Employees are in favour of adopting artificial intelligence in workplace as it minimizes human labour

“Sophia, please collect the printouts from the machine and Geminoid DK, schedule my meeting for this afternoon,” orders a manager to his humanoid robot worker. This is the future we are anticipating for artificial intelligence in workplace. However, companies across the globe are already adopting artificial intelligence to streamline day-to-day activities. Employees are also in favour of the changes as it minimizes their time spent on vague and lengthy labour works.

Workplace is one of the hundred things that artificial intelligence has touched in the 21st century. Even non-technology companies are looking for artificial intelligence solutions to better perform in their routine. Starting from the recruitment process to manufacturing goods and carrying out the execution processes, artificial intelligence has influenced the workplace totally. Besides, creating a satisfactory employee experience, using chatbots in HR, availing data and analytics to take critical decisions, implementing data security, etc are some of the benefits of artificial intelligence in the workplace. Unfortunately, the high embrace of technology has planted doubts on human minds. At least at one stage of our life, we must’ve wondered ‘if AI will dehumanise the future of work?’ Even though we can’t deny the fact that it could be true one day, humans are far from that stage. To be precise, we are just at the doorstep of adopting intelligent automation and have a long way to go.

According to Deloitte’s 2019 Global Human Capital Trends report, around 41% of respondents said that they are using automation ‘extensively’ across many areas of their organizations. Only 38% of them said they expect technologies like AI to eliminate jobs at their organizations over the next three years and merely 13% believed the numbers will be significant. Over the past few years, artificial intelligence in workplace has matured into a collection of powerful technologies that are delivering a competitive advantage to business across diverse industries. Henceforth, the future of work is anticipated to have humans and machines work together with mutual consent.

Intelligent automation to challenge the upswing 

The unexpected sweep by the Covid-19 pandemic has changed the mindset of people. Along with the mindset, their shopping preferences also shifted. Today, businesses are being hindered with new restrictions and 63% of business decision-makers feel they are struggling to meet customer demands. Fortunately, 93% of those decision-makers think that intelligent automation services will help solve emerging problems. Modern businesses are focusing more on strategies than groundwork. However, it helps them see improvement more than their expectation. Intelligent automation in workplace offers a handful of advantages like saving money, enhancing efficiency and improved communication. By implementing intelligent automation in the work environment, businesses can focus on more strategic work. Besides, business leaders are increasingly becoming familiar with the benefits of intelligent automation services. A survey conducted at the beginning of 2020 suggests that over 92% of business leaders already had plans to roll out intelligent automation in their organizations.

Augmentation to streamline routine process

When we take a close look at artificial intelligence in workplace, it is ‘augmentation’ that is outperforming every other automation features. Augmentation will help employees do their jobs more effectively, rather than replacing them. Augmentation in workplace is seen as a saviour that will take over the mundane tasks, leaving us free to do more intellectual works that involve creativity and human-to-human interaction. Besides, Robotic Process Automation (RPA) is used to monitor workflows and processes, and make intelligent suggestions about how things could be done more effectively or efficiently. The RPA tools will leverage the benefit to carry out repetitive tasks like scheduling and arranging a meeting or managing a diary.

Robotic Diaspora and not a dystopia

Humans fear robots more than any other artificial intelligence innovations, thanks to sci-fi movies and dramas. They have made us believe that robots will replace humans one day or even rise against mankind and take over us. This has also influenced workplace and induced a ray of insecurity among employees. They imagine a future where robots take control of most activities or decisions where human interactions or feeling do not matter. Unfortunately, humans should stop overthinking and see reality for a second. Today, robots in workplace are doing all the hard and even dangerous tasks. To be precise, they are ensuring that humans do not get hurt or face loss while doing tough labour works.

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Ludré Stevens is Chief Product Officer at Inbotiqa, a next-generation Intelligent Business Email for high-volume and group mailboxes utilising the power of AI. Here he shares how artificial intelligence can be used in the workplace during Covid-19.

The onset of the Covid-19 pandemic and the dramatic changes in working practices it necessitated have created many challenges, for organisations, teams and individual staff members. Entire workforces suddenly shifting to working remotely has meant banks needing to support thousands of employees as they work from home, keep track of work execution and maintain regulatory compliance. All this while also needing to ensure high standards of customer service and SLAs are met.

With a return to widespread normal office working looking unlikely, effective long-term solutions to elevated compliance risks and other challenges need to be adopted and embedded. Video conferencing and collaboration tools, artificial intelligence (AI)-driven automated solutions, analytics, and more, have helped operations to continue, recording and monitoring issues to be addressed, and have even improved productivity and management insights.

The vast amounts of data involved in the financial services industry are beyond human scale so the application of AI and machine learning (ML) in order to meet regulatory requirements, boost productivity and cut costs was already prevalent. The pandemic and the changes it has wrought have only increased the need for and accelerated the adoption of AI and ML solutions.

For example, AI and ML can be used for surveillance and behaviour tracking to find issues, meaning specialised AI and ML, in-house built or vendor-built tools, are being employed to support the new normal. Employee-surveillance software that utilises the likes of ML and behavioural rules engines was already being used by enterprises inside their office spaces.

Since lockdown started, there has been an increase in the use of such products to monitor remote-working practices. The use of surveillance tools when employees are working remotely throws up its own privacy issues, though, if they are deemed too intrusive.

A recent Bain Consulting report found that three out of four companies planned on accelerating automation initiatives across the board post-Covid, including those dependent on AI/ML, with the financial services industry having the largest percentage of respondents with this on their roadmap, at 93%. This despite the fact that many technology executives were already expressing uncertainty about their investments in machine learning and dissatisfaction with the way their companies were adopting it.

Dedicated AI-driven Regtech companies such as providers of behavioural analytics and big data and analytics or AI-powered biometrics that bolster document verification for anti-money laundering (AML) and know-your-customer (KYC) capabilities, were already established in the market place.

Companies often opt to buy rather than build solutions due to cost, time and talent issues, and there is an increasing choice of third-party tools on the market. Technologies already being built or bought to boost productivity and aid compliance have also proved uniquely helpful in facilitating remote working during this swift adjustment.

The flexibility of tools is also an important consideration. For example, our YUDOmail intelligent business email system uses its own ML to classify communications such as emails to route and classify work, meaning work can be allocated and tracked to people working remotely.

However, in addition, it also allows for vendor AI and ML tools as our structured email data can be easily ingested by them.

This structured email data including related metadata (audit trail and threads) saves other AI tools from having to search for email data and then structure it themselves. By also providing a delivery method for any workflow outcomes from these AI tools, the loop is closed.

When it comes to auditing, the man-hours alone spent on gathering data for audit purposes can be considerable, so it makes sense to integrate a compliance-driven recording strategy into communications so that an audit trail is created in real-time. Supporting asynchronous working, where employees are on more flexible hours now working from home, is another aspect of the new normal of increased remote working that needs to be considered and addressed and where AI can provide value.

Whatever the specific solutions opted for are, and whether employees are working from home or in the office, it’s clear that AI and ML are playing a big and rapidly increasing role in the financial services industry. The Bain Consulting report also notes that what separates the leaders from the pack is their ability to make real changes in the way they get work done and integrate AI into products and processes across the organisation, allowing them to differentiate themselves based on AI-driven insights.

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A recent study showing that data entry is one the most redundant and hated workplace tasks raises questions about why, in the age of artificial intelligence, data mining and smart technologies, this task is still being done manually.

Is there any way it could be less despised?

My ongoing fieldwork in a data-driven startup, referred to as Sage (a real company, but not its real name due to confidentiality requirements), suggests that technological solutions are not nearly as sophisticated as many assume — and are not going to replace human data entry any time soon.

For nearly two years, I’ve been studying the evolution of Sage’s hiring practices and jobs.

Sage’s initial plan was to develop and use AI to produce data it would sell to clients as part of its broader services. In the meantime, Sage asked its analysts to collect and enter that data manually. But when Sage ran a pilot project with an AI consulting firm, they discovered that AI would produce, at most, five per cent of the data that they were collecting manually, and at a substantially higher price.

They couldn’t afford the AI. As a result, they shifted their data-collection strategy from AI to human intelligence. Recognizing that analysts were too expensive and would be too dissatisfied doing this work exclusively, Sage hired dedicated data entry operators in a satellite office to do much of the work.

Technology doesn’t always work

This type of scenario is not rare. Technology doesn’t always work as expected, and has varied effects across different jobs and organizations. The failings of technology, however, offer only a partial explanation of why manual data entry still exists as a task, and could for quite some time. To understand more, we need to consider tasks in a broader context.

Tasks rarely exist in isolation. They are part of jobs and those jobs are made up of other tasks. They are associated with people who perform them, others who manage them or work with them, and with other jobs within occupations and organizations.

This broader context, and the relationships within it, makes it difficult for any task to be eliminated altogether but also means that a single despised task does not always mean an entirely despised job.

At Sage, the very rote task of entering data was almost inseparable from the act of collecting data. While some of the data could be found easily in annual reports, for much of the rest, the analysts, and then the data entry operators, had to search the internet for additional information. Once they found this information, they then had to enter it into a database.

It would make no sense and would be virtually impossible to automate the data entry part of this task.

This highlights that not all data entry work is created equal, and not all data entry jobs are the same. As a result, attitudes about data entry are much more complex than the recent survey suggests.

False expectations

The Sage analysts did despise the data entry work, but not only because of the nature of the task itself. Expectations shaped their attitudes. When they were hired, many of them had expectations that they would be doing what they called analyst work — things like creating reports from data, writing content and interacting with clients.

Some of them had initially applied for consulting jobs that were to be made of up of the aforementioned responsibilities. They were expecting work that was more glamorous, more fulfilling. In this context, it’s not surprising that they considered data entry drudgery and thought it was beneath them.

The data entry operators, on the other hand, were hired with expectations that they would be doing what the title of the job implied — data entry. Unlike the analysts, some of them reported being pleasantly surprised by the job and the amount of thinking and judgment it involved.

Even within the same job in the same organization, there can be variations in what employees do and these variations can lead to different outcomes.

For instance, one study showed that female Transportation Security Administration agents in the United States ended up doing many more pat-downs, an undesirable task, than male agents. The results of this were lower job quality and reduced opportunities.

Data entry operators promoted

The story at Sage is far from complete. I continue to watch as the tasks and roles evolve there. A recent strategy pivot at the company has meant that data collection became less central to firm’s goals.

With this pivot, the role of the analysts has evolved to include much more interaction with the company’s developers in creating products for the clients and doing more to produce content.

The data entry operator job, however, is largely unchanged. People do come and go from it, but some of the data entry people have been promoted to analyst positions, suggesting that the dreaded task may be a pathway to less dreaded work.

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