Artificial intelligence is redefining corporate finance

Sven Denecken, SVP and Head of Product Management and Co-Innovation at SAP, discusses how AI is changing finance functions

Artificial intelligence (AI) and its potential to transform business processes across industries has become a central focus for organizations across the globe. Whether its conversations in the boardroom, sessions at an industry conference or a small-scale team meeting of accountants, companies today are buzzing about AI and the opportunity it poses to help usher in digital transformation.

While many still speculate that AI is more hype than reality, AI is already deeply ingrained in many organisations, driving automation that simplifies business processes.

This is especially true in corporate finance, with a recent study from Oxford Economics and SAP finding that 73% of finance executives agree that automation is improving finance efficiency at their company.

What is AI?

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Defining artificial intelligence is perhaps the biggest initial hurdle that many finance stakeholders face in evaluating these technologies and weighing their potential impact in the enterprise. So, to start with the basics, AI can be broadly defined to include any simulation of human intelligence exhibited by machines.

One historical application that many organizations today are using is robotic process automation (RPA), which is rule-based robotic automation that can be extremely beneficial to companies in automating routine tasks. But beyond RPA, AI technology is a huge growth area that is branching into a multitude of areas when it comes to research, development and investment.

Other examples of AI include autonomous robotics, natural language processing or NLP (think of virtual assistants such as Apple’s Siri or Amazon’s Alexa), knowledge representation techniques (knowledge graphs) and more.

Machine learning is one specific subset of AI that has been gaining buzz in the industry today. Machine learning is learning based AI – it aims to teach computers how to accomplish tasks using data inputs, but without explicit rule-based programming that has historically been seen with RPA.

Drive efficiency in finance with AI

RPA is increasingly common within finance departments today, to help automate routine finance responsibilities, including streamlining transactional tasks and reporting. However, advanced AI technologies, like machine learning, have the power to take this a step further, removing the need for rule-based machines by implementing learning technology.

For instance, invoicing is a finance responsibility that can often be a nightmare for accounts receivable or treasury clerks. Often a customer might pay the incorrect amount for an invoice, combine several invoices together into one check, or even forget to include their invoice reference number. Rectifying this can be a huge time suck in trying to sift through invoices or track down the customer.

This is an area where machine learning could support finance teams in real-time by applying its learning technology to ultimately make suggestions to accounting teams on matching payments to invoices. With this, finance teams can not only better ensure accuracy in aligning payments, they can massively cut down the time spent manually tracking down the relevant information and apply themselves to other needs within the business.

Let AI have a seat at the table

The potential for AI doesn’t just lie in efficiency. As these machines get smarter, there is enormous potential for AI to support CFOs and finance directors in informing strategy and driving action.

In the consumer technology space, NLP applications like Siri and Alexa have helped to “humanize” technology and information for individuals, answering questions about the weather and news headlines – even occasionally entertaining the user with a bad joke. The use of these voice-enabled devices isn’t limited to the consumer setting, and in the coming years we will likely see an increase of NLP technology being applied in the B2B enterprise setting.

For instance, CFOs and other finance executives often receive questions in boardroom meetings around revenue forecasts, and a myriad of other topics. Often, the executive needs to spend countless hours prepping and pulling these figures to anticipate what information might be needed, or alternatively, halt an in-progress meeting to pull up the latest numbers.

These digital assistant devices could be used in the enterprise setting to let the CFO easily ask questions of his or her data analytics system in real-time. This technology would not only enable uninterrupted meetings, but also allow the CFO and other company stakeholders to make informed decisions that drive action quickly and with confidence.

Smart technologies will change the talent landscape

AI offers exciting promise for innovation as companies look to stay-ahead in today’s fast-paced, globalised business landscape, but as its popularity continues to grow, conversations have begun about the possible negative implications for workers.

For finance teams, while AI can have a measurable impact on efficiency, it cannot replace the human element. Human review and monitoring is still required when technology like machine learning streamlines some manual tasks, especially in cases that may be too complex for the machine to rectify.

Additionally, there is an opportunity for finance executives to build their teams by hiring people who are familiar with advanced technologies and can help support, improve and innovate their use within the finance function, ensuring human workers are equipped to excel in their roles.

Eighty-four percent of global companies cite digital transformation as an important factor for survival in the next five years, but to-date, only 3% of organisations have completed a company-wide digital transformation, according to another recent survey by Oxford Economics and SAP.

With this, finance executives in particular, believe that investment in digital skills and technology will have the greatest impact on company revenue in the next two years.

By exploring how AI technology can be implemented, not only in streamlining processes, but also as a valuable resource in informing strategy and driving action in finance, CFOs and other finance stakeholders can ensure their workforce is best armed to drive success in the digital economy.

Source:  Financial Director- Artificial intelligence is redefining corporate finance 


The Roots Of Robotics and Artificial Intelligence

Robotic automation is nothing new and goes back to ancient times. The science of robotics is perceived as a very recent development in today’s society but the truth is that history of artificial intelligence goes back centuries into ancient times.

AI dates back to 2000 BC where early legends like Cadmus mentioned artificial people in his mythologies. In Chinese legends it goes to the 10th century BC and Yan Shi who had written down ideas of transforming a human into an account.

Many other records and stories can be found in the Greek mythology, Christian legends and the Indian history. In Christian legends, one of the most famous legends includes plan for the construction of an entire android.

First Constructions of Artificial Intelligence

The first real concepts of AI are recorded from the 4th century BC on. The greek mathematician Archytas of Tarentum constructed “The Pigeon”, a mechanical bird driven by steam. The ancient greek philosopher Aristotle suspected that automation could one day end slavery because it could bring human equality. Another influential figure was Al-Jazari, who lived in the 12th century. He constructed a variety of different machines like kitchen appliances and automation machines powered by water.

Even the genius Leonardo da Vinci (1452-1519) mentioned AI and suggested designs of human robots with detailed plannings. Very interesting concepts were made the Japanese Hisaghige Tanaka who developed mechanical toys for the purpose of serving tea.

Over the centuries robotic concepts gained popularity and came to the attention of national leaders like Frederick the Great and Napoleon Bonaparte.

Industrial Revolution

Although there were already some really functional and remarkable examples of AI before, the Industrial Revolution and the progress in engineering and science gave robotics a major boost in the years to come. Charles Babbage (1791-1871) was an influential figure. He worked to develop the foundations of computer science in the early 19th century. Furthermore, in the 19th century factories started to use automation of robots to improve efficiency or the machine loads and the production numbers.

The 20th century to Today: The Modern Era of Robotics

Isaac Asimov came up with the concept of the “Three Laws of Robotics”. He wrote science fiction and robot stories that inspired other editors to write science fiction films. His ideas and concepts influenced the 2004 film I, Robot, starring Will Smith.

In the 1950s a robotic device was designed by George Devol that was used in plants of General Motors in the United States. Further other companies followed and successively implemented new technological advancements into their production and assembly lines. People started specializing in robotic and perfectioning ideas into reality. Robotic inventions become more and more popular and found appearance on the Tonight Show in 1966.

Over the decades, the size of the robotic industry has massive grown and AI found its place in many industries such as sales, retail, engineering, finance, construction, e-commerce, real estate. Companies spend millions and billions on research and development to be use AI to their advantage and to cope with trends and developments.

If you want to find out about the lastest technological trends in robotic automation, then check out Thoughtonomy.

The AI and robotic automation industry is expected to exponentially grow in the years ahead to 2020 and a large number of jobs and industries will potentially be affected by it. However, studies show that more robots don’t necessarily lead to a reduction in the number of jobs. In fact, the opposite effect has been observed in some instances because it opens many doors to use human creativity in other jobs. One such instance is online logo maker, Logojoy, and the way they’ve taken graphic design and made it

Source: Roots Of Robotics and Artificial Intelligence

Automation, robotics, and the factory of the future

Cheaper, more capable, and more flexible technologies are accelerating the growth of fully automated production facilities. The key challenge for companies will be deciding how best to harness their power.

At one Fanuc plant in Oshino, Japan, industrial robots produce industrial robots, supervised by a staff of only four workers per shift. In a Philips plant producing electric razors in the Netherlands, robots outnumber the nine production workers by more than 14 to 1. Camera maker Canon began phasing out human labor at several of its factories in 2013.

This “lights out” production concept—where manufacturing activities and material flows are handled entirely automatically—is becoming an increasingly common attribute of modern manufacturing. In part, the new wave of automation will be driven by the same things that first brought robotics and automation into the workplace: to free human workers from dirty, dull, or dangerous jobs; to improve quality by eliminating errors and reducing variability; and to cut manufacturing costs by replacing increasingly expensive people with ever-cheaper machines. Today’s most advanced automation systems have additional capabilities, however, enabling their use in environments that have not been suitable for automation up to now and allowing the capture of entirely new sources of value in manufacturing.

Falling robot prices

As robot production has increased, costs have gone down. Over the past 30 years, the average robot price has fallen by half in real terms, and even further relative to labor costs (Exhibit 1). As demand from emerging economies encourages the production of robots to shift to lower-cost regions, they are likely to become cheaper still.

Exhibit 1

Accessible talent

People with the skills required to design, install, operate, and maintain robotic production systems are becoming more widely available, too. Robotics engineers were once rare and expensive specialists. Today, these subjects are widely taught in schools and colleges around the world, either in dedicated courses or as part of more general education on manufacturing technologies or engineering design for manufacture. The availability of software, such as simulation packages and offline programming systems that can test robotic applications, has reduced engineering time and risk. It’s also made the task of programming robots easier and cheaper.

Ease of integration

Advances in computing power, software-development techniques, and networking technologies have made assembling, installing, and maintaining robots faster and less costly than before. For example, while sensors and actuators once had to be individually connected to robot controllers with dedicated wiring through terminal racks, connectors, and junction boxes, they now use plug-and-play technologies in which components can be connected using simpler network wiring. The components will identify themselves automatically to the control system, greatly reducing setup time. These sensors and actuators can also monitor themselves and report their status to the control system, to aid process control and collect data for maintenance, and for continuous improvement and troubleshooting purposes. Other standards and network technologies make it similarly straightforward to link robots to wider production systems.

New capabilities

Robots are getting smarter, too. Where early robots blindly followed the same path, and later iterations used lasers or vision systems to detect the orientation of parts and materials, the latest generations of robots can integrate information from multiple sensors and adapt their movements in real time. This allows them, for example, to use force feedback to mimic the skill of a craftsman in grinding, deburring, or polishing applications. They can also make use of more powerful computer technology and big data–style analysis. For instance, they can use spectral analysis to check the quality of a weld as it is being made, dramatically reducing the amount of postmanufacture inspection required.

Robots take on new roles

Today, these factors are helping to boost robot adoption in the kinds of application they already excel at today: repetitive, high-volume production activities. As the cost and complexity of automating tasks with robots goes down, it is likely that the kinds of companies already using robots will use even more of them. In the next five to ten years, however, we expect a more fundamental change in the kinds of tasks for which robots become both technically and economically viable (Exhibit 2). Here are some examples.

Exhibit 2

Low-volume production

The inherent flexibility of a device that can be programmed quickly and easily will greatly reduce the number of times a robot needs to repeat a given task to justify the cost of buying and commissioning it. This will lower the threshold of volume and make robots an economical choice for niche tasks, where annual volumes are measured in the tens or hundreds rather than in the thousands or hundreds of thousands. It will also make them viable for companies working with small batch sizes and significant product variety. For example, flex track products now used in aerospace can “crawl” on a fuselage using vision to direct their work. The cost savings offered by this kind of low-volume automation will benefit many different kinds of organizations: small companies will be able to access robot technology for the first time, and larger ones could increase the variety of their product offerings.

Emerging technologies are likely to simplify robot programming even further. While it is already common to teach robots by leading them through a series of movements, for example, rapidly improving voice-recognition technology means it may soon be possible to give them verbal instructions, too.

Highly variable tasks

Advances in artificial intelligence and sensor technologies will allow robots to cope with a far greater degree of task-to-task variability. The ability to adapt their actions in response to changes in their environment will create opportunities for automation in areas such as the processing of agricultural products, where there is significant part-to-part variability. In Japan, trials have already demonstrated that robots can cut the time required to harvest strawberries by up to 40 percent, using a stereoscopic imaging system to identify the location of fruit and evaluate its ripeness.

These same capabilities will also drive quality improvements in all sectors. Robots will be able to compensate for potential quality issues during manufacturing. Examples here include altering the force used to assemble two parts based on the dimensional differences between them, or selecting and combining different sized components to achieve the right final dimensions.

Robot-generated data, and the advanced analysis techniques to make better use of them, will also be useful in understanding the underlying drivers of quality. If higher-than-normal torque requirements during assembly turn out to be associated with premature product failures in the field, for example, manufacturing processes can be adapted to detect and fix such issues during production.

Complex tasks

While today’s general-purpose robots can control their movement to within 0.10 millimeters, some current configurations of robots have repeatable accuracy of 0.02 millimeters. Future generations are likely to offer even higher levels of precision. Such capabilities will allow them to participate in increasingly delicate tasks, such as threading needles or assembling highly sophisticated electronic devices. Robots are also becoming better coordinated, with the availability of controllers that can simultaneously drive dozens of axes, allowing multiple robots to work together on the same task.

Finally, advanced sensor technologies, and the computer power needed to analyze the data from those sensors, will allow robots to take on tasks like cutting gemstones that previously required highly skilled craftspeople. The same technologies may even permit activities that cannot be done at all today: for example, adjusting the thickness or composition of coatings in real time as they are applied to compensate for deviations in the underlying material, or “painting” electronic circuits on the surface of structures.

Working alongside people

Companies will also have far more freedom to decide which tasks to automate with robots and which to conduct manually. Advanced safety systems mean robots can take up new positions next to their human colleagues. If sensors indicate the risk of a collision with an operator, the robot will automatically slow down or alter its path to avoid it. This technology permits the use of robots for individual tasks on otherwise manual assembly lines. And the removal of safety fences and interlocks mean lower costs—a boon for smaller companies. The ability to put robots and people side by side and to reallocate tasks between them also helps productivity, since it allows companies to rebalance production lines as demand fluctuates.

Robots that can operate safely in proximity to people will also pave the way for applications away from the tightly controlled environment of the factory floor. Internet retailers and logistics companies are already adopting forms of robotic automation in their warehouses. Imagine the productivity benefits available to a parcel courier, though, if an onboard robot could presort packages in the delivery vehicle between drops.

Agile production systems

Automation systems are becoming increasingly flexible and intelligent, adapting their behavior automatically to maximize output or minimize cost per unit. Expert systems used in beverage filling and packing lines can automatically adjust the speed of the whole production line to suit whichever activity is the critical constraint for a given batch. In automotive production, expert systems can automatically make tiny adjustments in line speed to improve the overall balance of individual lines and maximize the effectiveness of the whole manufacturing system.

While the vast majority of robots in use today still operate in high-speed, high-volume production applications, the most advanced systems can make adjustments on the fly, switching seamlessly between product types without the need to stop the line to change programs or reconfigure tooling. Many current and emerging production technologies, from computerized-numerical-control (CNC) cutting to 3-D printing, allow component geometry to be adjusted without any need for tool changes, making it possible to produce in batch sizes of one. One manufacturer of industrial components, for example, uses real-time communication from radio-frequency identification (RFID) tags to adjust components’ shapes to suit the requirements of different models.

The replacement of fixed conveyor systems with automated guided vehicles (AGVs) even lets plants reconfigure the flow of products and components seamlessly between different workstations, allowing manufacturing sequences with entirely different process steps to be completed in a fully automated fashion. This kind of flexibility delivers a host of benefits: facilitating shorter lead times and a tighter link between supply and demand, accelerating new product introduction, and simplifying the manufacture of highly customized products.

Making the right automation decisions

With so much technological potential at their fingertips, how do companies decide on the best automation strategy? It can be all too easy to get carried away with automation for its own sake, but the result of this approach is almost always projects that cost too much, take too long to implement, and fail to deliver against their business objectives.

A successful automation strategy requires good decisions on multiple levels. Companies must choose which activities to automate, what level of automation to use (from simple programmable-logic controllers to highly sophisticated robots guided by sensors and smart adaptive algorithms), and which technologies to adopt. At each of these levels, companies should ensure that their plans meet the following criteria.

Automation strategy must align with business and operations strategy. As we have noted above, automation can achieve four key objectives: improving worker safety, reducing costs, improving quality, and increasing flexibility. Done well, automation may deliver improvements in all these areas, but the balance of benefits may vary with different technologies and approaches. The right balance for any organization will depend on its overall operations strategy and its business goals.

Automation programs must start with a clear articulation of the problem. It’s also important that this includes the reasons automation is the right solution. Every project should be able to identify where and how automation can offer improvements and show how these improvements link to the company’s overall strategy.

Automation must show a clear return on investment. Companies, especially large ones, should take care not to overspecify, overcomplicate, or overspend on their automation investments. Choosing the right level of complexity to meet current and foreseeable future needs requires a deep understanding of the organization’s processes and manufacturing systems.

Platforming and integration

Companies face increasing pressure to maximize the return on their capital investments and to reduce the time required to take new products from design to full-scale production. Building automation systems that are suitable only for a single line of products runs counter to both those aims, requiring repeated, lengthy, and expensive cycles of equipment design, procurement, and commissioning. A better approach is the use of production systems, cells, lines, and factories that can be easily modified and adapted.

Just as platforming and modularization strategies have simplified and reduced the cost of managing complex product portfolios, so a platform approach will become increasingly important for manufacturers seeking to maximize flexibility and economies of scale in their automation strategies.

Process platforms, such as a robot arm equipped with a weld gun, power supply, and control electronics, can be standardized, applied, and reused in multiple applications, simplifying programming, maintenance, and product support.

Automation systems will also need to be highly integrated into the organization’s other systems. That integration starts with communication between machines on the factory floor, something that is made more straightforward by modern industrial-networking technologies. But it should also extend into the wider organization. Direct integration with computer-aided design, computer-integrated engineering, and enterprise-resource-planning systems will accelerate the design and deployment of new manufacturing configurations and allow flexible systems to respond in near real time to changes in demand or material availability. Data on process variables and manufacturing performance flowing the other way will be recorded for quality-assurance purposes and used to inform design improvements and future product generations.

Integration will also extend beyond the walls of the plant. Companies won’t just require close collaboration and seamless exchange of information with customers and suppliers; they will also need to build such relationships with the manufacturers of processing equipment, who will increasingly hold much of the know-how and intellectual property required to make automation systems perform optimally. The technology required to permit this integration is becoming increasingly accessible, thanks to the availability of open architectures and networking protocols, but changes in culture, management processes, and mind-sets will be needed in order to balance the costs, benefits, and risks.

Cheaper, smarter, and more adaptable automation systems are already transforming manufacturing in a host of different ways. While the technology will become more straightforward to implement, the business decisions will not. To capture the full value of the opportunities presented by these new systems, companies will need to take a holistic and systematic approach, aligning their automation strategy closely with the current and future needs of the business.

Source: Mckinsey-Automation, robotics, and the factory of the future

6 Tips For Successful Robotic Process Automation

A smooth Robotic Process Automation (RPA) implementation is more likely to result where processes have gone through a thorough selection process using personnel the Business, IT and the RPA team. Typical selection criteria could include processes where there is an increased need for regulatory compliance and auditability, processes where errors are costly, or where the ability to scale up operations whilst minimising costs is important. Establishing clear selection criteria, and having relevant approval to proceed with automation should be the best route to success.


1. Get Business Stakeholder Support

RPA adoption isn’t normally driven by the IT department – but by business units instead. Therefore, for a RPA initiative to be a success, at the early project stages, support must be gained from key stakeholders – such as the CEO, or key IT personnel. This is because although RPA software sits within, and is managed by a business team, it’s still governed by the IT department using existing practices. With RPA, no robot can operate without a PC, a user account, or access to an application.

IT delivers the infrastructure required and applies roles and permissions to a robotic user account, so without its buy-in, getting a RPA programme up-and-running – may prove difficult. Although RPA is quick to implement, and minimises the need for costly systems integration, these benefits are not always fully appreciated by the IT department. To address this trend, there’s a growing list of scenarios that should be communicated that benefit IT department’s own internal operations. These can range from initial projects in service management and transaction processing – to resource-intensive, administrative and transactional work.

2. Keep Communicating With The IT Department Throughout Delivery

Liaison with IT should become an ongoing activity – as at various points in the delivery of a new RPA process, IT colleagues can provide real support to limit any operational impacts. For example, they can provide access to test environments for the purposes of building and testing processes, support on roles and permissions within an application, and crucially, knowledge of upcoming changes – as part of IT release cycles for an application – that may impact live processes. Regular contact with IT is important to ensure delivery of a new process is smooth – so the ‘live’ process remains operational.

3. Have A Clear Strategy For The Use Of RPA Across The Business

Ideally, any RPA project should make businesses operate more fluidly and efficiently. However, without a clear strategy on how RPA is going to be deployed and utilised, there is a risk that it just becomes the driving force of a standalone business function. Having a clear vision for the use of RPA ensures that the right RPA software is chosen to meet the collective needs of the many – not the few. For example, this could include linking RPA to strategic imperatives such as ‘increasing efficiency’ or ‘increasing agility’ outlined by the Board of Directors. This also ensures that the software can fully integrate into existing IT frameworks and support mechanisms – delivering a more harmonious infrastructure.

4. Be Prepared For The Hidden Costs Of RPA

Whilst recent developments in the RPA market have removed some costs, there will always be some initial expenditure to get RPA up-and-running and then to keep it operational. Budget for the build phase – including the provisioning of IT infrastructure such as databases, physical / virtual machines etc., and IT resource time to get RPA up-and-running. Also, account for additional consultancy costs from partner companies. Running costs are again largely time related and centre around the ongoing delivery and maintenance of processes, maintenance of underlying infrastructure and support etc. There may be additional roles created because of RPA, which may add salary costs. All of this needs to be factored into business cases for RPA.

5. Set Realistic Expectations

“RPA is a tool, not the tool.” RPA should not be the ‘go-to’ solution for every business problem; it is one of several options available and should form part of a wider strategy on the use of technology. There is still a need for human intervention to manage exceptions. So, taking a human user completely out of the equation through the implementation of RPA, is likely to lead to operational challenges later. Exceptions will be thrown due to business rules not being met and / or applications not responding as expected. Human users need to be on hand to help address these exceptions.

6. Choose The Correct Process For Automation

RPA is most effective where processes are repetitive, rules-based, high volume and do not require human judgement. It becomes more challenging where processes are non-standardised and require frequent human intervention to complete – such as interacting with customers or working with process variability. Even processes that pass the obvious criteria may not ultimately be the best ‘candidates’ for automation – at least not initially. For example, automating an inefficient process, can potentially only speed up the inefficiency. More benefit could be gained from either making the process more efficient, prior to automating – or by redesigning the process during the design phase of delivery.

Source: BCW-6 Tips For Successful Robotic Process Automation


In all the current spin around the use (or not) of robotic process automation, a practitioner’s voice came through clearly recently with real-life experience and learnings. Brian Halpin, Head of Automation at HSBC, joined HfS Research’s webinar: Achieving Intelligent Automation in Business Operations. We captured a few insights to share here… for more, feel free to access the replay and watch for the upcoming research report that includes analysis and insights from over 400 participants.

“I can’t get no satisfaction” from RPA unless it is associated with the right skills, data, process, and outcomes

Phil has covered this staggering research finding in great detail; barely half the RPA practitioners in our study are satisfied with the cost savings and business value generated. Brian was not surprised at our data, “It’s pretty logical… when RPA works, satisfaction is high; and vice versa. What contributes to RPA working: it’s not about the software; it’s about the ability to implement it in a way that drives the right outcomes. You need the skills to implement and manage RPA, quality of the data, the engagement of the team in operations, etc… “ Brian called out in particular the need to proactively address all aspects of operational change management.

“Recognize that you are trying to achieve operational change management,” explained Brian. “If you need to make a change to your standard operating procedure today, what process do you go through? I redesign the process, go through risk, financial, and other standard sign-offs on the controls, etc., because we are changing the way we work when we incorporate robots.  There is a strong cultural aspect to embed because I have to think differently about how I manage – I’m still fully responsible for what the people and now also the robots do and for the outcomes.”

Incorporating RPA into your business is not just about using technology; it is a cultural change

Is there a method/curriculum/approach for RPA? “There are steps to go through,” says Brian, “we have one and use the Blue Prism model. Don’t be tempted to skip through the stages too quickly. Do a proof of concept for each new business service to engage them in the learning experience. Go through the steps of the process, the ownership structure as an operational leader when it goes live, how to manage change, etc. You will build the organization’s maturity and learn what method works for your organization to follow. It’s like DevOps, but with RPA, it sits in the business unit instead of IT.”
Do you need a COE for RPA? “You have to build a COE to scale the use of RPA. In the early stages, you probably don’t need it, but you will likely have a federated model over time. Otherwise, everyone sees and manages RPA differently. You need to be able to leverage lessons, governance, and contracts with a centralized model that branches out into each business unit.” Brian sees HSBC evolving such that each business unit runs its own capability, but is managed centrally the way global business services runs today.
How do you bring in AI and cognitive? Brian notes that the business still defines the use cases, but AI technology needs to be housed in the CIO’s office. He says: “RPA and cognitive are complementary… a robot needs to call a machine learning component to assist in making decisions. RPA creates the framework that we will plug into with AI components.”

To effectively use RPA, you have to have a learning mindset… these skills are hard to find, and you need to develop them

People with skills in RPA – that have been through a full RPA implementation and realized benefits – are few and far between. Per Brian: “We see a lot of vendors that talk about ability to provide services that haven’t done it or the level of skills required… only a handful have been through the full cycle. The senior level skills are critical, not just certifications. It is very hard to find that depth in capability.” HSBC is looking at how to develop talent to automate at scale – and determine use cases to embed AI and cognitive capabilities to continue to add value.


Our research shows that what is holding back RPA adoption is the lack of immediate cost savings, followed by a general lack of understanding of the potential benefits. “It starts with cost reduction,” says Brian, “and quickly turns into efficiency, speed, customer experience, and risk benefits from data accuracy.”


The State of Automation and AI Study 2017: 400 operations leaders air the real deal

Finally, we can stop freaking out at all these lovely projections, such as “AI will eliminate 1.8M jobs but create 2.3M” in the next couple of years, and “47 percent of total US employment” being at risk and “AI being possibly the last event in human history”. Oh, and who can forget that recent whopper, “96% of clients are getting real value from RPA”.

We got so sick of this nonsense, we just went out and surveyed 400 enterprise automation and AI decision makers across the Global 2000, split across IT and business operations functions, and hit them with some very straight poignant questions about their attitudes, satisfaction levels and genuine plans for both AI and Automation across their business operations.

But let’s start with the hype: AI and Machine Learning is now one of the most critical strategic directives being dictated from the C-Suite onto the operations function

81% of operations leaders are feeling the pressure from their bosses to reduce the reliance on mid/higher skilled labor, viewing AI and Machine Learning as increasingly important or even mission-critical directives to drive this. Only cost reduction beats this out as a priority, but as we all know, we can’t reduce costs much further without investing in our digital underbellies:

Click to Enlarge

What’s clear is that enterprises are frantically evaluating their talent (81%) and looking to collapse these silos in the middle/back offices to improve their customer experiences. And they see AI, Machine Learning, and process automation as the levers to achieve this.

So let’s summarize the key findings from the study, and you can download your copy here :

  • Automation is the number one strategic priority four-fifths of enterprise C-Suites are placing on their operations. Enterprises see AI and machine learning (81%) and process automation and robotics (82%) as important C-suite directives toward operations strategy – higher than any priority other than cost reduction.
  • 98% of enterprises have an automation agenda, but a third already have embedded it into their service delivery. Every organization today needs to have an automation strategy and that is reflected in the responses in our survey; only 2% suggest not having a strategy as of now, while 20% are in the process of formulating their strategy. Already, 31% of enterprises are integrating automation into the fabric of their service operations. Others are setting up dedicated CoEs (18%) and working with service providers (13%).
  • Corporate leadership and IT are most active driving the automation agenda. Decision making is increasingly being led by the CEO (54%), CIO/IT Director (57%), and CFO/Finance Director (35%). Additionally, a diverse group of automation influencers and stakeholders emerge, notably the finance department (49% consider as influencers), procurement (47%), data center managers (51%) and purchasing managers (48%).
  • Deployments of RPA as well as AI starting to scale out with varying degrees of maturity. RPA is seeing rapid adoption and AI will become mainstream in two years. More than 70% of customers are planning to deploy RPA over the next two years and more than 50% believe that AI will be applicable for a broad set of processes within the same timeframe. Therefore, investments, planning, and training of talent around the notion of Intelligent Automation is pivotal for staying competitive.
  • Many customers are in an automation dichotomy: they want automation to drive long-term quality and agility, but need rapid cost takeout to sell the ROI. For a significant number of enterprises, their automation strategies are expected to deliver, primarily, better quality of operations (52%), more workforce agility and scalability (49%), and superior data accuracy (48%). Only a minority of respondents are seeking short-term cost savings (21%) or a way to displace employees (12%). However, when you ask what is inhibiting automation adoption, the top criterion is that the “Immediate cost savings are not high enough” (35%), indicating a disconnect in expected benefits and business case.
  • Satisfaction with initial automation deployments is mixed as customers struggle to define success and execute against it. Only a little over half the enterprises (58%) that have gone down the RPA path are satisfied with the level of business value and cost savings from their implementations thus far. Enterprises that have yet to explore technologies like RPA point to struggles with establishing business cases (41%), while 30% expect that automation capabilities will be absorbed by enterprise applications in the next five years. In addition, many enterprises struggle with developing an effective centralized governance structure for automation initiatives, citing that projects are too siloed, don’t have success milestones established, and lack organized training to use the tools effectively.
  • Despite the growing pains, RPA is starting to be used effectively in this era of innovation and the current satisfaction results reflect this. IT operations have the most satisfied clients for both cost savings (70% satisfied) and business value (72% satisfied), followed by marketing (70% satisfied with cost) and procurement (63% satisfied with business value). Regardless of the level of satisfaction on cost and business value as of today, operations leaders are making incremental progress, one process at a time. In the interim time between sawing off broken processes and legacy systems and replacing them with costly new systems and services, RPA seems to be helping enterprises get some level of access to new business value from their current processes.
  • Automation Centers of Excellence (CoE) proving a major success. Of organizations with the CoE approach, 88% believe that the automation CoE has been effective in delivering business value (scores of 4 or 5 on a 5-point scale). HfS has been hearing advisors in the RPA arena claim many clients are failing miserably with their CoEs, but this data proves, beyond doubt, these are scare tactics and those customers who are centralizing automation projects into one governance team are already reaping significant benefits.

Source: hfs-The State of Automation and AI Study 2017: 400 operations leaders air the real deal

Competing in the Age of Artificial Intelligence

Until recently, artificial intelligence (AI) was similar to nuclear fusion in unfulfilled promise. It had been around a long time but had not reached the spectacular heights foreseen in its infancy. Now, however, AI is realizing its potential in achieving human-like capabilities, so it is time to ask: How can business leaders harness AI to take advantage of the specific strengths of man and machine?

AI is swiftly becoming the foundational technology in areas as diverse as self-driving cars and financial trading. Self-learning algorithms are now routinely embedded in mobile and online services. Researchers have leveraged massive gains in processing power and the data streaming from digital devices and connected sensors to improve AI performance. And machines have essentially cracked speech and vision specifically and human communication generally. The implications are profound:

  • Because they know how to speak, read text, and absorb and retain encyclopedic knowledge, machines can interact with people intuitively and naturally on a wide range of topics at considerable depth.
  • Because they can identify objects and recognize optical patterns, machines can leave the virtual and join the real world.

A field that once disappointed its proponents is now striking remarkably close to home as it expands into activities commonly performed by humans. (See the exhibit and the sidebar.) AI programs, for example, have diagnosed specific cancers more accurately than radiologists. No wonder that traditional companies in finance, retail, health care, and other industries have started to pour billions of dollars into the field.

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Three milestone events made the general public aware of AI. Each one illustrates key aspects of the technology.

Deep Blue’s Defeat of World Chess Champion Garry Kasparov in 1997. Chess was originally considered an exercise that captures the essential tactical and strategic elements of human intelligence, and so it became the standard by which new AI algorithms were tested. For decades, programmers made little progress in defeating human players. But in 1997, Deep Blue, a computer developed by IBM, won the match against the world champion. Still, many people were disappointed when they realized that solving chess was not the same as solving artificial general intelligence. They did not like that Deep Blue relied heavily on brute force and memory. The program did not learn and certainly did not excel at any task but chess.

The event, however, revealed two important lessons. First, machines solve problems differently than people do. Second, many “intelligent” tasks are ultimately narrow and so can be solved by specialized programs.

With AlphaGo’s 2016 victory over Lee Sedol in Go, computer dominance of board games was complete. AlphaGo, developed by DeepMind Technologies, relied on deep learning—a neural network, or computational brain, with multiple layers—to beat a Go world champion. An intriguing fact about this match was how the machine prepared: having run out of human games to study, it spent the final months before the match playing against itself.

Watson’s Victory over Top Jeopardy Champs in 2011. By winning this challenging game show, IBM’s Watson effectively passed a Turing test of human-like intelligence. The performance showcased state-of-the-art speech recognition, natural-language processing, and search. The victory, however, was clinched by a different skill: Watson outperformed the other contestants in the “Daily Doubles,” in which players can wager all or part of their current winnings to secure a decisive lead. Making the best bet requires fast sequential reasoning, knowledge of game theory, and an ability to calculate probabilities and outcomes correctly. All these are areas in which humans are notoriously weak, as the Nobel laureate Daniel Kahneman observed in his famous book Thinking, Fast and Slow. Machines, on the other hand, think fast and fast in making data-heavy decisions.

Google’s Demonstration of a Self-Driving Car in 2012. Google is not the pioneer of self-driving cars. That distinction arguably goes to Ernst Dickmanns, a German computer vision expert who rode 1,785 kilometers in autonomous mode on a German autobahn in 1995, reaching speeds above 175 kilometers an hour.

Dickmanns, however, never had to turn left. In their 2004 book The New Division of Labor, Frank Levy and Richard Murnane argue that “executing a left turn against oncoming traffic involves so many factors that it is hard to imagine discovering the set of rules that can replicate a driver’s behavior.” Google’s self-driving car, however, routinely managed this exercise without incident. The car combined robots, computer vision, and real-time data processing to produce the ultimate intelligent agent that was capable of both exploring and learning from the real world.

Because AI systems think and interact, they are invariably compared to people. But while humans are fast at parallel processing (pattern recognition) and slow at sequential processing (logical reasoning), computers have mastered the former in narrow fields and are superfast in the latter. Just as submarines don’t swim, machines solve problems and accomplish tasks in their own way.

It is critical for companies to figure out how humans 
and computers can play off each other’s strengths as intertwined
 actors to create competitive advantage.

Without further quantum leaps in processing power, machines will not reach artificial general intelligence (AGI): the combination of vastly different types of problem-solving capabilities—the hallmark of human intelligence. Today’s robo-car, for example, doesn’t exhibit what we would consider common sense, such as abandoning an excursion to assist a child who has fallen off her bicycle. But when properly applied, AI excels at performing many business tasks quickly, intelligently, and thoroughly.

Artificial intelligence is no longer an elective. It is critical for companies to figure out how humans and computers can play off each other’s strengths as intertwined actors to create competitive advantage.

The Evolution of Competitive Advantage

In simpler times, a technology tool, such as Walmart’s logistics tracking system in the 1980s, could serve as a source of advantage. AI is different. The naked algorithms themselves are unlikely to provide an edge. Many of them are in the public domain, and businesses can access open-source software platforms, such as Google’s TensorFlow. OpenAI, a nonprofit organization started by Elon Musk and others, is making AI tools and research widely available. And many prominent AI researchers have insisted on retaining the right to publish their results when joining companies such as Baidu, Facebook, and Google.

Rather than scrap traditional sources of competitive advantage, such as position and capability, AI reframes them. (See the exhibit.) Companies, then, need a fluid and dynamic view of their strengths. Positional advantage, for example, generally focuses on relatively static aspects that allow a company to win market share: proprietary assets, distribution networks, access to customers, and scale. These articles of faith have to be reimagined in the AI world.

Let’s look at three examples of how AI shifts traditional notions of competitive advantage.

  • Data. AI’s strongest applications are data-hungry. Pioneers in the field, such as Facebook, Google, and Uber, have each secured a “privileged zone” by gaining access to current and future data, the raw material of AI, from their users and others in ways that go far beyond traditional data harvesting. Their scale gives them the ability to run more training data through their algorithms and thus improve performance. In the race to leverage fully functional self-driving cars, for example, Uber has the advantage of collecting 100 million miles of fleet data daily from its drivers. This data will eventually inform the company’s mobility services. Facebook and Google take advantage of their scale and depth to hone their ad targeting.

    Not all companies can realistically aspire to be Facebook, Google, or Uber. But they do not need to. By building, accessing, and leveraging shared, rented, or complementary data sets, even if that means collaborating with competitors, companies can complement their proprietary assets to create their own privileged zone. Sharing is not a dirty word. The key is to build an unassailable and advantaged collection of open and closed data sources.

  • Customer Access. AI also changes the parameters of customer access. Well-placed physical stores and high-traffic online outlets give way to customer insights generated through AI. Major retailers, for example, can run loyalty, point-of-sale, weather, and location data through their AI engines to create personalized marketing and promotion offers. They can predict your route and appetite—before you are aware of them—and conveniently provide familiar, complementary, or entirely new purchasing options. The suggestive power of many of these offers has generated fresh revenue at negligible marginal cost.
  • Capabilities. Capabilities traditionally have been segmented into discrete sources of advantage, such as knowledge, skills, and processes. AI-driven automation merges these areas in a continual cycle of execution, exploration, and learning. As an algorithm incorporates more data, the quality of its output improves. Similarly, on the human side, agile ways of working blur distinctions between traditional capabilities as cross-functional teams build quick prototypes and improve them on the basis of fast feedback from customers and end users.

    AI and agile are inherently iterative. In both, offerings and processes become continuous cycles. Algorithms learn from experience, allowing companies to merge the broad and fast exploration of new opportunities with the exploitation of known ones. This helps companies thrive under conditions of high uncertainty and rapid change.

In addition to reframing specific sources of competitive advantage, AI helps increase the rate and quality of decision making. For specific tasks, the number of inputs and the speed of processing for machines can be millions of times higher than they are for humans. Predictive analytics and objective data replace gut feel and experience as a central driver of many decisions. Stock trading, online advertising, and supply chain management and pricing in retail have all moved sharply in this direction.

To be clear, humans will not become obsolete, even if there will be dislocations similar to (but arguably more rapid than) those during the Industrial Revolution. First, you need people to build the systems. Uber, for instance, has hired hundreds of self-driving vehicle experts, about 50 of whom are from Carnegie Mellon University’s Robotics Institute. And AI experts are the most in-demand hires on Wall Street. Second, humans can provide the common sense, social skills, and intuition that machines currently lack. Even if routine tasks are delegated to computers, people will stay in the loop for a long time to ensure quality.

In this new AI-inspired world, where the sources of advantage have been transformed, strategic issues morph into organizational, technological, and knowledge issues, and vice versa. Structural flexibility and agility—for both man and machine—become imperative to address the rate and degree of change.

Scalable hardware and adaptive software provide the foundation for AI systems to take advantage of scale and flexibility. One common approach is to build a central intelligence engine and decentralized semiautonomous agents. Tesla’s self-driving cars, for example, feed data into a central unit that periodically updates the decentralized software.

Winning strategies put a premium on agility, flexible employment, and continual training and education. AI-focused companies rarely have an army of traditional employees on their payroll. Open innovation and contracting agreements proliferate. As the chief operating officer of an innovative mobile bank admitted, his biggest struggle was to transform members of his leadership team into skilled managers of both people and robots.

Related interview

Looking into the Future of Artificial Intelligence

Getting Started

Companies need to embrace the adaptive and agile ways of working and setting strategy that are common at startups and AI pioneers.

 Companies looking to achieve a competitive edge through AI need to work through the implications of machines that can learn, conduct human interactions, and engage in other high-level functions—at unmatched scale and speed. They need to identify what machines do better than humans and vice versa, develop complementary roles and responsibilities for each, and redesign processes accordingly. AI often requires, for example, a new structure, of both centralized and decentralized activities, that can be challenging to implement. Finally, companies need to embrace the adaptive and agile ways of working and setting strategy that are common at startups and AI pioneers. All companies might benefit from this approach, but it is mandatory for AI-enabled processes, which undergo constant learning and adaptation for both man and machine.

Executives need to identify where AI can create the most significant and durable advantage. At the highest level, AI is well suited to areas with huge amounts of data, such as retail, and to routine tasks, such as pricing. But that heuristic oversimplifies the playing field. Increasingly, all corporate activities are awash in data and capable of being broken down into simple tasks. (See the exhibit.) We advocate looking at AI through four lenses:

  • Customer needs
  • Technological advances
  • Data sources
  • Decomposition of processes

First, define the needs of your customers. AI may be a sexy field, but it always makes sense to return to the basics in building a business. Where do your current or potential customers have explicit or implicit unmet needs? Even the most disruptive recent business ideas, such as Uber and Airbnb, address people’s fundamental requirements.

Second, incorporate technological advances. The most significant developments in AI generally involve assembling and processing new sources of data and making partially autonomous decisions. Numerous services and platforms can capture incoming data from databases, optical signals, text, and speech. You will probably not have to build such systems yourself. The same is true on the back end as a result of the increasing availability of output technologies such as digital agents and robots. Consider how you can use such technologies to transform your processes and offerings.

Third, create a holistic architecture that combines existing data with new or novel sources, even if they come from outside. The stack of AI services has become reasonably standardized and is increasingly accessible through intuitive tools. Even nonexperts can use large data sets.

Finally, break down processes and offerings into relatively routinized and isolated elements that can be automated, taking advantage of technological advances and data sources. Then, reassemble them to better meet your customers’ needs.

For many organizations, these steps can be challenging. To apply the four lenses systematically, companies need to be familiar with the current and emerging capabilities of the technology and the required infrastructure. A center for excellence can serve as a place to incubate technical and business acumen and disseminate AI expertise throughout the organization. But ultimately, AI belongs in and belongs to the businesses and functions that must put it to use.

Only when humans and machines solve problems together—and learn from each other—can the full potential of AI be achieved.


Source: in the Age of Artificial Intelligence

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.

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.

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: Future of Robotic Process Automation

RPA satisfaction: lowest for finance and call center, highest for IT and marketing

So we’ve determined that 58% of enterprises which have adopted RPA are satisfied with both cost and business impact (see recent post). But how does this differ by business processes?

Let’s consider this data:

IT processes and apps are clearly the biggest beneficiaries of RPA. There’s nothing like music to the ears of cash-strapped CIOs and CFOs than prolonging the life of those once-expensive IT systems that just don’t integrate with each other. Plus, isn’t it great to make band-aid patches over those spaghetti codes to keep those cobol monstrosities functioning for a few more years yet? Suddenly that “technical debt” doesn’t feel quite so bad. The thing about writing off legacy, means you really only write off the stuff that just doesn’t work anymore… RPA is highly effective at prolonging the life of legacy systems by recording actions and workflows to give these things a new lease of life, allowing for technology investments to be made elsewhere (read our recent example of NPower).

Marketing functions have a lot of unnecessary manual fat that can be trimmed. There is one function that perennially suffers from excessive manual work and real issues integrating systems and processes, and that is marketing. Simple tasks (or tasks that should be simple), such as linking together databases of customers, subscribers, and prospects to align with campaigns, collateral, automated emails etc., are the bane of every CMO’s existence. So… rather than spending millions on consultants to recreate new processes, CRM capabilities and training people to use them, why not get what you have working better, while you figure out where to make those really valuable marketing investments in the future?

Procurement can really benefit from process automation. One function that has been cut to the bone – and still uses the fax machine as a mission critical tool – is procurement. RPA has the most positive impact on functions beset by poorly integrated processes, where the goal is to get things functioning better, than those functions where the goal of automation is really just to drive out cost. Being able to link together procurement systems, analytics tools and cognitive applications with the manual work that still creates major breakdowns in speed of execution and quality of data, is a major benefit for those customers which map out an RPA plan and execute against it. The more you can use procurement to support the business and speed up the cash cycle, the more effective the function becomes. HR is somewhat similar to procurement, in the sense that the fat has already been long-trimmed from most companies, and RPA adds value to processes in similar ways, such as supporting better analytics and linkages between legacy systems and processes. Payroll, in particular, is emerging as a major area where RPA can have a huge value impact, where all the critical employee data is housed and can be integrated with other knowledge systems to support better decision making. Another area is recruiting, where the whole process can be massively transformed simply by linking together actions, databases, social media, OCR etc. RPA can provide a great temporary fix while companies figure out where they really need to invest in the future – and “temporary” could mean a very long time indeed…

Finance and Accounting disappoints from a cost take-out standpoint. With only 40% of enterprises satisfied with the direct cost impact of F&A, we can conclude that many of them have their expectations set too high that RPA will drive short-term headcount elimination. On a more positive note, half of them are happy with the business value impact of RPA on F&A, so clearly there are process improvements, just not enough to remove the human cost of administering them immediately. Considering F&A is the number one process being used for F&A today (it dominates 50% of installs) it’s clear that the suppliers are playing the cost take-out game too aggressively and leaving many customers disappointed. As with outsourcing, it’s one thing separating tasks and removing workload elements from staff, it’s another being able to remove headcount simply my improving or digitizing processes. Customers must take a more transformative view that if they can free up 50% of an employee’s time, they need to focus on refocusing her/him on alternative activities. That is where the value is to be found.

RPA satisfaction in Customer Service functions is mixed. For a function that can truly benefit from intelligent data and digitized processes, it’s surprising that barely 50% of customers are experiencing either cost or business value benefits from RPA. The reason for this is two-fold: firstly, customer service functions are too mired in the legacy practice of managing shifts of low cost agents, whether they are inhouse or outsourced – and have little time or funds to investigate the value of RPA, which may require upfront investment and longer term planning. Consequently, with this short-term mindset to cater for, most the call center BPO suppliers have little pressure to change how their sell their services, if their clients are not clamoring for RPA solutions. While we are seeing significant interest in chatbots and virtual agent solutions, and established automation vendors in the call center space, such as Nice, have established relationships with many customers, the whole call center space seems to be lagging behind other functions when it comes to embracing how to leverage the benefits of RPA effectively – which could be significant when you take into account the dysfunctions across customer interaction channels.

The Bottom-line: RPA satisfaction is a lot higher when the motivation and mentality is one of process improvement, not cost-elimination

The main issue with RPA, in today’s market, is this misconception that customers will make significant headcount reductions in the short-term. With outsourcing, the cost savings are staged carefully over a 5 year engagement as work is moved to cheaper locations, better technology and processes are introduced, in addition to automation, and the processes are re-mapped over time to allow for work to get done, ultimately, with less people. Simply plumbing in RPA and not having a broader plan to transform the work, pulling several other value levers, in addition to the patching of processes and digitization of manual work, will likely result in a mismatch between expectations and reality. RPA needs to form part of a broader strategy to automate and streamline work, where people, processes, analytics tools, SaaS platforms, outsourcing models and carefully developed governance procedures, are taken into account as part of the broader transformation plan.

Source: hfs-RPA satisfaction: lowest for finance and call center, highest for IT and marketing

Using Robotic Process Automation To Prepare For GDPR Compliance

Many businesses are scrambling now, to be prepared for the impending changes in May 2018, to the General Data Protection Regulations (GDPR). The EU is going to the next level in its attempts to protect consumers from a data privacy (DP) perspective. One area that has a lot of companies very anxious is the right to be forgotten.

As of May 2018, any consumer can request to be forgotten. The request must be complied with to avoid significant fines. Each business will need a documented process of how they will scrub or remove the personally identifiable information (PII) connected to that consumer, in all their systems if there is no legal right or obligation to retain it. This can be a daunting task, depending on how many systems and cross system shares that may be in place.

This an area where Robotic Process Automation (RPA) may be the best answer. The first step in designing a “Forget Robot” is to document the details of all the places where data is stored (RPA 101 – requirements and process documentation). If this documentation doesn’t already exist, the RPA team needs to start compiling it now to be ready for May 2018! Once you identify all the places holding personally identifiable information, you will need to work with your data protection lead and your business stakeholders to decide if specific field data can be deleted or replaced, or if you need to delete the entire record. Some companies may wish to keep a record of a sale made to a male/female, in a specific age bracket, within a specific city for example, but would not be allowed to retain the PII connected to the transaction. A robot might just replace the PII fields with “*******”. System constraints may come in to play here also, with respect to how you may or may not be able to manipulate this data. In some cases you may have no choice but to delete the record. Clearly at this stage, you are designing the robot steps.

I have learned that PII fields sometimes come down to context. What other information is connected to a specific piece of data? If it is possible to derive a person’s identity through connected data, you will need to scrub the field in some manner. Your DP lead will be advising you to err on the side of caution as the fines can be significant.

The next challenge you will need to review with your DP Lead is what kind of detail that can be stored in the RPA logs relative to the task the “Forget Robots” carry out. The logs cannot contain any PPI information about the data that was just manipulated. At this stage you have moved from designing the Robot steps into the process, reporting and audit log documentation.

In some companies, there may not be resources available to carry out the right to be forgotten tasks. Based on the nature of the task, it is primed for RPA which adds a further degree of risk mitigation for your company as the robot will never miss a step or make a mistake. Your data privacy team likely has budget already, as most companies are anticipating new processes and controls will be required. This is your chance to show initiative, risk mitigation and save on costs by promoting “Forget Robots” to your organisation.

Source: Robotic Process Automation To Prepare For GDPR Compliance