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.

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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

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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.

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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

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

Unlocking the business opportunity of artificial intelligence

Today, if someone asked for thoughts on artificial intelligence (AI), your mind might paint a pop culture informed picture of a dystopian machine-ruled future or a chatbot with the lexicon of a seven year old.

That’s the problem with new technology. Our vision of the future is coloured by the realities of today, or in many cases, what we witnessed fifteen years ago in Minority Report.

Viewing AI through the lens of a futures market

It’s not a risky proposition to think that the value of AI will rise in the future. IDC suggests that $41 billion will be invested in AI systems for enterprises by 2024, and Forrester projects 13.6 million new AI jobs will be created in the next decade.

If you believe that AI will play a bigger role in business in the future, yesterday was a good time to begin the journey. Some businesses justify inaction by suggesting the technology is unproven; it introduces reputational and financial risk to a business. Why not sit on your hands for three years, and wait for the technology to mature.

Doing nothing is a high risk strategy. To begin with, first movers benefit massively from scaling their internal capabilities ahead of their competitors, particularly in a white hot recruitment market.

Second, to do nothing and to be seen doing nothing invites aggressive competitors to actively target those companies and their customers. Third, allowing competitors to shape the market is to defer to a process you have no control over.

Three areas of AI application

There are three immediate areas of business application for AI. The first is development of virtual assistants, designed to act on behalf of humans in order to better achieve our goals.

Today, there is a fast-growing trend for chatbots. This is perhaps unsurprising given the global popularity of instant messaging (IM) platforms. The format is familiar to anyone who has used IM, and with IM platforms being more popular than their social media equivalents, there is a large tech savvy audience.

WhatsApp alone has sends more messages than SMS globally. Consumers like the fact that messaging works both as an instantaneous, as well as an asynchronous, channel.

Today, chatbot adoption is fighting on two fronts. From a consumer perspective, if a chatbot is not a convenience upgrade on existing alternatives (such as Google search or mobile app functionality) the novelty value of chatbots will soon wear off. From a usefulness perspective, companies struggle to keep up with consumer expectations.

When Capital One launched one of the first Alexa skills in March 2016, customers immediately thought that they could conduct all their banking needs through it.

Capital One are early adopters of the platform and have learned a great deal in the last 18 months, pushing Amazon and the limits of the platform, in terms of ontology size and complexity, along the way.

Whilst chatbots may fade in time, the role of virtual assistants is here to stay. Whilst today many chatbots are merely the equivalent of a call center, interactive voice response (IVR) menu system or an FAQ knowledge retrieval system, over time their ability to handle more nuanced requirements and provide informed advice will grow.

Building successful virtual assistants requires a combination of magic and logic. Magic to build compelling experiences that change consumer behavior, and logic to build smart algorithms that continue to learn and improve decision making.

A second area of immediate business benefit is automation and augmentation. Automation of manual processes, particularly in legacy businesses with legacy technology, has significant cost base implications.

Whilst robot process automation (RPA) is nothing new, the smart application of machine learning to not just convert a manual process into an automated one, but to do so in an autodidactic way, constantly improving the effectiveness and efficiency of the process, is a prime application for AI.

Augmenting workforces with AI driven applications is another source productivity gain. Many forms of customer service interaction are now a combination of human and machine response.

Machines can make individual service representatives more productive by automating repetitive tasks and automatically prompting responses to commonly asked questions.

Inhibitors to value creation

Ultimately, the killer application of AI is the invention of new business models, products and services. It is alluring to think that a firm’s data contains a map to some hidden treasure of a previously undiscovered business model.

The reality is somewhat more mundane. Only those companies with access to the right analytical firepower, coupled with an ability to free their data from the shackles of legacy siloed databases, have a shot at legitimately creating new value from data. Both are serious undertakings with minimal shortcuts.

Talent availability is a serious inhibiter of AI growth. Without a sustainable capability model, businesses are struggling to attract people with the relevant skills, particularly when trying to compete with Google, Amazon and Facebook. Given the low supply, high demand nature of the AI labor market, workers are well compensated, with average salaries of $170k according to Paysa.

Legacy technology is the other hindrance to the implementation of AI applications. Identifying previously unknown relationships within data requires the integration of disparate data sources. Silos are the enemy of integration.

Those companies that have migrated their data to the cloud, have built robust APIs and have reached a higher degree of digitisation are generally in a better place to generate value from their data.

The clock is ticking

There are two ways of looking at generating business value today from AI. One is to get tactical. Developing proof of concept prototypes, getting real consumer feedback, and developing the opportunity to upskill colleagues and learn by doing.

The process of creating a backlog of prioritised use cases along with their respective business cases can help to focus development in small achievable chunks, with each new application building on the underlying knowledge model.

>See also: Is business data AI compatible?

The other is to take a longer term view, and begin to create the structure required to exist in a more AI mature world in three to five years’ time. While developing internal data analytics capabilities, migrating data from silos into an extensible cloud solution and building key strategic partnerships may not provide visceral evidence of progress in the short term, it is vital to long term sustainable success.

Either way, inaction is risky. As the world has been digitised, AI has begun to take off due to the exponential growth of data, reductions in costs of cloud computing and the scalability of virtual machines. Those that adopt an AI first mind-set early are in the best possible position to take advantage of this burgeoning field.

Source: information-age-Unlocking the business opportunity of artificial intelligence

AI & Automation are playing a major role in transforming businesses

Criticization and appreciation is a part of the game. And, something similar is happening with disruptive technologies such as Artificial Intelligence (AI) and advanced automation. Many see it as a force of change which will lead to growth, whereas few others criticize disruptive technologies for being the reason of job loss.

Putting aside all such debates, it is high time to admit that disruptive technologies have led to new forms of competition and it has become imperative for businesses to duke it out. However, if we turn history pages, we will be convinced that automation has always led to the creation of a new set of jobs. It is only that transformations in technologies demand patience, as it takes some time for the transition waves to settle down.

Similarly, advanced automation and Artificial Intelligence (AI) are in its stage of evolvement but at the same time, it has also encompassed people, businesses, and economies. It opens the world of enormous opportunities – where businesses can evolve at an incredible speed and the workforce can learn new skills to perform modern-day business operations.

According to a report published by Accenture, “companies that will grow and dominate their industries will be those that systematically embrace automation across their organizations using it to drive the changes to their products, services, and even business models as they continue to transform themselves and their industry.”

Advanced Automation Set to Change Business Landscape

Processes are becoming efficient, dependency on the human workforce has significantly reduced, tasks are being completed with more accuracy and precision, customers get an engaging and interactive platform – Artificial Intelligence (Ai) and advanced automation are changing the business landscape at an unprecedented speed.

It won’t be wrong to say that these disruptive technologies introduce a paradigm shift in the way companies function and the human workforce carry out diverse business activities. Enhancing efficiency is not the only aim, but with advanced automation, businesses can go beyond traditional boundaries of maximising productivity and profitability. Disruptive technologies rather support in long term growth of business organizations, where main focus lies on building a personalized relationship with customers, becoming a brand, delivering true value and enhancing customer loyalty and retention. In short, disruptive technologies such as intelligent automation enables business in remaining fit for even future performances.

Time for Humans to Team-Up with Their Digital Co-Workers

Gone are days when humans guided machines to perform various business operations. With cognitive abilities turning into a reality, it is the automated business system that guides the human workforce. Programmed machines are now performing tasks exactly as a trained employee. And, in some cases better than them.

With cognitive abilities, advanced automation solutions enable the business system to analyze and respond in an urgent situation. This gives businesses a responsive platform, which supports businesses in eliminating performance bottlenecks both inside and outside the organization. As a result, streamlined process supports in seamless business workflow, which significantly helps in achieving organizational goals efficiently.

It is high time the human workforce must team-up with their new digital co-workers. And, it can bring innumerable benefits for them. Programmed machines share much of the workload; employees get an opportunity to learn new skills required for modern business processes; advanced automation saves much of their time, which enables them to be more creative while performing diverse business tasks.

Benefits that Businesses Can Gain

Technologies have always supported business organizations in performing operational and productional activities. But with evolving technologies such as advanced automation, it is time for businesses to reap innumerable benefits.

Advanced automation solutions are designed with advanced business models such as SaaS, Six-Sigma, and Lean production. There various proven business models together play a key role in cutting operational costs. By enabling in updating the business system with the latest development in technology, ability to control entire business activity even from a remote location, supporting in optimum resource utilization and many more – advanced automation solutions in cutting costs by a great margin.

Disruptive technologies are being widely recognized for making business processes robust and efficient. With approaches such as Six-Sigma businesses can ensure that tasks are completed within allocated budget and time; variations in finished product and other services are kept to a minimum level, and most importantly it supports in reducing the error-rate. Lean production, on other hand, supports in eliminating wastes from processes which further aids in achieving organizational goals successfully.

Besides, advanced automation also enables in creating an interactive and engaging platform for customers. With automated processes such as Sales and Marketing, businesses can ensure that customers are informed of sales, discounts and other promotional offers right on time. Customers can put their queries and businesses can deploy advanced measures for an instant solution. This encourages customers to be loyal and also largely impacts their buying decision.

Therefore, it won’t be wrong to say that disruptive technologies enable business in performing robustly at all fronts. With competition getting steep, technologies such as advanced automation is key to success as it can play a larger role in delivering complete satisfaction to customers.

AI and Automation Is the Greater Force

With advanced automation technologies transforming businesses at a breakneck speed, leaders are set to gain a wider market share. As the report published by Accenture further informs “Intelligent automation will enable enterprises to innovate and evolve by increasing their agility, reducing the complexity of systems and operations, accelerating their time to market, and creating the ability to experiment continually with new products and services.”

In the prevalent technology-driven environment, businesses cannot grow using traditional business approaches. They should rather adapt to the change introduced by the new age disruptive technologies. Embracing intelligent automation, companies can drive demands and can successfully deploy advanced business methods to fulfill customer preferences.

Technological solutions are playing a larger role in transforming businesses and is helping them in securing long-term growth. But what matters is – how soon business leaders realize this fact that AI and intelligent automation solutions are the greater force, which can in no way be overlooked?

Source: itproportal-AI & Automation are playing a major role in transforming businesses

Industry and academia – the recipe for AI innovation

Academia and industry at first glance appear to be strange bedfellows. One focuses on the theoretical and conceptual, whilst the other is driven by the practicalities of deadlines, goals and ultimately, profit.

I’m in the privileged position of working on both sides of the fence. I’m Professor of Computer Science at the University of San Francisco as well as Chief Scientist at SnapLogic, a provider of application and data integration software. I have worked on AI in both of these roles, and throughout my 20 year career I’ve come to the realisation that, when it comes to driving innovation, these two distinct spheres need to work together.

AI promises to be the most important technology of the future, and if the lofty ambitions and out-of-the-box thinking of academia can find synergy with the can-do attitude, urgency and resources of industry, we’ll see an explosion in its applications. In fact, I believe that AI and ML technology won’t just be a nice feature but will be a requirement for all applications go forward.

Data, Data everywhere

This collaboration between industry and academia has been growing for some time and, like most things in technology, it all boils down to the data. For the first 10 to 15 years of my career I was on the traditional academic track, and whenever we were undertaking research or publishing papers, there was always one consistent stumbling block – we lacked real world data.

That all changed around 10 years ago with the emergence of social and search. The Google’s, Twitter’s and Facebook’s of the world were challenged by data growth problems that exceeded the capacity of conventional database technology, so they built custom solutions to house their vast datasets.

Of course, the large search and social companies didn’t curate all of this user and behavioural data for the distinct purpose of fuelling AI development, but they saw the potential that it held. The tipping point came when the likes of Twitter first invited academics to analyse this data. Suddenly and unexpectedly, a treasure trove of real world data was made available to academics. All that theoretical machinery that we had been developing in academia could be realised as real recommendations and meaningful predictive applications. Ph.D.s in academia started going into industry and working with real data, fuelling further theoretical developments and so on and so forth.

Many of the latest and most promising developments in AI have been forged through this symbiotic relationship, and if it can be strengthened, we’ll see many more breakthroughs in the pipeline.

Cultural Cross-pollination

The positive effects of academia and industry’s relationship aren’t solely limited to datasets. Cultural cross-pollination is, in my opinion, a vital aspect for driving further innovation in the field.

I’ve briefly alluded to this above, but it’s hard to imagine cultures much more different than industry and academia. As someone who was, for the majority of their career, primarily an academic, I’ve experienced first-hand how these two worlds differ, and how, on an individual level, being exposed to the other side of the coin is an enriching experience.

This is perhaps best illustrated with an example. I first joined SnapLogic in 2010, when it was a much smaller company than it is today. This was my first foray into industry, and I was tasked with building a prototype for a machine learning project to be implemented into our data integration platform. The prospect of putting my code in front of my new colleagues, industry veterans, unsettled me.

As someone who had been coding since the age of twelve or thirteen, it wasn’t a lack of skills or experience which caused this reaction, rather the prospect of doing so in a wildly different environment to that which I was used to. In academia, code is rarely reviewed. There isn’t an audience, it’s just an aspect of the job you complete. In industry it’s much more purpose-driven. There are goals and deadlines and milestones. Your work must deliver value for customers.

In the end everything was fine, and that code I wrote still exists in the platform to this day, but it shed some light on how sheltered we are in academia from the realities and stresses of industry. By spending time in industry I learned how to adapt to a different type of coding environment, and am a more well-rounded professional as a result.

These cross-cultural benefits are something I’m bringing into my classroom to better prepare my students for a career in industry. I’m now teaching some of the realities of building production software, which many academics might forego. We’re also bringing some of my students to work on-site at SnapLogic, on real-world AI projects, for course credit.

By exposing these younger people to industry in the earliest parts of their careers, rather than far later on as I did, the next wave of computer scientists will be equipped with not only the curiosity of the academic, but the practicality and work ethic of industry. This, I hope, will spur the next great developments in AI.

The AI future

I strongly believe that AI will have a huge impact on the world of business in the years to come. It will make companies more efficient, freeing up resources to be invested in other areas to enhance existing products or develop new ones. It will change job roles and allow employees to focus on more human tasks that rely on human skills, such as emotional insight, rather than burdened with rote, repetitive tasks.

However, it’s going to be an incremental process, and it won’t happen overnight. If we’re to have any hope of accelerating the timeframe, we’re going to need academics and industry pulling in the same direction, pooling their talents and resources and working in sync. The building blocks for this relationship are already in place, and both sides stand to benefit.

Source: itproportal.com-Industry and academia – the recipe for AI innovation