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The use of robots in the workplace has helped to increase numbers at workplaces and also in society. Robots nowadays have increased capabilities. This has led to a rise in the need to ensure that this robotic evolution isn’t the “beginning of the end” for the human race. However, there is an even more pressing and immediate need to ensure that the growth in the use of robots doesn’t mean a risk for the human workforce. This need is further increased by the stories highlighted of workers getting killed by robots. 

Presently, robots are re-categorised into three, i.e., collaborative robots, personal and professional robots, and industrial robots. There is now an emerging fourth category of robots popularly referred to as managerial robots.  

A brief history of robots

The first wave was introduced back in the ’70s, and they were used in the car manufacturing industry to assemble automobiles. The second wave started in the early 21st century, and that’s when service robots were introduced to the world. This second wave was expedited by the increasing sensory and anatomy capabilities robots had. This, coupled with the small size and cost of microprocessors’ controllers, paved the way for the creation of mobile robots. 

These mobile robots were able to perform autonomous operations even in unfamiliar environments like disaster zones. Thanks to the availability of collaborative robots, which can directly work with humans, the third wave of robotic workers are now in progress. 

Industrial robots

According to the ISO (International Organisation for Standardisation), industrial robots are programmable, multipurpose manipulators, and re-programmable. They can either be fixed in mobile or placed to be used in automation applications. 

These robots are characterised by precision, endurance, and high strength and are mainly used for testing, moving, assembling, painting and welding. 

Risks they pose

Many industrial robots aren’t aware of their environment and, as such, pose a risk to people. Some of the hazards they pose include:

Personal and professional service robots

According to the ISO, service robots are robots that perform useful tasks for humans. Professional service robots can be even further differentiated as being service robots used to conduct commercial tasks. On the other hand, personal service robots are used to perform non-commercial services. 

Physical propinquity between human workers and professional service robots is expected because both share a workspace. As such, worker isolation cannot be considered as a safety measure in such a scenario. Moreover, the complex environments pro service robots operate in require more mobility and autonomy. This mobile and autonomous behaviour can create dangerous situations for the human workforce. That is why robot designers are required to consider the ethical, social, and physical implications of that autonomy. 

Collaborative robots

According to the ISO, collaborative robots are specially designed to have direct human interaction. They aim to combine the precision, endurance and strength mechanical robots have. Considering that they work alongside human workers, isolation is still also not a viable option. That is why safety measures such as the use of proximity sensors, software tools, and other appropriate materials must be considered. 

What robots are used for in the automotive industry

Robots have been used in assembly lines for over 50 years. Some of their uses include:

  1. Vision: Robotic arms having “eyes” are used for operations that call for precision. Such a robot has a robot wrist that carries the camera and laser, giving the machinery feedback instantly. As such, they are used to install fenders, windshields, and door panels.
  2. Arc and spot welding: Industrial robots having long arms are used to spot-weld heavy body parts, whereas smaller robots are tasked with welding lighter parts like brackets and mounts. 
  3. Assembly: Some robotic arms are used to assemble pumps and motors at high speed. Other tasks that are performed by robotic arms in the automotive industry include windshield installation, wheel mounting, and windshield installation. 
  4. Coating, sealing and painting: Painting automobiles is no easy task, and it’s a toxic process. What’s more, there’s currently a labour shortage, making it nearly impossible to find professional/skilled painters. This is where robots fill the void and give the required consistency for a perfect finish. Machines are also used to spray primers, sealants, and adhesives. 
  5. Part transfer and machine tending: Pouring hot, molten metal, unloading and loading CNC machines, and transferring metallic stamps pose health and safety risks to human workers. Industrial robots are perfect for such jobs. More miniature robots are also used in unloading/loading tasks and machine tending. 
  6. Removal of materials: Robots can follow many complex paths without fail. This ability makes them the perfect tool for trimming and cutting jobs. Robots having force sensors can trim flash from cutting fabric, polishing moulds, and plastic mouldings. 
  7. Internal logistics: AMRs (Autonomous Mobile Robots) like forklifts are used in different factory settings. For example, Ford Motor Co. in Spain recently adopted the use of AMRs to deliver welding and industrial materials to different robot stations. 

More accessibility 

The automotive industry has fully embraced the use of robots in its assembly lines. Deploying and programming the robotic workforce is now more possible than it was some few years back. Their use has resulted in the creation of better quality products. 

Conclusion

Each vehicle comes with thousands of parts and wires. This means that the process of getting many components to work together is a complex one. Even with its complexity, the processes require the same quality standards when assembling all parts. That is why robots are perfect for these jobs. They can work even in lights out situations but still give out the high-quality standards set for production. 

These are some of the many different ways robots are used in the manufacturing industry. There have been whispers of there being more projects in the works aimed at enhancing productivity, security, and reliability. The expected outcome is reduced prices and fast-paced delivery times. 

Barclay’s ex-boss Anthony Jenkins recently said that technology could replace more than 50% of banking jobs. Finance Monthly heard from Ian Bradbury, CTO Financial Services, Fujitsu UK & Ireland, who shared his thoughts.

With the number of banking branches declining, the financial services sector is undeniably undergoing significant change, driven in no small part by the increasing adoption and implementation of emerging technologies. This of course has led to concerns of job displacement, and when we asked both the public and businesses which jobs most likely won’t exist in their current form 10 years from now, bank tellers was the top answer. One of the technologies said to disrupt the sector increasingly is Artificial Intelligence (AI), and in fact we found that seven-in-10 financial sector leaders believe technology such as AI will enable them to overcome many of the socioeconomic issues they are facing today.

The use of AI in financial services is nothing new. Trading businesses have used algorithms for many years, but what is new is the widening range of applications to which AI is being used for. The technology will not only replace existing manual processes, it will create new ways of doing things, which will add new value for businesses and their customers. For example, given the drive towards efficiency and agility, we can expect a lot of jobs to be created in the areas of automation, with more people employed to develop and implement AI-based automation solutions. It’s important to remember however that whilst some roles will disappear, many will surface in their place - 80% of jobs that will exist in the next decade haven’t even been invented yet.

It is the responsibility of us as a nation, from banks, government, to the companies creating these new technologies to ensure that we are equipping people with the right skills to manage this digital transformation that both the banking sector, as well as many others are currently and will be going through for the foreseeable future.

Artificial intelligence (AI) is infiltrating all industries, meaning a transformation in the way we live our day-to-day lives – and the way we work – is inevitable. But this is nothing to be afraid of and we should embrace AI to improve the way we work.

According to Adobe, 15 % of companies currently use AI, with 31 % expected to adopt it over the next 12 months. This significant technological disruption is set to affect everyone in some form, and many are worried that AI will displace our jobs and make humans irrelevant.

However, Reed Accountancy & Finance research found that almost half (47 %) of finance professionals asked are enthusiastic about AI in the workplace and are willing to embrace new technology. This shows there is a lot of enthusiasm about all the ways AI can improve our everyday activities. With this in mind, here are five reasons why we shouldn’t be panicking about the introduction of AI into the workplace.

 

  1. There is strength in humanity

Research from Deloitte shows that 61 % of companies are now actively designing jobs around robotics. However, it is expected that, in the coming years, the skills and traits that make us human and enable us to interact effectively will become increasingly important for employment and career advancement.  While machines and AI will be capable of performing many routine tasks, human cognitive skills will still be sought after, so businesses will still need to target candidates with these talents. The introduction of AI will also free up time for creative thinking and judgement work areas in which humans are naturally superior. AI can design solutions to complex societal issues, but only humans can implement them, as well as display empathy and compassion in a way machines never can.

 

  1. Enhanced productivity

A study by Accenture has revealed that AI could increase productivity by 40 %, and profitability by 38 %. This is in addition to our own research which found a third (32 %) of finance professionals believe AI will improve productivity and efficiency by having the capabilities to report and summarise accounts  taking away the menial tasks – understandably, businesses are interested. This means employees are free to concentrate their efforts on more stimulating, forward thinking work, making companies using AI very attractive. It can also help with recruitment, where AI can source, rank and arrange interviews with candidates. More accurate forecasting, predicting maintenance and repairs, personalisation, optimising manufacturing and replenishing stock automatically are all areas in which AI can also help companies become more efficient working within their budgets.

 

  1. Attracting Generation Z

By 2030, it’s estimated that Generation Z will represent 75 % of the workforce, meaning innovative methods of appealing to this group must be a priority for all organisations. One way to do this is by promoting the use of AI in the workplace, as this generation appreciates the value that technology brings.  The use of AI-driven foundational technologies, such as blockchain, may also help companies that are based on this technology present themselves as the more fashionable, innovative places to work.

 

  1. Saying farewell to unconscious bias

Unconscious bias has long been an issue in recruitment, and for those responsible for recruitment in an organisation. Some tech start-ups are already using AI to perform initial interviews, along with facial recognition software to detect body language and emotion cues when screening candidates, in order to eliminate the unconscious bias that is so often found in the human decision-making process. In fact, according to KPMG, 60 % of HR departments are planning to adopt cognitive automation in the next five years with the aim of making recruitment a bias-free procedure.

 

  1. New skills, new jobs

McKinsey research has found that, if AI is adopted by 2030, eight to nine % of labour demand will be in new types of jobs that didn’t exist before. History would suggest that, after a large technological disruption in society, over time, labour markets would adjust in the favour of workers. However, the skills and capabilities required for any job will shift, with the need for more social and emotional skills, such as logical reasoning and creativity, making candidates with these skills in heavy demand.

 

Navigating the unchartered territory of artificial intelligence can be daunting, but there is no need for businesses or candidates to panic. If used in the right way, AI can be incredibly helpful and vastly improve the effectiveness and efficiency of not just many organisations, but our everyday working lives.

 

Few would argue that artificial intelligence (AI) is making a considerable impact on many elements of Financial Services (FS), it’s computing power and automation helping to improve the overall customer experience and to extract incredible insight from big data held by FS companies. Below Dr. Dorian Selz, Co-Founder and CEO of Squirro, delves into a discussion about the keys augmented intelligence may carry in driving the future of FS.

As with many emerging technologies it was slow to hit the business mainstream, but that too is changing. Squirro recently conducted research into tier one banks’ use of AI, and it revealed that 83% have evaluated AI and more than two-thirds are already using it.

But for some people in finance, the words ‘artificial intelligence’ can signify fear just as much as they can opportunity. For all the potential of AI, there is a perception that jobs might be threatened as machines take over roles previously carried out by humans.

The idea that AI might facilitate a wholesale replacement of humans is fanciful at best. But perhaps it is time to talk about augmented intelligence instead, a technology intended to enhance human intelligence and one that is central to the future success of the financial services sector.

A human / machine collaboration

If artificial intelligence is the creation of intelligent machines that work and react like humans, augmented intelligence is essentially people and machines working together. This is a partnership that will see the augmentation and extension of human decision making, addressing specific challenges within FS and helping to deliver new and smarter services to customers that will encourage loyalty and improve the bottom line.

Improving the personal touch – much of FS – particularly corporate FS such as investment banking and real estate - is still heavily based on personal relationships. Account handlers speak to their clients and are expected to know about that client’s industry and be able to present them with strong opportunities for investment and growth.

That’s no a small undertaking, but augmented intelligence makes it much more straight forward. Augmented intelligence-based platforms are powerful at gathering data (both structured and unstructured) from across disparate and siloed systems and presenting that data in a form that gives account handlers a complete 360-degree view of each and every customer.

Because it can factor it so many disparate sources of data, users are then incredibly well-informed on what is happening in an industry that will affect that client, and what the opportunities are. They can retain the personal touch that is still so important in FS, but can now do so more informed than ever when speaking to clients

Deeper insight – the insight delivered by augmented intelligence is far deeper than what has previously been available to FS organisations. Because it is capable of managing and analysing so much data, the insight extracted from that data and then presented to the user is deeper and greater than anything previously possible.

Impact on the bottom line – the data insight generated by augmented intelligence can help FS firms greatly with their lead generation, not only identifying opportunities for clients but recommending the best product or solution for them.

Augmented intelligence solutions will look at data on competitors, partners and markets and identify catalysts that provide additional upsell or cross-sell opportunities to existing clients, and fresh approaches to prospective clients. In a competitive FS world, this is of the highest value.

The past decade has been a challenging one for FS organisations, with stiff competition from agile startups offering new and more effective services and a superior overall experience. Yet the emergence of augmented intelligence is a lifeline for the industry. It enables greater customer understanding and means FS providers can re-establish their market position, and augmented intelligence will be a key technology in FS for years to come.

Creating a balanced and even workflow will optimise productivity for robots – in the same way as it will for human workers.

Surely robots don’t get tired, can work 24/7, are fully skilled at what they are programmed to do, and don’t have any pesky motivational issues – so their productivity must always be consistently high? Absolutely not. This is according to Neil Bentley, Non-Executive Director & Co-Founder of ActiveOps, a leading provider of digital operations management solutions.

To believe this would be to forget everything we have learned about Lean Workflow and the way production systems work. For a processor (robot or human) productivity is best measured as a ratio of output:input. How much work did we get out for the amount of time we put in? For this to make sense we generally convert time into “capacity to do work” based on some idea of how much work could be done in a given time.

So, if Person A completes 75 tasks in a day and they had capacity to complete 100 then their productivity was 75%. Similarly, if Robot B completes 500 tasks in a day and had capacity to do 1,000 then their productivity would be 50%.

As we begin to increase our investment in Robotic Process Automation (RPA) and AI: the productivity of this (potentially) cheaper processing resource will matter – if not so much now then certainly when everyone is employing RPA to do similar tasks within the same services.”

But why would Robot B only do 500 tasks? They wouldn’t dawdle because they didn’t like their boss. They wouldn’t spend hours on social media, and they would surely only be allocated tasks that they were 100% capable of processing.

Maybe Robot B could only process 500 tasks because there were only 500 available to be done. Maybe the core system was running incredibly slowly that day, or there was so much network traffic that latency was affecting cycle times. Maybe someone changed a port on a firewall and the robot needed to be reset. Or there were hundreds of exceptions and the robot had to try them multiple times before rejecting them.

It is strange (isn’t it?) that if a person’s productivity is 50% we assume idleness, a propensity to waste time on social media, or a lack of skill but if it is a robot we quickly understand that it is the workflow that is the problem,” he continued.

Data-focused technologies such as Process Forensics and some digital operations management technologies or WFO technologies that seek to improve performance by URL logging or other screen monitoring techniques are totally missing the point: people’s productivity is far more influenced by the flow of work through the system than it is by their willingness to work or their skill level.

Workforce monitoring technologies seek to intimidate people into working harder, but you can’t intimidate people into having more work available to do. Equally, fluctuating demand, bottlenecks in the workflow, variations in work complexity will all drive variations in productivity – as with people, so it is with robots,” he added.

The answer is to introduce digital operations management solutions in the back office that will be the result of a blended human/RPA strategy made up of:

The plain fact of the matter is that with humans and robotics increasingly working alongside one another in service operations a blended and balanced approach needs to be taken on the issue of productivity.

Four out of five businesses will use chatbots by 2020, 85% of all customer interactions will be handled by them and they will generate $600bn in revenue in the same year, according to a recent Oracle survey. This week Chris Crombie, Product Manager at Engage Hub, believes now may well be the best time to start investing in chatbots.

In just under two years’ time, chatbots – conversation-mimicking computer programmes that provide your customers with an instant, personalised response – will be ubiquitous. Driven by innovation in artificial intelligence (AI) and the insatiable desire to enhance and personalise the customer experience.

Simply put, chatbots are one of the clearest concrete examples of how the “AI revolution” is impacting on the business landscape and on the day-to-day lives of millions of consumers worldwide.

Consumers happy to chat to bots

Consumer familiarity with chatbots has increased over the last decade, a result of our familiarity with things such as self-service machines in supermarkets and interactive IVR.

With the latest advances in AI technology pushing new boundaries, it’s easy to see why many are claiming that 2018 is set to be “the year of the chatbot”.

That’s because, for any company that has an interest in offering a great customer experience, the potential benefits of enhancing customer satisfaction and responding to customer’s needs in a faster and more efficient manner by using chatbots are immense.

Plus, new messaging applications such as Facebook Messenger, WhatsApp, WeChat and traditional SMS are proliferating, which means millions of new opportunities to reach customers and communicate with them using the communications channels they utilise and like the most.

Understanding innovation in AI, Machine Learning and NLP

To understand the latest chatbot innovations, it’s necessary to have an understanding of Artificial Intelligence (AI), Machine Learning and Natural Language Processing (NLP).

Artificial intelligence is the theory and development of computing technologies that can perform tasks that previously required human intelligence. Mainly relating to speech recognition, visual perception, decision-making or language translation.

As an extension of this, Machine Learning is the application of AI technologies in ways that use data to learn and improve automatically, without being given explicit instructions. While NLP is the branch of AI that helps computers understand human language as it’s spoken and written to be able to understand intent.

The computer chatbot uses AI and NLP to imitate human conversation, through voice and/or text. So, in addition to the above-mentioned text-based instant messaging systems, voice-controlled chatbots are becoming increasingly popular, both in the home and in business contexts.

Amazon Alexa, for example, has proven to be an immensely useful consumer technology over the last two years in terms of its educational benefits, teaching consumers about the ease-of-use of voice controlled tech and helping them to feel comfortable and happy using it.

Test chatbots properly, to boost business

So that’s a brief overview of the key technologies and the commonly-used acronyms behind chatbots. Yet the key thing you need to know if this: when implemented correctly, chatbots are a demonstrably fantastic way to increase engagement with your customers.

So, what’s the secret of rolling out chatbots in a way that resonates well with your customers and doesn’t risk you losing sales?

As with any new technology, rigorously test it out internally before you let your customers start to use it. This is particularly critical with chatbot applications, as the bot will start to learn from your team, which helps to ensure that it knows how to deal with a wide range of the most common customer questions, complaints and enquiries.

Thorough testing will ensure your chatbots work as efficiently as possible, giving the correct information to customers as rapidly as they demand it.

All of which means that you will gain a clear competitive advantage, future-proofing your business by improving the customer experience whilst also delivering operational excellence.

Connecting you to your customers 24/7

Businesses in all verticals, particularly finance, retail and logistics, and businesses of all sizes – from small start-ups through to global enterprise – need to be investing in the latest chatbot technologies in 2018 to stay ahead of the curve.

And in today’s market, enhancing the customer experience is all about providing a high quality ‘always on’ service to deliver the information that they need, on demand, 24/7.

“In the future, robots will be doing our jobs for us” is one of the most common sentences we hear from futurists, business people and technology leaders. Below Richard Acreman, Partner at WM Reply, discusses the overvalue of automated processes and the future of ‘robots’ on the front lines of business.

There are variations on the theme depending on your perspective, like “robots are stealing our jobs” or, at the other end of the spectrum, “robots will create a revolution in creative freedom.” But while all of these predictions will probably have a strong element of truth to them over the long term, they’re also the source of a dangerous misconception in the here and now: that businesses don’t need to worry so much about talent.

Broadly speaking, the exact opposite is true, but if it’s a misconception you share, you have some prestigious company. A major piece of opinion research recently revealed the difference between the value of human capital vs. physical capital to the economy now and in the future. As part the study, two thirds of CEOs and top leaders at global firms revealed that they believed technology would create greater value for their organisations than their workforces would. Almost half believed that automation, AI and robotics would make their workforce “largely irrelevant” in the near future.

In fact, the economic modelling that accompanied the research showed that human capital would be worth more than twice as much. The key distinction is having the right skills and the right people, who are valuable both in their own right, and as the people who are going to enable the technology to enhance their organisations’ value. That means engaging with, supporting and learning from the best workers to ensure that their knowledge and expertise remains within the business and can continue to unlock the business’s potential as the workplace changes.

A key battleground

Unsurprisingly, the first place that this issue is flaring up is on the front lines of business – among the customer facing or service delivering staff that form the grass roots of most organisations. It’s this audience who are often the most threatened by automation and it’s also this audience who are traditionally most neglected by their companies.

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As the above study shows, it’s all too easy for businesses who have never been particularly strong on engaging their front line workers to take technology as an excuse for inaction or to make the situation even worse, but overestimating the scale of the change or the speed of the transition means that those with this attitude will likely have years to regret their miscalculation before anything close to the future they’d imagined comes to pass.

Why front line workers continue to be so important

Looking at it from both sides helps to clarify how misleading it can be to think that technology is going to make staff less relevant.

If we assume that technologies that are good enough to fully replace staff are here already, or very close, then we might also make the assessment that the best staff will be needed to help inform the processes and approaches of that technology, look after it, and stand in for it when it goes wrong. Not to mention the cases where it doesn’t have the answer, or is dealing with a customer who insists on human interaction.

If we assume that the technology isn’t here yet, but is coming, then this group will remain essential to the work, but exist in a heightened state of anxiety about their future with automation seemingly in hot pursuit of them. They will therefore need not just a decent level of support and attention from the business (as should be standard), but also a certain amount or additional reassurance, the absence of which might well be seen as their death knell.

People and technology in perfect harmony

What then is the short-term answer? And how can companies ensure that they are not only engaging with, but also getting the most out of their front line workers, so that the technology will have to work even harder to offer a quantifiable advantage?

That’s probably something that’s best explained with some specifics. Bea Tartsanyi, enterprise innovation strategist at Sideways 6, talking at a recent event focused around Microsoft’s Yammer, pointed out that the value of grassroots innovation to a business is immense. With the analytics that her team is developing, they’re starting to get a handle on just how important that is. She’s proving that using tools like Yammer to access the knowledge and learnings of those on the front lines – those who ordinarily might not have much of a voice internally – is not only one of the most effective, but also cost efficient ways of driving innovation.

Bea also pointed out that companies are always looking at ways to bring the voice of the customer firmly into the business. To learn from the customer’s needs and to understand the best practice approaches to meeting them. What better way to do that than to directly connect front line workers to the centralised organisation through the engaging, non-hierarchical social tools like Yammer.

It works both ways: Front line workers can easily flag some of the everyday challenges they face for senior managers to tackle, while those at the top can offer up the challenges they face. Whether those challenges come from a loyalty, cost or another perspective entirely, allowing those at the coalface to share their experience of overcoming those challenges on an individual case basis will feed into an organisation-wide benefit.

Ultimately, automation will have an important role to play, but we’re a long way from that role superseding the role of front line workers. Giving those on the ground the tools to excel at their own jobs while also feeding into the wider strategy is an immediate answer to many of the challenges that companies face. Given how much front line workers – properly enabled – can contribute, ignoring the people-based solutions to those challenges in favour of the vague idea that up and coming technologies will fill in the gap is like saying you won’t buy an umbrella because at some point in the future it might not be raining.

Far from taking human jobs in future, Artificial Intelligence (AI) and Machine Learning (ML) technologies are going to free up finance professionals from spending too much time on monotonous tasks and allow them to focus on more strategic tasks of higher value to the business. Does this mean that finance roles will mostly be driven by robots? Below Tim Wakeford, VP of financials product strategy EMEA at Workday, discusses with Finance Monthly.

A recent EY study revealed that the majority (65%) of finance leaders said that having standardised and automated processes—with agility and quality built into those processes—was a significant priority when it came to investing in emerging AI and other technologies. And, following on from this, 67% of finance leaders said that improving the relationship between finance and the wider business strategy was also a key priority.

Again, this is an area where automation and AI technologies are helping free up time for finance to spend more time working with other teams within the business. This enables them to figure out where to go next as opposed to looking backwards and dealing with unproductive and time-consuming legacy finance systems.

Freeing up talent to focus on high-value tasks

Freeing people up from repetitive jobs to enable them to focus on high-value tasks is the opposite of the oft-cited “robots putting people out of work” narrative.

Indeed, automation is a huge opportunity to reduce the unnecessary burden and pressure that’s put on finance professionals, particularly around traditional tasks such as transaction processing, and audit and compliance.

The adoption of AI applications within finance enables forward-thinking executives to move info far more strategic business advisory roles. This means that they can focus less on number crunching and more on financial analytics and forecasting, strategic risk and resilience, and compliance and control. This shift to data-driven financial management delivers a much wider benefit across the business.

The Rise of the robots: AI in finance

Computer systems performing tasks that previously required human intelligence is the definition of AI, with experts viewing AI and automation as viable solutions to efficiently deal with compliance and risk challenges across different sectors.

With the rise of the ‘big data’ era comes a parallel growth in the need to analyse data for financial executives to be able to properly manage compliance and risk.

This is another reason why finance teams cannot ignore the opportunities that embracing AI technologies offers them. It allows them to process vast amounts of data faster and easier than large teams of humans can.

Individuals are then able to make better strategic decisions based on the information that AI is able to rapidly extract from what were previously time-consuming and repetitive and monotonous tasks such as transaction processing.

Jobs least likely to go to robots

Forward-thinking and highly-skilled financial executives are happily embracing AI, as they see the clear opportunity it presents to play a more valuable and strategic role within their organisation.

“The challenge for managers will be to identify where automation could transform their organisations, and then figure out where to unlock value, given the cost of replacing human labour with machines and the complexity of adapting business processes to a changed workplace.” This is how writers James Manyika, Michael Chui and Mehdi Miremadi so fittingly describe the process in their book These Are the Jobs Least Likely to Go to Robots.

“Most benefits may come not from reducing labour costs but from raising productivity through fewer errors, higher output, and improved quality, safety, and speed.”

AI and automation in finance has to be about reducing repetitive manual tasks and raising overall productivity through data-driven business strategy. The bottom line is this: any technology that can reduce manual input and the associated human errors for transaction processing and governance, risk, and control (GRC) will free up finance professionals for more strategic work.

Any organisation’s most important asset is its people. And finding out which emergent AI technologies and applications are the best for a business and its people is going to be key for the future of finance.

Giving skilled finance staff the autonomy and opportunity to move into far more strategic data interpretation roles and letting the machines take on the grunt work is a necessary shift in the finance function.

As well as automating a large part of the finance function, AI technology will also help skilled finance executives to make a far more sophisticated analysis of complex data sets and to provide genuinely valuable insight to drive the business forward.

There is very little doubt that the future of finance will be one that embraces technological innovations to improve effectiveness, increase efficiency, and enhance insight.

The AI technology has been picking up steam in the past couple of years. It’s no longer a gimmick or a faraway fiction. Scientists from all around the world are slowly but surely cracking this riddle. Sure, they are still a long journey away from creating a true Artificial Intelligence, but each year we see significant breakthroughs in this field.

Today, you can find some form of AI in many everyday places. For example, Alexa and Siri are world famous AI assistants. They will create appointments, answer your questions, set alarms, shop, and a million other things. Another great example is the Tesla car. Thanks to Tesla’s AI, self-driving cars are no longer a work of fiction.

But what about the poker industry? Surely there must be an AI capable of playing poker at high levels. The answer is yes, there is. This infographic will show you how the poker’s AI developed throughout the history, as well as where it is now. You can find a lot of interesting stats and information in this infographic, but if you are interested in reading more about poker related stuff, visit our website.

The robotic revolution is set to cause the biggest transformation in the world’s workforce since the industrial revolution. In fact, research suggests that over 30% of jobs in Britain are under threat from breakthroughs in artificial intelligence. Thanks to advances in technology, many jobs that weren’t considered ripe for automation suddenly are. Is your job next? Find out how many jobs per sector, are at high risk of being taken by robots by 2030.

(Source: RS Components)

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