Our takeaways from the 2018 AI day by the Finnish Center of Artificial Intelligence (FCAI)

Last Wednesday (12th of December) few members of our growing AI team were able to attend AI day by the Finnish Center of Artificial Intelligence (FCAI) at Aalto University. The yearly event brings together some of the top researchers in Machine Learning, Artificial Intelligence & Deep Learning, companies making an impact with AI and various other students studying within the emerging topic. During the day, we were able to hear some great talks delving deep into the different aspects of AI and interact with other movers and shakers working in the field. We also couldn’t help but notice some topics that seemed to be the most popular and common themes of this year and look forward to new advancements to answer these questions and more in 2019!

Most populat themes of discussion at the 2018 AI day:

  • Understandability of AI: How to improve user experience to allow more users to adopt the technology?
  • Social impact of AI : How will it transition in the future?
  • European and Finnish state of AI: What is going on in the world of AI in Finland and in the EU? What should happen in the future?

One of the most interesting talks we heard was one of the very first given by Ilona Lundström, the Director General within the Ministry of Economic Affairs and Employment of Finland. She spoke about the current strategy in the EU and Finland with the prospect of AI. It was great to see Finland in the age of Artificial Intelligence, whilst focusing on the wellbeing for its citizens, growth and development opportunities for businesses. She touched lightly on the other members of the EU states that also have targeted AI strategies in place, but that Finland is really the front runner in shaping the shared European AI agenda. The latter part of her talk was related to a theme that was mentioned above. “We need to ensure that an appropriate ethical and legal framework, for citizens to trust AI and for companies to take up business opportunities.” This was an interesting quote in respect to the AI Finland report that will be released in March 2019. So keep an eye out for that, as we can only assume that it will be a great read!

 

Building RPA at Helsinki University Hospital (HUS): Discussion with Minna Pekkala, Head of Robotics

Finland’s largest Hospital District, HUS – The Hospital District of Helsinki and Uusimaa – is a joint authority formed by 24 municipalities. Functioning as part of HUS, Helsinki University Hospital (HUH) is nationally responsible for treating severe and rare illnesses and ones calling for special expertise and technology. HUS hospitals employ over 24,000 professionals in 23 locations and received a total of 2,6 million patient visits in 2017.

 

Summary of our webinar with Minna Pekkala, Head of Robotics at Helsinki University Hospital (HUS)

How are digital workers delivered at HUS?

“HUS’s digital workers are delivered as a service (by Digital Workforce). This means we don’t have to buy our own licenses or servers, manage or update hardware, or worry about scalability. We think that the cost of service is predictable, and believe this can help us be cost-effective. We use service delivered from Azure cloud and Blue Prism (Robotic Process Automation, RPA) technology. We had already tested the technology over 2 years ago when we had our Proof-of-Concept.”

How is Robotic Process Automation managed?

“Because we are a large organisation we wanted to make sure that the project management wouldn’t be split in different units. That is why our (RPA) management is centralised in our IT management. Our duty is to make implementation possible and offer RPA development across the organisation.”

“We hit ‘go’ 7-8 months ago. With RPA, we want to help our employees automate routine processes and allocate more time to actual patient care.”

How do you choose processes for automation? 

“When we choose potential processes to automate we put weight on cost savings: How much is the work going to cost and what is the actual return on investment?”

“We have also required that the workflow can be copied. This means that the same process can be run in several different units. And of course, in our operating environment we always think about patient safety and customer quality.”

What has been done so far?

“At the moment we have two different processes in production and four more will follow soon. All in all, we have identified more than 50 potential processes and already 13 of those are in building.”

“Examples of our pilot processes include:

1) RADU-referrals: These radiology request forms are currently working in two different units, but we have 30 locations where the process can be copied.

2) Virtual referrals: Our hospital gets over 300 000 referrals a year. RPA is in operation at 6 locations doing classification, transfer and handling of referrals. If you think about scalability, we have still 37 more units where we can help with receiving referrals and redirecting them to specialists (by expanding the automation). We can use Machine Learning to help classify referrals.”

What have you learned?

“It’s not possible to communicate too much – to deploy RPA you have to concentrate on change management! Few people really know what Robotic Process Automation is and it raises questions among employees and management. People may be afraid of replacement so you need to commit management. The message of why we use RPA must come from line managers. Change often happens slowly in large organisations. There many parties and actors and everyone has their own opinion. You also can’t forget IT – without IT you can’t bring technology to use.”

“Then processes you are going to automate: Who owns them, who knows them best, who can give permission for (RPA) production? Does the process need changes to be automated with RPA? “

“Finally, you have robots, the users. Robots need identity. They have user access, but the robots are not human individuals and in certain systems it may cause problems. Robots can’t learn new system features without their model of work being updated. RPA updates must be synchronised with system changes.”

“RPA is not the final step. You must be ready to think about other options and possibilities as well. For example, RPA with Machine Learning can be very productive.”

Do you have a steering group for RPA?

“We do have a steering group. We have a head doctor who is responsible for clinical processes, a person from our administrative unit and two managers from IT management. We decide priorities, cost locations and project funding.”

“In my opinion, it is important that we have people involved from different sides of our organisation because we need to consider the benefits of the whole hospital.”

What expertise do u need to operate robotics in large scale?

“We buy software as a service (from Digital Workforce) and we do have a Centre of Excellence, but at the moment its only me. In the future, I would like us to have a project manager and perhaps a technical architect is also needed.”

What is your target for next year? How many processes will you automate?

“At least 50. But we would like closer to 100. Cost efficiency grows with scaling up.”

What do you think are the greatest obstacles when starting with RPA?

“It’s a lot of work to build the service up, I would recommend having a project manager for running tasks. In general, it’s good to have more hands and heads put together.”

 

Want to learn more? Listen to the complete webinar recording here

 

Business of AI webinar with UiPath: A successful merger between RPA and AI

Register free for our upcoming Business of AI webinar with Boris Krumrey, UiPath’s Chief Robotics Officer! The webinar takes place September 20th,10:00 EEST (8:00 BST).

Key take aways from the event include:
– UiPath’s ecosystem of expert RPA and AI capabilities – what is available and how?
– Automating complex, non-routine tasks
– Use cases demonstrating the successful merger

Read more and register here.

 

How is Intelligent Process Automation (IPA) changing business – a reflection by Professor Leslie Willcocks

Digitalization, robotization and artificial intelligence have become business buzzwords, frequently brought up during conferences and board meetings. How does the reality of a modern enterprise look? Are companies ready to embrace the potential presented by new intelligent technologies?

Two years after our first exclusive interview with Professor Willcocks of London School of Economics and Political Science, one of the most respected experts in the field of knowledge work automation, Digital Workforce had the opportunity to reunite with him, this time in London. Reflecting on what has changed over the past two years and looking ahead at the future of RPA (Robotic Process Automation) and other intelligent technologies, the discussion tackled some burning challenges companies face when implementing these solutions, how they currently stand with regards to knowledge work automation and how they should measure success.

How far have we gotten over the past two years in terms of technology and how managers see RPA?

RPA has gotten a much higher profile than it had two years ago, the technology has to some extent improved, in the sense of being more enterprise reliable than before. There are different, more sophisticated and highly customizable tools available now, solutions that are more scalable than others, but the underlying technology has not changed that much.

Business people have looked at RPA and realized they have to invest in it, due to its potential economic value. However, all managers have not come to this realization at once – we have observed three distinctive waves, implementing the technologies due to different motives. The first wave was a rather limited amount of relatively mature businesses who saw the value of RPA right up front. The second wave came in 2016, when a larger number of companies started investing heavily in process automation. Finally, the third wave started in early 2017, induced by heavily intensified marketing activities of RPA solution providers, and this enhanced visibility has been since than pushing companies to board the RPA train as well.

“RPA used to be sold as a “quick win”, easily adoptable and relatively cheap tool that would give you cost savings and a quick ROI – but people have now started to realize that it is rather a strategic weapon.” – Professor Leslie Willcocks of the LSE

Moreover, some risks have emerged that were not obvious two years ago. With regards to the main challenges RPA brings to businesses, only about 25% are directly related to technologies. The remaining 75% is about not managing it as a strategic project. Companies tend to neglect aspects such as good governance, quickly available resources, getting the C-suite on board, treating it as both a change management as well as a technical issue. There is a large number of steps the management should take and action principles that should be followed in order to reduce these risks.

What do you see as the major challenge for companies implementing knowledge work automation?

For sure, the biggest challenge is that companies are still not treating this strategically enough. They underestimate what they can achieve with it – something called a triple win, consisting of enhanced shareholder value, customer value and employee value. Their ambitions oftentimes do not aim high enough.

In the case of RPA – they are partially stuck with looking at it as a tactical tool rather than a strategic weapon, as a discrete tool rather than a potential uniting platform, as a software implementation and not as a change in the work processes. Ultimately, companies often miss on the real business value with RPA projects by this mistake. Looking for a quick ROI and hard business numbers to prove the added value while not factoring in that the main benefits might be unanticipated, such as improved customer experience and working morale of the employees, is the biggest mistake many modern enterprises make.

On top of that, cognitive automation has not yet taken off and is still at a promising, relatively immature stage. Some firms are implementing discrete uses of cognitive automation, which bring them real business value and progress. It seems so that the true synergies will arise from linking RPA with cognitive automation, eventually creating a platform that integrates seamlessly with other digital technologies in place.

Is this strategic approach towards RPA a necessary step on the way towards the implementation of cognitive automation and platform building?

Indeed, where RPA rests within the organization signals whether the company sees it as strategic or not. If you create a centralized center of excellence and you have senior executives involved in it, it is pretty clearly of a strategic interest. On the other hand, if you treat it as a lower level tool that you would apply increasingly across the organization, it seems to be more of a tactical approach without a sense of direction. The potential of RPA in relationship to cognitive automation is immense and the different automation technologies should be recognized as complementary pieces of a whole.

What is the most exciting development that you have seen in this field recently?

Lots of the cognitive automation technologies are truly exciting, carrying a massive promise. Once companies start combining them, they can get to an impressive level of automation, almost end-to-end in some cases, pushing the potential uses of the increasingly available technologies further.

Do you think that the organizational maturity is not necessarily there yet?

Mostly, the maturity of organizations with regards to their ability to absorb this level of change is not high enough yet. Companies are absorbed with way too many other IT problems and issues related to managing operations. This leaves them in a place where they are not ready to absorb even more technological change. As a result, learning to integrate the new advanced solutions is being postponed because people are still learning how to fit the previous ones into their businesses and to drive business value out of them.

How do you think the success of RPA and these first AI activities should be measured?

In some ways, measuring this presents the same problems as evaluating a success of an IT investment. There are some obvious costs and service improvement measures – you can reduce costs while offering a much superior service, the degree to which the automation does that is one of them. There is also a range of softer yet crucial benefits – such as customer experience that could be expressed by a plenty of measures. Especially in regulated industries, these solutions could help the companies to quickly and accurately comply with the imposed regulations, providing relatively cheap trial opportunities, compared to how would the companies do it without automation. Another set of metrics could revolve around employees – level of satisfaction, of morale, of productivity with machines as opposed to productivity without them and what the human beings bring to that combination. Last but not least, metrics around the level of innovation are also interesting – is the company innovating more in products and services?

What do you expect to happen in the upcoming two years?

I would expect to see a lot more RPA use-cases showing how they fit with cognitive automation, bringing lots of business value. Additionally, cognitive tools would improve on certain fronts – not the machine learning or the algorithms behind them, these are already advanced. Rather, image and data processing together with natural language processing is going to improve greatly, integrating the enhanced productivity and performance.

 

Leslie Willcocks, a professor of London School of Economics and Political Science, is considered one of the world’s most respected researchers, speakers and business publications writers in the field of knowledge work automation.

Professor Willcocks held the closing keynote at this year’s Blue Prism World event in London. You can check out the highlights of his speech titled “Robotic process automation 2018: Now, Soon, Later” here.

Business drivers to implement Intelligent Process Automation

Under unprecedented pace of technological development and increasing pressure of competitors from across the globe, businesses’ digitalization of some degree is inevitable. Companies are altering their offerings, processes as well as operations to keep up with the changing environment and grow their business further. As with every investment, the decision to devote company’s resources into process automation and digitalization must add value to the business. What are the most relevant aspects and general drivers of such business decisions, related to intelligent process automation?

OPERATIONAL EXCELLENCE

Companies strive to maximize revenues and minimize costs, and every investment into new equipment should facilitate either decrease in costs or increase in revenues, leading to increased efficiency of a firm’s resources. Thus, looking at the drivers from the perspective of business’s operations, decrease in cost is doubtlessly one of the key motivators when implementing intelligent process automation.

Process automation allows people and companies to focus on their most valuable work such as creative thinking and product crafting, customer service and leadership. When robots perform an organization’s supportive activities reliably and seamlessly, its human workers are enabled to do the work that is most valuable to the organization. It’s easy to consider how this change might improve both a customer’s service experience and a worker’s mood when they no longer have to be distracted by things such as registering data in various systems, searching it up and interpreting it whilst trying to serve their customer. Simply put, companies can benefit from a more sellable higher quality service/product without an increase in costs.

On the other hand, employing a secure and uniform robotic workforce can significantly cut operational costs as the robots are not subject to human error. Smart solutions can also dramatically increase operational efficiency through better optimization of resources, like decreasing a machinery’s downtime or cutting overlapping activities. Besides the obvious savings due to optimized use of resources, manufacturing firms may expect a significant drop in maintenance costs thanks to the potential of predictive maintenance, accompanied by savings in inventory costs and logistics, while service companies may benefit from more accurate reports and interpretation of data through better risk management, design of KPIs, internal training and services that accurately address customer needs.

Intelligent process automation enables companies to optimize their processes in real-time. This increase in productivity will not only bring higher levels of output, but also potentially much broader product/service spectrum of a better quality, targeting new segments of customers with tailor-made propositions.

BUSINESS AGILITY & NEW MODELS

Process automation in a reliable way enables the companies to direct their attention to high-margin parts of their offerings, or to completely new areas of business. Driving enterprises to explore untouched market opportunities, new digital technologies are bringing complex changes to the current business models. Core resources together with the central value proposition alter, enabling the firms to extend their portfolios. Additionally, hand in hand with business agility goes the idea of customer centricity. In case of a successful extension of the product portfolio (or of the offer as such thanks to additional customer-centric services), companies will grow their business, welcoming new revenue streams to their business models.

MASTERING THE DATA

The issue companies have today is not how to collect data – but rather how to make sense out of all the data available. Everybody is talking about terms such as Big Data and Internet of Things – yet only a few actually knows how to crack it. Thanks to a significant drop in costs of computation and the development of smaller and smarter sensors, the ever-expanding connectivity allows for unprecedented insights, assuming the data are correctly analyzed and understood. This combination of arising new hardware and increasingly sophisticated software holds an immense promise for businesses, eventually enabling physical objects to communicate autonomously among each other. The potential added value hidden in all the data a company produces but not yet fully understands is truly tempting.

With increased process reliability thanks to the data processing, companies can benefit from enhanced accuracy of operations once a system of smart process automation is in place. Eliminating the potential risk of a human factor, especially when talking about routine, standardized and non-creative processes, having an intelligent automated system in operations would enable the companies to redirect their most valuable resources – the human resources – towards more business-value-adding activities.

IT INFRASTRUCTURE & ARCHITECTURE

As much as a driver, the IT infrastructure can also be the most challenging factor when shifting towards more automated and digitalized processes. Digitalizing company’s operations will require more than a simple upgrade of current IT systems – rather, the companies must be ready to review their automation and digitalization processes in a deeply complex way.

Given that some form of computation technology is incorporated to virtually every organization active on the modern markets, companies might be motivated to unify their various systems, uncovering unexpected advantages and synergies. Creating a completely new IT infrastructure is demanding in terms of time, human as well as financial resources and oftentimes the readily-available solutions on the market are not sufficient. Overcoming this challenge through a highly customized solution would ensure the company retrieves the most relevant insights, enabling for flexible reactions.

 

Besides these key categories, there is one more crucial driver for companies to implement intelligent process automation and related technologies – the company’s culture. Through their leaders, companies must evolve a certain level of digital literacy and a corresponding company culture. To fully benefit from the intelligent process automation, firms should develop a strategy and a company-wide vision of a strong digital business culture, both customer as well as employee-focused.

Our journey and motivations to increase the intelligence of virtual workers – A reflection by Digital Workforce Head of AI, Gaurav Khullar

Recently, I’ve been devouring myself into critically thinking about innovation. Can there be a scientific framework for creating innovation and using it for the prosperity of businesses and humanity? Or does innovation happen due to random sparks of brilliance and ingenuity of the human brain. Whatever the reason, one thing is clear: Only innovation that results from solving someone’s need or want and is packaged at the right price will prosper.

There has been a lot of hullabaloo around Artificial Intelligence in the recent past and the fire has been further fuelled by media reports about spectacular AI achievements e.g. Deepmind’s AlphaGo, Microsoft’s machine translation reaching levels of human performance and many others. Sceptics or rationalists (as they may like to call themselves) would rather question the impact that such AI will have on humanity. Believers, on the other hand, would like to believe that AI will one day become a superpower.

For an organization like Digital Workforce, the goal is to create AI that will always empower humans rather than overpower them. Humans will be empowered if AI can help them in: a) making decisions that are unbiased and objective and require a lot of computation and data mining that is unwieldy for a human b) getting more satisfaction from their jobs by automating their routine tasks so that they can focus on more creative tasks.

Automation of routine tasks has been possible for many years by using automation scripts. However, more elaborate and industrialized commercial software have started to appear since the last couple of years. These software applications have gained the name of Robotic Process Automation (RPA). The RPA applications have not been as glorified as ML, but they have been providing a lot of value and the RPA industry is expected to be in multi-billion USD range by 2021.

What is especially interesting for me as an AI practitioner is the convergence of ML/AI and RPA. RPA robots have the limitations that the business rules have to be manually pre-programmed and they can only process structured digital data. They can therefore only automate a fraction of business processes where the format of digital data is structured and pre-defined. Also, these robots are not good learners – they don’t have the “digital brain” to learn patterns and rules from data. That said, their strength is that they can work with any existing IT application which means that cost of automation using RPA is minimal. RPA software also has the capability to integrate with AI components using REST API calls – this capability is a huge enabler to use AI as the brain and RPA agents as the hands to execute business processes.

The limitations of RPA no longer hold it back as integrated AI models can understand and process unstructured and unformatted data for it (including in non-digital format e.g. text in paper documents). Such a capability is termed as Intelligent Process Automation (IPA). The IPA market is an order of magnitude larger than the RPA market.

We at Digital Workforce are on a journey to create an IPA platform – a digital brain force for our RPA agents so that they can automate tasks requiring higher order of “brain” function. Hopefully, this will unlock a tremendous amount of business value which can be reinvested to develop more meaningful and innovative experiences. Our hope is that our innovation will fuel and empower corporations to further their own innovativeness.

Would you like to join us on the journey? We are growing our AI team! Find out more here.

 

Gaurav is responsible for defining, developing and delivering Digital Workforce’s AI strategy. He joined Digital Workforce from a Senior Manager & AI Lead for Data Science role at Accenture. He has strong experience in AI, e.g. context aware computing, personalised customer experience, churn prediction, econometric forecasting and predictive maintenance, gained from his previous positions at Nokia, Microsoft, Tecnotree, Accenture and a few co-founded start-ups. He has worked with technologies like Machine Learning, Natural Language Processing, Recommendation Systems and Deep Learning.

Contact: Gaurav Khullar Head of AI, Digital Workforce, +358 50 482 1216 gaurav.khullar@digitalworkforce.fi

Want to learn more about the latest developments in AI and how intelligent solutions can benefit your business? Gaurav hosts a monthly expert interview webinar series. Access all interviews free on ai.digitalworkforce.eu 

Robotics company Digital Workforce strengthens focus on Artificial Intelligence and appoints Head of AI

The leading robotics company in the Nordics, Digital Workforce, is moving towards more intelligent digital workers to drive new business. To support the growing market demand for solutions combining robotics and artificial intelligence, the company has appointed Head of AI, Gaurav Khullar, MBA, B.Eng.

Based in Helsinki, Finland, Khullar will be responsible for defining, developing and delivering AI strategy of Digital Workforce. Khullar joins from Accenture, where he was Senior Manager & AI Lead for Data Science. He has strong experience in AI solutions, e.g. context aware computing, personalised customer experience, churn prediction, econometric forecasting and predictive maintenance, gained from his previous positions at Nokia, Microsoft, Tecnotree, Accenture and a few co-founded start-ups. He has worked with technologies like Machine Learning, Natural Language Processing, Recommendation Systems and Deep Learning.

“Our customers have become skilled at utilising Robotic Process Automation to boost productivity and improve quality. The use of RPA is growing fast, but simultaneously new technologies emerge allowing more intelligent and learning solutions. We are delighted to welcome Gaurav Khullar to Digital Workforce, he brings unique multi-faceted, hands-on experience of AI technologies and business to our team”, said Jukka Virkkunen, one of the founders of Digital Workforce.

“Robotic Process Automation, or Digital Workers as we call it, can work perfectly on rule-based knowledge work tasks. Combining that with machine learning and other AI technologies allows organisations to automate more complex tasks and free up people to more strategic work. I am so excited by this unique opportunity to define and create this future with Digital Workforce”, explained Khullar.

Digital Workforce has automated over 500 knowledge work processes since the founding of the company in 2015. It was one of first companies in the world to provide industrial-scale RPA, robot-as-a-service, from cloud. Digital Workers help large organisations to process, classify and collect data across industries and functions. With AI led experiences RPA will become more intelligent and autonomous able to solve problems, do planning and provide knowledge & insight.

“AI and ML algorithms can be applied once the business problem and enough relevant data is available to solve the problem – these are the ingredients for any AI solution. For example, think of a car accident, parts of the insurance claims process are already being automated by RPA. With the help of AI technologies, bots can be taught to analyse pictures from the accident scene, work that is currently done by people. There are endless opportunities, but it must start from a business need,” told Khullar.

Contacts

Jukka Virkkunen, Partner, Digital Workforce, +358 50 670 47, jukka.virkkunen@digitalworkforce.fi
Gaurav Khullar Head of AI, Digital Workforce, +358 50 482 1216 gaurav.khullar@digitalworkforce.fi

About Digital Workforce

Digital Workforce is the only company specialising in Intelligent Process Automation services on an industrial scale. Our intelligent digital workers automate knowledge work processes in large organizations freeing up the time of human employees for more valuable work. The deployment of digital workers requires no changes to the existing information systems. Digital Workforce was founded in the summer of

2015 and it currently employs over 150 IPA specialists in Finland, Sweden, Norway, Denmark and Poland. www.digitalworkforce.eu

A guide into the world of intelligent process automation

The field of robotics is full of excitement and buzz-words, but it is often difficult to determine the concrete under the hype. The term Intelligent Process Automation, more commonly known as IPA, has recently joined the conversation. What is it all about? And why is it worth paying attention to?

What does IPA stand for?

IPA refers to a process automation solution, where the technology being used is smart – at least to some degree. There isn’t just one kind of IPA, rather the solution is tailored to fit the process requirements. The technologies utilised in the solution might include for example software robotics, chat bots, image recognition or machine learning. Put short, IPA is an umbrella term for a variety of different technologies that can be utilised (together) to automate processes.

IPA as a continuum of virtual work

It is reasonable to consider a company’s shift to using digital workforce as a continuation, where the journey starts at simple, repetitive and easily defined tasks. These routines of knowledge work are automated cost-efficiently with RPA (Robotic Process Automation), where every action of the robot is predefined in a manner following “if this then that”-logic. RPA best suits a situation, where different steps are repeated predictably and volumes are high – this way savings in expenses and the quality of operation rise enormously! RPA that is based on predetermined rules however doesn’t fit a situation, where steps can’t be well defined because there exists a large variety of possible actions following a situation.

In the next step of the continuum, focus shifts towards smart technologies, that take up tasks that require interpretation. To understand this step, it is useful to consider an RPA exception situation, where a software robot operating based on predetermined rules sends back a group of exceptions for the human worker to deal with. In practice, a situation like this might occur for example when a specific piece of information is logged in the used IT system’s open field in different formats. When the software robot processes the information, it only recognises the predetermined formats and reads the differing formats as errors. In a situation like this, a virtual worker utilising machine learning might provide a solution, by learning to identify that the different formats have the same meaning. If the smart virtual worker is delivered as a cloud-service, the solution also enables a flexible level of service, where more capacity and tools may be put to use when necessary.

Artificial intelligence in turn refers to completely autonomic systems, that can interact with their surroundings at any situation and reach their goals independently. These kinds of technologies are represented by for example IBM’s Watson and Google’s Alice. As computers’ abilities grow it becomes easier to recognise what technologies can’t be considered in terms of artificial intelligence, though defining true artificial intelligence remains difficult. For example, photo recognition – today considered to be routine technology – was previously considered a form of artificial intelligence.

Why is understanding the big picture so important?

The conversation around robotisation and future of work is often regrettably vague and leaves it unclear, what are the practical applications of the new technologies. Pumped up by the hype, it is easy to start doing things that sound great but do not meet needs in practice – at least not cost-efficiently or in a way that delivers on expectations. On the other hand, because the change in all fields only keeps accelerating, and because the competitive advantages reached by utilising virtual workers have been unprecedentedly large, this boat should not be missed!

The utilisation of virtual workforce should however always begin by considering the need, in a way that the choice of technology happens on the terms of the target. The best results are reached by climbing up from root to the top: transforming business processes into a digital form that can be utilised by robots and moving from rule-based automation towards smart solutions where the needs are clearly recognisable. The implementation of smart automation typically starts by recognising and prioritising the objectives. The concept of ‘IPA-continuum’ represents this way of thinking.

Intelligent Process Automation: A short glossary

Intelligent Process Automation, IPA

Intelligent Process Automation (IPA) refers to the practice of process automation with solutions that involve smart attributes. IPA solutions are tailored to the specific requirements of the target process by combining technologies like, chat bots, computer vision, machine learning and robotic process automation software.

Robotic Process Automation, RPA

Robotic Process Automation (RPA) refers to process automation with software robots. The robots are programmed to use the applications required for the execution of the target process in the same way as a human operator would. They always follow pre-programmed rules and are easy to integrate into existing business processes.

Software robot

Software robot is a software application that is used in robotic process automation to replace the actions of a human worker interacting with the user interface of a computer system. Software robots may have some vendor specific attributes, but implementing these technologies never requires changes to existing systems.

Enterprise RPA

Enterprise RPA refers to the use of RPA technology in a large and industrial manner to achieve automation requirements of a large enterprise. Enterprise RPA solutions are scalable, easily managed and maintained. These features are achieved by specialized services such as: managing component libraries that allow the re-use of automation objects, expert support, staff training, following and managing RPA performance, support in setting up internal robotic center of excellence.

Business Process Automation, BPA

Business Process Automation refers to a high-level strategy that aims to streamline all business processes. It involves recording – and re-designing- all processes within the business to digital format and then integrating them with an automation software.

Digital worker/ Virtual worker

Digital or virtual worker refers to a software robot that takes over a process or task otherwise performed by human workers. Digital workers deliver Robotic Process Automation (RPA) services where the used technology is a pre-programmable software.

Intelligent digital worker

An intelligent digital worker is a technology platform that combines pre-programmed software robots with different cognitive technologies. An intelligent digital worker is able to handle processes that consist of more complex tasks involving unstructured data and interpretation. Intelligent digital workers are used to deliver Intelligent Process Automation (IPA) services.

Robotic Center of Excellence, CoE

Robotic Center of Excellence (CoE) is the organization’s management center for the use of automation technologies. The job of the CoE-team is to create, measure and manage a virtual workforce that supports the organization’s strategic goals.

Robotic Desktop Automation, RDA

Robotic Desktop Automation (RDA) refers to computer-specific automation that is applied to speed-up or enhance the performance of a human worker using the desktop. While RPA technologies can be referred to as virtual workers, RDA technologies work hand-in-hand with their human counterpart and thus may be called virtual assistants.

Optical Character Recognition, OCR

Optical character recognition (OCR) refers to the conversion of images of typed or hand written text into machine encoded text. OCR programs analyze scanned-in images to detect light and dark areas in order to identify alphabetic letters and numeric digits. When a character is recognized, it is converted into code. OCR is often being used to digitize typed or handwritten information.

Natural Language Processing, NLP

Natural Language Processing (NLP) technologies enable computers to process large amounts of natural (human) language data. NLP technologies typically rely on machine learning to help them automatically learn new rules.

Computer Vision

Computer Vision technologies are used to enable computers to gain high-level understanding from digital images or videos. These technologies seek to automate tasks that would otherwise require the human visual system.

Chatbot

Chatbots are computer programs that can conduct a natural language conversation. They are designed to simulate a human conversation partner convincingly either via auditory or textual means. Depending on the level of sophistication of the solution, chatbots can make use of NLP technologies or analyze keywords.

Machine Learning, ML

Machine Learning (ML) gives computers the ability to “learn” (i.e. progressively improve performance on a specific task) by processing data. ML technologies can “learn” under supervision, i.e. learn general rules to map inputs to outputs based on a set of example inputs and their desired outputs. The “learning” may also be unsupervised, in which case no examples are given to the learning algorithm. Instead, it is left on its own to find structures from its inputs. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).

Cognitive Computing, CC

Cognitive Computing (CC) describes technology platforms that involve cognitive technologies such as machine learning, natural language processing, computer vision, and chatbots. The purpose of cognitive computing is to combine these technologies to find meaning from and understand a data set at a higher level. Rather than just pure data or sensor streams, cognitive computing can deal with symbolic and conceptual information. Cognitive computing enables computers to interpret information extracted from large data sets, while humans remain in charge of the decision process.

Artificial Intelligence, AI

AI is loosely used as an umbrella term for all cognitive technologies that enable computer systems to perform tasks normally requiring some form of human intelligence. A true AI machine would combine these different technologies in such a way that it would have augmented intelligence, surpassing humans in accuracy and insight. The individual technologies, on the other hand, that are performing tasks by simulating a specific area of human intelligence are called cognitive technologies.

Virtual Reality and Augmented Reality, VR/AR

Virtual Reality technologies are being used to create simulated environments. These technologies allow the user to experience the simulation around them – as they were part of it – not just looking in from the outside. Augmented Reality (AR) integrates digital information with live video or the user’s environment in real time to augment the video with artificially added elements or effects.

Business Process Management (Suite), BPM(S)

A business process management suite (BPMS) is a set of automated tools for designing, implementing and improving activities to accomplish a specific organizational goal. BPMS is designed to support the entire process improvement life cycle from process discovery, definition, monitoring and analysis, and through ongoing optimization. BPMS tools allow the organisations to redesign or re-engineer the whole process or set of processes and often also the related IT systems (unlike RPA which is solely using existing IT- system without changes).

Enterprise Cognitive Systems, ECS

Enterprise Cognitive Systems (ECS) are a form of cognitive computing. They are focused on action, not insight, and their intention is to assess what to do in a complex business situation. ECS makes evidence-based suggestions about how the business can achieve its goals. It does so by finding past situations similar to the current situation, and extracting the repeated actions that best influence the desired outcome.

 

Digital Workforce is the only company purely specialising in Intelligent Process Automation services on an industrial scale in the Nordic countries. We automate the routines of computer based knowledge work and liberate human employees’ time for more productive and important tasks. Learn more about us and our services on digitalworkforce.eu.

Digital Workforce presents: The Business of AI- webinar series

We are facing the most exciting revolution of our time: Intelligent technologies are fast advancing and disrupting the status quo in all areas of life. The way we work and do business is fundamentally changing. We must learn to swim in the new waters. What should you know right now, and what does the future entail? Digital Workforce’s new initiative, The Business of AI- webinar series connects business managers with the experts and visionaries of AI. Tune in to tap into the knowledge you need now! 

Digital Workforce recently launched a new initiative to cater to the needs of today’s business managers seeking AI-knowledge. The Business of AI- series hosts a monthly webinar discussion, each time with a different AI-expert providing new perspective to the topic. All the webinars are collected to one knowledge -base found on ai.digitalworkforce.eu.

The opening session of the series will be held with the best-selling AI-author Antti Merilehto April 11th, 10.00 EEST.

Key takeaways from the webinar will include:
How is AI changing the way we do business and how fast is the change?
What are the opportunities of AI?
What are the new requirements for business management and strategy?
How quickly and how should business management react?

Registration is now open – sign up free here!