21 Bot Experts Share Their 2017 Predictions

2016 was a huge year for bots, with major platforms like Facebook launching bots for Messenger and Amazon and Google heavily pushing their digital assistants. Looking forward to 2017, we asked 21 bot experts, entrepreneurs, and executives to share their predictions for how bots will continue to evolve in the coming year.

1. Andy Mauro, CEO, Automat

In 2017 brands will realize that Conversational Marketing is a better way to learn about and build relationships with their customer than today’s digital marketing which monitors their customers with cookies, pixels, search and social data. We’ll also see powerful case study data showing that opt-in and conversion rates and the quality of profile information that can be obtained conversationally far outweighs the benefits of email marketing, marketing automation and apps

2. Tania McCormack, Director of Product Management, Flowdock

Bots will be even more helpful, more intuitive, and most of all, more human. In Flowdock, we aim to have our users interact with bots like we do the people around us to get the information and updates we need. Best of all, bots will continue to keep work fun and to make us laugh.

3. David Mendlewicz, Co-Founder, Butterfly

We’re going to see more and more instances of bots helping us develop and grow as humans. To date, bots are primarily seen as a novel utility–a way to get things done more quickly or grasp information more immediately. Moving forward, the machine genius of bots will help understand our learning gaps and fill them in with relevant, personalized information that’s rooted in real data. In other words, bots will be a boon for education.

4. Ben Parr, CMO and Co-founder, Octane AI

There will be an explosion of unique content and experiences from bots as the barrier to creating and managing them drops. This will lead to some breakout bots. Some people will be famous primarily for their bots.

5. Justin Vandehey, Founder, Growbot

In 2017, I think we’re going to see bots grow up a bit, both in terms of standards for how they should be built and how they should be used. There’s a finite set of core workflows and jobs that can be improved. Bot builders who identify those workflows and fit in without requiring a ton of behavior change…those, are the money bots.

6. Jordi Torras, Founder & CEO of Inbenta, Inbenta

Chatbots will get increasingly smart, thanks to the adoption of sophisticated AI algorithms and machine learning. But also they will specialize more in specific tasks, like online purchases, customer support or online advice. First attempts of chatbot interoperability will start to appear, with generalist chatbot, like Siri or Alexa, connecting to specialized enterprise chatbots to accomplish specific tasks. Functions traditionally performed by search engines will be increasingly performed by chatbots.

7. Dan Reich, CEO, Co-Founder, Troops

The word “bots” will slowly go away as people realize that the value is less about talking to a computer or bot, and more about having intelligent workflow within a conversational platform.

8. Dmitriy Kachin, Head of Partnerships, Chatfuel

1) AI Technology – once the Machine Learning aspect of the current AI engines moves on to the next level, and the NLP functionality becomes more sophisticated, we should see some really interesting breakthroughs in terms of chatbot experiences that will appear as a result of that. 2) E-Commerce – when the ability to monetize your bot becomes more robust with solutions integrating CRM systems, warehouse management systems, order tracking, etc – there will be a lot more motivation to realize your offering in a chatbot form. The resulting increase in various e-commerce use cases and the corresponding user traffic should be interesting to watch. 3) As a result of 1 and 2, overall wider adoption of bots.

9. Rob MayCEO and Co-founder, Talla

This year, we’ll finally see large enterprises adopting chat. In our own lead flow at Talla we’ve seen that Fortune 1000s are exploring platforms and what they can do with them. This is how people want to work—and they’re seeing the vision too. We’re at the tipping point where they’re starting to cross over. As a result, we’ll see the integration ecosystem continue to mature into more robust solutions.

10. Lauren Kunze, CEO, Pandorabots, Inc.

Right now the industry needs data driven success stories as an antidote to hype, and this year certain bot applications will increasingly yield real business results. This will help brands filter the noise and differentiate upstarts from industry leaders. Beyond 2017, I predict bots will be the primary interface for casual interactions between people and brands, and people and connected things.

11. Amir Shevat, Head of Developer Relations, Slack

We will see conversational interfaces facilitating productive business workflows. We will see bots augment our life experiences in text and voice in consumer use cases.

12. Mikhail Naumov, Co-founder, CSO, DigitalGenius

In 2017 brands will understand when to use scripted chatbots and when to use machine learning algorithms. Customer Service functions in particular will be significantly transformed with latest advances in deep learning and artificial intelligence. Human and machine intelligence will be combined in a seamless way, to make great experiences for customers.

13. Zor Gorelov, CEO & Co-Founder, Kasisto

We expect the bot landscape to expand in 3 key areas: monetization, security and overall growth in capabilities. A marketplace on popular platforms will enable discovery and in-app transactions. This will drive a higher standard for security, especially in privacy-centric industries like banking, insurance or healthcare. The more companies and players in the space, the faster the bots will improve and the more useful they will become.

14. Marlene Jia, Chief Revenue Officer, TOPBOTS

Bots will be built with specific use cases and objectives in mind driving actual adoption of the consumer.. 2016 was a year of experimentation for both the brands and users, and there were a lot of learnings that emerged. In 2017, you’ll see brands and bot creators doing a better job identifying the use case for the bot and the narrow goals of what the bot should be able to do. We can’t guarantee AI will be at the stage it needs to be to make bots intelligent enough, but what we can do is have a clear idea of what the bot should do and design it based on that objective.

15. Matthew Hartman, Partner, Betaworks

We will start to see a set of bots that are growing, solving the discovery problem in unique ways. We’ll also start to see messaging services experiment with monetization that feels native and unique.

16. Oren Jacob, CEO, PullString

We will see Alexa voice experiences grow substantially in usage, reach, and complexity. A lot of amazing things are being built for the Alexa platform.

17. Jeff Pulver, Founder, MoNage

The way “we” experience the Internet is changing, and that the result of the shift in how communication evolves will be highly disruptive. Communications will be better, easier and more relevant for us Internet users as a result of AI. Summing up the change, the interface between humans and computers is rapidly changing from an “operational” interface (Websites, apps) to a “conversational” interface (ChatBots, voice interfaces). This is revolutionary, given that the “operational” interface has been the standard way to interact with computers since the earliest computers came on the market.

18. Dennis Yang, CoFounder & CPO, Dashbot

We are already seeing continued strong growth in the bot space across all platforms for the first part of 2017. I predict we will see a number of bots hit one million DAU by the end of 2017. Furthermore, we will begin to see more bots that fully embrace the capabilities of conversational UIs, differentiating themselves from the web & mobile experiences to which we are currently accustomed.

19. Tom Hadfield, CEO, Message.io

2017 will be the year of the conversational workplace. With the launch of Slack Enterprise Grid, Microsoft Teams, Google Hangouts Chat and Workplace by Facebook all in the first four months of the year, the enterprise messaging space is proving to be where bots are finding mainstream adoption. 2017 will be the year that conversational interfaces begin to transform the $620 billion enterprise software industry, just as the graphical user interface did in the 80’s, the web did in the 90’s, and mobile apps did more recently.

20. Sandeep Chivukula, Co Founder, Botmetrics

More push; Less pull. Today bots react to customers. The best bots of 2017 will predict what’s improtant to customers and help them take action.

21. Rachel Law, CEO/founder, Kip

The line between software bots and robots/drones will blur as physical bots integrate into platforms. Soon you’ll be able to control roombas and drones through Messenger!

 

Source: topbots.com-21 Bot Experts Share Their 2017 Predictions

Robotic process automation – a new frontier in customer service?

Customer service has always been a key business differentiator. However, recent technological progress, greater consumer choice and eroding loyalty means the empowered customer will no longer stand for sub-standard experiences. As a result, the past few years have seen a renewed focus on the consumer.

This is being reflected in internal business structures across sectors. Where once the customer would be the sole responsibility of the marketing department, they have now become a company-wide responsibility. From the c-suite to IT, brands are placing the customer at the heart of every department and designing their processes and structure with customer fulfilment at the centre of their thinking.

Digital transformation

The digital revolution has driven further advances in customer experience, allowing consumers to interact with their favourite brands whenever and however they want. Digital tools such as webchat, apps and social media are the norm – and in the race to provide the best customer service, forward-thinking brands are now experimenting with the latest technology to create novel, value-added solutions for their clients.

This is in part driven by journey mapping. At Firstsource, we use Interaction Analytics (FCI) to map the customer experience across all touchpoints, highlighting the ‘make or break’ moments – those reasons for customers to exit any given process – in the overall journey. From there, we work with brands to improve the pain points and policies and help make them more customer friendly.

Our experience tells us that customer experience is all about convenience, so it’s important businesses stay on top of what convenience looks like. This depends on integrating new channels to help businesses interact on customers’ own terms, which is why we’re currently looking at how we can use popular messaging platforms such as WhatsApp to deliver tailored communications in real-time, for example.

While traditional contact channels such as voice and email will always be important, new technologies helps connect brands with digitally-savvy customers. As an added bonus, businesses also get the kudos that come with appearing as an innovative and customer-focused brand.

The automation opportunity

Robotic Process Automation (RPA) is one of the newest frontiers in customer service. At its core, RPA is the application of a computer software or “robot” to process transactions, manipulate data or trigger responses, depending on the scope of the request. This technology has the potential to unlock value across a wide range of different industries and business functions. In particular, regulated industries with high volume and transactional business processes stand to gain significant benefits from the application of RPA.

Done well, it can deliver more cost efficient, streamlined and compliant processes. At the same time, automation allows employees to focus on higher value activity that will drive customer experience. It’s a win win for businesses who get it right.

However, while appetite for robo-advice among consumers is growing, it’s clear that automation requires careful due diligence to understand the opportunities, risks and requirements for delivery.

And consumers understandably still have their doubts when it comes to automated advice. A recent study conducted by Firstsource showed that 44 per cent of consumers see the availability of a bank branch as their number one deciding factor when choosing their banking provider – telling a cautionary tale for businesses undergoing digital transformation.

RPA transformation

Needless to say, integrating RPA is a significant undertaking. While the specifics will depend on the business, the sector they operate in, and the extent to which they are aiming to automate their processes, there are three critical ingredients for a successful RPA transformation.

The first – and perhaps most important – is that RPA must be a strategic fit for the company. RPA needs to be understood not as a process but as a strategic capability that increases business value. This re-engineering will be key to increasing the impact of automation and maximising ROI, and must be given due diligence – so it’s vital businesses understand which processes will deliver the biggest business benefit when automated, and construct a careful roadmap accordingly.

Next, there also needs to be buy in for transformation and automation from the C-suite for RPA to be a success. Cultural adoption may often require education and careful articulation of the business benefits of the solution, and lack of internal support at a senior level can be one of the major stumbling blocks to RPA implementation.

Unsurprisingly, successful automation also relies on IT engagement. Legacy IT systems and resistance from existing IT departments can often be a barrier to transformation and automation. Bringing the IT function on board at the beginning of the automation journey will help to set a clear roadmap for transformation and identify any potential roadblocks that lie ahead.

RPA in practice

When thinking about the ways businesses can use RPA, most peoples’ thoughts turn to chatbots. Microsoft, Uber and Twitter are just a few brands who have recently launched bots for customer service – although some with more success than others.

While they have the potential to go very wrong, chabots can be useful to solve straightforward transactions and simple queries. They can also help shepherd customers on a relatively linear journey, such as answering delivery questions on an order.

But RPA can be used for more than just straightforward customer engagement. It also has the potential to transform back office processes, freeing-up employees from repetitive tasks to focus on more complex and value-added work. And it can also be used to transform more complex processes, such as commercial finance operations. This is particularly valuable in the financial services industry, where many businesses rely on the efficient and cost-effective running of their commercial finance division to keep them in business.

But often, the smooth running of these operations are hampered by inefficient, expensive and cumbersome legacy technologies. And while many businesses recognise that this is holding them back, they lack the skills, resource and expertise to overhaul the outdated systems and processes. Outsourcing commercial finance operations can be an effective way to transform a business through automation with lower risk and resource.

By simplifying and automating processes and redesigning operating models, many large-scale financial organisations should be able to increase productivity in their commercial finance operations by between 30-50- per cent, while reducing cost to serve by 25 per cent.

Whether it’s customer-facing or in the back-office, brands have a lot to gain by integrating RPA in their operations. What’s clear is that automation is here to stay and evolve – and businesses must determine how it can play a key role in delivering the best customer experience possible.

Source: itproportal.com-Robotic process automation – a new frontier in customer service?

Automation across financial services: hype or reality?

Whether the displacement of human labour by automation is, as is often depicted, another nail in the manual coffin seems a moot assertion. But what cannot be denied is its increasing role across the financial services (FS) sector, with some players even seeing automated environments as a panacea.

Universal remedy or not, what does seem clear is that the FS sector is ripe for automation. Supporting this assertion is a 2017 report by Infosys – ‘Amplifying Human Potential: Towards Purposeful Artificial Intelligence’ – which found that FS companies across the globe (based on a poll of 1600 senior business decision makers at some of the world’s largest organisations) are looking to automation, and its subset, artificial intelligence (AI), to boost revenues and streamline structures.

“Financial institutions (FIs) are looking at automation across a fairly wide spectrum of activities,” says Tom Kimner, head of global risk marketing and operations at SAS. “One recent area of interest has been an investigation of current processes around governance and compliance. With many of these processes stabilising to some degree, FIs are looking at improving and streamlining them with some form of automation to not only reduce costs but to make them more robust and repeatable.”

Clearly, the use of automation and AI is expanding and evolving across many industries, with the FS sector a particularly active participant. Indeed, according to Infosys, companies in this space have each invested, on average, $14.5m in AI technologies to date, compared to an average of $6.7m in other industries.

The Infosys survey further reveals that: (i) 76 percent of senior decision-makers believe AI is fundamental to the success of their company’s strategy; (ii) by 2020, those currently or planning to use AI technology anticipate a 39 percent boost to their company’s revenue, on average; and (iii) eight in 10 companies that have replaced, or plan to replace, roles with technology will retrain or redeploy those who are displaced.

In its 2016 analysis of the automation debate – ‘How can RPA and other digital labour help financial institutions’ – PwC states that technology is now allowing FIs to automate many computer-based operational tasks like searching, matching, comparing, filing and more, which frees up staff to do much higher value work. Also highlighted are some of the repeatable and logic-driven activities which are deemed ideal for automation, such as: trade mismatches; management reports; regulatory information such as CCAR stress tests; client reporting; asset servicing; account opening processes, such as anti-money laundering and know your customer; and reconciliation and data remediation initiatives.

Many commentators expect the shedding of costly and cumbersome legacy IT architecture to continue apace. The stage is set for FS automation and AI to move from what was, only a few years ago, relatively vague concepts to bona fide, strategic business imperatives.

The shape of things to come

Automation can of course be found in some shape or form in virtually every industry. Its creation has greatly improved efficiency and substantially increased quality thresholds. Add to this the ongoing development of AI technologies – with numerous applications for insight, increased productivity and expanded possibilities – and the benefits of automation are abundantly clear, with the FS sector an obvious beneficiary.

Intelligent automation has truly landed in FS, with the aim of reducing costs, simplifying processes and improving performance,” says Christopher O’Driscoll, a financial services expert at PA Consulting. “Specific trends and developments in robotic process automation, cognitive computing and Internet of Things (IoT) are being seen across banks, insurers and asset managers. These include machine learning being applied to credit risk processes, robo-advisers delivering investment advice and natural language processing being used for tasks such as speech recognition for account access. These trends will continue as automation is increasingly applied to an even wider variety of tasks.”

“The stage is set for FS automation and AI to move from what was, only a few years ago, relatively vague concepts to bona fide, strategic business imperatives.”

Further fleshing out the nature of such tasks is Capgemini’s 2016 report ‘Robotic Process Automation Solutions for Financial Services’, which proffers that optimising and improving efficiency means more than just upgrading systems or outsourcing processes – it means harnessing innovation. Repetitive tasks – which the Capgemini report estimates 40 percent of staff spend their time on – are essentially algorithms and therefore can be automated, with robotic process automation (RPA) innovation that combines user interface recognition technologies and workflow execution to follow predetermined computer pathways.

The effects of such innovation, from fighting fraud to improving the customer experience and even predicting the direction the market will head in, are already visible. “Contact tools such as the AI virtual agents introduced at a Japanese bank are a good example of how automation is already benefiting the FS landscape,” says Indivar Khosla, executive vice president and global head of FS business services at Capgemini. “They can respond and solve customer enquiries much faster, resulting in fewer calls to the bank’s main contact centre, therefore freeing up time for staff while improving the customer experience.”

Chatbot technology, which carries out complex calculations instantaneously, allowing customers to check their finances, evaluate their spending habits and monitor their credit score, is also having a big impact. The speed and efficiency of core business processes received a significant boost from the chatbot innovation.

Pros and cons

Like any endeavour poised to be a major disruptive influence on an industry’s status quo, automation in FS comes with a range of pros and cons. According to Mohit Joshi, president and head of banking, financial services & insurance at Infosys, by using automation, banks are distinguishing themselves by being technologically sophisticated and capable of meeting the financial and security needs of digitally savvy customers. “Some of the world’s largest credit card issuers, HSBC and JP Morgan Chase & Co. among them, utilise AI to analyse the buying patterns of their cardholders. Any anomalies are red-flagged and preventive measures taken before a cyber thief can do lasting damage. The 2016 Forter and PYMNTS.com ‘Global Fraud Index’ found that in the first quarter of the year, $4.79 of every $100 in online transactions was considered at risk. That is up from $2.90 year-over-year.”

Automation enables FS firms to create a measurable audit trail of activity, reduce human error, speed up transaction times, reduce costs and improve overall customer experience. But the reality, says Chris Gayner, marketing director at Genfour, is that many firms are still trying to deliver a significant return on investment (ROI) to justify further investment. “In our experience, those FS firms which are seeing ROI from automation, view automation as a journey and not a project – which typically means cross functional working, robust governance and employing the right skills to drive out maximum benefit,” he says.

Further obstacles to automation include budgetary concerns and a lack of technical capability, not to mention redundancies and the associated impact on company brand. Potential job losses is a sensitive issue in the automation debate, with no easy answers. That said, not all processes lend themselves to automation and, for those that do, the result may not be job losses but rather a form of job transfer to more important tasks like deeper, more thorough analytics. “It is important for organisations to have an understanding of what types of skills are needed now and in the future for any evolution toward automation they may consider,” suggests Mr Kimner. “Anticipating the right mix of skills is important as it will influence the types of training and the hiring that organisations may undertake.”

Dismantling architecture

An unavoidable issue when moving to automation is the need to dismantle existing IT architecture, protect underlying systems and, at the same time, keep costs under control. To help minimise costs during this process, IT architecture should be simplified and a service layer added, to allow a company to integrate its IT with other systems and intelligent automation technologies.

“Due to the size of FS organisations’ operations, changing their IT systems and ways of working can be a challenge,” says Mr Khosla. “However, investing in ways to improve the IT infrastructure and processes can help save on future costs which will only increase as systems and processes become increasingly outdated. These savings can then be passed on to other areas of the business, allowing other processes to be updated.”

One option favoured by Mr Gayner is for companies to have recourse to a considered automation roadmap – the first step toward minimising the cost of automation. Yet, given that automation tools can readily be found online and installed onto machines without involving IT, it makes sense for organisations to take a company-wide view of automation – who owns it, how it is managed and where it should be used. “FS sector firms should invest in a robust proof of concept to ensure the technology fits with their wider IT initiatives and complies with corporate policies, but also that the approach to automation is the right one. It is important that automation adds value, and is not just a replacement for bad processes,” explains Mr Gayner.

The transition from legacy systems to newer, more agile technologies and platforms is clearly a difficult and costly enterprise, with many companies mistakenly looking only at the technology costs of replacing various systems and not the total costs, which include, for example, change management, process improvement and resource training. “Organisations often start by acquiring some new technology and then trying to implement pieces of it through a patchwork of small projects that often seem like iterative, trial and error exercises,” attests Mr Kimner. “Thorough planning and an understanding of the impact and downstream effects of technology changes on people, as well as processes, must be part of a sound programme in order to keep overall costs in check.”

Embrace or resist?

With automation in FS continuing to evolve, the extent to which the sector will play ball with this evolution, whatever form it takes, is a matter of debate.“As traditional banks grapple with the challenges posed by FinTechs, legacy constraints and traditional operational models, AI is emerging as the saviour,” claims Mr Joshi. Indeed, according to the Infosys survey, 23 percent of 250 FS sector respondents confirmed that AI technologies have been fully deployed in their organisations. Moreover, 47 percent view AI as being fundamental to the success of their organisation’s strategy. “It is likely this trend will continue to accelerate and transform the financial services landscape. Furthermore, as AI is deployed more regularly and employees become increasingly familiar with it, adoption will be the common sense option,” adds Mr Joshi.

On the flipside, less focused firms could find themselves struggling to keep up with competitors taking advantage of the benefits that automated business processes can bring. “It is too early to say how far automation can go, but the next five to 10 years and beyond will certainly be exciting when it comes to the application of AI to business processes,” suggests Mr Khosla. “For under pressure FS firms, gross operating expense (GoE) reduction, return on equity (RoE) maximisation and transformation of the operating model are all key priorities. Moreover, as the technology behind automation develops, we will see it start to take on more complex tasks and create greater efficiency and potential within the workforce. Although challenges exist, AI has the capability to allow the industry to develop new highly personalised customer propositions and improve their experience.”

A tool for the future

In a landscape where competition, complex processes and regulatory demands are all challenging profits, automation is assisting the FS sector to reduce costs and reconfigure existing practices and business models. Furthermore, by making tasks more predictable and easier to control, automation is also improving performance and process quality, eliminating human error and improving efficiency.

“With the market becoming more competitive, FS companies are recognising the need to differentiate themselves,” says Mr O’Driscoll. “Automation can help with this, enabling FS providers to carry out processes faster so that new products and services can be brought to market quicker than the competition. From the robo-adviser to the automated back office, FS will continue to be a leader in the digital development, and an early adopter of RPA, cognitive technologies and AI. As the combination of high transaction volumes, level of customer service and regulation becomes ever more costly, automation will be increasingly applied.”

Going forward, it is of course difficult to predict the types, uses or limits of automation across the FS sector. However, it stands to reason that there will continue to be cases where the automation of repetitive processes, compliance activities and reporting, will be more cost effective and, in some cases, necessary. According to the 2017 ‘Robotic Process Automation: A Guide for Banks and Financial Institutions’, the global automation market is expected to see a compound annual growth rate of 75 percent, reaching $835m by 2020 – an adoption rate which strongly indicates that the sector will focus on investing for training and ownership of automation technologies.

“There will most likely be cases where automation is used to attract, convert and retain customers through various channels,” says Mr Kimner. “There may even be cases where automation is used in business decision management or perhaps portfolio optimisation. However, what is clear is that FIs need to look for ways to reduce costs and improve margins if they want to remain profitable and competitive – and automation is one of the tools that may well help them achieve this.”

 

Source: financierworldwide.com-Automation across financial services: hype or reality?

The robots are coming: better get used to it

Every society needs an enemy: something which threatens the fabric of the nation. Whether it’s the barbarians at the gates of Rome, Reds under the bed, or the job-destroying stocking frames attacked by the Luddites, every age has its own perceived existential threat. Ours is robots.

This world will not just survive the rise of the robots, but benefit from them greatly. It’s true that they will disrupt the workforce, but the apocalyptic forecasts of mass unemployment are simply hyperbole, more fitted to a Brothers Grimm tale than a rational discourse of the near future.

What society needs are facts, not scaremongering. However, facts about the future (excluding death and taxes), especially with the pace of change and the uncertainty in the world are difficult to come by.

So let’s start with Forrester’s prediction that by 2019, a quarter of all job tasks will be offloaded to software or robots. It seems alarming and is certainly headline grabbing, until you read further and find, in the same report, that these technologies will create a further 14 million jobs in the same period.

No-one denies that a world powered by automation and AI will look very different from today, and no doubt some existing jobs will go the way of ostlers, farriers and blacksmiths. At the same time, technology will create entirely new careers, many of which people can only guess at today.

People should be accepting of this inevitable rise of the machine, because, for all the capabilities of AI, machine learning, RPA and robots, no technology comes close to the ingenuity of even the most average human mind.

The problem is not that robots will steal our jobs in the future: it is that humans have been wasting their faculties on tedious tasks that are much better performed by artificial intelligence or software, such as rekeying data or answering routine queries.

It will also help with the UK ‘productivity gap’, that the country has been suffering from for several years. The UK has been at the forefront of offshoring, lots of that efficiency gained from labour arbitrage can instead be delivered by robots and managed locally; driving more flexibility, control and efficiencies.

Far from being a jobs thief, new technology will augment the workforce, freeing them from repetitive, mundane tasks and using their higher abilities on more meaningful activity.

Take chatbots and AI assistants. Already these are replacing the time-consuming task of scheduling meetings, creating schedules – even taking notes in meetings. This means that the “cognitive load” (not to mention the time) can be spent on more productive, creative, and valuable activity.

Businesses must adapt to the great changes that have just begun to take shape and embrace the opportunities that technology represents. Because, if history teaches us anything, it is the futility of trying to stem the tide of change.

Source: information-age.com-The robots are coming: better get used to it

RPA and AI are not the future – they are the Now!

Robotic Process Automation (RPA) and Artificial Intelligence (AI) are becoming more prevalent in business and society. As the technology becomes more accessible and efficient, more and more organisations are looking at RPA and AI. Although both of these technologies are not new it does seem that they are now beginning to come of age and radically changing the way the world does business.

So, what is RPA and AI? Let’s consider RPA first of all. Perhaps the most important thing to say about RPA is that it is not a robot! At least it is not a physical robot. RPA is a type of software that is able to interface with computer systems in the same way as a person does. RPA software is able to ‘type’ and is able to ‘click’ and is able to move a cursor. This enables it to open and close programs and to use programs. This is why the term ‘robotic’ was coined – there’s no physical robot but the software behaves in a robotic way. What is key though is that the RPA software is able to carry out tasks with a much greater level of efficiency than a human operator – and it never gets tired.

RPA has been shown to be a highly effective option for carrying out certain types of tasks. It has delivered huge cost savings for organisations and eye wateringly massive returns on investment of 100s of per cent in some instances. It is certainly worth every organisation taking a serious look at how they might take advantage of what it is able to do.

Shop Direct, Telefonica, RAC and nPower are just some of the organisations that have reported substantial benefits to their businesses. Some of those benefits include the reduction of costs of processes. It isn’t however only a matter of cost reduction. Using RPA helps to further improve the quality and consistency of outputs – why wouldn’t it? It also provides organisations with greater auditability / trackability of their processes down to key stroke level. A boon for those in say the financial services sector.

Neither is RPA just about the commercial sector. In 2015, Sefton Council became the first local authority in the UK to trial RPA in its revenues department. At the beginning of the project, leading international service provider, Arvato automated three processes in Sefton’s revenues department to ensure the RPA solution was accurate, robust, auditable and scalable, before extending it to cover a number of high-volume tasks across the department. The tasks vary in complexity, from indexing documents and assigning them to specific workflows to signing up people to direct debit payment of Council Tax and processing discount applications.

Alastair Bathgate, CEO of Blue Prism is confident that RPA has a greater role to play for local government in the future, “We believe there is huge potential for RPA to make a difference in local government thanks to the large number of repetitive back office tasks which can be automated”. His view is support by a a recent study by PricewaterhouseCoopers it was estimated that 45% of work activities could be automated, creating $2 billion of savings in global workforce costs.

RPA is clearly an important tool for organisations to consider. There is though considerable confusion that surrounds it. Many wonder what all the fuss is about given that most organisations already have very high levels of automation and have had for many decades. This gets us to the key to RPA’s appeal. We pointed out that RPA software uses other systems, like a human operator. This is crucial. It means that we can improve the efficiency of a process that is largely automated without necessarily having to make any changes to the existing legacy systems. For anyone who has wrestled with old legacy systems and over stretched IT teams the prospect of being able to make improvements, without instigating costly and resource hungry IT projects, is a very attractive proposition indeed.

RPA often acts as a link, bridging the gaps between systems or it can act as an effective work around for a system that could not quite accommodate a particular set of tasks. Often these system shortcomings have been addressed by getting people to fill the gap. Not only is this often quite inefficient it also creates mind numbingly dull tasks that someone has to do. RPA offers the opportunity to improve the efficiency of a process, reduce cost and often take away tedious tasks, allowing people to focus on more added value, more highly skilled tasks – like talking to customers.

This takes us nicely to the difference between RPA and AI. RPA is a dummy. It is pretty stupid. It does exactly what it is told to do – exactly. There’s no thinking – no judgement – just a set of rules which it blindly follows. It can only work with structured data. If the task requires working with less structured data RPA is struggling. If the rules for what it needs to do – down to individual key strokes – cannot be defined, then RPA is going to struggle.

Enter Artificial Intelligence. AI does have the capacity to work with less structured data. It can find patterns in data and be programed to make choices. AI can learn, based on what it experiences and that learning can then inform future choices. AI is advancing quickly and has evolved into an incredibly useful tool for organisations. Unstructured data such as emails and phone calls can be sifted with AI programmes by identifying key words or phrases before checking parameters and categorising what is required. Virgin Trains have been using AI for some time now to analyse emails received from customers. They are able to make sense of what the email is about by analysing key words. They are then able to decide to make for example further checks on the validity of the email content by perhaps checking if a specific train journey mentioned in the email actually exists. It might then go on to check if there was any reported issue with that train journey. It can then pass the task on to person who is now able to make a judgement about what needs to happen next having been saved the chore (and the time) of checking key facts.

In some instances AI can be used to help structure data allowing it then to be offered perhaps to an RPA solution to progress further. A combination of AI and RPA can potentially transform a process, massively reducing costs through reducing FTE’s while being more efficient and effective at completing tasks than human employment.

What has happened alongside the growing popularity of exploring and implementing RPA and AI is a growing appreciation of not only the benefits of these solutions but also the challenges. Understanding implementation and the processes that RPA and AI can improve is crucial to getting the best return on your investment. There has perhaps been a view that RPA certainly could be almost bought off the shelf and implemented by a school leaver with a GCSE in woodwork. The reality is somewhat different. More people are recognising that there is in fact a lot to consider to get the most from RPA. How to choose the best processes to RPA. How to gain buy-in and support. How to design the new target operating model. What software to choose. How to manage long term. How to ensure the fit with IT . How to set up effective governance.

RPA and AI are not the future, they are the present. A great return on your investment that efficiently and effectively gets the job done. Whether or not you and your organisation ultimately invest in RPA and AI solutions importance of investing in finding out more about it the case for investing some time and energy in finding out more about it and how it might add value to your business is extremely compelling. Those that don’t run the risk of missing out on a very good thing.

Source: sourcingfocus.com-RPA and AI are not the future – they are the Now! 

The Future of Robotic Process Automation

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

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Transcript:

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

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

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

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

[music ends]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Adam: Exactly.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[background music]

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

Adam: Jon, my pleasure. Thank you.

Source: georgianpartners.com-The Future of Robotic Process Automation

How AI Is Changing The Way Companies Are Organized

Artificial Intelligence may still be in its infancy, but it’s already forcing leadership teams around the world to reconsider some of their core structures.

Advances in technology are causing firms to restructure their organizational makeup, transform their HR departments, develop new training models, and reevaluate their hiring practices. This is according to Deloitte’s 2017 Human Capital Trends Report, which draws on surveys from over 10,000 HR and business leaders in 140 countries. Much of these changes are a result of the early penetration of basic AI software, as well as preparation for the organizational needs that will emerge as they mature.

“What we concluded is that what AI is definitely doing is not eliminating jobs, it is eliminating tasks of jobs, and creating new jobs, and the new jobs that are being created are more human jobs,” says Josh Bersin, principal and founder of Bersin by Deloitte. Bersin defines “more human jobs” as those that require traits robots haven’t yet mastered, like empathy, communication, and interdisciplinary problem solving. “Individuals that have very task-oriented jobs will have to be retrained, or they’re going to have to move into new roles,” he adds.

The survey found that 41% of respondents have fully implemented or made significant progress in adopting AI technologies in the workforce, yet only 15% of global executives say they are prepared to manage a workforce “with people, robots, and AI working side by side.”

As a result, early AI technologies and a looming AI revolution are forcing organizations to reevaluate a number of established strategies. Instead of hiring the most qualified person for a specific task, many companies are now putting greater emphasis on cultural fit and adaptability, knowing that individual roles will have to evolve along with the implementation of AI.

On-the-job training has become more vital to transition people into new roles as new technologies are adapted, and HR’s function is quickly moving away from its traditional evaluation and recruiting function—which can increasingly be done more efficiently using big data and AI software—toward a greater focus on improving the employee experience across an increasingly contingent workforce.

The Deloitte survey also found that 56% of respondents are already redesigning their HR programs to leverage digital and mobile tools, and 33% are utilizing some form of AI technology to deliver HR functions.

The integration of early artificial intelligence tools is also causing organizations to become more collaborative and team-oriented, as opposed to the traditional top-down hierarchal structures.

“To integrate AI, you have to have an internal team of expert product people and engineers that know its application and are working very closely with the frontline teams that are actually delivering services,” says Ian Crosby, cofounder and CEO of Bench, a digital bookkeeping provider. “When we are working AI into our frontline service, we don’t go away to a dark room and come back after a year with our masterpiece. We work with our frontline bookkeepers day in, day out.”

In order to properly adapt to changing technologies, organizations are moving away from a top-down structure and toward multidisciplinary teams. In fact, 32% of survey respondents said they are redesigning their organizations to be more team-centric, optimizing them for adaptability and learning in preparation for technological disruption.

Finding a balanced team structure, however, doesn’t happen overnight, explains Crosby. “Very often, if there’s a big organization, it’s better to start with a small team first, and let them evolve and scale up, rather than try to introduce the whole company all at once.”

Crosby adds that Bench’s eagerness to integrate new technologies also impacts the skills the company recruits and hires for. Beyond checking the boxes of the job’s technical requirements, he says the company looks for candidates that are ready to adapt to the changes that are coming.

“When you’re working with AI, you’re building things that nobody has ever built before, and nobody knows how that will look yet,” he says. “If they’re not open to being completely wrong, and having the humility to say they were wrong, we need to reevaluate.”

As AI becomes more sophisticated, leaders will eventually need to decide where to place human employees, which tasks are best suited for machines, and which can be done most efficiently by combining the two.

“It’s a few years before we have actual AI, it’s getting closer and closer, but AI still has a big problem understanding human intent,” says Rurik Bradbury, the global head of research and communication for online chat software provider LivePerson. As more AI software becomes available, he advises organizations to “think of those three different categories—human, machine, or cyborg—and decide who should be hired for this job.”

While AI technologies are still in their infancy, it won’t be long before every organization is forced to develop their own AI strategy in order to stay competitive. Those with the HR teams, training program, organizational structures, and adaptable staff will be best prepared for this fast-approaching reality.

 

Source: Fast Company-How AI Is Changing The Way Companies Are Organized

10 Success Factors for Deploying Software Robots in the Enterprise

The implementation of software robotics and smart technologies frees a workforce from routine tasks while improving efficiencies, data accuracy and compliance.

Make Sure IT Is Involved from the Start

There often is tension between what IT resources a company’s lines of business need to operate most effectively and the allocation of said resources. While the overarching mandates are to improve service and reduce costs, the resources and priorities of the two groups often are misaligned, constraining business growth and performance. Many RPA implementations emanate from business operations teams, leaving IT on the sidelines in favor of speed and creating shadow RPA projects outside of IT’s oversight. This is a mistake. The most successful, scalable deployments of RPA are implemented in full collaboration with IT leadership.

IT Must Demonstrate its Willingness to Collaborate

It also is important for IT and the business teams to work on the same page. IT must recognize the urgent need for RPA in the business in terms of mandates to improve efficiencies, improve customer satisfaction and other drivers and offer appropriate levels of support and partnership to avoid shadow deployments. Collaborating and agreeing on priority deployments upfront will alleviate alignment issues later.

Begin with an Automation Strategy that Sets Direction

One way to align priorities for the business is to work together on setting and aligning expectations with a common vision. Beginning this process with a documented automation strategy is important. What is the target state of RPA within the operations team? What does the roadmap look like? Establish executive sponsorship upfront, agree to the scale of investment and quantify the expected benefits of that investment so it can be measured. Also consider including a proof of concept or pilot project that supports the defined strategy and vision.

Identify Ideal Process Candidates for Automation

For most businesses, the best candidates for automation often are back-office processes wherein the goal is to provide faster, easier service to customers—such as activating a new SIM card in five minutes rather than 24 hours. These processes are mundane and require entering repetitive data into multiple systems that don’t talk to each another. The goal is not to reduce jobs, but to minimize mundane tasks so people can focus on more value-added and fulfilling work.

Don’t Stop with Quick Tactical Wins

You’ve likely identified a large number of existing processes that can be improved with automation. You can move to automate those quickly, secure wins and demonstrate the success of RPA. But RPA presents an opportunity to drive transformational change in your business. Now is the time to take a step back and allow teams to imagine what is possible. Brainstorm with different groups within the business and allow them to be creative in identifying game-changing and high-impact opportunities to create competitive advantage. What would your business do if time, people and resources were unconstrained?

Choose the Right RPA Technology To Enable Process Automation

As business-line and IT leaders work together to choose the right RPA solution, it’s important to understand the difference between simple desktop scripting, software development kits (SDKs) and enterprise RPA. A desktop automation solution offers a quick solution for a team with short, recorded and replay tactical automations aimed at navigating systems on the desktop. Automated tasks, often manually triggered, are coded or recorded individual keystrokes of a user. They are not connected to enterprise systems and are often deployed without the knowledge of IT. SDKs give IT a better, faster way to deliver on business teams’ expectations, but often don’t involve the operations teams in the process.

Make Security a Key Requirement for Vendor Selection

Business and operations leaders should engage IT early on in the process to ensure proper security, infrastructure and support. Enterprise-class RPA should be deployed in the data center or in the cloud, but never on the desktop. If there is a record button on the desktop, IT can’t monitor or provide security or meet regulatory requirements. Desktop deployments should scream “shadow RPA” to the IT organization. It’s important to ensure RPA software meets required compliance requirements such as PCI-DSS, HIPAA and SOX to provide the necessary security and governance.

Find a Strong Implementation Methodology

There are well-defined methodologies that already have been tried and tested for implementing software robots in the enterprise. Make use of these in your environments: 1) Identify the processes that are best-suited to robotic process automation; 2) Establish the benefits case for robotic process automation, encouraging organization wide recognition and adoption; 3) Implement the required infrastructure, governance and support framework to enable a robotic process automation capability to run efficiently and effectively; 4) Define a best-practice approach for process configuration, which increases the potential for automation and accelerates the development life cycle; and 5) Provide the necessary skills to operational resources via a role-based training and mentoring accreditation program.

Welcome ‘Bots’ to Workforce with Change-Management Best Practices

Both IT and business operations should incorporate change management best practices when introducing software robots as part of the workforce to bring teams along with the vision. Introducing bots into the workforce is new and different, and it requires careful concept selling and implementation. Sharing the company’s vision for how the software robots will add value and improve the business is important, but it’s equally important to help employees understand what’s in it for them: How will these robots help them do their jobs better and more efficiently?

Measure Impact to Demonstrate Value

When helping teams understand the total value of RPA, calculate expected benefits across shareholders, customers and employees. Focusing on one area only will sell the initiative short and miss an opportunity for driving broader enterprise value and scale. Use your RPA software to collect meaningful business intelligence data and real-time operational analytics to report on decisions and actions taken by each software robot. Use this data to see how the organization is performing, where process improvements can be made and what new opportunities for revenue and customer satisfaction can be identified.

Source: eweek.com-10 Success Factors for Deploying Software Robots in the Enterprise

6 business cases where RPA delivers proven value

Today, across the world, many millions of hours of staff in customer services, business support and operations are being consumed with mundane, manual, labour intensive activities. While much of the headline-grabbing may come from the concept of Artificial Intelligence and self-learning systems, the fact is that if these mundane tasks were automated, the benefits would be huge, with staff freed up to focus on higher value activities, improved speed and accuracy and significantly reduce costs.

This white paper looks at the various areas within a business where Robotic Process Automation (RPA) can offer benefits along with some proven real world use cases where RPA has already delivered game-changing results for some of our customers.

If you’ve ever asked yourself or others the question “why isn’t that automated?” then it’s highly likely RPA can help you.

Download the Thoughtonomy_RPA_whitepaper to learn more

Source: thoughtonomy.com- 6 business cases where RPA delivers proven value

A look to the future with Professor Leslie Willcocks: RPA and the changing world of work

With 2017 fast approaching, a glance at the future seems only appropriate. In our exclusive interview with Professor Willcocks we look ahead in two areas: the future of utilising RPA (Robotic Process Automation) and its implications to the market at large.

Past November Digital Workforce organized a unique breakfast seminar discussing the role of RPA in digital strategy and excitedly welcomed the event’s keynote speaker, Professor Willcocks. Leslie Willcocks, a professor of London School of Economics, is considered one of the world’s most respected researchers, speakers and business publications writers in the field of knowledge work automation. Following the seminar, Professor Willcocks sat down in private to answer some of our questions.

The larger value of RPA is tied to business processes and institutionalization of the technology. How do organizations reach these benefits as they move forward with RPA?

“One of our researched organizations had an interesting model they worked with, that in my opinion could well be worth coping elsewhere. The company had identified eight key in-house competences which they combined with client assets to form a third entity – a service delivery vehicle. One of the organization’s key competences was process re-engineering, an area in which Robotic Process Automation falls perfectly.

RPA isn’t a technology in a vacuum. It has to sit with something and it fits process best, but the technology has to sit with people too. This is a late learning, as the early adopters often focused on fixing individual processes; almost like sticking a plaster – though a good one – on a wound. Fixing individual processes offers limited benefits compared to adopting the technology on a strategic scale, but doing so requires willingness to build new capabilities. Luckily we found, that all good process principles, such as Six Sigma and Lean, fit RPA extremely well – these principles demand companies to take a broader look at their business strategy and key performance indicators as well as consider their alignment with the organization’s process technologies and people. New adopters of RPA are doing exactly this.”

Considering future advances, could RPA be utilized to tap into even more opportunities?

“RPA technologies are one small piece of the bigger automation jigsaw. Digitalization should be looked at as a whole. The organizations I know of work with automation centers of excellence. This should be the approach even if RPA is the only tool in the box right now. Things like business analytics and amplifying automation by analyzing unstructured data with solutions such as cognitive intelligence stick on top of what can be done with RPA. Creating a platform compatible for integrating all these solutions should be the obvious next step.”

What kind of impact do you expect RPA will have to the market at large?

“Compared to other robotic technologies RPA faces less issues related to ethical conflicts or underdeveloped regulations. On the contrary, RPA is often used to conform with regulatory requirements. It is however, important that the modern tech area is well regulated. Having regulations in place helps steer the impact of fast moving change while the social implications of growing business efficiency depend on the power and wealth being spread fairly.

Studies suggest jobs being both created and lost as a result of RPA automation. There is also a distinct difference between using a technology as a complementary or replacement solution. The full data is poorly incorporated to most studies. Such flawed publications speak of 47% job loss due automation. Based on our research, 14-16% fewer jobs in the sectors where RPA operates seems realistic. However, the most of the eliminated work load won’t translate to loss of total jobs but partial jobs.

Using the term “robot” seems sometimes unnecessarily bias, when you consider that RPA could just as well be described as a software solution. When describing RPA I often use the line “taking the robot out of the human” as it accurately describes what the technology does. The amount of knowledge work has dramatically increased in every sector from health care to banking. Over the last 10 years of conducting interviews, no one has ever told me that their work load has stayed the same or decreased! This fact has gone largely unnoticed in the public discussion but resulted to a situation where, for all the over-worked individuals, implementing RPA is simply great!”

Source: digitalworkforce.eu -A look to the future with Professor Leslie Willcocks: RPA and the changing world of work