CIOs, the age of the algorithm takes hold — for better and worse

The International Institute for Analytics released its annual list of predictions and priorities. Their big prediction for 2018: Companies will experience the pros and cons of what they dubbed the age of the algorithm.

Tom Davenport, co-founder of IIA, fellow at the MIT Initiative on the Digital Economy and the president’s distinguished professor of information technology and management at Babson College, and Bill Franks, chief analytics officer at IIA, said that algorithms are nearly ubiquitous and will proliferate in the enterprise.

The good news is that the age of the algorithm will mark a level of analytics maturity for the enterprise: Algorithms will be easier to access, easier to use, and even self-learning, making it possible for companies to interrogate and take advantage of their data like never before. The bad news is algorithm ubiquity will also lead to complexity, impacting technology buying decisions, enterprise architecture strategies and even how job titles are perceived.

IIA’s 2018 predictions were accompanied by corresponding priorities, providing companies with advice on what analytics hurdles to expect in the age of the algorithm and how they can be avoided.

Prediction No. 1: The age of the algorithm arrives

Algorithms have become ubiquitous and will play a more prominent role in day-to-day corporate activities next year, according to Franks. They are now easy to deploy, easy to access, embedded in applications and can be rented in the cloud, eradicating the careful choices analytics experts used to make about which algorithms matter and how to expend precious processing power. “Today, we’re basically able to go ahead, tee up and test a whole range of algorithms and then pick the best,” he said.

Priority: Automate algorithmic testing

Data scientists should embrace tools that automate the testing process as a timesaver. Franks was quick to point out that using these tools, which companies can build or buy, doesn’t negate the need for data scientists. Problems still need to be well defined, data still needs to be prepared and results of the testing process still need to be interpreted.

Prediction No. 2: AI projects grow, but disillusionment rises

Franks and Davenport disagreed on the latter part of this prediction. Davenport said almost every company he talks to has a portfolio of concrete AI projects underway; he believes companies have moved away from “moon shots” to smaller, easier-to-get-at problems, and that they tend to be bullish on the technology. But Franks believes enterprise AI in 2018 will follow a pattern similar to big data, which started out hot and then fizzled before finding its footing. He sees the lack of AI skills as the biggest contributor to enterprise disillusionment with AI. The talent dearth is especially acute for the more complex forms of AI, such as deep learning. That prowess is even scarcer than data science skills. “As demand rises, that’s going to be a challenge,” he said.

PRO+

Content

Find more PRO+ content and other member only offers, here.

Priority: Incorporate AI into your plans — rationally and incrementally

The key is to be strategic in how AI is incorporated into the analytics roadmap by making it an extension of what the business is already doing with analytics. “About 90% of AI has a statistical underpinning,” Davenport said, which means the foundation for AI isn’t new to data science departments. The roadmap should include top priorities, including AI, according to Franks. Doing so will give the company a contextual understanding of how aggressive it needs to be with artificial intelligence.

Prediction No. 3: Hybrid analytics

The proliferation of analytics products has given rise to freedom of choice — open source or proprietary tools, on premises or the cloud, or, increasingly, a mix of both. But with added choice comes added complexity, Franks said.

Priority: Allocate resources to determine the right technology mix

Companies will need to think hybrid analytics if they want to remain competitive, according to Franks. In 2018, analytics professionals should start to consider not only what a tool does, but where it best performs and how well it integrates with other technology. And in partnership with IT, they should start building flexible analytical architectures, making it easy to snap in or get rid of tools. Added flexibility will ensure a level of resilience if startups go under, if more established vendors stop supporting a product or if the analytics team makes a bad investment. “The architecture can be an insurance policy,” Franks said.

Prediction No 4: Beware the so-called data scientist

The title of data scientist has become watered down. Reporting tools and data preparation tools often have models baked in, making them easier to use and accessible, according to Franks. “People who traditionally would not have had any ability to claim being an analytic professional/data scientist suddenly, in theory, can say I use tools that do this so, therefore, I’m going to put that on my card,” he said.

Priority: Inventory your analytics skills and define job titles

Davenport suggested companies collaborate with HR to inventory and classify the analytics, data science and AI skills they have in-house. Doing so will help to distribute talent appropriately and pinpoint potential skills gaps. An example of classification is to implement a categorization and certification process for data scientists, with junior data scientists taking on simpler tasks such as regression analysis and senior data scientists taking on complex tasks such as developing new algorithms.

Prediction No. 5: Blockchain is a roadblock for analytics

Blockchain, an immutable distributed ledger, won’t have a profound impact on enterprise analytics programs, according to Franks. But it will pose performance issues when analyzing transactional data. Data on the blockchain is distributed and not centralized, it’s repetitive and compressed, and, unlike SQL databases, it isn’t designed for analytics. Data scientists will have to extract data out of the blockchain format and “make it into something friendly,” Franks said.

Priority: Prepare to analyze blockchain data

Franks said understanding how blockchain works and the new issues the technology presents is a good first step. He suspects that, at least in the short term, analytics professionals won’t have a lot of direct interaction with blockchain data. Instead, they will build “enterprise analytic views” into the data to do the analysis. “Those views can then be updated on a somewhat frequent basis and made into an extract or it can run live,” he said.

Prediction No. 6: Analytics are widely applied to improve data

When Davenport mentioned ditching moon shots for easy-to-get-at AI projects, one example he had in mind was applying AI to the data preparation process. For some companies Davenport has talked to, machine learning algorithms took months and sometimes just weeks to find duplicated records in multiple databases — a project that would have taken humans years to complete. “We hear a lot about what analytics and AI can do, but you don’t hear so much about this one, even though it may be one of the quickest ways to value for a lot of organizations,” he said.

Source: searchcio.techtarget.com-CIOs, the age of the algorithm takes hold — for better and worse

Advertisements

Accommodating GDPR email marketing regulations a top priority

Is AI working for your organization? Can you prove its ROI? Are you in the pilot stage and wondering what key metrics warrant rolling it out for marketing automation and at what point to cut bait?

We can’t answer those questions for you, but we can — and did — ask a number of industry leaders and observers to talk about where AI is going in the next year, as well as how it’s reshaping marketing automation.

One thing’s for sure: If you’re marketing to customers in Europe, you’d better get your act together before the European Union’s General Data Protection Regulation (GDPR) takes effect in May 2018. GDPR email marketing rules could mean a crackdown for businesses that are ill-prepared to comply with them. The regulation carries the force of law, and it harmonizes a patchwork of privacy rules across the EU’s member states.

GDPR email marketing rules remake workflows

Michelle Huff, CMO, Act-On Software: “The GDPR requires that all companies doing business in the EU — or online with EU citizens — protect the personal data and the privacy of those citizens.

A marketer will need to treat cookie data with the same level of protection as they would a customer’s address or birthdate. This means data security and privacy are no longer just IT’s problem. Marketers need to educate themselves on what data they have, how they use it and how it is protected, then limit access appropriately.

The days of exporting a huge CSV file of user data and uploading it to your email marketing platform are fast drawing to a close. For email, the best way not to run afoul of the GDPR [email marketing rules] is to institute tighter controls on email marketing programs.

Express consent must be granted by your customers, for both the data you are collecting and how you will use it. And, once collected, that data should never leave utilities that have been vetted and approved to meet the required level of data security.

The GDPR will have the biggest fundamental impact on marketing in the last decade, and the biggest impact on how companies go to market since the invention of the cloud. The onus is on marketing technology providers to ensure that their users have access to the tools they need to market safely and securely in the new world of marketing under the new GDPR [email marketing rules].”

2018: Year of the unsubscribe

Matt Harris, co-founder and CEO, Sendwithus: “If you weren’t doing double opt-in before, do it now. 2018 will be the year of the unsubscribe.

People are collectively realizing that they’re being bombarded by far too much online content, and unsubscribing or flagging irrelevant email is a fast and easy way to turn down the noise. Not only will double opt-ins help with regulatory compliance, they will ensure you have only interested, engaged and invested customers on your list, which will prevent unsubscribes and spam reports, which will, in turn, protect your reputation.

PRO+

Content

Find more PRO+ content and other member only offers, here.

From a technical standpoint, a double opt-in requires a simple click to confirm auto-response upon sign up. But you could use the opportunity to collect more data points to further personalize the user experience. A simple — but optional — checklist on the confirmation landing page would allow the user to select preferences, such as email frequency, product categories or content topics.”

Marketing integrates across channels

Joe Stanhope, VP and principal analyst, Forrester: “For 2018, I see a major shift in how marketers orient themselves — and, by extension, their marketing automation efforts — with respect to multitouch, multichannel customer engagement.

Historically, often by necessity, marketing automation and customer interactions have been very siloed by channel. Marketers are rapidly evolving beyond this state, and we’ll see major progress in this area in 2018.

Marketers will view customer engagement less as a series of independent or lightly related interactions, but rather as a continuous customer journey comprised of highly personalized moments that create opportunities to create value between the customer and brand. This approach will necessarily lead marketers to seek advances in their marketing automation capabilities to support the orchestration and delivery of interactions in line with customer journeys across any engagement point, regardless of channel, touch point or device.”

Chat marketing comes of age

Srivatsan Venkatesan, Freshworks product head, Freshsales CRM: “Consumers will prefer chat as a medium over other forms in 2018. Bots powered with context will become the real enablers.

In addition, bots will begin to adopt the look and feel of the application or website you integrate them with, thereby providing a native or personal experience.”

Enterprises struggle with ‘digital laziness’

Daniel Siegel, independent digital product architect: “Trends change every year, but what seems to stick is something I refer to as digital laziness. Instead of fixing the actual, hard and sometimes messy problem, we come up with an easy technological solution.

We prefer a CRM instead of picking up the phone. We prefer an email reminder instead of meeting someone face to face. We prefer a fully automated website and newsletter instead of staying in contact with our clients ourselves. We become lazy because we think the computer is taking care of it.

Now, we can use websites, drip campaigns, newsletters and digital marketing strategies to get more and better clients, but we’ll fail utterly if we don’t assert the fundamental goal we’re trying to achieve. Instead, we have to see the above as tools we can use to reach these goals and augment parts of our businesses.”

Marketing turns to influencers

Collin Holmes, founder and CEO, Chatmeter: “Consumers are tired of traditional, intrusive marketing messages, and [are] instead turning to their peers to influence what they do and buy. Coupled with the rise of ad-blockers and cord-cutting consumers, we can presume that we will all become influencers as online reviews and social sites become prevalent influencer marketing tools over the next few years.

This evolution is already beginning, as we know from the 92% of consumers who report making a purchase after visiting Yelp — a higher conversion rate than search engines and social platforms where most influencers currently reside. This is arguably for the better, as ad, marketing and content providers are facing pressure to be more creative, personal, relevant and timely with content, while simultaneously continuing to manage spend.”

AI marketing wears thin

Matt Nolan, Pegasystems Marketing Automation director of product marketing, Pegasystems: “The term artificial intelligence is being overused and is increasingly wearing thin on marketers who are way ahead of the CRM learning curve and already leveraging much of the AI tech being showcased, [such as] predictive analytics, machine learning, natural language processing and customer decision management engines.

Marketing practitioners, particularly those focused deeply on martech capabilities, see clearly through the veneer put in place by vendors — and know that a lot of the truly powerful AI tech, like deep learning platforms, won’t fully mature and add functional business value for years. So the challenge isn’t finding a place to leverage new AI — it’s finding a way to consolidate and operationalize the AI components they already have to provide a compelling customer experience and keep those individuals engaged.

In a sector with more than 5,000 unique marketing solutions, the average campaign response rate is less than one percent. There’s one question every company needs to ask itself: What are we actually trying to accomplish with our marketing? Because the answer isn’t ‘to run campaigns.’

Campaigns aren’t the end goal, they are just a means to an end. And despite how marketers are driven to behave, the goal isn’t simply to sell products either — that’s shortsighted. Instead, the goal must be to increase revenue and profit for the company as a whole and, ideally, to increase customer satisfaction at the same time.”

Source: searchcrm.techtarget.com-Accommodating GDPR email marketing regulations a top priority

Why Digital Transformation is a Must for Organizations in 2018 | Analytics Insight

With innovation cycles getting shortened accompanied by intense competition and globalization related challenges, the analytics-driven transformation has become go-to for almost all businesses. With challenges also come opportunities from which the companies can benefit. HCL’s corporate vice president, Anand Birje says, “Over the past four or five years, enterprises were pushed hard to do anything in the field of analytics, big data and digital transformation. They were being pushed because there was this fear about what their competitors might be doing, so there was this feeling that they had to do something digital.” Digital transformation presents rich opportunities and developing a strategic plan to manage data assets can lead to long-term success.

Customers now have a wide range of providers, thanks to globalization that has made it possible for competitors to emerge from anywhere and with any kind of price range. To maintain a value-driven relationship with customers, companies are expected to operate with a slim margin, balancing both profits and product innovation. According to a Gartner report, almost 32 percent of top leaders in big organizations have confirmed to be undergoing a digital transformation in their processes.

Rather than simply enhancing or supporting traditional methods, the transformation stage of digital signifies new types of innovation using software and computerized technologies strategically. Industry 4.0, also referred to as the fourth industrial revolution, emphasizes the importance of bridging the gap between the physical and digital realms. Technologies like cloud computing, cyber-physical systems, Internet of Things (IoT), cognitive computing together help create what is called as a ‘smart factory’.

One of the major banks was using an agile and scrum method for proof of concepts with a timeframe between 8 to 12 weeks. With numerous product cycles and users not willing to wait for final products, the entire process got confusing. An organization must first develop a business value chain which specifies the objectives and goals clearly and then proceed to build its big data and analytics capabilities.

Large volumes of data can be turned into assets for organizations with proper digital efforts and value chain development. Data related to research and development, product, engineering, supply chain, manufacturing, production keep on piling up. A unified view of the business helps extract data of significance from disparate systems like ERP, SCADA, and CRM. This helps gain useful insights that further helps in the planning and decision making course for a company.

In addition to the big data an organization owns, IoT data in the data cloud adds significant volume to datasets. Effective big data analytical solutions need to be deployed to manage the datasets and generate perishable insights to take actions at the moment. Variances and consequence of any kind of business processes can be linked using predictive analytics tools. For instance, production lines slowing down is a consequence which can be attributed to say a variance in a supplier’s component having a design defect that doesn’t match what is required in the product assembly. Most importantly business processes are time sensitive and big data provides a real-time decision-making opportunity that can strategically improve a company’s performance and take it above other competitors.

As is true for any kind of change, re-architecting the business structure also means changing the way employees work, changing their responsibilities, reporting relationships and overall changing the organizational culture. Training and support of those involved in transformation efforts should be taken care of by the human resources department for a smooth and effective transition to take place.

Now the hype surrounding big data, analytics and digital transformation has reached a dead end. Companies can be sure of success only when they do their homework on sound strategic planning and align project initiatives with a long-term vision and deploy the best available tools and resources in line with absolute strategic clarity.

Source: Analytics Insight-Why Digital Transformation is a Must for Organizations in 2018

In a world of bots, AI and big data, how can employees and businesses survive?

With the Fourth Industrial Revolution hailed as bringing about a digital boom on the global economy, many may think: “Are we not we already well into the digital economy era?”.

It is true that there are now countless apps and computing technologies that allow people to conveniently hail a taxi, book a hotel, or clean floors with a robot. Smart machines can also already drive cars, diagnose patients, and manage finances more effectively than humans. But in a new analysis – What to do when Machines do Everything – we found the real boom is only just beginning.

In the years to come, AI will create further value, for example around safeguarding financial health, insuring families, and enabling people to heal and govern themselves – and this is just the beginning. Systems of intelligence, which combine hardware, AI software, data, and human input will help improve countless customer experiences, business processes, products and organisations.

Jobs and businesses will undoubtedly be impacted. One of the most common concerns is that the bots will take over everything. While it is true that machines will replace some occupations, and make some current skills irrelevant as robots do more of the everyday, mundane tasks, people will also become even more vital to helping an organization innovate and grow.

Machines are getting smarter every day and doing more and more; they will soon change our lives and our work in ways that are easy to imagine but hard to predict. The debate has, thus far, been in the hands of theoreticians: it is now time for pragmatists to take over. These pragmatists – whether companies or individuals realize that machines will replace some occupations, putting pressure on wages for some jobs and making some current skills irrelevant. However, machines will also enhance the human element of work. In fact, more than 80 percent of teaching, nursing, legal and coding jobs will be made more productive, beneficial and satisfying through artificial intelligence. While machines will learn to do more things, and will perform tasks more economically, more efficiently and with fewer errors, this will augment the human experience, generating more jobs, even creating professions that do not even exist yet.

As we expect 20 percent of the more administrative portions of a job go to a machine, the future workforce will require more people to fill jobs currently in short supply: data scientists, designers, technologists, and strategists, as well as create jobs that do not even exist yet.

Materials, Machines and Models – the formula to ‘win’ the Fourth Industrial Revolution

The digital revolution is fundamentally a growth story. While the future of an automated workforce can be frightening, the artificial intelligence (AI) revolution will create a huge wave of opportunity for businesses and individuals who are prepared. Typically, every previous revolution has followed such a pattern: innovation bubble, stall, and then boom. The Fourth Industrial Revolution will be no different. Early digital economy winners have aligned the Three Ms – materials, machines and models – and use them to their advantage.

Firstly, sensors will be required on nearly every “thing” – IoT devices, RFID sensors, accelerometers, motion sensors, etc. – to create massive amounts of data that is the new raw material of the digital economy. Secondly, systems of intelligence (machines) will be required to “process” this new raw material data to improve business productivity and customer. Finally, new commercial models will emerge that monetise services and solutions based on these systems of intelligence.

However, without the right business model to support data-fuelled machines, companies will struggle to be successful. Business leaders will need to decide how to instrument everything, how to harvest all the resulting data, how to ask the right questions of the data, and to “teach” the AI systems what to look for, what is meaningful, and what is immaterial.

Five essential plays for winning with AI

Each of the Three Ms in today’s business success formula must be activated to move AHEAD. There are five distinct approaches for not only winning with AI but surviving and thriving in this time of transition – automation, halos, enhancement, abundance and discovery.

1. Automation: Outsource rote, computational work to the new machine. This is how Netflix automated away Blockbuster.

2. Halos: Maximise the data products and people generate – via their connected and on-line behaviours – to create new customer experiences and business models. GE and Nike are instrumenting their products, surrounding them with halos of data, creating more personalised customer service and products as a result.

3. Enhancement: View the computer as a colleague that can help increase job productivity and satisfaction. For example, a car’s GPS system improves driver performance by enhancing navigation, providing alerts for road hazards, and ensuring the fastest route is taken on any given journey.

4. Abundance: Use the machine to open up vast new markets by dropping the price-point of existing offers. For example, UK-based start-up, Brolly, has created an AI enabled insurance advisor to allow customers to understand, manage and buy the insurance they need.

5. Discovery: Maximise use of AI to conceive new products, new services, and new industries. Just as Edison’s light bulb led to discoveries in radio, television, and transistors, today’s new machines will lead to the next generation of invention.

The world is changing faster than ever before. Our children and grandchildren will study the advances of the Fourth Industrial Revolution, just as we studied the great technological innovations of Albert Einstein and Thomas Edison. Automation and the rise of AI are truly deep and unstoppable forces – they are the core of this incredible pace of change. The shift to the new machine and AI is inevitable but if managed wisely, it will ultimately be a positive force for companies, individuals, and society. Leaders can compete and win in the next phase of global business by driving productivity, customer intimacy, and innovation if they align the three Ms and think AHEAD.

It is time to build our own future, complete with a sense of optimism and confidence. When machines do everything, there will still be a lot for companies to do. It is time to start now or risk being left behind.

Ben Pring, Vice President, Cognizant’s Center for the Future of Work and co-author of What to do when Machines do Everything

Source: itproportal-In a world of bots, AI and big data, how can employees and businesses survive?

How to institute an agile IT outsourcing process

Traditionally, IT organizations have spent six months to a year or more on the IT outsourcing transaction process, finding the right providers and negotiating a suitable contract. But as IT services — and, increasingly, as-a-service— deals have gotten shorter, that lengthy process may no longer make sense.

Industry advisors and consultants have debated the potential benefits of speedier sourcing for several years. In today’s rapidly changing business and technology landscape, it may become an imperative. But an effective outsourcing engagement demands more than just an accelerated version of the traditional IT services transaction process.

“Typical attempts to speed up the process include leaving out important activities or rushing to a solution to meet completion dates or budget objectives. In some cases activities that are skipped can be picked up and completed during transition,” says Michele M. Miller, director of KPMG’s Shared Services and Outsourcing Advisory. “However, we find that in most cases these activities are never completed and result in lost value and dissatisfaction in the outcome of the outsourcing project.”

Preparing for an agile approach to outsourcing

CIOs must take four steps to make sure they prepare their organizations for a new, agile approach to outsourcing, Miller says. First, they must define their business strategy, including the future state of IT and business services, in order to accurately asses how outsourcing will impact their companies down the road. Second, they need a clear understanding of their base case — the current cost of doing business today and down the road. Third, they need to define their target operating model (aligned with business strategy) in order to calculate the potential benefits of internal optimization vs. outsourcing or resourcing.

Finally, they must assemble a dedicated and experience outsourcing transaction team that was involved in building the strategy and is empowered to work closely with providers on day-to-day planning, design, and documentation of the solution as well as oversight of desired business outcomes. This preparation takes time an effort. However, “these steps are required for a successful outsourcing engagement,” says Miller, “and most companies are willing to put in the effort.” In fact, part of the reason Miller’s group began to document this agile approach to outsourcing was the fact that some companies had already established these key components.

 

With that foundation in place, IT leaders can attempt a more agile approach to outsourcing. Like its namesake software development approach, an agile outsourcing transaction process involves constant communication and collaboration between the IT organization and its providers throughout the outsourcing lifecycle, adapting as needs change. Unlike the traditional sourcing approach in which IT service customers approach the process in a linear fashion — gathering requirements, creating an RFP, engaging providers, and drawing up a contract — agile outsourcing transactions are more fluid.

Agile outsourcing starts with a series of sprints. “The sprints focus on collaborative ‘solutioning’ vs. the traditional approach, where the client outlines a solution up-front, often excluding other potentially beneficial alternatives from serious consideration,” Miller says. “Via these sprints, the parties consider alternatives together and jointly build a solid, viable solution. This results in a more-accurate RFP response, less time required in the due diligence phase, and more precise pricing during final pricing submissions or best and final offer.”

Because the process is collaborative, with both parties knowledge of requirements and solutions early in the process, timelines can shrink significantly. In fact, business requirements and solutions are so well understood that a traditional 26-week timeline can be condensed to as little as 12 weeks, Miller says. But increased speed is just one of many benefits. Agile outsourcing can sharpen the focus on business outcomes and instill greater collaboration not just between client and provider, but also among a company’s ecosystem of suppliers, in delivering those outcomes, according to KPMG.

Most companies are drawn to the agile outsourcing concept, but not all can make it work. “These projects are not shorter because we leave out critical processes; the client needs to have completed the four key requirements mentioned and be willing to work in the fast-paced iterative environment and make decisions quickly throughout the project,” Miller says. “Similar to many components of outsourcing there isn’t a single approach which works in all situations.”

IT service providers are game for the new approach, according to Miller. “They understand that a collaborative approach to sourcing tends to result in a more successful outcome for both parties because each shares in the responsibility for the design of the solution.” However, it does require that they, too, have done the upfront work of designing and documenting their solutions for ease of integration into the process.

Source: cio.com-How to institute an agile IT outsourcing process

Automation & AI – the human workforce’s new best friend?

Could AI and automation become the employee’s new best friend at work? Gareth Hole at NICE answers that question in this exclusive op-ed for CBR.

Robotic Process Automation is a huge trend with businesses across the world. By combining artificial intelligence with other technologies, organisations are automating routine, repetitive businesses processes to improve efficiency and drive better results. According to the National Association of Software and Services Companies, RPA can already reduce operations costs as much as 65 per cent, with ROI within as little as half a year.

Every business has processes that can be automated from start to finish by an unattended robot, working without intervention, 24/7, without errors, collecting and executing tasks from a queue. But what about the business processes that have decision points requiring human intervention or communication skills?

This is where attended desktop automation comes into play. Attended desktop automation allows a dedicated, smart desktop robot to help a human with certain tasks. This robot can mimic human actions, from copying and pasting information, to data inputting and even opening up applications and performing actions. It can even exceed what a human could achieve on their own, by gathering and analysing large amounts of data 100% accurately and rapidly in real-time and taking actions based on the results. All of these activities can occur on an employee’s desktop, in the background, triggered by any type of event, such as a button click, switching tabs, checking a field has specific data or even a complex combination of multiple events.

Recent improvements in algorithmic techniques and the expansion in the use of deep neural networks have also enabled significant improvements in the technology’s ability to deliver value.

In the contact centre industry, for example, where agents often have to juggle multiple tasks, (talking to customers, sourcing information, inputting data etc), attended automation certainly lightens the load. It allows those agents to focus on talking to a customer whilst tasks like looking for relevant data in multiple applications or figuring out what the best next step to take is, are done for them in real-time.

With this approach, the human and the desktop robot are working side by side, in full collaboration, with humans overseeing the execution of each activity. Humans can then focus on more interesting, valuable work, while also being empowered to make the best decisions in real-time.

Large businesses that run attended desktop automation robots, enabling their employees to make the most of their expertise and focus on the essence of their jobs, report high customer and employee satisfaction and operational efficiency which leads to a better bottom line. One of the UK’s largest mobile network provider, has automated 32 processes across a wide variety of process types, realising a saving of over 4 million seconds per month in automations alone. Using both attended and unattended automations, there is also the expectation of delivering about £1 million per year of additional benefits from these processes.

Attended automation can also identify events and processes that require training, enabling managers to take the necessary steps to improve the employee’s performance. For example, if the employee is required to read a disclaimer as part of the call but the disclaimer text was only open for two seconds, the system can detect that it is most likely that the employee did not read the disclaimer, and an alert will be sent to the manager.

Managers, in turn, will be better equipped to focus their limited time on delivering tailored coaching and support. This will help employees further hone their customer service skills, as they focus on delivering the sort of service that builds loyalty and satisfaction, and not on mundane, routine tasks.

Deciding on what to automate can be a huge challenge. With customer expectations constantly changing and new channels of engagement emerging, deciding on where to begin, as well as the long-term plan is not always straightforward. This is where attended desktop automation can again help, this time by gathering process information to uncover further opportunities to optimise those processes in real-time.

There is also the issue of how automation fits in with existing systems and processes. For example, does process automation still have value against a backdrop of investing in ‘best of breed’ IT systems? With traditional automation, the value can often be challenged but with attended desktop automation, a smart desktop robot can leverage even more value out of those systems to help their users in real-time.

Every organisation, in every vertical, has repetitive, time-consuming, error-prone processes which demand accuracy and speed, and don’t necessarily rely on human ‘out of the box’ thinking. The automation of these processes has become a reality but there is a growing realisation that humans can add more value to an organisation when freed from this repetitive and mundane work.

Attended desktop automation does not just save organisations valuable employee time. You could go as far as calling it the employee’s new best friend, keeping them engaged in the most important and valuable tasks for the business. After all, who wouldn’t want their own smart robot helper?

Source: cbronline.com-Automation & AI – the human workforce’s new best friend?

Robots May Not Take ­­­Your Job After All

Over 150 years ago, British author Samuel Butler predicted the rise of artificial intelligence, calling for a “war to the death” against machines – and arguing that that “the time will come when the machines will hold the real supremacy over the world and its inhabitants.”

Today, the inevitable conflict between man and AI-powered machines permeates our national discourse, as the threat of technological unemployment looms large. Elon Musk recently told a gathering of governors that AI is “the greatest risk we face as a civilization.”

Photo by Chris McGrath/Getty Images

But while the national narrative tends to reflect Butler’s dystopian fears for the 3.5 million truck drivers rendered obsolete by autonomous vehicles, or Mark Zuckerburg’s often mischaracterized vision of an educational future devoid of teachers – early adoption of AI suggests a far more collaborative reality.

Because in practice, AI often shines a light on areas where replacing humans with robots leads to suboptimal results – but cooperation between humans and machines, create outcomes that are better than either might achieve independently. Google Translate results may be technically accurate, for example, but fail to transpose idioms or slang that human translators can interpret. Interactive voice response (IVR) systems, unable to deal with the breadth and complexity of customers’ needs, trap frustrated consumers within the endless computerized loops until a human, armed with information gathered by computers, can direct them toward a solution. And in the education context, outcomes for AI-driven courses have failed to produce results, at scale – without the thoughtful support and encouragement of real-world teachers.

Of course, the concept of human-machine symbiosis isn’t entirely new. Average chess players paired with laptops famously took down chess masters and supercomputers in one 2005 match. PayPal’s founders paired human analysts with sophisticated algorithms to tackle complex fraud challenges – giving birth to the technology that undergirds mercurial tech titan, Palantir.

At American colleges and universities, human-computer symbiosis is allowing faculty like University of Michigan Professor Perry Samson to leverage students’ mobile phones to collect real-time information about student behaviors, and modify instructional strategies to improve learner outcomes. Technology helps teachers understand how students answer problem questions, whether they take notes, get confused, tag content in books, or review lecture material after class. Rather than replace them, technology is making human teachers more effective, at a time when even the most advanced AI isn’t able to data mine its way to non-obvious hypotheses that improve student learning. Educators, empowered by data, are drawing on human intuition and creativity to identify correlations between student behavior and outcomes. Technology isn’t just making teachers more effective, it’s enabling a new era of pedagogical innovation as a growing number of educators experiment with flipped, blended, and adaptive courses.

This approach is not unique to education. In customer service, the failure of IVR to deliver a positive customer experience has led to a blended approach that leverages AI to match customers with the customer service reps most likely to address their content needs – or even personality. Rather than replace call center operators, companies like T-Mobile are using AI platforms (like little-known “unicorn” Afiniti) to drive improved results of existing employees – by simply using AI to learn from historic interactions to better pair customers with their call center representatives. Same team, same customers, but upwards of 5% improvement in sales outcomes and retention with significant impact on the bottom line.

The applications of blended AI are broad. Stanford researchers have used advances in machine learning to develop a human-machine hybrid for translation, allowing bilingual human translators to move faster than they could if translating everything manually, while also improving the accuracy of machine translation. The blended approach allows for quicker translation on the basic language and uses humans to finalize the more subtle portions of the translation for context and culture.

A final dimension that most futurists are missing in discussing AI is that it actually creates whole new categories of jobs in training AI technology, explaining the contextual situations that AI machines don’t handle well (sarcasm) and measuring efficacy. Just as computers never really eliminated paper, we realize that AI – like the internet and renewable energy – will create whole new career paths.
In each case, AI is transforming the way humans interact with each other, in ways that make those interactions more efficient, effective, and even more meaningful. Could it be that AI might actually facilitate more authentic connections between individuals? The robots may still be coming for our jobs. But rather than competing with people, AI may turn us into the real supercomputers – and, in an ironic twist, make human interaction even more human

Source: Forbes-Robots May Not Take ­­­Your Job After All