still very young and we’re only at the start for so many organizations, so let’s not get too carried away until we see really robust solutions with proven ROI and long-term business value.

In short, every siloed dataset restricts the analytical insight that makes process owners strategic contributors to the business. You can’t create value – or transform a business operation – without converged, real-time data. Digitally-driven organizations must create a Digital Underbelly to support the front office by automating manual processes, digitizing manual documents to create converged datasets, and embracing the cloud in a way that enables genuine scalability and security for a digital organization. Organizations simply cannot be effective with a digital strategy without automating processes intelligently – forget all the hype around robotics and jobs going away, this is about making processes run digitally so smart organizations can grow their digital businesses and create new work and opportunities. This is where RPA adds most value today… however, as more processes become digitized, the more value we can glean from cognitive applications that feed off data patterns to help orchestrate more intelligent, broader process chains that link the front to the back office. In our view, as these solutions mature, we’ll see a real convergence of analytics, RPA and cognitive solutions as intelligent data orchestration becomes the true lifeblood – and currency – for organizations.

Source: HFS-The Robotic Process Automation market will reach $443 million this year

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RPA and AI – the same but different

For a conference run by the Institute of Robotic Process Automation (IRPA), there sure was a lot of talk about Artificial Intelligence (AI). Unfortunately, most of that talk only seemed to confuse people about this latest, and most-hyped of, technologies. There were frameworks presented which showed RPA and AI as a ‘continuum’, there were models that seemed to suggest that there was a natural ‘journey’ from RPA to AI, whilst others talked about AI being a ‘must have’ if RPA was to realise its full value. Some presenters talked about a ‘choice’ between RPA or AI. None of which really helped educate the conference attendees on the benefits of either technology. Let’s unravel each of these points so that everyone can be clear on the relationship between RPA and AI.

The RPA/AI Continuum – whilst it can be argued that RPA is the relatively simpler of the two types of technologies, they are very different beasts indeed. The key difference is that the robots of RPA are ‘dumb’ whilst the AI is ‘self—learning’. The robots will do exactly what you tell them to do, and they will do it exactly the same way again and again and again. Which is perfect when you have rules-based processes where compliance and accuracy are critical. However, where there is any ambiguity, usually when the inputs into a process are unstructured (such as customer emails) or where there are very large amounts of data, then AI is the appropriate technology to use because it can manage that variability and, most importantly, get better at it over time through its own experiences. So, if you do want think of a technology continuum, make sure you put a large gap between RPA and AI.

The RPA to AI Journey – there are a number of case studies where companies have implemented RPA and then implemented AI, but only because RPA is a more mature technology than AI. There are far more examples of companies implementing RPA and not implementing AI at all because they actually don’t need the AI. RPA does a fantastic job of delivering labour arbitrage, accuracy and compliance without AI coming anywhere near it. And, of course, some companies implement AI without RPA. It’s not a journey, just a set of choices based on specific demands.

The RPA Dependency on AI – another view that was put forward was that RPA is only valuable when it has AI in support. This is clearly a self-fulfilling view put forward by the vendors that are able to offer both technologies, but it is simply not correct. As mentioned above, many (in fact, most) companies implement RPA without any consideration or need for AI. If you want compliant, repeatable processes, and can feed the robots with structured data, then why complicate and confuse matters by introducing AI?

The RPA/AI Choice – There was yet another the view put forward (which actually conflicts with much of the above) that companies need to make a choice between RPA and AI – in other words which is the best one for them to implement that will deliver their objectives? As should be clear by now, the two technologies actually complement each other very well, for example by using AI to structure unstructured data at the beginning of the process, by using the robots to process the transactions, and then potentially using AI for decision making and/or data analytics at the end.

So, why all this confusion and mis-information? Part of it is obviously self-interest from vendors and providers to create frameworks and models that align with their own capabilities and marketing messages. And, although RPA is now pretty well defined (with that badge of maturity: its own acronym) some of the confusion surely arises from the multiple terms used to describe artificial intelligence; AI, cognitive computing, machine learning, NLP, etc. For now, it is much the best approach to think of AI in terms of how it can help your business, without worrying about what to call it. As the technology develops though a more robust approach is required, which is why at Symphony Ventures we are working on an ‘AI taxonomy’ that will clarify the different types of AI, and therefore help to explain the practical opportunities and uses for AI in our clients. We look forward to sharing this with you and de-bunking much of the confusion around RPA and AI that we have seen over the past few months.

Source: Symphony-RPA and AI – the same but different

Blue Prism Collaborates with Microsoft to Deliver Digital Workforce Capabilities to Global Enterprise Client

Blue Prism, a leading global Robotic Process Automation (RPA) provider, today announced it will launch its Operating System for the Digital Workforce on Microsoft Azure. Building on the certified cloud reference architecture, Microsoft and Blue Prism will jointly collaborate to add intelligent automation capabilities on Microsoft Azure and distribute to the Microsoft partner network.

Enterprises can now leverage Blue Prism’s intelligent automation platform powered by Azure Machine Learning and Microsoft AI, and benefit from leading-edge artificial intelligence (AI), machine learning, analytics and cloud capabilities to help drive comprehensive digital transformation.

The joint collaboration will see Blue Prism platform increasingly optimized to run on Azure delivering unprecedented performance and scale while enabling native access to cognitive and AI services. This collaboration also includes building integrations into Azure Analytics and other partner services.

For example, in the banking sector, Microsoft, Blue Prism and Identitii have teamed-up to streamline financial transactions and address money laundering using blockchain technology. Together this solution enables banks and financial institutions to use Blue Prism to automate and audit financial transactions while Azure provides the machine learning analytics and Identitii the database capabilities. In this way, Blue Prism offers enterprise businesses a differentiated technology offering on the cloud that is able to seamlessly integrate with any system in the enterprise landscape, regardless of it’s interface, to provide a next generation digital workforce.

“Our customers and partners are changing the way they think about automation. They are looking to deploy a digital workforce that can easily integrate with their other best-in-breed AI and cognitive solutions,” said Alastair Bathgate, CEO, Blue Prism. “Working with Microsoft Azure enables this vision by providing everyone with unprecedented access and flexibility to the lastest automation capabilities, while ensuring that it is done in a sustainable, scalable way. Our digital workforce safeguards existing IT investments while enabling the next generation of enterprise-grade applications.”

Blue Prism’s Operating System for the Digital Workforce incorporates investments in over a decade of software development and includes insights from more than 400 global enterprise customers. These include leading Fortune 500 companies in highly regulated industries, including finance, insurance, utilities, telecom, healthcare, retail and manufacturing. An RPA industry leader, Blue Prism pioneered the Operating System for the Digital Workforce, coined the term RPA in 2012 and was recently acknowledged by MIT Technology Review as one of the 50 smartest companies globally.

“Today’s enterprises are always looking for new ways to make their operations faster, more secure and more efficient—which is what drives them to seek out cloud and automation solutions,” said Janet Lewis, vice president of Worldwide Financial Services at Microsoft. “Working with Blue Prism to bring their best-in-class software robots to the cloud not only delivers on that promise but enables enterprises to truly realize the digital transformation they’re working toward.”

“Organizations must include intelligent automation processing as part of their digital transformation strategy,” said Judith Hurwitz, president and CEO of Hurwitz & Associates. “Transformation requires a well thought out roadmap. Automation needs to be aligned as part of a broader business strategy for developing differentiated service offerings. This collaboration is all about making processes run smarter digitally, so organizations can be more competitive and responsive to customer demands.”

Source: BluePrism-Blue Prism Collaborates with Microsoft to Deliver Digital Workforce Capabilities to Global Enterprise Client

15 Ways Artificial Intelligence And Automation Can Help Us Get Better At Work

Even if we are not always aware of it, artificial intelligence is already a big part of our lives, having a major impact on how we live and how we work. From customer service applications to voice-powered assistants such as Apple’s Siri or Amazon’s Alexa, there are several examples of AI and automation tools in use today.

As technological advancements continue, AI’s role in our lives will only grow bigger. There are even concerns that AI will soon make most human-filled jobs obsolete and ultimately leave millions unemployed. And according to the National Science and Technology Council’s Subcommittee on Machine Learning and Artificial Intelligence, these concerns are not entirely unfounded. In a report put together for the previous U.S. administration, the subcommittee suggests that up to 47% of jobs risk becoming irrelevant because of technological advancement and that 83% of jobs that pay under $20 an hour will most likely be automated within the next few years.

Whether that prediction comes true or not, the fact remains that widespread AI adoption can have multiple benefits for a business and its employees, such as higher quality work, improved reliability, increased and consistent output. Automation of routine tasks can actually help workers spend more time on creative tasks that provide enhanced value to the company and its customers. Below, 15 Forbes Agency Council members discuss ways in which artificial intelligence and automation can actually help workers become better at their jobs in the next few years.

Artificial intelligence is going to continuing changing agency professionals' daily workflow.All photos courtesy of Forbes Agency Council members.

Artificial intelligence is going to continuing changing agency professionals’ daily workflow.

1. Give Deeper Insights

Artificial intelligence (AI) and automation will transform the job of PR professionals. Tasks such as news monitoring, researching, reporting and building media lists will no longer need to be done manually. AI and predictive analytics will give PR professionals deeper insights into market trends and movements. The combination will help free up mind space so PR can think more strategically, creatively and high-level.   – Chi ZhaoHokku PR

2. Replace Day-To-Day Low-Level Cognitive Tasks

AI, automation and machine learning will impact day-to-day tasks considerably in the next five years. The main focus will be on low-level cognitive tasks: scheduling calendar invites, routinely ordering food, determining emails to answer/review/delete based on facts. They’ll also be working their way into aiding high-level cognitive tasks: identifying connections, analyzing correlation and assessing conclusions.   – Alan MorteThree Ventures Technology, Inc.

3. Act As Life’s Concierge Service

With the popularity of AlexaWatson and Einstein, consumer expectation will soon be for tech to act as concierge. Big Data companies now anticipate general real-time needs and provide info (weather, traffic, etc.) but AI’s evolution post-purchase to provide highly personalized info means it will soon anticipate my daily individual needs to recommend when, where, and how I accomplish my tasks.   – Elizabeth PostonHelios Interactive

4. Make Marketing Less Artificial And More Intelligent

AI will enable companies to develop even deeper relationships with people. Cognitive technology, such as IMB’s Watson, enable us to analyze data like unstructured text, audio, images and video. Sensing and processing personality, tone and emotion will help us make even greater personalized recommendations and help companies engage in conversations through chatbots.   – Debbie WilliamsSPROUT Content

5. Automate Customer Support

Using chatbots for automated customer support will save a lot of time. Chatbots that are available 24/7 for customers and can automatically answer your questions, provide recommendations, or direct you to the next step in your workflow can take a lot of weight off your customer support system. Furthermore, bots can gather intelligence on customer needs, engagements and emotions.   – Solomon ThimothyOneIMS

6. Put Great Minds To Good Use

We often find ourselves and our staff stuck doing mundane tasks. This realistically holds us and our teams back from unleashing our problem-solving skills and our creativity. Jumping on AI and workflow automation will help us clear our plates of tasks including sorting, organizing, responding, or reporting, and will leave us and our teams more time to be productive.   – Ahmad KarehTwistlab Marketing

7. Edit Video Content On The Fly

AI will eventually edit content that we produce in real time, developing an infinite number of variations instantaneously once published. Shots will be switched out, music and sound effects will change on the fly, all dictated by real-time user engagement and personal characteristics of the viewer. We’re already creating multiple versions of each video we create – it’s time a robot does it for us!  – Chris CarterRep Interactive

8. Create Jobs And Integrate Human Workflows Together

Artificial intelligence in the workplace will break apart established workflows, which in return will create jobs to help integrate the workflows together. The structure of the workplace will continue to change due to AI and automation. However, humans will play an integral role in making sure these worlds combine efficiently and effectively.   – Ryan PezzottiKnowzo.com

9. Free Up Time For Strategy And Storytelling

In the ad operations space, automation and programmatic platforms have grown over the past few years. This has reduced the tactical need to spend time on the nuts-and-bolts, managing every last technical detail. This will allow agencies to focus on high-value strategic work, especially creative audience targeting, analysis and storytelling.   – Dan GoldenBe Found Online

10. Inform Future Strategies

While humans will always be needed in the PR industry as relationship building is an essential component, the data that our teams can collect through AI will help to inform future strategies. This intelligence will be especially beneficial when it comes to media monitoring and ensuring that we’re able to stay abreast of competitor news and coverage.   – Jennifer HawkinsHAWKINS International Public Relations Inc.

11. Condense 40 Hours of Analysis In Four Minutes

Manual analysis of marketing efforts is time-consuming. The future of marketing efficiency is condensing tasks that normally take 40 hours into four-minute report runs. One example? Creating a social media strategy. Instead of analyzing all of the status updates that have been posted and categorizing them into a strategy, future automation tools will be able to better direct marketing efforts.   – Brett FarmiloeMarkitors

12. Keep Us Productive During The Commute

By far the most exciting application of AI and machine learning is automated driving. Being a Tesla P90D owner, I understand the value of autopilot, especially in terms of reduced driver fatigue on long trips. However, I’m most excited by the idea of fully autonomous driving, which I suspect will have a dramatic impact on productivity during one’s commute to and from work.   – Kristopher JonesLSEO.com

Forbes Agency Council is an invitation-only community for executives in successful public relations, media strategy, creative and advertising agencies.Do I qualify?

13. Increase Engagement

AI can help us craft customized experiences in real time to increase engagement with brands. We recently created an experience for an industry event that allowed passers-by to interact with and affect a change within a branded environment. Once inside, consumers answered five simple questions and we created custom content based on their answers. The engagement level was off the charts.   – Chris CavanaughFreeman

14. Make Routine Processes Easier

Automation, whether in a manufacturing facility or a programmatic advertising platform, exists to make routine processes easier. Advanced technologies such as AI just extend what can conceivably be automated. As an entrepreneur, I look at AI as the ultimate efficiency driver. It enables me to put the day-to-day in digital hands, freeing mine and making each day more productive.   – Julien VerdierAdyoulike

15. Give A Competitive Edge

AI implementation will allow us to simplify multi-faceted processes by replacing the manual process of sorting and identifying complex data, key insights and actionable plans. We can create strategies to help our clients gain a competitive edge over competitors by having better decision-making, improved ROI, operational efficiency and cost savings.   – Revecka JalladDivisa

Source: Forbes-15 Ways Artificial Intelligence And Automation Can Help Us Get Better At Work

It’s OK If Robots Take Over Our Jobs So Long As This Happens

Barely six months ago, President Donald Trump rode a wave of anti-establishment sentiment to the White House. One of his central campaign promises was to bring back jobs to American workers, vowing to “Buy American, Hire American.”

The political merits of this approach can be debated, but there’s something that can’t be denied: Some jobs can’t be brought back, especially those replaced by technology and artificial intelligence. Automation stands apart from the debate around immigration or globalization. Robots can now assemble cars, around the clock, without much help from humans. Machines can write stories for news publishers. It’s simply the way it goes — technology does displace workers – but, perhaps more surprising, is that many people don’t object to artificial intelligence (AI) taking our jobs. In some cases, they actually are in favor of it.

Because there’s so much noise and hype around AI. It’s difficult to determine what matters and how it will impact the future of business.

AI’s potential to boost business outweighs the potential downside of job losses, according to a PWC survey of 2,500 business executives and consumers. But this comes with one key caveat: People want AI platforms that replace humans to provide more affordable solutions and products to the wider population. For example, 80% of respondents say it’s of greater importance to have access to more affordable legal advice than to preserve the jobs of lawyers. And 69% would rather have more affordable, convenient and reliable transportation than preserve the jobs of taxi drivers. Respondents felt that if human jobs are replaced, the AI platform replacing them needs to benefit the wider population.

Businesses are already making the necessary decisions and investments to utilize AI to a greater degree (54% said they are making “substantial investments” in AI). Executives cite the potential for AI to elevate employees from minute, tedious projects to allow them to do more important work, use digital assistants to better manage schedules, and detect data trends to better inform strategy. But to make this all work and not have mass unemployment, everyone needs to be prepared to gain the competencies to work effectively with new technologies, as people will need skills for platforms that may have not even existed just a few years ago. The majority of experts expect technology like AI to create more jobs than it displaces by the year 2025, according to Pew, but these jobs may be completely different from roles that exist today.

What’s most surprising is that executives are willing to trust AI for such important decisions as promotions and salaries: 69% thought an AI platform would be as fair or even more fair as a human in making promotion and salary decisions, but 86% of respondents would still want to talk to a human after a review decision was made by AI, suggesting that people want the intelligence of an AI platform, but paired with the empathy of a human. This theory goes beyond the business world: Even in a day and age when it’s difficult to arrange an appointment with a doctor, the survey found that 77% prefer to visit a doctor in person versus taking an assessment at home with a robotic smart kit. These results suggest that the safest human jobs will be those that involve a person-to-person connection that can’t be easily replaced by machine.

People do have concerns about AI. Privacy is a big concern for people, with 87% citing privacy as a “major concern” for AI, while 23% of respondents believe AI will have serious, negative implications. While people see the positive potential of AI, they also want safeguards to ensure it’s not abused.

Overall, people are more excited about the potential for good than they are worried about the negatives. AI provides the potential to make services vastly more accessible, more affordable, more efficient for everyone, and even more personal. And that’s something that all of us, no matter what side of the political aisle we’re on, should be able to get behind.

Source: Fortune-It’s OK If Robots Take Over Our Jobs So Long As This Happens

Working with Robots: Human and Machine Coexistence in the Workforce

The pervasive fear that artificial intelligence (AI) will take over human economic livelihood has been felt in places like the manufacturing sector, as large swaths of the industry automate labor formerly done by humans. However, proponents of machine learning say ultimately AI and robotics will improve the way we do virtually everything, and ultimately create new jobs.

Still, nearly 40 percent of U.S. jobs were slated as a “high risk” for automation by the early 2030s in a March 2017 report by PricewaterhouseCoopers (PwC). While the PwC report acknowledges it’s unlikely all those jobs will be automated for “a variety of economic, legal, and regulatory reasons,” PwC also acknowledges that new tech typically means the creation of new jobs for human workers as well, conceding “the net impact of automation on total employment is therefore unclear.”

Many technologists purport that the new job creation will offset some of the pain of displacement; retraining programs and continuing education opportunities are key to bringing in displaced workers into the new high-tech fold.

“Ever since the industrial revolution, we’ve created technology that in theory has displaced workers, and yet growth continues,” Chris Volinsky, assistant VP or inventive science at AT&T Labs, told Business News Daily. “It’s a displacement of work from more menial tasks to those tasks which require more education and more technology, so work gets displaced, but workers are constantly evolving and being retrained.”

However, the pace of technological growth is so fast that many workers might not find this a truly viable option, said Moshe Vardi, professor of computer science at Rice University and fellow at the Institute of Electrical and Electronics Engineers.

“As AI becomes more effective and complex, the zone of ‘automated jobs’ will continue to widen across industries and verticals,” Vardi said. “Workers are racing against the machines, and to stay ahead of the game, they need to be willing to continually refresh and upgrade their skills. The jobs least likely to be automated are those that combine nonroutine technical skills in combination with people skills.”

What’s on the horizon?

The pace of change has workers both excited and anxious about what the future holds. According to a survey conducted by Atlassian, 87 percent of respondents expect AI to change their jobs by 2020, with 76 percent responding that some or half of their job could be performed by an algorithm or robot. And while 64 percent said they trust AI’s ability to properly complete a task, 80 percent are concerned about a subsequent spike in unemployment.

“If harnessed correctly, AI can become our team’s ‘sixth man,’ moving beyond digital assistants and chatbots, and freeing up time and headspace for us to tackle society’s most complex problems,” Atlassian’s report reads.

“AI is, first and foremost, a tool that makes humans more productive, not unlike a hammer or a steam shovel,” said Manuel Ebert, founder of AI and machine intelligence consultancy summer.ai. “If one human can produce more in the same time, that means we need fewer humans to satisfy the same demand. That is where displacement comes from.”

However, Ebert continued, when productivity increases and costs decrease, oftentimes the demand for those goods and services increases and helps drive the creation of new jobs.

“Think the printing press and books or the assembly line and cars,” he said. “So, the interesting question is where can AI create demand for things that were previously (prohibitively) expensive?” [See Related Story: AI Comes to Work: How Artificial Intelligence Will Transform Business]

The PwC report anticipates a rise in average pretax incomes because of mass adoption but acknowledges “these benefits may not be evenly spread across income groups.”

“There is therefore a case for some form of government intervention to ensure that the potential gains from automation are shared more widely across society through policies like increased investment in vocational education and training,” the report reads.

Others have suggested a universal basic income of some kind, which would essentially offer payments to citizens that could cover necessities like groceries or rent and mortgage payments.

How will the job market transition into automation?

While it is generally agreed that some steps need to be taken to ameliorate the pain of transition, AI proponents like Volinsky argue that the benefits of these technologies far outweigh the negatives. For example, he said, AT&T is utilizing drones and machine learning to expedite inspection and maintenance of cell towers. Instead of sending a worker up, the company now flies drones to inspect the antennae.

“(The drone) flies up with HD video and sends footage back to a technician on the ground to inspect,” Volinsky said. “It might take a half hour to do a full detailed inspection of one of those towers, even though the technician is only interested in certain parts of that video.”

That’s where AI comes in: Machine learning can be used to identify potential problem areas and highlight key points of interest the human technician needs to analyze, Volinsky said. By doing so, it can reduce the half hour task to a matter of minutes, removing the technician’s need to scan through useless pieces of video to find the value.

AI is also making headway in customer service, internal decision-making and the way companies track their customer relationships, to name a few examples. Each of these in-roads represent only the beginning of the AI revolution, Volinsky said, and these tools will be essential as they proliferate.

“I like to think of AI as taking the mundane parts out of peoples’ work and helping humans focus on their real expertise, which is identifying problems and focusing on what else needs to be done,” Volinsky said.

As AI more prominently enters the workforce, humans will need to prepare for continued waves of automation by learning new skills and adapting to a changing economy, while harnessing the capabilities of AI to solve problems that were previously out of reach. A symbiotic relationship between man and machine, then, appears far more desirable than a war for prominence.

Source: businessnewsdaily.com-Working with Robots: Human and Machine Coexistence in the Workforce

Eight ways intelligent machines are already in your life

Many people are unsure about exactly what machine learning is. But the reality is that it is already part of everyday life.

A form of artificial intelligence, it allows computers to learn from examples rather than having to follow step-by-step instructions.

The Royal Society believes it will have an increasing impact on people’s lives and is calling for more research, to ensure the UK makes the most of opportunities.

Machine learning is already powering systems from the seemingly mundane to the life-changing. Here are just a few examples.

1. On your phone

Using spoken commands to ask your phone to carry out a search, or make a call, relies on technology supported by machine learning.

Virtual personal assistants – the likes of Siri, Alexa, Cortana and Google Assistant – are able to follow instructions because of voice recognition.

They process natural human speech, match it to the desired command and respond in an increasingly natural way.

The assistants learn over a number of conversations and in many different ways.

They might ask for specific information – for example how to pronounce your name, or whose voice is whose in a household.

Data from large numbers of conversations by all users is also sampled, to help them recognise words with different pronunciations or how to create natural discussion.

2. In your shopping basket

Many of us are familiar with shopping recommendations – think of the supermarket that reminds you to add cheese to your online shop, or the way Amazon suggests books it thinks you might like.

Machine learning is the technology that helps deliver these suggestions, via so-called recommender systems.

By analysing data about what customers have bought before, and any preferences they have expressed, recommender systems can pick up on patterns in purchasing history. They use this to make predictions about the products you might like.

3. On your TV

Similar systems are used to recommend films or TV shows on streaming services like Netflix.

Recommender systems use machine learning to analyse viewing habits and pick out patterns in who watches – and enjoys – which shows.

By understanding which users like which films – and what shows you have watched or awarded high ratings – recommender systems can identify your tastes.

They are also used to suggest music on streaming services, like Spotify, and articles to read on Facebook.

4. In your email

Machine learning can also be used to distinguish between different categories of objects or items.

This makes it useful when sorting out the emails you want to see from those you don’t.

Spam detection systems use a sample of emails to work out what is junk – learning to detect the presence of specific words, the names of certain senders, or other characteristics.

Once deployed, the system uses this learning to direct emails to the right folder. It continues to learn as users flag emails, or move them between folders.

5. On your social media

Ever wondered how Facebook knows who is in your photos and can automatically label your pictures?

The image recognition systems that Facebook – and other social media – uses to automatically tag photos is based on machine learning.

When users upload images and tag their friends and family, these image recognition systems can spot pictures that are repeated and assigns these to categories – or people.

6. At your bank

By analysing large amounts of data and looking for patterns, activity which might not otherwise be visible to human analysts can be identified.

One common application of this ability is in the fight against debit and credit card fraud.

Machine learning systems can be trained to recognise typical spending patterns and which characteristics of a transaction – location, amount, or timing – make it more or less likely to be fraudulent.

When a transaction seems out of the ordinary, an alarm can be raised – and a message sent to the user.

7. In hospitals

Doctors are just starting to consider machine learning to make better diagnoses, for example to spot cancer and eye disease.

Learning from images that have been labelled by doctors, computers can analyse new pictures of a patient’s retina, a skin spot, or an image of cells taken under a microscope.

In doing so, they look for visual clues that indicate the presence of medical conditions.

This type of image recognition system is increasingly important in healthcare diagnostics.

8. In science

Machine learning is also powering scientists’ ability to make new discoveries.

In particle physics it has allowed them to find patterns in immense data sets generated from the Large Hadron Collider at Cern.

It was instrumental in the discovery of the Higgs Boson, for example, and is now being used to search for “new physics” that no-one has yet imagined.

Similar ideas are being used to search for new medicines, for example by looking for new small molecules and antibodies to fight diseases.

What next?

The focus will be on making systems that perform specific tasks well which could therefore be thought of as helpers.

In schools they could track student performance and develop personal learning plans.

They could help us reduce energy usage by making better use of resources and improve care for the elderly by finding more time for meaningful human contact.

In the area of transport, machine learning will power autonomous vehicles.

Many industries could turn to algorithms to increase productivity. Financial services could become increasingly automated and law firms may use machine learning to carry out basic research.

Routine tasks will be done faster, challenging business models that rely on charging hourly rates.

Over the next 10 years machine learning technologies will increasingly be part of our lives, transforming the way we work and live.

 

Source: BBC-Eight ways intelligent machines are already in your life

How Companies Are Already Using AI

Every few months it seems another study warns that a big slice of the workforce is about to lose their jobs because of artificial intelligence. Four years ago, an Oxford University study predicted 47% of jobs could be automated by 2033. Even the near-term outlook has been quite negative: A 2016 report by the Organization for Economic Cooperation and Development (OECD) said 9% of jobs in the 21 countries that make up its membership could be automated. And in January 2017, McKinsey’s research arm estimated AI-driven job losses at 5%. My own firm released a survey recently of 835 large companies (with an average revenue of $20 billion) that predicts a net job loss of between 4% and 7% in key business functions by the year 2020 due to AI.

Yet our research also found that, in the shorter term, these fears may be overblown. The companies we surveyed – in 13 manufacturing and service industries in North America, Europe, Asia-Pacific, and Latin America – are using AI much more frequently in computer-to-computer activities and much less often to automate human activities. “Machine-to-machine” transactions are the low-hanging fruit of AI, not people-displacement.

For example, our survey, which asked managers of 13 functions, from sales and marketing to procurement and finance, to indicate whether their departments were using AI in 63 core areas, found AI was used most frequently in detecting and fending off computer security intrusions in the IT department. This task was mentioned by 44% of our respondents. Yet even in this case, we doubt AI is automating the jobs of IT security people out of existence. In fact, we find it’s helping such often severely overloaded IT professionals deal with geometrically increasing hacking attempts. AI is making IT security professionals more valuable to their employers, not less.

In fact, although we saw examples of companies using AI in computer-to-computer transactions such as in recommendation engines that suggest what a customer should buy next or when conducting online securities trading and media buying, we saw that IT was one of the largest adopters of AI. And it wasn’t just to detect a hacker’s moves in the data center. IT was using AI to resolve employees’ tech support problems, automate the work of putting new systems or enhancements into production, and make sure employees used technology from approved vendors. Between 34% and 44% of global companies surveyed are using AI in in their IT departments in these four ways, monitoring huge volumes of machine-to-machine activities.

In stark contrast, very few of the companies we surveyed were using AI to eliminate jobs altogether. For example, only 2% are using artificial intelligence to monitor internal legal compliance, and only 3% to detect procurement fraud (e.g., bribes and kickbacks).

What about the automation of the production line? Whether assembling automobiles or insurance policies, only 7% of manufacturing and service companies are using AI to automate production activities. Similarly, only 8% are using AI to allocate budgets across the company. Just 6% are using AI in pricing.

Where to Find the Low-Hanging Fruit

So where should your company look to find such low-hanging fruit – applications of AI that won’t kill jobs yet could bestow big benefits? From our survey and best-practice research on companies that have already generated significant returns on their AI investments, we identified three patterns that separate the best from the rest when it comes to AI. All three are about using AI first to improve computer-to-computer (or machine-to-machine) activities before using it to eliminate jobs:

Put AI to work on activities that have an immediate impact on revenue and cost. When Joseph Sirosh joined Amazon.com in 2004, he began seeing the value of AI to reduce fraud, bad debt, and the number of customers who didn’t get their goods and suppliers who didn’t get their money. By the time he left Amazon in 2013, his group had grown from 35 to more than 1,000 people who used machine learning to make Amazon more operationally efficient and effective. Over the same time period, the company saw a 10-fold increase in revenue.

After joining Microsoft Corporation in 2013 as corporate vice president of the Data Group, Sirosh led the charge in using AI in the company’s database, big data, and machine learning offerings. AI wasn’t new at Microsoft. For example, the company had brought in a data scientist in 2008 to develop machine learning tools that would improve its search engine, Bing, in a market dominated by Google. Since then, AI has helped Bing more than double its share of the search engine market (to 20%); as of 2015, Bing generated more than a $1 billion in revenue every quarter. (That was the year Bing became a profitable business for Microsoft.) Microsoft’s use of AI now extends far beyond that, including to its Azure cloud computing service, which puts the company’s AI tools in the hands of Azure customers. (Disclosure: Microsoft is a TCS client.)

Look for opportunities in which AI could help you produce more products with the same number of people you have today. The AI experience of the 170-year-old news service Associated Press is a great case in point. AP found in 2013 a literally insatiable demand for quarterly earnings stories, but their staff of 65 business reporters could write only 6% of the earnings stories possible, given America’s 5,300 publicly held companies. The earnings news of many small companies thus went unreported on AP’s wire services (other than the automatically published tabular data). So that year, AP began working with an AI firm to train software to automatically write short earnings news stories. By 2015, AP’s AI system was writing 3,700 quarterly earnings stories – 12 times the number written by its business reporters. This is a machine-to-machine application of AI. The AI software is one machine; the other is the digital data feed that AP gets from a financial information provider (Zacks Investment Research). No AP business journalist lost a job. In fact, AI has freed up the staff to write more in-depth stories on business trends.

Start in the back office, not the front office. You might think companies will get the greatest returns on AI in business functions that touch customers every day (like marketing, sales, and service) or by embedding it in the products they sell to customers (e.g., the self-driving car, the self-cleaning barbeque grill, the self-replenishing refrigerator, etc.). Our research says otherwise. We asked survey participants to estimate their returns on AI in revenue and cost improvements, and then we compared the survey answers of the companies with the greatest improvements (call them “AI leaders”) to the answers of companies with the smallest improvements (“AI followers”). Some 51% of our AI leaders predicted that by 2020 AI will have its biggest internal impact on their back-office functions of IT and finance/accounting; only 34% of AI followers said the same thing. Conversely, 43% of AI followers said AI’s impact would be greatest in the front-office areas of marketing, sales, and services, yet only 26% of the AI leaders felt it would be there. We believe the leaders have the right idea: Focus your AI initiatives in the back-office, particularly where there are lots of computer-to-computer interactions in IT and finance/accounting.

Computers today are far better at managing other computers and, in general, inanimate objects or digital information than they are at managing human interactions. When companies use AI in this sphere, they don’t have to eliminate jobs. Yet the job-destroying applications of AI are what command the headlines: driverless cars and trucks, robotic restaurant order-takers and food preparers, and more.

Make no mistake: Automation and artificial intelligence will eliminate some jobs. Chatbots for customer service have proliferated; robots on the factory floor are real. But we believe companies would be wise to use AI first where their computers already interact. There’s plenty of low-hanging fruit there to keep them busy for years.

Source: Harvard Business Review-How Companies Are Already Using AI

The rise of the machines: not so doom and gloom

As technology perforates every aspect of our professional and personal lives, it is clear that a robotic revolution is upon society. While robots have been used in industries such as manufacturing and automotive for years, today’s systems are ever-improving and, in some cases, are now exceeding human limitations.

For instance, Google’s artificial intelligence (AI), DeepMind AlphaGo, is beating the world’s number one player in tournaments of Go (an incredibly complex Chinese grid game). While this represents the higher end of robots’ current capabilities, there is a growing, but unfounded, fear that with the rate of development, it’s only a matter of time before all jobs are completed by machines.

Losing jobs to technology is not simply a modern day worry. For example, the early days of the industrial revolution saw everyone believing that their jobs were at risk. In the 1930s, economist John Maynard Keynes coined the term ‘technological unemployment’, believing that the rise of technology would lead to a permanent decline in the number of jobs. In both cases, the impact was not quite as dramatic. Technology helped employees to do their jobs better, it didn’t necessarily replace them. It even created new positions.

It’s a similar story today. An accountant, for instance, is likely to use tools that automate certain aspects of their role. Data collection and report creation are both arduous time-consuming activities to complete manually, and the ever-growing deluge of information only increases the chances that something vital will be omitted. Automating the processes provides accountants with the data they need to analyse, enabling them to increase accuracy and productivity.

Many will argue that automated production lines are a testament to the fact that machines can do some jobs better, and businesses do indeed invest heavily in order to have work floors almost free of humans.

>See also: VR and machine predictions for 2017

But the belief that robots will take all jobs is wrong. Semi-skilled positions will bear the biggest brunt, while low- and high-skilled jobs (such as caretakers and data scientists respectively) will be impacted less, due to cost or the complexity of roles. In fact, the rise of machines will also help stimulate employment figures by creating new roles.

Despite having all the ‘bells and whistles’, robots can’t design and service themselves. Humans can try and create another robot to do the job, but then what happens if that one breaks down too?

The truth is, the cost of developing a machine to do such a role will always outweigh paying a few humans a salary; meaning the number of positions will increase in proportion to the number of robots. Furthermore, as machines become even more intelligent there are simply more things that can go wrong, exacerbating the need for skilled workers.

Some humans will be working for or following orders from robots. In warehouses and other logistics operations, for example, machines already tell humans what to do, including which items to select off shelves and where to process them. Robots can more quickly analyse orders and delegate the relevant responsibilities to ensure that they are fulfilled in almost real-time.

While it may seem incredibly ‘overlord’, it’s worth noting that people all already work for machines in some capacity. Every time someone uses Facebook or Google, they are providing AI systems with data and they pay people back in services. People also take their driving directions from apps on their smartphones. So, just as an employee generates value for an employer, humans are all worth something to those machines.

There will also be humans who own the machines, a role that is not just going to be filled by the billionaires developing machines today. Today’s independent lorry-driver who can operate just one lorry today will, in just a few years, be able to invest in and operate several autonomous lorries.

Ultimately, while reports will continue to conclude that robots are coming for people’s jobs, it’s often forgotten just how intensively competitive humans are. Elon Musk argues that humans will have to become cyborgs to beat machines, but people compete with other people, they don’t compete with machines. Humans will always find a way to win, even if they adopt technology from machines in order to compete with each other. Even if some jobs are replaced by robots, those affected will simply re-skill themselves, and perhaps upgrade themselves, to find another profession.

Source: Information Age-The rise of the machines: not so doom and gloom

Automation doesn’t have to be a dirty word…

Without a doubt, the impact of automation on the IT Services industry is a topic of much debate and contention. The challenge is that speculation drives much of the discussion, rather than quality data and analysis.

 

While the subject of automation has been discussed a few times on the blog, I feel compelled to add my experiences and those of the IT professionals I met on my travels to the discussion.

Not long before joining HfS, I spent several months presenting research on automation at events and conferences across the UK. While the research covered a broad range of topics, automation in IT services was by far the most popular. After a few presentations discussing the increased adoption of automation and the growing capability of the tooling, it became apparent where the popularity of the topic originated – fear. After each session, a small gathering of IT professionals would question me on job security, headcount decreases and how automation augered a bleak future for the industry.

It’s not difficult to see why the audience felt this way. The mainstream media and even some analyst firms have been stoking the climate of fear with considerable vigor.

So I went back to the drawing board and changed my presentation. I took a fresh look at the data to examine what was happening in the industry – did we genuinely need to worry? Beginning with an impactful quote most media outlets were running with – something along the lines of “be terrified, the robots are coming” – I started to dismantle these theories with my research data on employment trends, headcount increases, and industry perception.

While many argued that automation would lead to job cuts, my data showed the opposite. Organizations recognized the importance of technology to their businesses and were investing in the services needed to support it. The data revealed that in organizations with higher levels of automation, workers were not disappearing, they were moving to higher value areas of the support structure – taking on strategic projects or developing services.

At the end of the presentation, I concluded that the reality of automation’s impact on modern IT services was far from the bleak picture painted by other analysts and consultants.

Nevertheless, a few minutes after the session ended the same horror stories started to emerge: IT leaders facing a backlash from staff as automation projects ramp up and professionals working themselves into a frenzy over their job security if projects continued. It was frightening stuff.

Crucially, my research revealed that the cause of this panic doesn’t come directly from the automation itself – there were almost no real-life examples of automation leading to sweeping changes in any of the organizations I was working with. Without a doubt, much of the fear was generated by analysts and media outlets whipping up this distorted perception, but surely there must have been another force at work.

After a bit of digging around the real cause of the hysteria became clear. In organizations with little or no perception issues, it was clear that the leadership team had taken the time to communicate with their teams. Conversely, those with stressed and worried staff had not.

When I questioned an executive who sought advice on soothing fears in his team if he had clearly explained his vision, and what the outcome of the project would be, he replied that it was obvious what he was trying to achieve. If that were true, the perception crisis in his organization would not be there.

Successful automation projects have an engaged team working behind them. The most effective I have seen understand what will be automated and why. They know what impact it will have and, for the most part, agree it was an area of manual work they found repetitive, boring and unfulfilling anyway. They eagerly anticipated a time when they could dedicate their efforts to more meaningful and valuable work.

Under different circumstances, this committed group would be dealing with the same fear and stress as their peers in organizations with less effective communication.

In the noisy information age we now live in, it’s easy to get caught up in the hype. Business leaders have an obligation to provide clear, effective communication that outlines the vision and journey of automation projects. Without the context and understanding they provide, an engaged team can quickly turn into a stressed one. And a stressed team will undoubtedly hold your project back. It’s not hard to understand why an individual afraid of becoming obsolete may not be working towards your goals with total enthusiasm.

Source: HFS-Automation doesn’t have to be a dirty word…