Making AI and robotics work for your business

The use of robotics and artificial intelligence in businesses is on the rise, but there are still significant challenges for organisations adopting the technologies. Two executives from global IT consulting and outsourcing group Capgemini spoke to IoT Hub about how best to meet these challenges and why the returns make the effort worthwhile.

“The amount of data that’s available now in places like social media and enterprises means it is becoming for efficient for machines to make decisions rather than humans, taking the human bias out of it and making decisions objectively,” said Saugata Ghosh, senior manager of digital services at Capgemini.

This trend, together with the maturity of robotic process automation (RPA) technologies over the last three to five years, has contributed to the growth in adoption of robotics and AI, Ghosh said.

“If you look at the spectrum of robotic automation, at one end you have simple rules-based automation where the economics of those are such that they are quite easy to implement and have strong returns on investment,” he explained.

“At the other end, towards the cognitive and artificial intelligence side, you’re also seeing accelerated maturity, with things such as driverless vehicles making it possible to automate tasks that we wouldn’t have previously thought of automating a few years ago.”

Ghosh is also observing convergence between both ends of the automation spectrum.

“In real life, many processes have an element of both. For example, in the case of email feedback analysis, the interpretation of the body of the email is within the realms of cognitive or pattern recognition, while the processing of the email once it has been analysed could be rules-based,” he said.

Ghosh has noticed a trend in the motivations of deploying RPA technologies from that of cost-saving to improved accuracy and customer experience.

“Initially, everybody was after headcount reduction. Most people are telling us now that their focus is on reducing errors, improving compliance, or improving the customer experience,” he explained.

“We’re certainly seeing maturity in this area and the focus has shifted from the tactical to more strategic and sustainable objectives.”

Hilda Carmichael, director of digital program delivery for digital services at Capgemini, added: “The ambition particularly around more traditional finance, HR and IT functions is to have better business partnering capabilities by eliminating more of those manual tasks, freeing capacity to properly engage with customers instead of being distracted with repeated, administrative tasks.”

How to meet the challenges

Despite the benefits that automation technologies can provide, Ghosh said that there are a number of challenges that businesses face when adopting AI.

“All organisations recognise the potential for RPA to significantly transform their business, but they have questions as to how they get started,” he said.

“These organisations may also have a good sense of what it takes financially to do a pilot or a proof-of-concept, but are aware that just because the entry barrier to adoption is low, they must also prevent uncontrolled proliferation of these technologies across the enterprise.”

“It all comes down to scope,” Carmichael added. “Companies need to pick a candidate set of processes by which they have a span of control that they can deploy initially.”

“Processes that cut across multiple functions within an organisation will require a greater set of engaged stakeholders.

“So start small, start with a number of high-volume, manual, repetitive set of processes that’s within your span of control, and go away and prototype that.”

Carmichael also said that business units should work together to build the business case and realise the potential of RPA.

“It doesn’t matter who leads the charge, whether it’s the business or IT, but there has to be a partnering component to it,” she explained.

“The business needs to determine and help codify the business rules, and IT needs to determine the infrastructure and scalability of the solution.”

Source:  – Making AI and robotics work for your business

Image Credit: Thinkstock

Service Robots Open New Avenues for an Automated Workforce

Robots are no longer a Sci-Fi dream, and are well on their way to establishing a reality that we all fantasized about. Artificial intelligence coupled with advancing technology has ensured that the next generation robots are brought to the aid of the service sector. The bots are finally demonstrating a remarkable ability to perform hard, dangerous, or menial jobs. These include tasks such as moving around heavy objects, providing customer assistance, aiding disabled people & patients, or even for security purposes in the defense sector. The service robotics industry is touted to soon take over from the human work in the next few years. This is due to its increasing adoption by diverse verticals for domestic as well as commercial purposes.

Warming up these cold machines, the populace is slowly accepting that robots make lives much easier and fruitful by saving them a lot of time. The exciting possibilities of employing the use of these intelligent machines in different applications has promised great potential for the service robotics market. Experts at Allied Market Research have observed that professional service robots contribute to a greater share in total market revenues, as compared to personal service ones. The industry promises endless prospects for tapping the potential of these smart bots.

Agro-robots: Revolutionizing agricultural techniques

The humble farmer who usually brings the grain to our table will soon be a state-of-the-art intelligent machine. Agriculture industry is set to witness a major revamp as increasing number of farm jobs can be accomplished by using robots. The bots will soon replace human labor force in agriculture, and eliminate the high costs associated with employing people to sow and harvest crops. The rising demand for essential food crops can be met with the deployment of robots in the various farm activities and minimize the time taken by each task.

Robotic engineers and researchers are coming up with new innovations to program robots for working in the agriculture sector. For instance, researchers from UK’s Harper Adams University have attempted to grow and harvest a complete hectare of cereal crops through intelligent machines. The project is entitled as Hands Free Hectare, and is based on the idea of precision farming. It aims to completely do away with the need for human workforce on the field. The project is one of the most ambitious projects, as it had been led in collaboration with precision farming specialist, Precision Decisions.

“We believe there is now no technological barrier to automated field agriculture. This project gives us the opportunity to prove this,” says Kit Franklin, one of the Hands Free Hectare researchers at Harper Adams University. Similar such researches are aimed at developing autonomous agriculture technologies that can introduce driverless tractors, crop irrigators and harvesting machine.

Humanoid robots to make banking fun & interactive

Banks, usually considered as uninteresting and serious places are now ready for some introduction of fun elements. Developers have come up with robots that are aimed at making banking a more interactive and personal experience. To see a cute robot, walk up and communicate politely with you when you enter a bank would certainly add to the “cool” factor. Alderan Robotics and SoftBank has teamed together and designed a humanoid robot, Pepper, which has the ability to read emotions. The artificial intelligence-backed robot was announced in 2014 and has already found place in major stores and banks across Japan and Europe. Asian countries, such as Taiwan have been quick to adopt these bots in their workplaces. Taiwan’s biggest insurer, Cathay Life Insurance, introduced its first mini ‘Pepper’ robot in its branch. The intelligent humanoid machines greet customers as they walk in, read their facial expressions and body language, and interact accordingly. They have made for a more engaging and fun experience, and are expert marketing tools. Pepper robots also provide information on financial products and even guide customers through the bank to intended departments.

“Pepper’s job is to greet customers and introduce products to make the wait for services less boring,” said Rachel Wang, the insurer’s executive vice president. Apart from providing ease of service and serving different functions, such robots are extremely effective in ensuring that customers are impressed by the organization’s marketing skills and stay loyal to the brand.

Intelligent machines to be the future of healthcare

It is extremely vital that the most intelligent machines created by mankind should also serve for human wellbeing. Healthcare is another area where robots can help revolutionize the practices in the industry. Engineers and medical researchers together have been continually developing robots that can cater to the needs of the patient and decrease the recovery time. Nano robots are the new rage among surgeons, where these tiny engineered devices are inserted into the body, and programmed to tackle cell damage and repair tissues within the body. This can potentially alter the way medicine is combined with technology for advanced healing techniques.

Moreover, robots are also being used by surgeons for aiding in complex surgeries and treatments. Along with these, the cold machines are also being made more humane and gentle, to help patients recover in hospitals. Elderlies and physically challenged patients are also reaping the benefits of having a human robot at their disposal, which can provide assistance to perform simple tasks, as well as remind them about medication. The robotic revolution is expected to be of great support to the healthcare industry, as it can realize tasks that are menial, intricate, or even potentially dangerous.

With the robotics revolution taking the world by storm, people across various verticals are warming up to the idea of these cold machines. Millennials are fascinated as well as amazed at the wide gamut of operations a robot is capable of achieving. Emerging nations are investing their resources to bring life to machines and create a task force of robots that can do menial, odd, or boring jobs that would make human beings more employable in other deserving areas. Robots are achieving more complicated levels of functionality and thus proving more effective than a human labor force. With a more futuristic outlook being adopted, the service robotics market can be counted on for meeting the demands of a fast-paced dynamic world.

Source: – Service Robots Open New Avenues for an Automated Workforce

The Opportunities and Challenges of BPaaS

Forward-thinking organizations are on the hunt: They’re searching for cost savings, strategic capabilities and scale while minimizing capital investment, time to market and risk. Cloud computing, in the form of SaaS, IaaS and PaaS, has been a big part of making that happen.

KPMG defines business processes as-a-service (BPaaS) as the combination of technology, people and process requirements of a business function into a fully-managed service, provided in a leveraged environment and measured on the basis of an accepted business outcome.

And now, it’s become a tremendous opportunity. The sweet spots include business units with unique repetitive transaction bases, such as human resources, finance and accounting (think payroll and accounts payable). Spending on BPaaS, in fact, is expected to reach $13.7 billion in 2016, up from $12.95 billion in 2015 (Gartner, Forecast Analysis: Public Cloud Services, Worldwide, 1Q16 Update, May 2016).

There are plenty of benefits to explain the growing popularity of BPaaS, including reducing costs and improving speed to market. According to KPMG Managing Director Randall Wiele, there are several other key benefits to focus on, such as capital avoidance — not just for underlying capabilities, but also the ability to have a constant refresh of the services.

“With traditional outsourced services, we found there was often a stale period where service wasn’t improved all that dramatically,” he says. “By sharing scale with other clients and by being responsible for the process and the technology, the service provider can constantly refresh the solution and achieve best practices.”

In addition, with BPaaS the business no longer needs to be the expert on all regulatory changes, he adds. Generally, the business can also take advantage of more secure usage that is also elastic, and pricing, which is resource-based and pay-as-you-use.

BPaaS Challenges Bring Lessons

The clear benefits and opportunities of BPaaS don’t mean there are not challenges and lessons organizations have learned as they move through the journey of implementing these cloud options. Wiele offers three important tips for organizations to make the move towards implementing BPaaS as smooth as possible:

1. Don’t rush the setup.

More work on the front end can solve a lot of problems that could occur down the road, says Wiele. Deals that are rushed into just to save costs, for instance, don’t last very long and are seldom satisfactory. “It’s important to take the time to define the solution and understand the existing environment, but also to prepare for the new environment with a transition and transformation program to achieve a positive result,” he explains.

2. Let the provider be the expert.

If you simply want a new service that does the processes and activities in the same way they are currently performed, you won’t achieve the maximum value from BPaaS, warns Wiele. “There is a reason the provider created the offering and has brought it to a best practice standpoint,” he says. “It’s important to allow the provider to move you through the redesign you need and make the necessary changes to achieve the benefits and the value.”

3. Live within the new structure, not the old ways.

There are many people involved in various business processes, so typically a lot of change is required when moving to BPaaS, says Wiele: “You need to live within the new structure that’s established, not try to make it what it was before.” In-house resources will no longer be managing the process, but will be managing relationships with the service provider, which is a very different skillset. “If the parties aren’t working well together and don’t understand their new roles, we find there can be a lot of conflict, which diminishes value from the overall solution,” he explains.

CIOs Need to Lean In for BPaaS Success

Opportunities to implement BPaaS may come from within the IT organization through the CIO, or through the business. Either way, it’s important to support the initiative — Wiele emphasizes that CIOs need to lean in and respond to the business:

“A joint approach is very important, because it generally takes equal amounts of effort from IT and the business to come together to make BPaaS work effectively,” he says.

The CIO also needs to take time to integrate BPaaS within what is most likely a large and growing suite of products and services in the IT portfolio — IT needs to provide the integration and orchestration required to make it work. Finally, the IT organization itself will change as key elements are outsourced, with the CIO overseeing organizational redesign and change management.

“BPaaS affects IT as with any kind of outsourcing,” he explains. “It never eliminates overall responsibilities, and IT will continue to provide the integrated environment and controls, and the organization needs to be prepared for the changes.”

Growing Intersection Between BPaaS and Robotics

The growing demand for BPaaS is aligned with the growth in the global market for RPA (robotics process automation), which is expected to reach $8.75 billion by 2024, according to a new report by Grand View Research. As with other digital disruptors, RPA — including advanced software automation and digital labor — is driving a new generation of BPaaS offerings that provide a virtual workforce, says Wiele.

“Robotics and digital labor are already a key component of next generation BPaaS engagements that employ various cognitive tools, and RPA is rapidly changing and enhancing BPaaS capabilities,” he says. Wiele points out that RPA-enabled BPaaS moves business services from a shared scale and large-scale labor reduction to labor elimination with accompanying significant reductions in cost.

“It’s really a game-changer,” he says, cautioning that contracts need to reflect this new reality: “From a contracting perspective, certainly in any current or next-generation BPaaS engagement, we need to make sure that the savings achieved through the addition of RPA to the BPaaS shows up in the contracting and is shared among all the users,” he says.

Source: – The Opportunities and Challenges of BPaaS

The Rise of the Robots.

The Rise of the Robots

As we have seen in the news this week robotics and AI are quickly becoming a real thing in both business and the home. In the next few years we will see more and more of this type of technology taking on more and more of the administrative type roles in businesses around the world. In this blog, I share my thoughts on this trend and some of my very own personal experiences…

Rise of the Robots.

I am strong believer that we can predict B2B technology trends by looking at what happened in the B2C world 12 to 24 months earlier. In my experience, business technology trends usually follow those that happen in the home a couple of years later.

If you look at some of this year’s B2C trends, I think that 2016 will be remembered as the kick starter year for AI in the home. £50 devices like the Amazon Echo are transforming our homes into a fully connected internet of things. These new devices allow us to get accurate information easily and automate complex tasks with simple commands.

I recently bought a £50 Amazon Echo, Alexa (Note to my wife: purely for research purposes, honest!) and taught it that when I say “Good Night”. It turns off my TV, sets the home alarm, turns the down stairs’ lights off, sets the thermostat to 19c, turns the bedroom lights on, looks in calendar for the next day and sets my wake up alarm accordingly. In the morning when the alarm goes off, Alexa can tell me the traffic to work, the day’s weather, turns on my kitchen lights and coffee machine!

Power vs. Simplicity.

There is no doubt that these devices are amazing and are impressive when you use them, however they are not clever enough to do all of the above on their own. The key to these devices are their ability to connect to the cloud to get information. But what it does really well is allow third party products to connect and work with it.

For example – and this shows just how clever these devices are; after only a few days my wife and I started to treat Alexa like a real person – if I ask Alexa to play a certain song, it makes a request to Spotify.

Spotify then decodes this request and finds the right song for me. If I say “Good Night” to Alexa and the lights go out – that is your smart bulb manufacturer communicating to Alexa via the API – and this is the Key. Amazon have done an amazing job of harnessing the power of the cloud but also creating a way that other providers can interface their products with it.

Speed of light.

For me this means that robotics and Artificial Intelligence will only be as good as the integration methods that it ships with them.

If HCM technology companies want to keep pace, we all need to make sure we are easy to integrate with to be able to get the support for the services we think are the future. Going back to Alexa, the makers of smart devices’ are falling over themselves to support it as they see it being a key to getting people bought into their systems. When that Robotic game changer hits businesses, we all need to be ready.

Source: – The Rise of the Robots.

How to get IT on board with RPA

What is the initial feedback from IT? Where does the resistance lie?

As you might expect, resistance generally comes from engaging any team late in the process or without sufficient information or sponsorship. Generally, we have found IT teams to be hugely supportive of RPA when it is deployed within IT governance and addresses a challenge that IT is not already addressing through other programs of work.

Information security is generally the IT team we spend most of our time with. RPA provides a new model for understanding and constructing appropriate controls. The thought of a robot performing transactions on an unlocked machine accessing sensitive data has obvious risks. Working with the information security team to propose, review and implement controls to manage these risks is essential to a successful deployment.


How should business leaders message the value of RPA to IT?

RPA has numerous benefits to a business, such as improved quality and consistency, reduced transaction times, business continuity and agility – not to mention the obvious cost savings. That said, it is not the only tool in the tool box and IT teams may have different approaches they are already pursuing to solve the same challenge the business is trying to solve with RPA. Understanding the IT roadmap prior to embarking on any implementation is therefore imperative to avoid conflicting agendas.

It is also essential that a business sponsor with sufficient seniority is identified to allocate project budget and prioritize RPA among other initiatives. We’ve found the key to obtaining sponsorship is to perform a ‘Future of Work Assessment,’ or FOWA, across the area of the business. The FOWA evaluates potential solutions, proposes a Target Operating Model (TOM) and compiles a business case to articulate the value of RPA and the cashable and non-cashable benefits it will bring. Once the investment and benefit are quantified, it’s easy to justify RPA and resources in supporting its implementation.

Why is it important for IT to be involved in implementation?

While RPA is often managed by operations teams to provide a virtual workforce, it is still an IT implementation, and therefore has to be deployed and managed within an IT governance framework so that the risks associated with automation can be effectively managed.

We have seen some organizations take a different route in implementing RPA without the involvement of IT. In every single instance, this has caused additional delay and/or risk to the business, and in the vast majority of cases, has resulted in a lag in adoption or the RPA initiative being shut down altogether.

IT teams tend to be the budget holders for the infrastructure that is put in place (new robots), and are responsible for infrastructure and system availability, up time and recovery. IT teams also tend to hold the licensing and roadmaps for the target applications RPA is automating (office, SAP etc.).

Regardless of which function manages the implementation, for RPA to be successful both operations and IT have to be bought into the initiative and actively involved.

When should IT get involved?

In our experience, it is best to involve IT from the outset. This doesn’t mean heavy involvement – just the socializing of the investigation, which may or may not lead to a business case for RPA. The earlier the engagement, the less risk to the project as you may discover that the application you wish to automate is due for replacement in the following year or that there is already another RPA pilot in your organization that you could leverage.

Once the idea has been socialized, and ideally once the sponsorship is in place, the next step is to perform a FOWA assessment. We have found that a strong business case is far more convincing to leadership than a compromised proof of concept that proves the software can work in your business as it does now in countless other organizations. Once the FOWA is complete, IT needs to be involved in validating the security model and providing the governance within which the deployment can take place. Often the IT team will be required to provide access to non-production environments that mirror the live systems for the purpose of developing and testing RPA.

Once the implementation is complete, IT involvement is more important than ever, as they need to manage any upgrade or change to the systems being automated to ensure continuity of automation.

Where has IT seen value? How have their jobs been made easier?

RPA is a great means to address the projects that IT cannot prioritize. We’ve worked with several CIOs and CTOs who have told us that they now look at RPA in their triaging of investment requests. If the opportunity is not significant enough to make it onto the roadmap, then the IT teams look to see if RPA can provide a lower cost pragmatic solution. This is a great dynamic to create as our clients that deploy RPA do not want to compete with the initiatives to replace systems or upgrade their functionality, rather they want to find a more effective solution than dealing with these problems manually as they do today.

With many projects delivering a typical payback period of less than one year, there is often a case for implementing RPA even when there are longer term strategic solutions for addressing the same challenge.

Another way RPA can be used by IT teams is as a means of prototyping automations which can then be transferred into the underlying applications when stabilized.

How do you see RPA impacting IT day to day in the next five years?

I think RPA will become a more valuable tool to IT departments where they can provide operations teams with a means to provide automated solutions to problems that are not addressed through the IT roadmap.

We do not see it as a means to reduce IT spend or channel away the limited funds IT teams usually have available to them. Instead, we see it as a way to extend the amount of opportunities they can support by enabling operations with tools that have been approved by IT and are managed effectively and securely.

There is also the potential to grow hybrid IT / business roles by bringing the functions closer together.

Source: BluePrism – How to get IT on board with RPA

Move Your Analytics Operation from Artisanal to Autonomous

Many organizations today are wondering how to get into machine learning, and what it means for their existing analytics operation.

There are many different types of machine learning, and a variety of definitions of the term. I view machine learning as any data-driven approach to explanations, classifications, and predictions that uses automation to construct a model. The computer constructing the model “learns” during the construction process what model best fits the data. Some machine learning models continue to improve their results over time, but most don’t.

Machine learning, in other words, is a form of automating your analytics. And it has the potential to make human analysts wildly more productive.

To illustrate the movement from “artisanal analytics” to “autonomous analytics,” I’ll provide an (anonymous) detailed example. The company involved is a large, well-known technology and services vendor, with over 5 million businesses as customers, 50 major product and service categories, and hundreds of applications. Each of its customer organizations has on average four key buyers. The company needed to target sales and marketing approaches to each company and potential buyer. To do this, it created a score for each customer executive, reflecting their propensity and ability to buy the company’s offerings, so that sales and marketing approaches could be more effective.

This approach is called “propensity modeling,” and it can be done with either traditional or autonomous analytics approaches. Using traditional human-crafted modeling, the company once employed 35 offshore statisticians to generate 150 propensity models a year. Then it hired a company called Modern Analytics that specializes in autonomous analytics, or what it calls the “Model Factory.” Machine learning approaches quickly bumped the number of models up to 350 in the first year, 1500 in the second, and now to about 5000 models. The models use 5 trillion pieces of information to generate over 11 billion scores a month predicting a particular customer executive’s propensity to buy particular products or respond to particular marketing approaches. 80,000 different tactics are recommended to help persuade customers to buy. Using traditional approaches to propensity modeling to yield this level of granularity would require thousands of human analysts if it were possible at all.

There is still some human labor involved. Modern Analytics uses fewer than 2.5 full-time employees to create the models and scores. 95% of the models are produced without human intervention, but in the remaining cases people need to intervene to fix something. The technology company does have to employ several people to explain and evangelize for the models to sales and marketing people, but far fewer than the 35 statisticians it previously used.

If your company already has some analytical skills, it may be able to do machine learning models by itself. Cisco Systems, for example, went from doing tens of artisanal propensity models to tens of thousands of autonomously generated ones. A small group of analysts and data scientists in a group called Global Customer Insights generates these models each quarter.

Turning your current analytics operation into a machine-learning-savvy “model factory” requires some changes, of course. First of all, your analytics experts are going to need some new skills. Instead of painstakingly identifying variables and constructing models, machine learning analysts or data scientists need to focus on assembling large volumes of data and monitoring outputs for relevance and reasonability.

They may also need to work with some new tools. Vendors of proprietary analytics software are rapidly adding machine learning capabilities, but many algorithms are available in open source formats that provide less support to users. And since machine learning models typically operate on large amounts of data and are computationally-intensive, it’s important for analysts to employ in-memory or elastic cloud (infinitely expandable) hardware environments. These technologies can handle the largest datasets and can dramatically accelerate computation speeds.

If there is already a central analytics group or center of excellence in place, it probably already has the statistical expertise in place to interpret machine learning models to some degree. But full interpretation is very difficult. When there are thousands of models in place to address a business process, it may be impossible to interpret each one. And some variations on machine learning—neural networks and their more detailed cousin, deep learning—are virtually impossible to interpret. We end up knowing which variables predict an outcome, but we don’t know why.

The lack of transparency from machine learning is one of the greatest cultural and leadership challenges to overcome with the technology. Managers must learn to trust models that they don’t fully understand. The key is to be vigilant about whether the models are actually working. If, for example, they no longer do a good job of predicting lift from a marketing program or sales from concerted sales force attention, it’s probably time to revisit them.

The world is a big and complex place, and there is increasingly data available that reflects its size and complexity. We can’t deal with it all using traditional, human-crafted analytical methods. Organizations with some familiarity with those methods, however, will have an easier time transitioning to more autonomous approaches involving machine learning. The time is now to begin such a transition.

Source: Harvard Business Review – Move Your Analytics Operation from Artisanal to Autonomous

To Lead a Digital Transformation, CEOs Must Prioritize

Given the pace at which digital innovation is disrupting industries globally, it’s not surprising that most CEOs feel pressure to find and deploy the right technology as fast as their budgets will allow. Many are discovering, however, that becoming a digital leader isn’t simply a matter of technological savvy. It’s about creating an agile organization that can detect what type of change is essential and respond quickly with the most competitive solution.

In our experience, most companies are already steeped in technology and learning fast about how it can transform their businesses. Typically, teams in the field are well aware of the digital threats and opportunities within their area of the organization – usually more so than the corporate center. They have launched their own apps, deployed robotics, established partnerships with digital players, or are using data to analyze their business and make better decisions.

The problem is that that these efforts tend to be ad-hoc and uncoordinated. Without the proper framing and orchestration at the overall company level, the best initiatives will fail to get the attention and investment they need. While it is important to encourage local ownership of ideas and projects, turning them into game-changers requires clear, sometimes ruthless direction from the center around which projects to scale and in what order. Only the CEO has the power to provide this kind of direction across the entire enterprise.

To do that effectively, CEOs need a holistic view of the digital threats and opportunities facing key parts of the business, and a way to link them to an overall vision for how digital is reshaping the competitive landscape. This brings order to the chaos of initiatives and provides a clearer basis for narrowing down priorities and managing the cross-functional interdependencies that the best digital solutions often present. Three ways to manage the digital transition are:

Define where change is needed most: Digital technology affects every company differently, but it tends to create or destroy value in four critical areas of the organization: customer engagement, digital products and services, operational performance, and preparing for disruptive new business models. Developing a clear point of view on the opportunities or threats in each area will suggest which capabilities need the most attention and where to concentrate investment.

Consider how General Electric arrived at the decision to develop and launch its Predix cloud-based industrial operation system. The initiative began when GE’s CEO encouraged his organization to explore how the accelerating trend toward value-added services in the industrial sector might eventually affect the company’s growth. In essence, he challenged his team to act as a change leader – to disrupt before being disrupted — by interpreting the weak signals coming from the market. He asked them to pay special attention to how digital native companies were creating shifts in customer behaviors and to diagnose digital solutions for business challenges that had not yet taken a toll on the company’s profit and loss statement.

Launched in August 2015, Predix helps companies see how their machines and infrastructure are performing so that they can improve them constantly. GE has treated the platform as open source, reasoning that it will power the growth of the industrial Internet, which will in turn accrue major benefits to GE. Its early success also highlights the CEO’s critical role in challenging the organization to assess its digital competence and to determine how urgently it needs to respond to threats and opportunities.

Choreograph the change: Even the clearest digital strategy will fail if your people are unprepared to embrace it. As critical as defining where you need change is setting up the capabilities and processes that will enable it. IT, for instance, is very often the tightest digital choke point because it is mired in old processes and needs significant reshaping to link it more closely to strategy, while creating a more agile approach to development. It is also essential to develop key capabilities in data analytics to make better decisions using the flood of new information flowing through the organization.

Ensuring that change sticks involves the hard work of defining new roles, adding new skills and adopting new ways of working. And it is important to carefully choreograph the change, defining who will lead the effort and how it will be sequenced.

Mobilizing for this kind of change inevitably means shaking up the status quo and leaders themselves need to be prepared to manage the company differently. Consider the challenge companies face in the rapidly changing market for power train compressors. Increasingly, competing in this market means equipping compressors with hundreds of sensors that send information back to the manufacturer about power consumption, vibration, wear and output. Manufacturers then analyze the data remotely to predict problems their customers might face and offer solutions proactively. Blending the hardware with digitally enabled services creates measurable customer value. But taking full advantage of it requires significant cultural changes. Product development teams have to work with field maintenance and commercial teams. Data management teams have to develop the predictive algorithms to improve the customer experience in coordination with the customer-facing teams. Marketing, commercial and finance have to work together to develop new pricing models. All of this has to happen fluidly and rapidly. The old system of passing possible solutions across silos, wading through validation loops and meeting threshold tests simply isn’t fast enough.

Empower people: One clear implication of this approach is the central importance of an orchestration model for digital —prototyping, risk-taking, and mobilizing the frontline to push concrete initiatives. Many of the leading digital models to date have been distributed globally throughout an organization via “digital relays” or champions within each geography and business unit. They are centrally orchestrated at a cadence that improves uptake and so the design remains consistent where appropriate.

This “project team”-based approach relies on empowering people at every level of the organization to work together to devise and implement solutions. Again, that requires some critical organizational and cultural changes. Everybody, for instance, needs access to customer data and the analytics and visualization tools used to interpret it – information that is typically hoarded in a particular part of the organization. This “democracy of data” frequently puts pressure on the middle managers in charge of it and passes decision rights to many others.

Only the CEO can manage this process by breaking down the appropriate boundaries, giving teams permission to set new rules, and providing the strategic framework to buttress the new order. It often makes sense for the CEO to delegate to an “orchestrator,” in the form of a Chief Digital Officer. But that person needs to be fully empowered to compel change across the organization in the name of the CEO. There isn’t time for anything else.

Source: Harvard Business Review  – To Lead a Digital Transformation, CEOs Must Prioritize


The Rise of the Robotics in Banking

Many people believe robots are a distant reality, however, the reality is that technological advancements in robotics is currently underway and are beginning to enter the workforce.

In partnership with Kofax from Lexmark, OmniChannel Media hosted a roundtable discussion in Brisbane regarding the phenomena that is Robotics Process Automation (RPA).

Moderated by Daniel Fowell, attendees included some of the industry’s leading experts and featured a keynote presentation from Tim Sheedy, Principal Analyst at Forrester.

“RPA is a very simple piece of technology capability that is taking the world by storm at the moment,” Sheedy said.

RPA is the application of technology to change normal day-to-day processes that are easily repeated, and shifting them to automated procedures which can increase the efficiency and cut unnecessary costs.

“RPA is a simple solution to save a lot of money and time, and cut costs out of the business to take humans out of the process.”

With robotics integrating itself into every industry, there are many examples of RPA which casual customers don’t notice but take for granted.

Key industries that illustrate this change are the food and transport industries. The most prominent case is seen in grocery stores where self-serve checkouts have replaced many of the aisle checkouts, and therefore nullified the purpose of human assistance.

Many organisations are increasingly realising that technology can be more dependable than humans, with Sheedy providing GE as an example of an organisation that is shifting towards RPA in order to avoid human error.

“GE, one of the world’s largest organisations, are talking about closing all of their books at the end of every month, quarter or year and using…RPA capability to do that. Their justification is that humans make 1-2 mistakes each and there’re 200-300 people involved in closing the books globally, and so that’s 600 mistakes that flow into their accounts.”

In the transport industry, the arrival of self-driving cars is becoming a further example of how RPA is expanding rapidly across multiple industries.

Although the emergence of RPA has caused many citizens to begin fearing for the safety of their jobs, this will inevitably encourage humans to up-skill and specialise in order to harness the evolving digital world.

Impacting FWOW

US bank Citi recently suggested that its retail banking workforce could be reduced by 30% over the forthcoming years on the account of RPA. Although we are yet to see the full force of RPA transpire, it has the potential to disrupt the fabric of organisations from the bottom up.

Source: -The Rise of the Robotics in Banking 

How has RPA played a role in increasing data accuracy and predictability in your healthcare operations?

Are you using robotic process automation (RPA) as a way to drive better outcomes for your healthcare organization?

In our research, we are hearing that companies using RPA find the greatest value from it in the quality, predictability, and speed that results from the use of the software to automate rules-driven business processes (there’s your definition of RPA, by the way). And we’d like to hear more examples –stories to share –of how it is being integrated into healthcare operations to impact health, medical and financial outcomes.

Notice in Exhibit 1, that 65% of the respondents in our cross-industry survey say the most value they get from RPA is in driving more predictability and quality in the processes, and half add that speed is of value, while rounding out the top three is freeing up staff to move to other projects. Healthcare respondents mirrored this top three, adding that number four is “creating more reliable data sets for analytics.”

I’d venture to guess that the value of more reliable data sets will increase exponentially as value based care and reimbursements take center stage in the industry. Predictable, accurate data will be increasingly important in, for example, segmenting patient populations, identifying appropriate and timely care interventions, and capturing and reporting appropriate feedback and insight for reimbursement. Reporting results for reimbursement is absolutely dependent on accurate and timely data.

Where to use RPA in healthcare operations

We heard from one enterprising organization that “every activity, every process is an opportunity for RPA.” Most of the examples we see are in claims processing and coding changes, followed by provider data management where there are many steps that require checking and / or moving data from multiple systems. EXL will share examples of the applicability in care management, for example, on an upcoming webinar, Robotics: A Call To Action In Health Care Management.

But while a number of tasks, activities, and even processes are automated, it is still too often in isolation from a broader process, which can really make an impact. What we have yet to see is dramatic change and impact on the healthcare consumer experience through the use and integration of RPA into a business operation. We’d like to see a significant change to the experience of a healthcare consumer in their patient visit to payment processed, for example, involving RPA, analytics, and customer service.

Think big, and start small—and find your champions. Start where there is the greatest interest in the benefit from the use of it, and the willingness to experiment. It can be anywhere in the operation, really. The key is to identify people who have a passion for using RPA; and in them you will also find the people who will help drive interest, momentum, business rationale, and results. Results should be about business outcomes, such as reduced fraud or waste, increased medical adherence, reduced readmissions, or better member or patient satisfaction.

In order to get people excited about the prospect of these potential results, its important to develop a story around RPA for your internal stakeholders. At a recent HfS Summit discussion with operations leaders, Lee Coulter, Senior Vice President at Ascension and Chief Executive of the Shared Services Subsidiary said that what worked for them was to build a 10 second message, a 30 second speech, and a three-minute story that should include a demo or video clip to “show” how it works. Focus on the impact and results that RPA can drive—the accuracy, speed, and predictability, for example.

Partnering for results

Service provider partners can play a strategic role in identifying opportunities to better leverage RPA. While they are at different stages of maturity, they have been developing capabilities and tools over the past few years on the processes they manage. The use of automation is becoming increasingly sophisticated, especially when you as a service buyer partner with an operations service provider to use RPA in a shared strategy. You’ll find a snapshot of how service providers and service buyers are incorporating automation into their operating models and infrastructures in my recently published POV, Getting the Ball Rolling with RPA in Healthcare Operations.

What’s your greatest challenge, success story, or tip to share?

Just with any change, it takes learning and collaboration to create something meaningful. We look forward to your questions, comments, examples, and stories over the coming months as we figure out how as an industry, healthcare can better leverage RPA to drive better health, medical, and administrative outcomes over time.

Source: has RPA played a role in increasing data accuracy and predictability in your healthcare operations?

Automation Will Make Us Rethink What a “Job” Really Is

As businesses enter the unchartered waters of machine intelligence – where machines learn by experience and improve their performance over time – researchers are trying to predict its impact on jobs and work. Optimists suggest that by taking over cognitive but labor-intensive chores the intelligent machines will free human workers to do more “creative” tasks, and that by working side by side with us they will boost our imagination to achieve more. Experience with Robotic Process Automation (RPA) seems to confirm this prediction. Pessimists predict huge levels of unemployment, as nearly half of existing jobs appear prone to automation and, therefore, extinction.

More nuanced analysis points to a less dystopian future where a great number of activities within jobs will be undertaken by intelligent systems rather than humans. This view, in effect, calls for a re-examination of what a “job” actually is: how it is structured, and how it should be reconfigured, or perhaps redefined, in the age of intelligent automation. How should companies rethink the value of a job, in terms of increased performance through machine intelligence? What set of skills should companies invest in? Which jobs should remain within the company, and which should be accessed via talent platforms, or perhaps shared with peers, or even competitors?

Conventional wisdom has long suggested that, as job performance increases, so does the value or return to the company. This myth of a consistent relationship between job performance and value across all jobs within a company has since been debunked, most recently in Transformative HR, which illustrates the variance in roles where great talent makes a difference and where good enough suffices.

However, with technology, digitalization, and artificial intelligence accelerating changes to jobs, the relationships between performance and value become even more complex and yield potentially exponential opportunities for value creation. Return on Improved Performance (ROIP) – similar to Return on Investment – measures the value of improved performance in a given position (i.e., not just the value of average performance in a job). Let’s look at an example that most of us directly interact with for hundreds, if not thousands, of hours annually: the airline industry.

Pilots are a critical pool of talent for an airline; there must be a sufficient supply with appropriate skills to operate the airline. But this is a segment where “good enough” suffices. As the chart below illustrates, beyond a certain standard, having higher performing airline pilots will not yield additional business value (defined as customer loyalty) to the organization, although having even one pilot “below minimum standards” can have a significantly negative impact on the performance and reputation of the organization as well as compromise the integrity of the business model.

This is the reason airlines invest in elongated career paths for pilots. For instance, it takes 20 years to move from the “right seat” of an Embraer 175 doing a short haul flight to the “left seat” of a Boeing 747 going across the Pacific Ocean. Significant investment also takes place in cockpit technology as well as in training and development (e.g., minimum simulator hours required) among other things, in order to take the left side of the curve out of play. This is a classic proficiency role: though the skills are high level, beyond a certain standard, higher performance won’t yield more value.

Nevertheless, as airlines increasingly pursue competitive advantage by differentiating the customer experience – particularly for premium passengers – flight attendants become a pivotal workforce segment. Often they are only “face of the organization” to most passengers – which suggests that higher levels of performance, particularly when it comes to delivering an experience that truly delights a passenger, can yield significantly greater customer loyalty, as the work of the flight attendant steadily shifts from the transactional to the relational. This is a classic pivotal role: higher performance yields more value.

So armed with this insight about the differential relationship between employee performance and value to the company, how can we apply the rapid advances in artificial intelligence to further enhance the impact of these roles? Indeed, how can we ensure that task automation does not merely reduce labor cost but also delivers increased performance for the human workers? To answer these questions, we need to begin disaggregating work and understanding how automation and AI can differentially handle various aspects of work.

Let’s go back to our flight attendants and think specifically about how cognitive automation might enable them to take the work of delivering the optimal customer experience to a whole new level – in this case with augmented reality powered by cognitive computing to deliver an unprecedented level of insight. If we deconstruct the job into the three categories defined in the chart above, you would ensure that the legally required and airline minimum elements of work were highly standardized and performed to the minimum acceptable standard while empowering and enabling the flight attendant to unleash all his discretionary effort on a highly personalized level of service. Imagine flight attendants wearing a version of Google Glass, through which they can access customer data and personalized preferences. No nut dishes served to Charles in 3C given his allergy, but black coffee and a predisposition for onboard duty free. Early seating meal for Sarah in 2A so she can get to sleep quickly. And so on.
In a scenario such as this, machine intelligence overlaid on augmented reality further increases the steepness of the curve for the discretionary portion of this pivotal role’s work. For the flight attendant using this technology a unit improvement in individual performance provides even greater increases in organizational value, as premium passengers are treated with a level of personalized service where it matters that would be otherwise unfathomable.

Conversely, consider how robotic process automation can change the left side of the curve for a pilot (i.e., the legally required element). Instead of investing the aforementioned resources to minimize the possibility of human error, AI (in this case, robot pilots or autonomous airplanes) can replace the routine and repetitive elements of the pilot role, flattening that portion of the curve. The emphasis could shift to having highly skilled pilots act as overseers from a distance for multiple flights, intervening when an unforeseen event moves the work beyond the routine. This would allow airlines to leverage the experience and insight of skilled pilots in a much more efficient way. The net effect is both a reduction in labor cost (as fewer pilots are required) and a reduction in the risk of an accident.

And yet….as we have seen countless times, the very idea of a robot making a mistake is terrifying to humanity. Consider the difference in the public reaction to the recent news of the Tesla autopilot accident versus the statistics about the countless lives lost every day due to human drivers’ texting while driving. It doesn’t matter that we know that IBM Watson’s success rate in diagnosing lung cancer is close to 90% while our human oncologists average 50%. We trust humans and expect robots to be infallible. Will we as a society be willing to allow the robots to learn? How long will it take the flying public to get comfortable putting their lives in the hands of a robot?

Given these challenges, here are five steps we recommend companies take to rethink work in light of automation and AI:

  • Gain clarity on pivotal vs. proficiency roles in your organization
  • Understand the specific nature of the relationship between performance and value for your pivotal and proficiency roles
  • Disaggregate the different parts of the curve shown in the chart above and determine how AI can play a role
  • Determine the specific activities that these different forms of AI might transform, and the relevant cost, capability, and risk implications
  • Plan for how stakeholders can be engaged in understanding and embracing the potential changes to work, recognizing the aforementioned biases and resistance factors

Recognizing how technology and AI can transform the performance and value equation provides a significant competitive advantage. Successful leaders will translate the evolving pivot points in their business models into specific implications for work, looking beyond jobs, and understand the transformative role AI can play in redefining the performance curve for the work of the future.

Source: Harvard Business Review –  Automation Will Make Us Rethink What a “Job” Really Is