Artificial intelligence is redefining corporate finance

Sven Denecken, SVP and Head of Product Management and Co-Innovation at SAP, discusses how AI is changing finance functions

Artificial intelligence (AI) and its potential to transform business processes across industries has become a central focus for organizations across the globe. Whether its conversations in the boardroom, sessions at an industry conference or a small-scale team meeting of accountants, companies today are buzzing about AI and the opportunity it poses to help usher in digital transformation.

While many still speculate that AI is more hype than reality, AI is already deeply ingrained in many organisations, driving automation that simplifies business processes.

This is especially true in corporate finance, with a recent study from Oxford Economics and SAP finding that 73% of finance executives agree that automation is improving finance efficiency at their company.

What is AI?

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Defining artificial intelligence is perhaps the biggest initial hurdle that many finance stakeholders face in evaluating these technologies and weighing their potential impact in the enterprise. So, to start with the basics, AI can be broadly defined to include any simulation of human intelligence exhibited by machines.

One historical application that many organizations today are using is robotic process automation (RPA), which is rule-based robotic automation that can be extremely beneficial to companies in automating routine tasks. But beyond RPA, AI technology is a huge growth area that is branching into a multitude of areas when it comes to research, development and investment.

Other examples of AI include autonomous robotics, natural language processing or NLP (think of virtual assistants such as Apple’s Siri or Amazon’s Alexa), knowledge representation techniques (knowledge graphs) and more.

Machine learning is one specific subset of AI that has been gaining buzz in the industry today. Machine learning is learning based AI – it aims to teach computers how to accomplish tasks using data inputs, but without explicit rule-based programming that has historically been seen with RPA.

Drive efficiency in finance with AI

RPA is increasingly common within finance departments today, to help automate routine finance responsibilities, including streamlining transactional tasks and reporting. However, advanced AI technologies, like machine learning, have the power to take this a step further, removing the need for rule-based machines by implementing learning technology.

For instance, invoicing is a finance responsibility that can often be a nightmare for accounts receivable or treasury clerks. Often a customer might pay the incorrect amount for an invoice, combine several invoices together into one check, or even forget to include their invoice reference number. Rectifying this can be a huge time suck in trying to sift through invoices or track down the customer.

This is an area where machine learning could support finance teams in real-time by applying its learning technology to ultimately make suggestions to accounting teams on matching payments to invoices. With this, finance teams can not only better ensure accuracy in aligning payments, they can massively cut down the time spent manually tracking down the relevant information and apply themselves to other needs within the business.

Let AI have a seat at the table

The potential for AI doesn’t just lie in efficiency. As these machines get smarter, there is enormous potential for AI to support CFOs and finance directors in informing strategy and driving action.

In the consumer technology space, NLP applications like Siri and Alexa have helped to “humanize” technology and information for individuals, answering questions about the weather and news headlines – even occasionally entertaining the user with a bad joke. The use of these voice-enabled devices isn’t limited to the consumer setting, and in the coming years we will likely see an increase of NLP technology being applied in the B2B enterprise setting.

For instance, CFOs and other finance executives often receive questions in boardroom meetings around revenue forecasts, and a myriad of other topics. Often, the executive needs to spend countless hours prepping and pulling these figures to anticipate what information might be needed, or alternatively, halt an in-progress meeting to pull up the latest numbers.

These digital assistant devices could be used in the enterprise setting to let the CFO easily ask questions of his or her data analytics system in real-time. This technology would not only enable uninterrupted meetings, but also allow the CFO and other company stakeholders to make informed decisions that drive action quickly and with confidence.

Smart technologies will change the talent landscape

AI offers exciting promise for innovation as companies look to stay-ahead in today’s fast-paced, globalised business landscape, but as its popularity continues to grow, conversations have begun about the possible negative implications for workers.

For finance teams, while AI can have a measurable impact on efficiency, it cannot replace the human element. Human review and monitoring is still required when technology like machine learning streamlines some manual tasks, especially in cases that may be too complex for the machine to rectify.

Additionally, there is an opportunity for finance executives to build their teams by hiring people who are familiar with advanced technologies and can help support, improve and innovate their use within the finance function, ensuring human workers are equipped to excel in their roles.

Eighty-four percent of global companies cite digital transformation as an important factor for survival in the next five years, but to-date, only 3% of organisations have completed a company-wide digital transformation, according to another recent survey by Oxford Economics and SAP.

With this, finance executives in particular, believe that investment in digital skills and technology will have the greatest impact on company revenue in the next two years.

By exploring how AI technology can be implemented, not only in streamlining processes, but also as a valuable resource in informing strategy and driving action in finance, CFOs and other finance stakeholders can ensure their workforce is best armed to drive success in the digital economy.

Source:  Financial Director- Artificial intelligence is redefining corporate finance 


The Future of Robotic Process Automation

Artificial intelligence. Is there any term that’s more used in tech these days or that has a wider range of meanings? Any one that conjures up more excitement, hyperbole and fear? In this episode Jon Prial talks with Adam Devine, the CMO of WorkFusion, one of Georgian Partners’ newest portfolio companies, about a very practical application of the technology: Using AI to improve and even automate what have traditionally been human-driven processes in the workplace. You’ll hear about robotic process automation, an emerging field that is bringing AI-powered software robots into the workplace to help make companies more efficient and effective.

Jon Prial: Artificial intelligence. Is there any term that’s more used in tech these days or that has a wider range of meanings? Is there any one that conjures up more excitement, hyperbole and fear? Today, we’re going to focus on a very practical and a real application of this technology, using AI to improve and automate what have traditionally been human-driven processes.

We’ll take a journey, looking at how technology has evolved to help automate the work of traditional back-office business processes. The latest step in the evolution has been the development of robotic process automation, an emerging field that’s bringing AI-powered software robots into the workplace to help make companies more efficient and effective.

We’ll find out how on today’s episode, when I talk to my guest, Adam Devine, head of marketing at WorkFusion. WorkFusion is one of the newest members to our portfolio, and it’s using AI to help large companies use intelligent automation to work more efficiently.

I’m Jon Prial, and welcome to the Impact Podcast.

Jon: At one point, I was looking at a survey. I’m not sure if it was on your website or something I found, but McKinsey had said that 49 percent of the activities that people are doing today in the global economy can be automated with a currently demonstrated technology. Can you take me through your view of what you think of when you think of automation?

Adam Devine: Sure. First, I would invite everyone listening to close their eyes and imagine the huge expanse of a back office of a large financial institution or insurance company. Hundreds if not thousands of super-smart, capable people spending 30, 40, 50, 60 percent of their day doing things like operating the UIs of SAP or Oracle, super-repetitive swivel chair work, or looking at a PDF on one monitor and an Excel sheet on another monitor and simply, routinely transferring the information from that PDF, which you can’t manipulate, to an Excel sheet.

I think McKinsey is very much right. There’s a high percentage of work that the average so-called knowledge worker, people who work with information all day long, can be automated.

Jon: The thought of taking the data from the PDF to the Excel spreadsheet has to get codified somewhere. How do you approach that in terms of that’s something that could be done more efficiently, that needs to be automated?

How do you figure that out? How do you get the algorithms behind all these changes, perhaps?

Adam: There is this notion of writing rules or having rules learned. In the old days, like two years ago, there was scripting — if/then/else automation. You’d have teams of engineers and maybe some data scientists writing rules for scripts to follow, and that meant, as you say, codifying each and every action that a machine would take so that there is absolutely no ambiguity about how the work is done.

This, today, is an old-fashioned way of automating a process. What we can do today with machine learning — and it’s not just our business, this is a growing trend — is having machines that learn. Learn is the key word.

Rather than writing the rules, people do as they do. They open up an Excel sheet. They open up a document. They click here. They click there, and over the course of time, machines can detect patterns that people can’t. This is what I mean by learning. Where someone clicks on a document once it’s been digitized, what the context is of that information.

With enough repetition — typically 400, 500 repetitions — the software is able to identify a pattern and train an algorithm to do what a person had been doing.

Jon: I started, one of my early careers we did a lot in the world of workflow and image processing, taking electronic versions of paper and moving it through a process, maybe reading the paper, managing workflow. That evolved from paper-based processes to human-based processes.

Can you talk to me more, then, about robotic process automation, what that market is and what it was a few years ago and what it’s evolving to?

Adam: Sure. If workflow yesterday was the movement of paper, the movement of information, RPA is one level above that, or one step up the ladder, in that it doesn’t just move the information, it can transform it and transfer it. A good example would be moving structured information from SAP to Oracle or from Oracle to Workday.

These are systems that don’t inherently talk to one another. They’re different formats, and they require what you’d call human handoffs between these applications. RPA can operate these systems at either a UI level, meaning at a virtual desktop a bot will enter credentials automagically and run an operation to do a transaction or to move the information, or it can operate at an API level where — I guess you could call it— diplomatic code serves as an intermediary between these two applications.

I would say that RPA is the next level up above old‑fashioned BPM or workflow.

Jon: Does that involve AI, or does AI then come to the next level?

Adam: It can involve AI. One of the problems with scripting and with RPA is exceptions. What happens when something changes about the process or the content and the bot, which has been programmed to do a very defined task, says I don’t know how to do this? That means the process breaks. That means the bot breaks.

What happens, with just RPA, is that a person discovers that a bot has broken, because the business process has failed, and has to go in and manually retrain that bot and fix the business process. When you add AI to RPA, you have automated exceptions handling. You have an intelligent agent identify that the bot doesn’t know what to do and route that work to a person.

The person handles the change if the bot can’t figure it out, and that creates a contribution to the knowledge base. It teaches the bot what has gone wrong so that the same mistake or a similar mistake doesn’t happen in the future. What AI does for RPA is business continuity.

Jon: When you talk about RPA getting improved by managing the exceptions, and you’re managing the exceptions because you’re learning things — it’s a learning opportunity. Obviously, you’re learning from data. What new type of data is being brought in to a system to allow that learning to take place?

Adam: There is a lot of new learning that takes place when AI assists RPA. One of the more interesting things is workforce analytics. Rather than having opacity around who your best human performers are, around what their capacity is, what their capability is, what their aptitude is, when AI gets involved and can monitor the actions of a person that’s intervening in a process, you very quickly figure out who your star performers are and what they’re good at. You very quickly have transparency on what the capacity is of a workforce and how work should be routed.

A good example would be the back offices of a large bank. Most offices are highly distributed across Latin America and India and the US and Europe, so when workforce A in Costa Rica blows out of capacity or doesn’t have the capability, AI can look at that workforce and say, “OK, I’m going to move this task, this business process, to a supplementary workforce in the Philippines or in Omaha.”

The number one set of data you get when AI is involved in a business process is not just the automation of the work, but an understanding of how people are performing it and how best to perform the work in the future.

Jon: As you get started, as you do an implementation, I assume the first focus area is how to make a process better and focus on that data. I know you even do some crowdsourcing of data around that. Let’s talk about making a process better, and then we’ll take a step back and do a little more about the people.

Adam: We get this question a lot, about how our software enables transformation. I was talking to an executive from a shared services organization just yesterday at a big conference down in Orlando, and I used the word transformation, and he flinched. Apparently, transformation is a four-letter word in a lot of these big organizations.

They’re not necessarily trying to transform. They are truly trying to automate. What we see is that by using software such as ours, there needn’t be a focus on transformation for the sake of transformation. When you allow an intelligent automation to do its thing by automating — for example, import payments in trade finance, or claims processing in insurance — the byproduct of automating that work, by letting algorithms see how data is handled, see what the sources are, see how people extract and categorize and remediate information and thus automate it, the process, the byproduct of this automation is transformation. Does that make sense?

Jon: The transformation, it still involves automation. I’m talking about the conflict you had with the customer you were talking to. Doesn’t that transformation get them to automation, or not? I’m trying to think what the end goal might be here. They’re not mutually exclusive, are they?

Adam: They’re definitely not mutually exclusive. Most businesses simply have a remit to either cut costs or improve service and capacity. It’s one of those two things, and in these days, it’s both. Most shared services, product lines, operations, wherever the genesis of automation is, wherever the genesis of these initiatives are, they’re starting with their KPIs.

Their KPIs are not impacted by simply transforming work. Their KPIs are impacted by eliminating the amount of manual work done in the operation. That elimination of manual work and the freeing up of human intelligence to focus on higher-value work is, in effect, transformation.

Jon: The results, you’re looking at the KPIs and you’re getting better business results, then everybody should be happy, because the topline numbers matter the most.

Adam: Exactly.

Jon: In terms of industry, you’re mostly in fintech, but what do you see is the opportunities for the automation of these types of processes against different industries? What’s your take on that?

Adam: That’s a great question. Martin Ford, who’s the author of “Rise of the Robots” — we’re actually featured prominently in that book — super-smart guy, true futurist, he spoke at this conference I was at yesterday in Orlando, the Shared Services and Outsourcing Week, and he said that to ask what the impact of AI will be on different industries would be like asking the impact of electricity on different industries.

His perspective, and I share it, is that AI will have a ubiquitous impact across every industry. It’s going to touch everything that we do. We’re not going to feel it until it’s ubiquitous and we stand back and say, wow, it really has transformed everything.

To drill into it specifically, we at WorkFusion have made a strategic choice to focus on banking, financial services, and insurance. We’re now getting into health care very quickly. We have a lot of interest from utilities, from telecom. I don’t think there’s any one industry that we won’t touch. It’s just a question of sequence, and it’s also a question of internal drive.

Banking and financial services, because of regulatory compliance, have had an unusually high amount of pressure to digitize, to automate. I think health care is very closely behind, and then the general Fortune 1000, I don’t think there’s been quite as much pressure, but some of the things that’s happening with the optics of offshoring and outsourcing will probably catalyze automation efforts even faster.

Jon: Let’s talk a little bit about the analytics. A lot of it’s rooted in basic machine learning. There are semantical challenges sometimes for people understanding the difference. I see a lot of you saying this is AI, and it’s really just machine learning. Give us your thoughts on how a technology like machine learning can evolve into something a little richer in terms of a solution set with AI.

Adam: As I understand it, machine learning is really the only practical application of what we refer to as artificial intelligence right now — algorithms that take in massive amounts of data and are configured, the feature sets are programmed to do something like extract data from an invoice.

Another subset of that question is what’s the difference between analytics and AI. A lot of businesses, as they get into their AI journey, confuse the two. Machine learning automates the manual work in business processes. Analytics, that may or may not be powered by AI, tells you something about the way a business is performing. These are two very different things.

Automation replaces manual effort. Analytics tells you something about the way things are happening. That’s the simplest definition I could give.

Jon: You earlier mentioned about training. What’s your approach to training and making sure you’re learning the best algorithms, you’re actually codifying the right actions — avoiding biases, avoiding codifying bad behaviors. What’s your approach to training?

Adam: There are two things to remember about how intelligent automation and machine learning learns to do the work of people and automates the work of people. The first problem is how do you get lots and lots of good quality data. This was a problem that we solved back in 2010 at MIT’s Computer Science and Artificial Intelligence Lab, which is where the company was born.

The researchers back then used the same approach to identify human quality that banks use to identify fraud in financial transactions, and that’s called outlier detection. If there are 100 workers doing a specific task and 90 of them are performing the same keystrokes, the same speed, on the same content, but 5 of them are going really fast and using only numeric characters, whereas the vast majority are using alphanumeric, and 5 of them are going really slow and using just alpha, then that means those 10 on other end are outliers, and that means that they get an increased level of scrutiny.

Maybe there is adjudication between two workers where two workers do the same thing and the results are compared. That first problem of how do you get quality data was solved by using machines to perform outlier detection and statistical quality control on workers, the same way that assembly lines ensure quality.

The second challenge is how do you take that quality data and train machine learning models. In the old days, you’d need data scientists and engineers to perform countless experiments on Markov models, traditional random fields, deep learning neural nets, and run all of this sequentially to figure out which model and which combination of models and features was going to best perform relative to human quality.

We solved that second problem about three or four years ago, which was to do with software what data scientists had done. We call it the virtual data scientist, where once the good-quality data is generated, the software automatically performs experiments with different models and feature sets and then compares them all in real time.

Once confidence is high enough, that means there’s been a winning algorithm. You can think of it like an algorithm beauty pageant where they’re all competing to do the best work and the software chooses the best model to deploy as automation.

Jon: Does this go across both broad processes, long-running processes across a day or multiple days, as well as some of the small micro tasks that individuals might do? What’s the difference?

Adam: The question is around what’s the level of granularity and application of this process of learning and training. It happens at an individual worker level in a matter of seconds, where maybe a worker handles a task and that makes a small adjustment to the way an algorithm performs, and it happens across entire lines of business where the data generated by hundreds of workers impacts the way automation performs.

That’s the beauty of machine learning is that it isn’t a blunt object. It’s an incredibly specific and — I guess you could say — perceptive capability in that the slightest adjustment by a person can change the way a machine performs.

Adam: You really are sourcing the logic from the data that you’re collecting across multiple companies, individual companies? What’s your view of data rights and learning and normalizing and learning from different companies together?

Adam: Jon, this could be a whole other podcast. We get this question a lot from our customers. Dealing with some of the most data-rigid security concerns, compliance-ridden businesses in the world in these giant banks, one of the very first questions we get from C-level stakeholders is, what are you doing with my data, and do I own it?

There are a couple of different answers within that question. The first is that our customers own their data. You could think of us as a car wash, the car being the data. The data comes into our business, into our software. It is manipulated. It is improved. It is stored into a place in a customer environment, and we no longer touch it. Our software does not store our customers’ data.

The second part of that question is do our algorithms retain the intelligence created by one customer, and can we take the intelligence from one customer and apply it to the next? There is some level of retention, but if, for example, we’re talking about something like KYC — know your customer in banking — we do not and will not take the insights generated by one customer’s very specific, proprietary business process and apply it to another customer’s.

We consider that proprietary to the customer. Would it be nice if we were like Google and my search history could be applied to your search history to improve the search results for all of mankind? Sure, but that’s not what our customers want.

Jon: You mentioned that you might change into other industries over time and grow. Absolutely that makes sense in fintech and potentially in health care, although perhaps learning about how health care procedures work across different hospitals and different solutions they may be willing to share some of the data rights and allow you to aggregate.

Today, the answer is here you are today within fintech. Could that change in other industries?

Adam: It really could, even within fintech. There’s another process called anti-money laundering, and this is essentially massive-scale fraud detection for banks. Banks don’t really consider the way they execute compliance a competitive differentiator.

There is a stream of thought among our customers to pool their intellectual resources on our software to create a ubiquitous software utility to solve non-differentiating processes like anti-money laundering, like KYC to an extent, like CCAR and BCBS 239. There are 80,000 regulations out there or something like that.

Industries and our customers, we may be a forum for them to decide where they want to compete and where they want to collaborate. In healthcare, this is particularly true, given that it is outcome-based. There’s actually a really cool company in San Francisco called Kalix Health, and they have the remit to democratize outcome based on democratizing good-quality data.

I think we’re going to see a big trend in healthcare to do more sharing than siloing.

Jon: Let’s put some CEO hats on as we get toward the end of this thing and think about CEOs. I’m going to make the assumption, the mental leap, that they have already figured out what we’ve been calling applied analytics. They’ve got analytics. They’re beginning to inject insights into processes, but they haven’t really taken the next step yet, in terms of degrees of automation and efficiencies that they could get.

They’ve got better outcomes, but they really haven’t thought about where these efficiencies are. If you’re talking to a CEO and talk about the managing and improving of processes and leveraging of the AI, where would you have the CEO start? Where should he or she start?

Adam: There’s a strategic answer and a tactical answer. Sometimes the tactical one is more insightful. In terms of the geography of a business, we see a lot of companies beginning in what’s called shared services, where there is a large aggregated workforce that serves as an internal service provider to the business, like handling processes like employee onboarding or accounts payable, the high-volume, common processes that different lines of business all employ.

Shared service is a great place to start, because it’s where outsourcing typically happens, and where there is outsourcing or offshoring, there is a large amount of work that can be automated. I would say, tactically, I would say CEOs should look toward their global business services or shared services to start their automation journey.

Strategically, I think every business needs to decide, do I want to do the same with less, or do I want to do more with the same, or do I want to be extreme and do even more with fewer? That’s the existential question of a business. If a business is healthy, they’re going to want to continue to grow their headcount but then exponentially grow their productivity.

That is the power of our software, of other software like it, that can do with machines what had been done before with people. The other thing, too, is how do you elevate the application of human intelligence? You do that by automation. You hire the same great people, but you expect them to perform at a higher level because machines are doing for them what they had done before with their hands and their minds.

Jon: As I’m building my team and I’m thinking about this and I’m going to embrace this model of being more efficient, you mentioned that you deliver a virtual data scientist. How much do you look for your customers to help, in terms of them owning and building a data science team? What are you looking for these companies to provide as they get started?

Companies are going to embrace this. These aren’t necessarily your customers, but customers are going to embrace AI. We’ve talked about data quite a bit. It does get rooted in that. What kind of skills should they be looking for?

Adam: The honest answer is that the only skills that a business truly needs to be effective with WorkFusion are subject matter experts on the process — people who understand the progression of work. Any big, successful company is going to have people within the organization that understand the methodical flow of work — first do this, then do that.

The problem with AI in the past, and actually some other vendors that are out there, is that they’re black boxes, and these black boxes require data scientists and engineers to build — you could say to fill the gap between the black box and the practical business process.

The reason why WorkFusion has been so successful and why we’re going to be such a big, important company to C-level executives across all industry is that there is no need for them to fill the gap between our capabilities and the practical business process, because we are rooted in the practical business process, and we do not require teams and teams of data scientists and engineers.

Sure, if you’re a power user and you want to radically automate across an entire business, you’re going to need some level of technical capability. You’re going to need some java engineers. IT is certainly going to need to provision environments just like they do with any software, but the beauty of this next generation of practical machine learning powered software is that it can do the dirty, highly complex work of teams of data scientists automatically.

Jon: I like the thought of making sure the CEOs stay focused on what they do well — know their business process. Stay in your swim lane. Keep going and the application of AI might get you into deeper water, but you’ll keep going. That’s the key.

Adam: That’s the key. There is the D word — disruption. I don’t see this as a disruption to the way a business works. I think a disruption would be expecting a business to go out and hire 500 data scientists and use some magical black box that has no transparency. That’s disruptive.

What is non-disruptive, what is purely evolutionary but exponential in its benefit, is a software that can seamlessly integrate into the way a business is doing their processes now and make those processes slicker and faster and more automated. That’s what every CEO wants.

[background music]

Jon: I can’t think of a better way to end it than that. That’s a perfect message. Adam, thank you so much for being with us today.

Adam: Jon, my pleasure. Thank you.

Source: Future of Robotic Process Automation

Using Robotic Process Automation To Prepare For GDPR Compliance

Many businesses are scrambling now, to be prepared for the impending changes in May 2018, to the General Data Protection Regulations (GDPR). The EU is going to the next level in its attempts to protect consumers from a data privacy (DP) perspective. One area that has a lot of companies very anxious is the right to be forgotten.

As of May 2018, any consumer can request to be forgotten. The request must be complied with to avoid significant fines. Each business will need a documented process of how they will scrub or remove the personally identifiable information (PII) connected to that consumer, in all their systems if there is no legal right or obligation to retain it. This can be a daunting task, depending on how many systems and cross system shares that may be in place.

This an area where Robotic Process Automation (RPA) may be the best answer. The first step in designing a “Forget Robot” is to document the details of all the places where data is stored (RPA 101 – requirements and process documentation). If this documentation doesn’t already exist, the RPA team needs to start compiling it now to be ready for May 2018! Once you identify all the places holding personally identifiable information, you will need to work with your data protection lead and your business stakeholders to decide if specific field data can be deleted or replaced, or if you need to delete the entire record. Some companies may wish to keep a record of a sale made to a male/female, in a specific age bracket, within a specific city for example, but would not be allowed to retain the PII connected to the transaction. A robot might just replace the PII fields with “*******”. System constraints may come in to play here also, with respect to how you may or may not be able to manipulate this data. In some cases you may have no choice but to delete the record. Clearly at this stage, you are designing the robot steps.

I have learned that PII fields sometimes come down to context. What other information is connected to a specific piece of data? If it is possible to derive a person’s identity through connected data, you will need to scrub the field in some manner. Your DP lead will be advising you to err on the side of caution as the fines can be significant.

The next challenge you will need to review with your DP Lead is what kind of detail that can be stored in the RPA logs relative to the task the “Forget Robots” carry out. The logs cannot contain any PPI information about the data that was just manipulated. At this stage you have moved from designing the Robot steps into the process, reporting and audit log documentation.

In some companies, there may not be resources available to carry out the right to be forgotten tasks. Based on the nature of the task, it is primed for RPA which adds a further degree of risk mitigation for your company as the robot will never miss a step or make a mistake. Your data privacy team likely has budget already, as most companies are anticipating new processes and controls will be required. This is your chance to show initiative, risk mitigation and save on costs by promoting “Forget Robots” to your organisation.

Source: Robotic Process Automation To Prepare For GDPR Compliance

Companies are using AI to screen candidates now

Human job recruiters can only physically juggle so many candidates at once. HireVue, a company with a “video interview intelligence platform,” wants to make that easier by using artificial intelligence to do the heavy lifting for you and screen multiple candidates at once.

Candidates can use HireVue’s mobile or desktop app to set up a video interview with an employer and record answers to interview questions at their convenience.

When I tested the demo on my smartphone, I was asked to answer what my ideal career would be. It was slightly awkward seeing my own face staring back at me, but HireVue gives you an unlimited number of chances until you can record with saying “ummm.” So far, it seems like any normal video application.

Then the A.I. kicks in.

Using voice and face recognition software, HireVue lets employers compare a candidate’s word choice, tone, and facial movements with the body language and vocabularies of their best hires. The algorithm can analyze all of these candidates’ responses and rank them, so that recruiters can spend more time looking at the top performing answers.

HireVue’s AI can judge your tone and vocabulary for employers

HireVue said it doesn’t want to replace recruiters; instead, it wants to make the job interview process more efficient. At its best, it can serve as an initial screener before job seekers can get to the promised land of interviewing with a human.

As part of its positive testimonies, HireVue said SHIPT, a grocery delivery service, tripled its recruitment rate as recruiters no longer had to deal with technical difficulties and coordinating video times. Goldman Sachs, Under Armour, Unilever, and Vodafone are also among the companies that have used the platform.

By having each candidate answer the same questions, HireVue said it makes its process more structured, which can help eliminate biases.

“Structured interviews are much better and subject to less bias than unstructured interviews,” HireVue founder Mark Newman told Fast Company. “But many hiring managers still inject personal bias into structured interviews due to human nature.”

In other words, the algorithm is only as objective as the human minds that guide it. So if the employer’s ideal candidate is already biased against certain characteristics, HireVue’s platform would only embed these biases further, potentially making discriminatory practices a part of the process. Human recruiters would need to recognize their own personal biases before they could stop feeding them into HireVue. It’s one more reminder that behind each robot lies a human who engineered it.

Source: are using AI to screen candidates now

Robots will not lead to fewer jobs – but the hollowing out of the middle class

Moravec’s paradox says that robots find difficult things easy and easy things difficult, which might lead to humans taking lower-paid manual work. Photograph: Fabian Bimmer/Reuters

Throughout modern history there has been a recurrent fear that jobs will be destroyed by technology. Everybody knows the story of the Luddites, bands of workers who smashed up machinery in the textile industry in the second decade of the 19th century.

The Luddites were wrong. There has been wave after wave of technological advance since the first Industrial Revolution, and yet more people are working than ever before. Jobs have certainly been destroyed. Banks, for example, no longer employ clerks to log every transaction in ledgers with quill pens. At this time of year, 150 years ago, the fields would have been full of people with scythes and pitchforks bringing in the harvest. That work is now done by motorised harvesters.

The reason new technology has not been the cause of mass unemployment is that new kit will only be used when it makes the productive process more profitable. Higher productivity frees up the resources to buy other goods and services. The rural workers that Thomas Hardy described in Tess of the D’Urbervilles found work in factories and offices. What’s more, it was better paid work, and so the upshot was an increase in living standards.

Similarly, the age of robots will lead to more jobs. Kallum Pickering, analyst with Berenberg, says there is a big hole in the argument that artificial intelligence (AI) will lead to vast numbers of workers joining the dole queue.

“Producers will only automate if doing so is profitable. For profit to occur, producers need a market to sell to in the first place. Keeping this in mind helps to highlight the critical flaw of the argument: if robots replaced all workers, thereby creating mass unemployment, to whom would the producers sell? Because demand is infinite whereas supply is scarce, the displaced workers always have the opportunity to find fresh employment to produce something that satisfies demand elsewhere.”

That, though, is not the end of the story. Robots will create more jobs, but what if these jobs are less good and less well paid than the jobs that automation kills off? Perhaps the weak wage growth of recent years is telling us something, namely that technology is hollowing out the middle class and creating a bifurcated economy in which a small number of very rich people employ armies of poor people to cater for their every whim.

This is certainly a much more likely threat than mass job destruction. What’s more, it fits with the history of the recent past, the theory of automation, and recent trends in the labour market.

Christian Siegel from the University of Kent’s school of economics has found that labour markets in the advanced countries of the west started to polarise as far back as the 1950s as they became more dominated by the service sector. Growth was strong during this period, but the job creation tended to be either at the top end of the pay scale or at the bottom end, while employment opportunities in traditional middle-class sectors of the economy declined. The arrival of IT in the 1980s merely accentuated a process already underway.

Robots are likely to result in a further hollowing out of middle-class jobs, and the reason is something known as Moravec’s paradox. This was a discovery by AI experts in the 1980s that robots find the difficult things easy and the easy things difficult. Hans Moravec, one of the researchers, said: “It is comparatively easy to make computers exhibit adult-level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.” Put another way, if you wanted to beat Magnus Carlsen, the world chess champion, you would choose a computer. If you wanted to clean the chess pieces after the game, you would choose a human being.

Dhaval Joshi, economist at BCA research, believes Moravec’s paradox will have a big impact on the labour market. He considers two scenarios for a stylised economy with three jobs: a high-income innovator, a middle-income manufacturer and a low-income animal tender.

In scenario one, the innovator comes up with a machine that dispenses with the need for the animal tender. The machine is more productive than the animal tender and so the innovator uses his extra income to buy manufactured goods. That provides the opportunity for the animal tender to retrain as a more highly paid manufacturer.

In scenario two, the innovator invents a machine that makes the middle-income manufacturer obsolete. Again, the innovator has more disposable income and uses it to purchase animal tending services. The middle-income manufacturer now has to make a living as a more lowly paid animal tender.

In the modern economy, the jobs that are prized tend to be the ones that involve skills such as logic. Those that are less well-rewarded tend to involve mobility and perception. Robots find logic easy but mobility and perception difficult.

“It follows,” says Joshi, “that the jobs that AI can easily replicate and replace are those that require recently evolved skills like logic and algebra. They tend to be middle-income jobs. Conversely, the jobs that AI cannot easily replicate are those that rely on the deeply evolved skills like mobility and perception. They tend to be lower-income jobs. Hence, the current wave of technological progress is following scenario 2. AI is hollowing out middle-income jobs and creating lots of lower-income jobs.”

Recent developments in the labour market suggest this process is already well under way. In both Britain and the US, economists have been trying to explain why it has been possible for jobs to be created without wage inflation picking up. Britain has an unemployment rate of 4.4% but average earnings are rising by just 2.1%. Something similar has happened in the US. The relationship between unemployment and pay – the Phillips curve – appears to have broken down.

But things become a bit easier to understand if the former analysts and machine operators are now being employed as dog walkers and waiting staff. Employment in total might be going up, but with higher-paid jobs being replaced by lower-paid jobs. Is there any hard evidence for this?

Well, Joshi says it is worth looking at the employment data for the US, which tends to be more granular than in Europe. For many years in America, the fastest-growing employment subsector has been food services and drinking places: bar tenders and waiters, in other words.

AI is still in its infancy, so the assumption has to be that this process has a lot further to run. Wage inflation is going to remain weak by historic standards, leading to debt-fuelled consumption with all its attendant risks. Interest rates will remain low. Inequality, without a sustained attempt at the redistribution of income, wealth and opportunity, will increase. And so will social tension and political discontent.

Source: The Guardian-Robots will not lead to fewer jobs – but the hollowing out of the middle class

1 in 4 would trust robots for insurance advice


Men were found to be more accepting of insurance advice given by robots.

Be it car, pet, home or life insurance, one in four UK adults would trust an automated robotic service to provide insurance advice.

The CenturyLink EMEA survey of over 1,200 adults in the UK also revealed what type of advice consumers would feel comfortable in taking from a robot. Nearly one in five (19%) would trust robotic guidance on how to claim for something, for example a car accident or contents theft.

A further 18% would seek robotic advice as to which insurance provider would give them the best offer for their needs, but only 15% would trust robotic technology to manage and send relevant documents required to set up a policy, such as passports and proof of identification documents.

Car insurance came out top as the insurance policy that consumers most trusted to be led by robotic services (19%), beating pet insurance (12%), holiday insurance (13%) and phone insurance (9%). This could point towards the frequency that people renew insurance policies for certain products, or could expose consumers views on how often they might need to interact with, or claim for something, with a specific type of policy.

“It is interesting to see the growing trust that consumers have in robotic advice for insurance matters, particularly that almost a fifth (19%) would trust automated advice just as much as they would from a human. Businesses must take note of the views of the consumers and adapt their strategies to reflect this shift in the way buyers like to receive their services,” said Jay Hibbin, director of insurance and financial services CenturyLink EMEA.

Interestingly, the research findings also revealed a generational split in views towards robotic advice. Those between the ages of 16 and 34 placed most trust in automated services (33%), whilst only 21% of those between the ages of 45 and 55+ would be happy with this form of interaction. Men are also more accepting of this kind of advice, with almost a third (30%) placing trust in insurance robots, as opposed to only 23% of women.

“The insurance industry is going through a rapid period of change and businesses are having to think about how they streamline services in order to survive and thrive. Time-poor IT teams at insurance enterprises have been, perhaps unfairly, described as “anti-innovation” and “risk averse” but now is the time to shake off these stereotypes,” said Mr Hibbin.

“By working on, and investing, in digital transformation strategies, IT leaders that can meet the new demands, and expectations, of consumers. If insurers are to stay competitive and keep up with the challengers that are nipping at their heels, they must look to how they implement and maintain technologies such as these to provide services fit for the future”.

Source: cbronline-1 in 4 would trust robots for insurance advice

Do You Know Your Business Like the Back of Your Hand?

If you asked both an executive and an associate what their company does you might receive surprisingly different answers. Executives, understandably, often have limited knowledge of the every-day business processes, (for example it is not a CEO’s job to know how many reports are run per day), but this gap in knowledge could be costing your business.

The Key Assessment Questions

In order to boost your understanding of company operations, the following questions must be considered:

  • What processes exist?
  • How are they executed?
  • What problems exist? (I.e. where are mistakes made?)
  • What causes the greatest client satisfaction/dissatisfaction?
  • What causes the greatest employee satisfaction/dissatisfaction?
  • Which activities require the greatest amount of financial investment?
  • Where is that investment beneficial and/or wasted?

These questions are the cornerstones to process improvement. Without adequate insight on the current process state, one cannot identify what needs changing and how to implement these changes. Creating a fluid and robust global process is the first step in approaching the Future of Work. Only once the workflow has been transformed and optimized should automation be applied.

The Value of Process Maps

The creation of “As-Is” process maps is incredibly valuable in a business assessment. The maps should be captured with the assistance of employees who perform the tasks day-in, day-out. This will highlight how the work is actually being done, as opposed to how executives think it is being done. No one designs bad processes, but individual and unstructured practises develop easily due to changing client demands, updated system architecture and associates finding ways to reduce the time-consuming and tedious parts of their work. The identified workarounds can then be pin-pointed and addressed in process improvement initiatives.

Process Mapping Must Be Designed to a key-stroke and click level

When the automation is being considered, process maps must be designed to key-stroke and click level. Robotic Process Automation (RPA) requires very granular instructions and it is important to demonstrate that the work is entirely rules-based and structured. Having detailed process maps will greatly assist an RPA developer and will lead to quicker configuration and implementation.

The Legacy of BPO

Over the last three decades Business Process Outsourcing (BPO) established itself as the solution for managing business costs. Repetitive and transactional tasks were sent abroad to locations where resources were plentiful and work could be done for a fraction of the in-house cost. This was the best solution at the time, but it has often led to disconnected global processes. Companies have had to sacrifice some control over the process and human workers have become the integration point for technology. This means that employees are being held prisoner by systems that cannot perform effectively and the business is driven by systems instead of by processes and people.

As technology has progressed, BPO solutions have stagnated. Eventually we will run out of cheaper offshore options, yet many businesses have not advanced much beyond this model and many industries are lagging behind in leveraging new innovations. As a result, companies are not making the most of their human workforce. With the ability to think critically, people are our great problem solvers. Their skill set could be offering businesses far greater benefits if they were placed in less manual and tedious roles.

One company that was prepared to innovate its operations was the communications company BT. Fifteen years ago they undertook a £15 million automation initiative with the aim of examining all existing systems and linking them together into one seamless workflow. Providing the integration between systems is truly what RPA was built for and in harnessing this new technology, BT has continued its development as one of the leading global communications companies and secured a strong foundation of processes for further Future of Work technologies.


Insight is invaluable to businesses and can inspire process improvement and transformation. It is important to assess exactly what occurs in a process rather than what is expected to occur, because standard operating procedures are easily adaptable by employees.

Additionally, modern automation technology is now competitive in both price and functionality. By transforming processes for digital automation, people are freed to focus on the value-added tasks that are more stimulating and rewarding. In turn, companies will see higher levels of job satisfaction and employee retention.

Source: symphony-Do You Know Your Business Like the Back of Your Hand?

Top RPA Conversations So Far

Earlier this year, we made several interesting predictions as to what would dominate the conversation throughout 2017 in the automation landscape as part of a preview for the year in RPA. After consulting with industry thought-leaders, analysts, and RPA specialists, we zeroed-in on a handful of critical topics poised to drive important discussions about RPA trends and developments. As we pass the midpoint of the year, we thought it would be interesting to revisit our predictions and determine which conversations have not only been taking place, but have also proved to be critical talking points for those within the automation industry.

Why is this look back to the beginning of 2017 in terms of automation so important? A 2016 study from the industry research firm Gartner suggested the demand for RPA tools is growing quickly…”at about 20 percent to 30 percent each quarter.” This growth was projected to continue into 2017 and thus far industry analysts believe it has, at least through Q1 and Q2 of this year.

With these statistics and figures in mind, let’s take a moment to check the pulse of the automation industry thus far in 2017 by considering which predictions and projections have evolved to be critical conversations in RPA.

RPA deployment will increase across new industries

At the outset of 2017, RPA adoption patterns were poised to experience a significant shift as companies in new and emerging industries discovered the power of automation solutions to enhance their business operations. While the adoption of automation technologies has functioned in somewhat of an opportunistic manner by early adopter companies, wider adoption moments have come to the forefront in such industries as healthcare, insurance, banking, manufacturing, and retail. This also dovetails with industries that have already experienced the benefits of RPA increasing their deployments into the front-office and other customer-facing tasks, actions which have yet to be fully realized by many companies.

In a 2016 report that surveyed the adoption of eight new technologies — including robotics and automation — by the professional services firm Deloitte suggested that:

More companies are increasing investments in these technologies. New technology investments over $1 million have increased…[and some companies are planning to] spend at least $100 million on new technologies over the next two years.

Rather than just being a way for companies to streamline their business operations, RPA and other automation platforms are transitioning from a luxury to a necessity. This conversation has not only persisted as the year progressed, but the drumbeat for automation platforms as a key aspect of any given company’s operation strategy has only increased as global economies and industries become more and more connected.

Automation will replace more tasks, not actual jobs

The emergence and proliferation of automation has caused a certain degree of panic over the possibility these technologies could replace the need for human employees in the workplace. Especially as automation moves from the back-office to more customer-facing tasks, customer relations-related areas such as call centers and other customer service platforms have suddenly become the subject of how and when automation could render these functions less necessary from a manual intervention standpoint. However, as we’ve moved through 2017, this concern has continually been minimized be the actuality of automation deployment.

Automation certainly has the ability to replace certain tasks or remove human personnel from specific department or business moments, especially those that are tedious, repetitive, and time-consuming: in fact, that’s what it is meant to do. However, this doesn’t mean the entire workforce will be replaced by robots. In fact, a 2017 publication by the McKinsey Global Institute suggests that:

The right level of detail at which to analyze the potential impact of automation is that of individual activities rather than entire occupations. Given currently demonstrated technologies, very few occupations—less than 5 percent—are candidates for full automation. However, almost every occupation has partial automation potential.

This kind of automation paints a hopeful picture for the future, one where humans and automation work side by side, which is the critical point of this conversation: debunking the myth RPA will essentially replace all methods of human intervention. In point of actual fact, RPA and human personnel will work in conjunction with each other which will allow human employees to focus on higher-level tasks that are meaningful and interesting, thus creating a space for automation to work through more repetitive, high volume tasks.

AI and machine learning will advance RPA

It has long been discussed how intelligent technologies like artificial intelligence, cognitive computing, and machine learning are expected to develop in the coming years and decades. In addition, a critical element of discussion within the automation landscape is how these technological developments will integrate and bolster automation functionality.

In a recent discussion with Forbes, Ash Ashutosh, the founder and CEO of Actifio, predicted that:

Just as most companies evolved to include cloud capabilities and features, 2017 will bring machine learning to almost every aspect of IT…[these technologies] will usher in a new era of data understanding and analysis.

While this is certainly a salient point, what’s less often considered is how RPA solutions will combine with intelligent technologies to deliver even greater automation potential. No longer can these technologies be viewed as disparate from each other when companies consider automation and how RPA can help enhance their business operations.

But what’s perhaps most important in this discussion is how intelligent technologies will be able to learn and make decisions beyond their initial programming. This means they are able to learn from previous actions and deal with unforeseen exceptions in a business process. Because RPA is able to quickly generate and gather data, combining RPA with intelligent technologies means that the “learning” process can take place at accelerated rates. While these two technologies are only starting to be used together, the smart automation they can produce means that companies will be able to foster both increased productivity and creativity going forward.

Source: UIpath-Top RPA Conversations So Far for 2017

Robotic Process Automation The Question of If Rather When

RPA is one of the most efficient emerging technologies that can deliver results in terms of business efficiency, customer acquisition, customer engagement, and experience.

Robotic Process Automation (RPA) is in its early days. Having said that at times, the hype could get ahead of the reality. It is no longer a question of “if?” rather “when?”. RPA promises a lot for the enterprises especially in the areas of:

  • Customer Experience by improving process quality, increasing accuracy, reducing response-time, and allowing higher predictability
  • Business Efficiency by increasing the consistency in processes and tasks to reduce output variation, improves productivity by freeing up the brains and hands to do more value added tasks, enhances flexibility to respond to flux in demands and allows geographical independence.

This, in turn, enables enterprises to undergo a significant Digital transformation and expedite business processes, reduce errors and cut operational costs.

The physical and digital worlds are converging at esoteric speed. In 2004, when we all watched the famous Sci-fi action movie, iRobot, we never anticipated that a significant piece of work being taken over by robots would be a reality, but yes, it is knocking our front door now, is real and here to stay.

RPA virtually connects multiple disparate systems while executing repetitive work more accurately and reliably than people can do. At the same time, it necessarily doesn’t mean the complete replacement of human workforce by the robots, making humans redundant in their work environments. If RPA has to be effective, along with right intervention and human touch points, it is important to identify the right work that can be automated and executed under RPA by careful evaluation.

  • Type of transactions
  • Availability of knowledge to power automation
  • End to end process improvement
  • Total cost of ownership
  • Ease and time to deploy
  • Scalability & flexibility for the future requirements
  • Return of Investment.

RPA should be considered as an enabler that will free up people from doing high volume, highly mundane tasks that are better suited for robots, who can work tirelessly and continuously without making errors and the human workforce can take on more creative, collaborative and interactive work that makes them productive and efficient. Leveraging technologies to make machines work for you than they replacing you, that is the message that needs to be driven to avoid the fear of jobocalypse.

Any RPA project, undertaken, will be changing the nature of the work and the way it is executed currently, however, with better quality and improved efficiency. At the same time, it warrants to be conscious of balancing automation with right decision-making ability. Technologies like big data, analytics, Artificial intelligence and Machine Learning, in tandem with RPA can drive improved decision making and there by providing a new layer of engagement and transform enterprises to become customer centric.

With RPA, applications attempts to understand natural human communication such as touch, gesture, speech or even to the extent of reading neural signals and convert that to actions and communicate back in similar natural language. Artificial Intelligence variants like natural language processing, knowledge representation and reasoning, in the form of the algorithms equipped with machine learning capabilities, self- trains the applications to take right decisions. In parallel, the transactions and processes are being enriched with insights; predictive & prescriptive, and intelligence generated from ever growing repository of data; real time or operational, video streams, photographs, handwritten comment cards, data from security kiosks and other varied sources.

RPA is one of the most efficient emerging technologies that can deliver results in terms of business efficiency, customer acquisition, customer engagement, and experience.

What we are witnessing is the tip of the iceberg, and with Artificial Intelligence and Machine Learning evolving in tandem, RPA will become the catalyst, orchestrating the next level of digital transformation.

Source: Process Automation The Question of If Rather When

AI Demystified: Shaping the future for positive change

The debate between Mark Zuckerberg and Elon Musk on the misunderstandings of artificial intelligence (AI) has brought to the forefront concerns and dangers of a robot takeover.

Often misrepresented and misunderstood, AI continues to serve as a source of significant intrigue. It has long been lauded as the future of work, but according to notable Hollywood movies, is also a harbinger of a robot takeover.

Futuristic movies, like I, Robot, and Avengers: Age of Ultron, portray AI as the precursor to a robot revolution wherein a seemingly innocuous utilization of the tool devolves into dystopia. And in many cases, despite being an effective money making tool, it is a mischaracterization of AI. Still, it is believable because of the lack of public fluency on the issue.

As the idiom states: “We fear what we don’t understand.”

With anxieties abound, it is important to understand that every technology shift has its own set of winners and losers. The advent of the car was initially rejectedby the public, and even ridiculed by horse owners. The only difference, is that the pace of advancing technology is now much quicker than it was in the past. When we do not understand a technology, we automatically tend to demonize it.

Similarly with AI, being able to define it and have awareness towards how it is impacting various industries for positive change will offer a more profound understanding that may ease concerns and lead it to be more widely accepted.

What is AI?

The first step in busting AI myths is to arrive at a reasonable, inclusive and thoughtful definition of the term.

Oxford Dictionary defines AI as, “The theory and development of computer systems able to perform tasks that normally require human intelligence. Examples include tasks such as visual perception, speech recognition, decision making under uncertainty, learning, and translation between languages.”

This definition is effective because it makes clear that AI is, in many instances, simply streamlining a process to make it more efficient. And while it is executing tasks that “require human intelligence,” the tasks themselves – like mass data analysis or translation, complex calculations or immediate responsiveness – are rarely those which people are otherwise capable of or willing to perform.

The Robot Workforce

There are concerns about how AI will impact the workforce and the global economy. For example, some fear that the rise of AI will lead to the replacement of jobs. In fact, researchers at Oxford University projected that 47 percent of U.S. employment may end up “at risk” with the expansion of AI.

However, it is important to keep an open mind on the opportunities it presents.

AI alone, is not enough. It requires humans to help AI understand language and make subjective decisions for a business. With the availability of online education, workers are able to receive the training and schooling that will present new employment opportunities. There are tailored courses for data scientists or machine learning engineers specifically designed to assist with AI.

While the concern exists that a sizeable number of jobs across all levels will be displaced by AI, a new study from Forrester Research argued that the development of AI and automation will actually transform and advance current jobs as humans get familiar working alongside their machine counterparts. Furthermore, Forrester estimated that in the next decade, 15 million new jobs will be created in the US as a result of AI and automation technology.

As the workforce modernizes, the door will open for new, previously unexplored jobs.

Change for Good

Healthcare is seen as one of the industries which will see tremendous benefits from AI-powered tools.

At a recent Stanford University conference, Andy Slavitt, former acting director, Center for Medicare and Medicaid Services (CMS), said that the expansion of AI in healthcare is designed to address productivity concerns. Specifically, “We need to be taking care of more people with less resources, but if we chase too many problems and business models or try to invent new gadgets, that’s not going to change productivity. That’s where data and machine learning capabilities will come in.”

CB Insights reported that there are now over 100 AI-based healthcare startups. The companies have wide-ranging aims, from aiding oncology treatment to reducing administrative responsibilities for doctors and nurses to powering digital journaling tools. In each case, AI is enhancing productivity through machine learning and deep data analysis.

This is precisely why AI-anxiety is misguided. These are unexplored tools, each of which has the potential to revolutionize healthcare delivery and improve outcomes for the issue which they are intended to address.

As the healthcare industry undergoes several paradigm shifts – from fee-for-service to value-based care, impersonal to precision medicine, traditional to digital healthcare delivery – AI is becoming essential. There are, for example, an overwhelming number of cancer variations that depend upon one’s family history, upbringing, DNA, environment, work and medical history. Coordinating care delivery and analyzing treatments and outcomes is essential, but with finite manpower and resources, impossible without the use of AI.

AI is a catch-all and a flashpoint, a source of concern and of intrigue. But it does not need to be. Instead, it should be recognized for what it is – a state-of-the-art way to utilize limited resources to advance an industry. It is not without its share of concerns, like other innovation groundswells before it. But it is also not an issue to be feared.

Eliezer Yudkowsky, an American AI researcher and writer who champions friendly AI, wrote in Singular Hypotheses: A Scientific and Philosophical Assessment, “By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.” Misinformed generalizations that lead to stigmatizing attitudes, fears and misconceptions, limit the public’s scope to further the conversation surrounding the benefits of AI development.

With a broader and deeper understanding of the technology and the opportunities it presents, I am hopeful that fear and hesitation will become excitement. Unlike it had initially been thought to be, AI is not all doom and gloom.

Source: itproportal-AI Demystified: Shaping the future for positive change