Robotics Process Automation: 5 Lessons Learned on How to Get Started

I suspect that a high percentage of people reading this article are already familiar with the basics of Robotics Process Automation (RPA), and the significant cost savings and efficiencies that RPA can bring to your organization by automating high volume, transactional processes in multiple functions including Finance, HR and IT.

Now that it’s clear that you need to start evaluating RPA and how to implement it in your organization, I’m sure there’re many open questions about how to get started. The purpose of this article is to briefly point out five key lessons learned on how to jump start a Robotics Journey, based on our experience as both an advisor and a managed services operator of RPA:

1. Don’t drink the vendor “Kool-Aid”

As Gartner cited in a recent RPA study, “The expectations of robotic process automation (RPA) are as significant as the confusion about the technical capabilities of the RPA tools themselves.”

Many RPA software vendors will try to convince you that almost anything can be automated and tend to oversimplify the actual deployment process. While the power of their tools can’t be denied, programming a dysfunctional process wastes time and effort, and is less stable upon deployment. In our experience, processes will often require some degree of standardization or redesign during the automation journey.

Plus, RPA vendors often talk about processes that require dozens of robots and hundreds of workers. While these processes and operations clearly exist (e.g. Financial Services), and the benefits can be significant, they are not the “norm” for most businesses. Most companies we have spoken with are looking to leverage RPA for back office processes that often utilize dozens of employees, not hundreds. In these scenarios, the investment in RPA may not yield the ROI that the vendors are touting, and a more practical implementation approach is required.

Deep business process expertise and knowledge of your back office environment is equally as important as selecting the right RPA tool. Process expertise and knowledge of your back office do not come from the RPA software provider. And their business case methodology needs to be adapted to a much smaller footprint for most businesses.

2. Partner with an RPA expert

This second lesson learned goes hand in hand with the first. Many organizations try to implement robotics by themselves, with no internal or external RPA expertise. Companies often do not realize this can cause their RPA initiatives to go off track, take longer and cost more money.

We’ve seen how some companies have chosen the wrong RPA tool because they didn’t get the right advice. They just selected a solution based on price or market presence, and then realized it had many limitations, was not scalable, or simply did not deliver the expected (and needed) ROI.

Another common mistake is for companies to task one of its employees to lead the RPA initiative, but the person is so embedded in the day-to-day operation, and the way processes currently operate, that they lack the necessary objectivity, and fresh eyes to identify how activities can be changed and adapted to RPA.

Most RPA advisors or managed services providers can quickly assess your organization, identify the key automation opportunities and provide a realistic implementation timeline based on having delivered these initiatives multiple times. This initial assessment will provide you with a good estimate of the savings and efficiency opportunity before you decide to embark on this journey.

3. Understand your deployment model options

RPA can be deployed in a model where it is managed internally or through an outsourced Managed Service model. An organization will need to determine which RPA deployment model fits its “DNA.” Many midsize firms do not internally possess the process excellence, technical expertise, and change management skill set to manage the deployment internally successfully.

If built internally, the firm will need to document processes, train the robot (through development and configuration) to perform the process, and build an ongoing support function to monitor the robots and continually reconfigure the robot as systems and process evolve. Their IT departments may be too overloaded managing their existing systems and other priorities and do not have time or resources needed to support the RPA software and ongoing environment adequately. It is often less costly for these firms to turn to an Outsourced BPO Provider to build these capabilities as part of their scope of services.

4. Embrace your workforce

It’s not a secret that the impact of RPA on your workforce is huge (according to ISG Insights the rate of RPA/AI adoption is set to double by 2019). But this doesn’t mean your employees should feel disengaged with the initiative.

Whether they want it or not, automation is going to come sooner or later, and the sooner they get involved with it, the bigger competitive advantage they will have if they want to make a career within the back office and process optimization. If not, they better look at shifting their career path.

RPA requires a new set of skills and capabilities that will need to be learned and performed by someone, ideally by those “operators” currently executing the tasks manually. In the end, the tasks to be automated are those that most employees would gladly relinquish. Educating employees on how RPA will allow them to focus their time on higher value-added activities will help get them on board. It is not uncommon for your highly engaged best performers to embrace the change and jump at the opportunity to perform higher value work, while less engaged bottom performers resist change and look for an exit.

5. Start small

It is important to realize that RPA should be viewed as an integral component of your back office operation going forward, and not as a “project.” The key to success is to start small, prioritize initiatives and build momentum towards full deployment over time. An initial “Fit Analysis” can quickly identify & prioritize processes that deliver the most benefit with the least amount of complexity.

A typical best practice is to perform an initial Proof of Concept by automating a small number or processes with limited investment. If successful, the Proof of Concept confirms the value of Automation while also delivering savings to fund additional deployment. Additionally, success will build organizational and executive buy in. Any major change initiative needs quick wins to build and sustain the momentum needed to push through an organization’s inherent discomfort with change. It also creates an opportunity to reflect on lessons learned and adjust the implementation strategy based on the initial experience.

After a successful pilot, you are now ready for a broader deployment across the organization. This phase of the RPA journey involves an iterative process of business analysis, BOT development, and configuration, training, and testing. Incorporating the lessons learned from the pilot. During this time you should also be building a support structure for ongoing operations and management. The BOTS are now part of your ongoing operation and like other aspects of your business require continuous monitoring, support, and tweaking. The benefits of RPA can be significant for an organization, but like anything else, it requires commitment, investment, and planning.

Our experience has shown that the mistakes that companies make in implementing RPA are from under-estimating the effort and knowledge needed to implement it. Having realistic expectations, and good guidance and support, coupled with a well-thought out implementation strategy and a rigorous deployment methodology, will help to ensure RPA success.

Watch this quick demo for a real-world example of RPA in action.

Ready to start your RPA journey?

Source: Process Automation: 5 Lessons Learned on How to Get Started


Leveraging Cognitive Computing for Business Gains

Cognitive computing systems have been one of the trendiest aspects of modern day technologies. Deploying computerized models to simulate the human cognition process to find solutions is what cognitive computing systems do. A cognitive computing system is used in complex situations for ambiguous and uncertain outcomes. The term cognitive computing is closely associated with IBM’s cognitive computer system, Watson and overlaps with Artificial Intelligence (AI) using the same technologies to power cognitive applications, like neural networks, expert systems, virtual reality (VR) and robotics.


The Technology Behind Cognitive Computing

Cognitive computing systems can synthesize data from multiple information sources, analyzing the context and conflicting evidence to offer the best-suited solutions. For the best solutions, Cognitive computing systems apply self-learning technologies which use data mining, natural language processing (NLP) and pattern recognition to mimic how the human brain works.

Cognitive systems aggregate vast amounts of structured and unstructured data which are fed into machine learning algorithms for further analysis. With technological upgrades, cognitive systems are poised to refine the way they identify patterns and process data to anticipate new problems and give the best solutions on a case to case basis

To achieve the best solutions, cognitive computing systems must employ five key attributes, as pointed by the Cognitive Computing Consortium.

•  Adaptive: Cognitive systems must be flexible to learn and relearn information changes as priorities change. These systems must be adaptive to real-time dynamic data adjustments as business environment change.

•  Interactive: Human-computer interaction (HCI) is a critical component that is indispensable to cognitive systems. User interaction with cognitive machines, processors, devices and cloud platforms for requirement gathering must be top notch.

•  Iterative: Cognitive computing technologies should be able to perform iteration to the maximum levels. They must identify problems by asking questions or pull additional data if a problem is vague or incomplete by historical analysis about similar situations that have previously occurred.

•  Contextual: Understanding context is critical to the business problem, thus cognitive systems must understand, mine and identify contextual data like syntax, domain, location, time requirements, user profile, tasks or end goals. This contextual data may be drawn from multiple sources of information, like visual, auditory structured and unstructured data or sensor data.


Harnessing the Power of Cognitive Performance Computing

Cognitive performance computing has taken the leaders in business, management consulting and government around the globe by storm.  As the policymakers analyze and debate how they can leverage cognitive computing for their work, cognitive operations are increasingly being adapted in organizations where there is a constant set of unknowns. Senior officials, trusted advisors are setting the best practice for internal users and clients alike. Regulators are working forward to create the laws requiring cognitive compliance from organizational leaders. The next evolution of cognitive computing answers to fulfilling the organizational goals including helping executives and management consultants work through their risk-reward trade-offs matrix also called as cognitive performance. Organisations focus to enhance their cognitive performance as it leads to improved critical thinking, stakeholder communications, advisory collaboration, decision-making, uncertainty monitoring and cognitive compliance.


Mixed Bag Performance

So far the initial results to leverage the gains from cognitive computing have been mixed. Even Watson sometimes gets into a trouble filtering solutions from often conflicting datasets. Cognitive system scores high as it has the ability to learn-relearn and adapt to changing environments. This leads them to improve their results without manual coding. The path to autonomy is leading to a very real possibility that very soon business systems will be largely managed by autonomous, self-learning platforms.

But the path to this development is a two-way street. As cognitive evolves and change to exponential learning and continuous, self-directed optimization, business enterprises must learn to adapt to a radical change in their working to gain an advantage in blockchain, the IoT and advanced 3D printing technologies.

To successfully navigate this transition businesses and organizations need to adopt changes with a clear mind. As industries go digital there will be an opportunity to create service driven lines or entirely new ones for an increasingly connected world.


Digital Footprint in Cognitive Performance

Many organizations have a complex structure involving multiple teams who are responsible for operating processes and digital transformation. These teams are organizations and business enterprises that spend time to embrace the cognitive performance of their teams and will leapfrog their competitors as they leverage the competition.  There is still a long, bright road ahead for reaping the maximum gains from cognitive performance.

Cognitive technology may be referred to sometimes as “thinking” computer, but this is not entirely true and correct. The mysteries of the human thought and consciousness are still unfathomed, as cognitive systems make a sincere attempt to mimic the human intellect through highly advanced algorithms. A definite change has been made as cognitive solutions can outperform the human brain, particularly in processing large, complex datasets. But ultimately, the human brain is the winner for its unique and mysterious thinking and capability to achieve the unconquered.

Source: Cognitive Computing for Business Gains


Digital Operations through AI-Driven Software Robots

A common problem affecting enterprise companies is how to best expand revenue growth, while at the same time, minimize cost growth. Businesses like Amazon and Google have a significant operational advantage through the use of digital technologies like artificial intelligence (AI), which drive exponential growth while at the same time, contain costs. This video introduces how digital operations can be performed through AI-driven software robots.

Source: etftrends-Digital Operations through AI-Driven Software Robots

The Future of Human Work Is Imagination, Creativity, and Strategy

It seems beyond debate: Technology is going to replace jobs, or, more precisely, the people holding those jobs. Few industries, if any, will be untouched.

Knowledge workers will not escape. Recently, the CEO of Deutsche Bank predicted that half of its 97,000 employees could be replaced by robots. One survey revealed that “39% of jobs in the legal sector could be automated in the next 10 years. Separate research has concluded that accountants have a 95% chance of losing their jobs to automation in the future.”

And for those in manufacturing or production companies, the future may arrive even sooner. That same report mentioned the advent of “robotic bricklayers.” Machine learning algorithms are also predicted to replace people responsible for “optical part sorting, automated quality control, failure detection, and improved productivity and efficiency.” Quite simply, machines are better at the job: The National Institute of Standards predicts that “machine learning can improve production capacity by up to 20%” and reduce raw materials waste by 4%.

It is easy to find reports that predict the loss of between 5 and 10 million jobs by 2020. Recently, space and automotive titan Elon Musk said the machine-over-mankind threat was humanity’s “biggest existential threat.” Perhaps that is too dire a reading of the future, but what is important for corporate leaders right now is to avoid the catastrophic mistake of ignoring how people will be affected. Here are four ways to think about the people left behind after the trucks bring in all the new technology.

The Wizard of Oz Is the Wrong Model

In Oz, the wizard is shown to run the kingdom through some complex machine hidden behind a curtain. Many executives may think themselves the wizard; enthralled by the idea that AI technology will allow them to shed millions of dollars in labor costs, they could come to believe that the best company is the one with the fewest people aside from the CEO.

Yet the CEO and founder of Fetch Robotics, Melonee Wise, cautions against that way of thinking: “For every robot we put in the world, you have to have someone maintaining it or servicing it or taking care of it.” The point of technology, she argues, is to boost productivity, not cut the workforce.

Humans Are Strategic; Machines Are Tactical

McKinsey has been studying what kind of work is most adaptable to automation. Their findings so far seem to conclude that the more technical the work, the more technology can accomplish it. In other words, machines skew toward tacticalapplications.

On the other hand, work that requires a high degree of imagination, creative analysis, and strategic thinking is harder to automate. As McKinsey put it in a recent report: “The hardest activities to automate with currently available technologies are those that involve managing and developing people (9 percent automation potential) or that apply expertise to decision making, planning, or creative work (18 percent).” Computers are great at optimizing, but not so great at goal-setting. Or even using common sense.

Integrating New Technology Is About Emotions

When technology comes in, and some workers go away, there is a residual fear among those still in place at the company. It’s only natural for them to ask, “Am I next? How many more days will I be employed here?” Venture capitalist Bruce Gibney explains it this way: “Jobs may not seem like ‘existential’ problems, but they are: When people cannot support themselves with work at all — let alone with work they find meaningful — they clamor for sharp changes. Not every revolution is a good revolution, as Europe has discovered several times. Jobs provide both material comfort and psychological gratification, and when these goods disappear, people understandably become very upset.”

The wise corporate leader will realize that post-technology trauma falls along two lines: (1) how to integrate the new technology into the work flow, and (2) how to cope with feelings that the new technology is somehow “the enemy.” Without dealing with both, even the most automated workplace could easily have undercurrents of anxiety, if not anger.

Rethink What Your Workforce Can Do

Technology will replace some work, but it doesn’t have to replace the people who have done that work. Economist James Bessen notes, “The problem is people are losing jobs and we’re not doing a good job of getting them the skills and knowledge they need to work for the new jobs.”

For example, a study in Australia found a silver lining in the automation of bank tellers’ work: “While ATMs took over a lot of the tasks these tellers were doing, it gave existing workers the opportunity to upskill and sell a wider ranges of financial services.”

Moreover, the report found that there is a growing range of new job opportunitiesin the fields of big data analysis, decision support analysts, remote-control vehicle operators, customer experience experts, personalized preventative health helpers, and online chaperones (“managing online risks such as identify theft, reputational damage, social media bullying and harassment, and internet fraud”). Such jobs may not be in your current industrial domain. But there may be other ways for you to view this moment as the perfect time to rethink the shape and character of your workforce. Such new thinking will generate a whole new human resource development agenda, one quite probably emphasizing those innate human capacities that can provide a renewed strategy for success that is both technological and human.

As Wise, the roboticist, emphasized, the technology itself is just a tool, one that leaders can use how they see fit. We can choose to use AI and other emerging technologies to replace human work, or we can choose to use them to augment it. “Your computer doesn’t unemploy you, your robot doesn’t unemploy you,” she said. “The companies that have those technologies make the social policies and set those social policies that change the workforce.”

Source: HBR-The Future of Human Work Is Imagination, Creativity, and Strategy

UK SMEs shouldn’t fear falling behind on RPA

A recent industry survey has found more than half (57 per cent) of the UK’s SMEs fear big businesses use of robotic process automation (RPA) will help to drive them out of business in the next five years.

Robotic process automation, put simply, is the use of software robots to automate business processes, for example, in back-office functions or your other core business processes. By automating time-consuming, repetitive tasks SMEs stand to improve their productivity and gain competitive advantage.

With a lack of time and resources, it might seem that such a digital transformation is a daunting prospect for many small and medium-sized businesses. But SMEs need to embrace the new technologies available and shouldn’t fear them. Digital transformation – and by that I mean the transformation of your business through the use of digital technology to fundamentally improve business efficiency and productivity – will be key to staying competitive.

How does it work? RPA increases productivity by speeding up the time taken to do mundane tasks; it ensures greater accuracy and compliance by removing human error, and it also ensures greater security of data and information – a bonus as we approach GDPR deadlines too. RPA can be used to improve business processes in many areas including, HR, legal, finance and IT.

It’s not surprising 57% of UK SMEs fear big businesses use of it will put them out of business, as until now the technology has been out of the reach of SMEs and was only available to the large enterprises that could afford it. But this is changing and SMEs need to know that.

On a positive note, the One Poll survey we conducted of SME c-suite executives found that two-thirds of businesses want to use robotic process automation. Sixty-five per cent of companies reported that they either plan to or already automate repetitive, time-consuming tasks. The financial services sector leads the charge, where more than 80 per cent of companies either plan to or already automate at least some business processes.

As a firm, we have started to automate our own processes, including some IT, HR and finance processes. For example, we now use software robots to handle the processing of tickets that come into our IT managed services desk. Our software robots are available 24/7, 365 days of the year so we are able to respond to customer needs faster and more accurately as the robots leave no room for human error.

One of the most exciting ways we are using RPA is to automate some of the forecasting and planning tasks within the business. Our software robots collate real-time sales and marketing information and now process all the information they collect during the day overnight to produce detailed forecasts and business intelligence. To collate this information and analyse it would have taken approximately 8 to 10 hours per day of staff time. Now we have improved business intelligence to plan with, and staff have more time to spend on customer service and strategic thinking than before.

We are also using RPA to conduct some of the more mundane HR aspects of processing the needs of joiners to the firm. For example, ordering their equipment and setting them up on financial and IT systems. In addition, we have automated some of our invoice posting activities. Overall I would estimate we have increased our productivity as a firm by a factor of about two, which means we are better able to focus on growing the business and building an improved customer experience for our clients.

For SMEs now is the time to sit down and think about which of their internal processes could be automated to create efficiencies in their business. We have only automated five key processes at the moment, but the returns have been dramatic. Most businesses will have several processes they can automate; some businesses will have a myriad of processes that can be automated. Working out which ones to automate should be done on a clear ROI basis and by looking at where mundane tasks are hampering staff’s ability to work on more important tasks.

Importantly, RPA doesn’t necessarily mean job losses. McKinsey’s research has shown clearly that employees welcomed the technology because they hated the boring tasks that the machines now do, and it relieved them of the rising pressure of work. We call our software robots ‘Virtual Workers’ as they are there to work alongside humans to do the work they don’t need or want to do. They allow SMEs to free up their staff to spend more time on strategic and creative projects that will give them a competitive advantage, while also improving productivity. In the longer-term, as Professor Leslie Willcocks at the LSE says, ‘it will mean people will have more interesting work.’

Interestingly, the survey backed-up our belief that RPA will help employees become free of the uninteresting tasks and able to focus on more strategic work. 77% of respondents want to use RPA to automate mundane, transactional tasks, and 56% saying freeing up staff time to focus on more strategic work was a key driver for using RPA.

For SMEs, the cost of purchasing RPA has appeared prohibitive. Many have understandably felt they would be left behind as only larger enterprises can afford such technology. But RPA is now an affordable option thanks to the ability to provide RPA as a SaaS (software-as-a-service) offering. With a simple cloud deployment and as-a-service delivery SMEs can now access RPA without having to build costly infrastructures and re-architect applications.

Now is the time for SMEs to embrace the opportunity of implementing RPA in their businesses to increase productivity and help them remain competitive. RPA and the digital transformation that it brings by automating tasks and procedures that allow specialist teams to focus on higher-value tasks is exciting. It means that smaller businesses will be able to deliver tasks at a scale and speed that would only have previously been imaginable for a traditionally ‘big’ organisation. SMEs really need have no fear of falling behind on RPA; indeed they should see it as a huge opportunity to help them compete alongside the ‘big boys’.

Source: SMEs shouldn’t fear falling behind on RPA

These 100 Companies Are Leading the Way in A.I.

Whether you fear it or embrace it, the A.I. revolution is coming—and it promises to have an enormous impact on the world economy. PwC estimates that artificial intelligence could add $15.7 trillion to global GDP by 2030. That’s a gargantuan opportunity. To identify which private companies are set to make the most of it, research firm CB Insights recently released its 2018 “A.I. 100,” a list of the most promising A.I. startups globally (grouped by sector in the graphic above). They were chosen, from a pool of over 1,000 candidates, by CB Insights’ Mosaic algorithm, based on factors like investor quality and momentum. China’s Bytedance leads in funding with $3.1 billion, but 76 of the 100 startups are U.S.-based.

COMPANY COUNTRY SECTOR FUNDING($ Mil.) AEYE U.S. AUTO TECH 16.27 Affirm U.S. FINTECH & INSURANCE 525 Afiniti U.S. MARKETING, SALES, CRM 80 AiCure U.S. HEALTHCARE 30.74 Algolia U.S. ENTERPRISE AI 74.02 Amplero U.S. MARKETING, SALES, CRM 25.5 Anki U.S. ROBOTICS 182 Appier Taiwan COMMERCE 81.5 Applitools U.S. SOFTWARE DEVELOPMENT & DEBUGGING 10.5 Appthority U.S. CYBERSECURITY 23.25 Aquifi U.S. COMMERCE 32.76 Arterys U.S. HEALTHCARE 42 babylon U.K. HEALTHCARE 85 Benson Hill Biosystems U.S. AGRICULTURE 34.21 Brain corporation U.S. ROBOTICS 114 Bytedance China NEWS & MEDIA 3,100 C3 IoT U.S. IOT 130.94 Cambricon China HARDWARE FOR AI 101.4 Cape Analytics U.S. FINTECH & INSURANCE 14 Captricity U.S. CROSS-INDUSTRY 49.02 Casetext U.S. LEGAL TECH 24.28 Cerebras Systems U.S. HARDWARE FOR AI 85 CloudMinds U.S. ROBOTICS 130 CognitiveScale U.S. CROSS-INDUSTRY 40 Conversica U.S. MARKETING, SALES, CRM 56 CrowdFlower U.S. ENTERPRISE AI 55.95 CrowdStrike U.S. CYBERSECURITY 281 Cybereason U.S. CYBERSECURITY 188.62 Darktrace U.K. CYBERSECURITY 182.3 DataRobot U.S. ENTERPRISE AI 124.61 Deep Sentinel U.S. PHYSICAL SECURITY 7.4 Descartes Labs U.S. GEOSPATIAL ANALYTICS 38.46 U.S. AUTO TECH 77 Dynamic Yield U.S. COMMERCE 45.25 Element AI Canada ENTERPRISE AI 102 Endgame U.S. CYBERSECURITY 96.05 Face++ China CROSS-INDUSTRY 608 Flatiron Health U.S. HEALTHCARE 313 FLYR U.S. TRAVEL 14.25 Foghorn Systems U.S. IOT 47.5 Freenome U.S. HEALTHCARE 79 Gong U.S. MARKETING, SALES, CRM 26 Graphcore U.S. HARDWARE FOR AI 110 U.S. MARKETING, SALES, CRM 264.3 Insight Engines U.S. CROSS-INDUSTRY 15.8 Insilico Medicine U.S. HEALTHCARE 8.26 Invoca U.S. MARKETING, SALES, CRM 60.75 Kindred Systems Canada ROBOTICS 43 KYNDI U.S. CROSS-INDUSTRY 9.6 LeapMind Japan ENTERPRISE AI 13.4 Liulishuo China EDUCATION 100 MAANA U.S. IOT 40.14 Merlon Intelligence U.S. RISK & REGULATORY COMPLIANCE 7.65 Mighty AI U.S. AUTO TECH 27.25 Mobalytics U.S. E-SPORTS 2.65 Mobvoi China CROSS-INDUSTRY 257 MOOGsoft U.S. IT & NETWORKS 52.93 Mya Systems U.S. HR TECH 29.5 Mythic U.S. HARDWARE FOR AI 19.42 Narrative Science U.S. CROSS-INDUSTRY 47.87 NAUTO U.S. AUTO TECH 182.6 Neurala U.S. ROBOTICS 15.95 Numerai U.S. FINTECH & INSURANCE 7.5 Obsidian Security U.S. CYBERSECURITY 9.5 Onfido U.K. RISK & REGULATORY COMPLIANCE 59.53 Orbital Insight U.S. GEOSPATIAL ANALYTICS 78.7 OrCam Technologies Israel IOT 47 Osmo U.S. EDUCATION 38.5 PerimeterX U.S. CYBERSECURITY 35 Petuum U.S. ENTERPRISE AI 108 Preferred Networks Japan IOT 112.8 Primer U.S. CROSS-INDUSTRY 14.7 Prospera Israel AGRICULTURE 22 Recursion Pharmaceuticals U.S. HEALTHCARE 118.62 Reflektion U.S. COMMERCE 45.91 SenseTime China CROSS-INDUSTRY 637 Shape Security U.S. CYBERSECURITY 106 Spain PERSONAL ASSISTANTS 8.2 Shield AI U.S. PHYSICAL SECURITY 13.15 Shift Technology France CYBERSECURITY 39.72 Socure U.S. RISK & REGULATORY COMPLIANCE 33.25 SoundHound U.S. NEWS & MEDIA 114.1 SparkCognition U.S. CYBERSECURITY 43.88 Sportlogiq Canada SPORTS 7.2 Tamr U.S. ENTERPRISE AI 41.2 Tempus Labs U.S. HEALTHCARE 70 Text IQ U.S. RISK & REGULATORY COMPLIANCE 3.34 Textio U.S. HR TECH 29.5 Tractable U.K. CROSS-INDUSTRY 9.82 Trifacta U.S. ENTERPRISE AI 76.3 Twiggle Israel COMMERCE 35 UBTECH Robotics China ROBOTICS 521.39 Upstart U.S. FINTECH & INSURANCE 584.73 Versive U.S. CYBERSECURITY 57 Vicarious Systems U.S. ROBOTICS 118.03 Workey Israel HR TECH 9.6 WorkFusion U.S. RISK & REGULATORY COMPLIANCE 71.3 ZestFinance U.S. FINTECH & INSURANCE 268 Zoox U.S. AUTO TECH 290 Zymergen U.S. LIFE SCIENCE 174

Source: Fortune-These 100 Companies Are Leading the Way in A.I.

9 AI trends to look for in 2018 RPA 2.0 initiatives

WorkFusion’s president Alex Lyashok originally wrote this post for his own feed, but we thought it was so fascinating that we decided to publish it here.

We all know that Artificial Intelligence is developing at breakneck speed. But what you may not know, is how these AI technology advancements will benefit your Intelligent Automation or RPA 2.0 programs, making them more powerful and manageable.

Here are nine trending solutions for common automation issues that will help your digital workforce improve and evolve in 2018.

Theme: AI in a Dynamic Enterprise

1. Continual Learning

Most Machine learning (ML) models are trained very infrequently (maaaybe hourly). At the same time, they are making decisions on inputs very frequently (in seconds or less). This can cause models to make incorrect decisions when they operate in a dynamic, quickly shifting environment. If a model was trained on a set of documents from one market and documents from another market start to rapidly come in, this can cause problems.

Look for:

  • Online Learning where models are trained in real-time as new data arrives.
  • Ensembles that combine frequently trained models with infrequently trained (think long-term/short-term memory).
  • Reinforcement Learning that focuses on learning the policies (not the inputs) and updating the policies based on feedback.

2. Robust Decisions

Making important decisions with machine learning means being able to deal with noisy or even adversarial inputs. For example, decisions made on inputs that the model has never seen before can be hard to evaluate.

Look for:

  • Data Provenance to track and understand where exactly the training inputs came from.
  • Confidence Management to develop more nuanced understanding of the ML model output (confidence intervals) and manage and detect unforeseen inputs.

3. Explainable Decisions

Decisions that are made in regulated or sensitive industries, such as banking or healthcare, need to be explained to people in the context of the regulatory or legal framework in which they were made. This means that you need to establish a set of preventive controls to support audits for an automated process.

Look for:

  • Interpretation to be able to interactively review a model in terms and concepts that SME is familiar with.
  • “What-if” Simulation to understand what other inputs could have led to the same decision.
  • Record and Replay to be able to trace, repeat and analyze computations that led to the decision.

Theme: Secure AI

4. Secure Enclaves

Securing AI means being able to run models in an isolated (software or even hardware) environment.

Look for:

  • Enclaves to run code in a secure environment that protects data, privacy, and decision integrity.
  • Secure Modularization to split AI code into parts where the smaller, sensitive part can be run in an enclave and the larger unprotected part can be run in an untrusted environment

5. Adversarial Learning

The adaptive nature of ML systems makes them vulnerable to new types of attacks. Broadly, they can be classified into evasion and data poisoning attacks. Evasion attacks target the inference stage, where data that can be correctly processed by a person, but is processed incorrectly by a ML model is crafted. For example, two documents may look the same to a person, but get classified differently by a computer. Data poisoning attacks target the training stage, where data is injected into the training set to cause the ML model to behave incorrectly in the future.

Look for the Data provenance and Explainable Decision capabilities described above. They can mitigate these risks and can be effectively combined with human-in-the-loop (HITL) capabilities to create reliable preventive controls in ML-based automation.

6. Shared Learning on Confidential Data

When you conduct learning across data that belongs to multiple organizations, you will derive much better results. However, when you train models on sensitive data, prohibiting leaks of confidential information can be a challenge. Hence, exploring secure multi-party learning is increasingly becoming a priority for many organizations.

Look for:

  • Differential Privacy to mix noise into data to securely obscure sensitive inputs.
  • Multi-party Computation (MPC) to allow each party to compute private inputs on joint models without learning of other party’s inputs.

Theme: AI-specific Enterprise Architecture

7. Composable AI systems

Modularity is essential to scaling systems. Breaking down complex software into modules helps reduce cost and improve manageability at scale.

Look for:

  • Model Composition to be able to assemble smaller models into ensembles where each individual model can be added or removed to improve overall output and manageability.
  • Action Composition to combine model outputs into options thereby shifting decision making to higher levels of concepts. For example, options to decline or accept claim vs. data on specific document fields.

8. Cloud-edge systems

Cloud systems are used extensively today to run and manage AI systems. At the same time, most enterprises operate systems in their data centers or on the edge of the cloud. Systems that combine cost benefits of the cloud with control advantages of the edge systems can bring the best of the both worlds together.

Look for Model Composition and Action Composition described above to be able to take advantage of secure, fast learning on the edge with the power of centralized cloud systems.

6. Domain specific hardware

Many enterprises increasingly adopt hardware architectures that increase performance, reduce cost, or improve security of AI systems.

Look for:

  • GPU and FPGA Support to reduce cost and improve scalability in data centers or public/private clouds
  • Google Tensor Processing Unit (TPU) Compatibility to accelerate certain compatible AI payloads
  • Enclave Support to take advantage of secure computation such as Intel’s SGX and ARM’s TrustZone

Visit to learn more about AI, Cognitive Automation, and how to expand your RPA program to take advantage of all our Intelligent Automation techniques.

Source: AI trends to look for in 2018 RPA 2.0 initiatives

B2B PAYMENTS UiPath Lands $3B Valuation For Robotics Process Automation Tech

robotics process automation (RPA) startup has just secured a $3 billion valuation as B2B FinTech continues to explore how the technology will disrupt the market. A press release issued Tuesday (Sept. 18) said CapitalG and Sequoia Capital led the $225 million Series C round for UiPath.

The funding, which also saw Accel participate, followed only a few months after the company raised $153 million, pulling UiPath’s valuation up to $1 billion. The latest investment means UiPath has tripled its valuation in less than six months, reports noted.

UiPath’s RPA technology links businesses with software that can deepen their automation capabilities beyond less sophisticated automation solutions. Reports pointed to corporate processes like accounts payable (AP), procurement and reconciliation as key areas that are prime for RPA disruption, particularly as the technology negates the need for businesses to replace their existing systems.

In its announcement, UiPath pointed to its accelerated growth, which saw its annual revenues spike from $1 million to $100 million, though it did not indicate how quickly it achieved that milestone. Still, the company said it could become “the fastest growing enterprise software company in history.” At present, the startup has 1,800 corporate customers averaging six new clients added each day. By the end of the year, the company expects its annual revenues to have quadrupled from 2017 levels.

We are enabling a future where employees at every organization are empowered to automate tedious and time-consuming work, enabling them to focus on creative, challenging problems, said UiPath Co-founder and CEO Daniel Dines in a statementWe are delighted by the strong support of our customers, partners and investors toward making this future of automation a reality. UiPath is driven by the incredible potential for our platform to be the gateway to transform our customers’ digital business operations with machine learning and AI.

UiPath did not specify what it plans to do with the latest funding round.

Source: PYMNTS-B2B PAYMENTSUiPath Lands $3B Valuation For Robotics Process Automation Tech

Cultural Counterparts: The Advantage of Nearshoring

When a business is choosing which company to outsource with, location can often be overlooked in favour of the most appropriate specialist for the project. However, location – and especially proximity – should be a critical part of the decision process. For example, if your company is based in Europe, it will be more difficult to outsource from a provider based in Asia, due to a mixture of time, travel, language, and perhaps cultural differences. To ease this dilemma, nearshoring offers greater potential in outsourcing because it has the strongest collaboration opportunities for a number of additional reasons.

Shared culture 
The key benefits of nearshoring are the shared time zones and ease of travel to meet your outsourcing partner. The further apart the companies are, the more expensive flights will be, and with the added flight time, more work time will be hindered. In addition, the working hours of the two companies based nearshore will be similar, so if a problem occurs, the teams can organise an immediate call to fix the problem. Alongside this, if both companies have working hours that are either within the exact same time zone, or very similar, weekly meetings can also be secured easily, as opposed to one party having to use time in their early morning or evening.

On top of this, you’re far more likely to be aware of—and share—the national holidays of your nearshoring partner, and will consequently be aware of which working days your outsourced partner won’t be present in their office. With these dates in mind, project deadlines can be calculated accordingly. If a country has a national holiday shortly before a deadline, you will be able to adapt the workload accordingly, rather than find out about the holiday last minute.

The work culture of each country also impacts how companies communicate with each other. For example, misreading a message can have disastrous consequences if interpreted incorrectly, affecting both the direction and development of your partnership. The same applies to languages. If both company representatives share the same language, or at least share knowledge of a language, then the two companies can work much easier. This is far more likely to happen in regions where one language is commonly used amongst neighbouring countries.

Local legislation 
And yet, other benefits to nearshoring, such as the company having a greater understanding of the country’s changes in legislation and law, are more often ignored. For example, companies based in the EU, or with customers in the EU that store customer data will be impacted by the incoming General Data Protection Regulation (GDPR) legislation, but an outsourcing partner in Asia may not be aware of this new legislation. By nearshoring, the businesses will be aware of changing legislations or laws, and can consequently understand new processes that the company needs to undertake.

Shared culture and location are undoubtedly the biggest advantages to nearshoring. With smoother and more efficient communication and date planning comes a product that is created quicker and with greater quality, with a higher chance of having as little miscommunication dilemmas along the way as possible.

Source: Counterparts: The Advantage of Nearshoring

GDPR: Lose money if you comply, lose money if you don’t

Dive Brief:

  • If close to one-third European Google users decide to opt out of data sharing when GDPR comes into effect in May, it could translate to a 2% impact on the company’s ad revenue, according to a Deutsche Bank note to investors, reported by Business Insider. Deutsche Bank estimates the internet giant earns one-third of its revenue from the continent, and users opting out would reduce ad efficacy by 20%.
  • Tech giants like Google and Microsoft have had the resources and manpower to get up to speed, but global companies in general are lagging behind on compliance and may incur high costs that way. Only one-third of executives said their company has a GDPR plan in place, although in Europe around 60% reported having a compliance plan, according to an EY survey of global executives.
  • The adoption of forensic data analytics (FDA), which can help companies reach compliance, rose 51% year-over-year in 2017, according to the study. More focus is also being given to advanced FDA technologies. Close to 40% of respondents said they were planning on implementing robotic process automation as well as AI.

Dive Insight:

Complaints that businesses, at home and abroad, are woefully unprepared for the upcoming set of data privacy regulations are widespread. Many companies have hesitated because the cost of becoming compliant can push into the millions of dollars — although studies have shown that noncompliance is still more costly.

The narrative of GDPR tends to focus on costs associated with becoming compliant or the costs of noncompliance, which, given potential for fines of 4% of global turnover, is unsurprising. But companies could be facing losses on the compliance front too as users begin taking more control over data.

Companies collect a mind-boggling amount of data on the users or affiliates of their platforms. Just ask the woman who asked Tinder for the data it had on her and got 800 pages in response. But under GDPR, users will have more power to decide what information a business collects about them and the right to erasure.

Google makes the majority of its revenue off of its ad business, and such a hit in the European region could break a sweat on CEO Sundar Pichai’s brow. The company may have high hopes for its cloud segment eventually bringing in as much revenue as advertising, but it is still years out from that point, if it hits it at all.

But from the international behemoth to SMBs that handle much smaller amounts of personal data, the shift in the relationship is starting to favor people and data over companies.

Source: Lose money if you comply, lose money if you don’t