Automation Archives - Page 7 Of 11

Can Indian Tier-2/3 Cities Fit the Bill for Digital Services Delivery? | Sherpas in Blue Shirts

December 18, 2018

India continues to offer an attractive service delivery location proposition for global companies, given its unique combination of a low-cost, scalable English-speaking talent pool, and the breadth and depth of available skills.

As the global digital services industry matures, and with increasing competition in the tier-1 cities, companies are looking to reduce the costs of talent and access additional untapped talent pools for digital services delivery.

Can tier-2/3 cities in India fit the bill? Let’s start by looking at the current state of digital services delivery in these cities.

Existing Landscape

Today, India is the largest destination for digital services delivery, with 75 percent of the market. Tier-2/3 cities in the country currently hold 14-16 percent of the market share, and we expect this proportion to grow by 15-20 percent in the next couple of years. Ahmedabad, Chandigarh, Coimbatore, Indore, Jaipur, Kochi, Lucknow, T-puram, and Vadodara are the top nine tier-2/3 locations, accounting for 55-60 percent of the digital services headcount in tier-2/3 cities.

Tier-2/3 cities are mostly leveraged to provide social & interactive (41-43 percent), cloud (21-23 percent), analytics (16-18 percent), and automation (10-12 percent) related services. When it comes to sophisticated digital technology services, such as cybersecurity, mobility, and Artificial Intelligence (AI), service providers still prefer tier-1 locations such as Bengaluru.

Now, let’s evaluate how tier-2/3 Indian cities’ value proposition stacks up against tier-1 cities.

What’s ahead for India’s Tier-2/3 Cities?

Here are some of the key findings from our recently published report, “Will Tier-2/3 Indian Cities Carve a Niche in the Digital Story?”

Tier-2/3 cities will continue to be leveraged predominantly as spokes to major hubs in tier-1 cities for the next two to three years
Because of a lack of skilled talent, delivery of advanced digital services such as machine learning, cyber security, and mobility from tier-2/3 cities will remain a distant dream for the next few years
An increasing number of enterprises will set up global in-house centers (GICs) or shared services centers for delivery of digital operations, due to increasing confidence and improvements in infrastructure quality
Reskilling/upskilling for digital capabilities will be paramount for companies operating in these cities
A few large service providers will invest in training talent, and benefit from early mover advantage by becoming distinguished employers in a less competitive market

To learn more – including the metrics around availability of talent, market maturity, cost of operations, business and operating risk environment, and implications for market participants including buyers, service providers, investment promotion councils, and industry bodies – please read our recently published report, “Will Tier-2/3 Indian Cities Carve a Niche in the Digital Story?.” We developed the report based on deep-dive discussions with leading shared services centers, service providers, recruitment agencies, and other market participants.

Have RPA Vendors been MARVELous? | Sherpas in Blue Shirts

December 7, 2018

The relationship between RPA vendors and their clients isn’t so different from the relationship between Marvel Studios and its fans.

Since the movie Iron Man hit the big screen in 2008, fans’ expectations of superhero films have skyrocketed. Despite the rising and evolving expectations, Marvel has satisfied its audience and has made a little pocket change in the process.

In a similar way, RPA buyers are expecting increasingly more from their RPA vendors. So, have RPA technology vendors been MARVELous in their customers’ eyes?

The Drivers

Our recent research study among 50 enterprise RPA buyers makes it clear that vendors have excelled in addressing their primary drivers, which are cost reduction and process optimization.

However, vendors didn’t score as high on secondary drivers such as improved customer experience, governance, and top-line growth. With increasing awareness about the potential impact of RPA beyond immediate cost and efficiency benefits, enterprises have started to view RPA as a primary contributor to their digital strategy, rather than a tactical measure.

Consequently, technology vendors should focus on continuously evolving their RPA solutions with a host of capabilities to help enterprise buyers achieve their strategic business outcomes.

The Capabilities

As to be expected, the buyers in our research study found their RPA vendors excelled in certain areas and had work to do in others.

The key strengths for those vendors who were identified as the Leaders as per our PEAK Matrix™ assessment on RPA included:

Customer support and service
Ease of use and robot development
Vision and strategy

Key improvement areas for Leaders included:

Responsiveness
Product vision and strategy
Product training and support

The X Factors

As there are so many RPA tools available in the market, each with its own strengths and weaknesses, it can be daunting for enterprises to select the right vendor for their unique needs. One critical part of the decision-making process is to focus on the X factors that are most important to their strategic agendas.

Our study found that factors including “ease of use and robot maintenance” and “scalability” highly correlate to buyers’ overall satisfaction levels. This is not surprising, as these are factors that buyers typically face issues with during RPA adoption. “Product vision and strategy” – and in some cases vendor expertise in a specific vertical industry or function – are also important buyer X factors.

While it’s clear that RPA vendors can do more to satisfy the needs of their customers – and that they’ll need to continually evolve their solutions – they have indeed been relatively MARVELous in delivering value and overall satisfaction to their buyers.

To learn more, please read our report “Buyer Satisfaction with RPA – How Far or Close is Reality From Hype.”

AI for Experience: From Customers to Stakeholders | Sherpas in Blue Shirts

December 5, 2018

Everest Group’s digital services research indicates that 89 percent of enterprises consider customer experience (CX) to be their prime digital adoption driver. But we believe the digital experience needs to address all stakeholders an enterprise touches, not just its customers. We touched on this topic in our Digital Services – Annual Report 2018, which focuses on digital operating models.

Indeed, SAP’s recent acquisition of Qualtrics and LinkedIn’s acquisition of Glint indicates the growing importance of managing not only CX, but also the digital experience of employees, partners, and the society at large.

AI Will Usher in the New Era of the Digital Experience Economy

Given the deluge of data from all these stakeholders and the number of parameters that must be addressed to deliver a superior experience, AI will have to be the core engine powering this digital experience economy. It will allow enterprises to build engaging ecosystems that evolve, learn, implement continuous feedback, and make real time decisions.

AI’s Potential in Transforming CX is Vast

Today, most enterprises narrowly view the role of AI in CX as implementing chatbots for customer query resolution or building ML algorithms on top of existing applications to enable a basic level of intelligence. However, AI can be leveraged to deliver very powerful experiences including: predictive analytics to pre-empt behaviors; virtual agents that can respond to emotions; advanced conversational systems to drive human-like interactions with machines; and even to deliver completely new experiences by using AI in conjunction with other technologies such as AR/VR, IoT, etc.

Digital natives are already demonstrating these capabilities. Netflix delivers hyper personalization by providing seemingly as many versions as its number of users. Amazon Go retail stores use AI, computer vision, and cameras to deliver a checkout free experience. And the start-up ecosystem is rampant with examples of cutting-edge innovations. For instance, HyperSurfaces is designing next-gen user experiences by using AI to transform any object to user interfaces.

But focusing just on the customer experience is missing the point, and the opportunity.

AI in the Employee Experience

AI can, and should, play a central role in reimagining the employee journey to promote engagement, productivity, and safety. For example, software company Workday analyzes 60 data points to predict attrition risk. Humanyze enables enterprises to ascertain if a particular office layout supports teamwork. If meticulously designed and tested, AI algorithms can assist in employee hiring and performance management. With video analytics and advanced algorithms, AI systems can ensure worker safety; combined with automation, they can free up humans to work on more strategic tasks.

AI in the Supplier and Partner Experience

Enterprises also need to include suppliers and other partners in their experience management strategy. Using predictive analytics to automate inventory replenishment, gauge supplier performance, and build channels for two-way feedback are just a few examples. AI will play a key role in designing systems that not only pre-empt behaviors/performance but also ensure automated course correction.

AI in the Society Experience

Last but not least, enterprises cannot consider themselves islands in the environment in which they operate. They must realize that experience is as much about reality as about perception. Someone who has never engaged with an enterprise may have an “experience” perception about that organization. Some organizations’ use of AI is clearly for “social good.” Think smart cities, health monitoring, and disaster management systems. But even organizations that don’t have products or services that are “good” for society must view the general public as an important stakeholder. For example, employees at Google vetoed the company’s decision to engage with the Pentagon for use of ML algorithms for military applications. Similarly, employees at Microsoft raised concerns over the company’s involvement with Immigration and Customs Enforcement in the U.S. AI can be leveraged to predict any such moves by pre-empting the impact that a company’s initiatives might have on society at large.

Moving from Customer to Stakeholder Experience

As organizations make the transition to an AI-enabled stakeholder experience, they must bear in mind that a piecemeal approach will not work. This futuristic vision will have to be supported by an enterprise-wide commitment, rigorous and meticulous preparation of data, ongoing monitoring of algorithms, and significant investment. They will have to cover a lot of ground in reimagining the application and infrastructure architecture to make this vision a distinctive reality.

What has been your experience leveraging AI for different stakeholders’ experiences? Please share with us at [email protected] and [email protected].

Upskilling and Reskilling: Is It Just Good L&D or Something Different? | Sherpas in Blue Shirts

December 4, 2018

Is upskilling and reskilling little more than a thinly disguised attempt by HR departments to rebrand Learning and Development (L&D)? The answer, as one practitioner pointed out at a conference in Poland, is “no.”

I recently presented to the Association of Business Services Leaders (ABSL) Chapter in Krakow, Poland about the talent acquisition challenges that digitization poses to Shared Services Centers (SSCs.) The argument runs roughly like this:

Robotic Process Automation (RPA) is replacing human agents in transactional roles, freeing up capacity in the workforce. This can mean lay-offs at worst, or unqualified internal candidates reapplying for roles at best
There is greater demand for people with new skills both technological (design thinking, robotics, autonomics, analytics) and soft (pattern-recognition, complex problem solving, leadership, intuition) than can be met by simply recruiting externally
As automation takes care of transactional processes, organizations can enhance the value of their brands and the service they provide by having more people in roles which emphasize first contact resolution, emotional intelligence, listening, etc.
This new value chain focuses on outcomes: people are measured against quality of outcome rather than throughput (for instance, a shift from average handling time to CSat), which in turn requires new management thinking around staff incentives, culture, and business model.

The data in the presentation was based on the Everest Group survey of 81 SSC leaders in Poland, the Philippines, and India, published earlier this year (see “Building a Workforce of the Future – Upskilling/Reskilling in Global In-house Centers.”)

So obvious was the message that emerged from the survey that one or two skeptics in the audience questioned why retraining that part of the workforce most affected by the trend of automation was even worthy of discussion. Is it not just good L&D practice? And surely survey respondents would not admit to anything other than good practice when asked the question?

Not quite true: there were survey respondents, albeit no more than 10 percent of them, who said that they were not planning to undertake upskilling and reskilling as a means of addressing talent shortages. A small majority, 58 percent, said upskilling/reskilling was the highest priority in addressing this same problem, while 10 percent, possibly the same nagging 10 percent, said it was a low priority.

The discussion continued after the presentation. Without experience as a practitioner, I wrestled with an explanation as to why this 10 percent stubbornly refused to fit the theory. Thankfully, the HR head of a Krakow-based SSC rode to my rescue and gave the answer.

This is the group, she said, which understands that reskilling and upskilling is indeed good L&D practice but remains wedded to external hiring of permanent and temporary staff. It is the group that fails to see that existing employees must be recognized as the key pool to meet scarce talent requirements in SSCs.

Her explanation, thankfully, echoed our contention that successful application of reskilling/upskilling to talent acquisition needs:

Senior leadership backing to ensure adequate resource and profile within the organization
Implementation of a skill-specific talent acquisition strategy to identify both critical areas of shortage and those most suitable for reskilling/upskilling
Quick roll-out of pilots in critical areas of shortage to build confidence and to learn
Breaking down of functional barriers and giving employees wider exposure to functional roles
A combination of effective duration and appropriate method (job rotation, on-the-job training, mentoring, peer-to-peer learning, and specialist external providers)
Clear communication of career paths, internal opportunity, incentive, and compensation
Patience and persistence!

She explained further. In her experience, the real difference between reskilling/upskilling as good L&D practice and reskilling/upskilling as a talent acquisition solution is simple. The talent acquisition solution approach is not considered aspirational, “something that HR does,” or nice to have. Rather, it is a strategic imperative.

How nice to have somebody who really knows what they are talking about answer a difficult question on my behalf!

Using AI to Build, Test, and Fight AI: It’s Disturbing BUT Essential | Sherpas in Blue Shirts

November 15, 2018

Experts and enterprises around the world have talked a lot about the disturbing concept of AI being used to build and test AI systems, and challenge decisions made by those systems. I wrote a blog on this topic a while back.

Disquieting as it is, our AI research makes it clear that AI for AI with increasingly minimal human intervention has moved from concept to reality.

Here are four key reasons this is the case.

Software is Becoming Non-deterministic and Intelligent

Before AI emerged, organizations focused on production support to optimize the environment after the software was released. But those days are going to be over soon, if they aren’t already. The reality is that today’s increasingly dynamic software and Agile/DevOps-oriented environments require tremendous automation and feedback loops from the trenches. Developers and operations teams simply cannot capture and analyze the enormous volume of needed insights. They must leverage AI intelligence to do so, and to enable an ongoing interaction channel with the operating environment.

Testing AI Biases and Outcomes is not Easy

Unlike traditional software with defined boundary conditions, AI systems have very different edge scenarios. And AI systems need to negate/test all edge scenarios to make sense of their environment. But, as there can be millions of permutations and combinations, it’s extremely difficult to manually assure or use traditional automation to test AI systems for data biases and outcomes. Uncomfortable as it may be, AI-layered systems must be used to test AI systems.

The Autonomous Vehicle Framework is Being Mirrored in Technology Systems

The L0-L5 autonomous vehicle framework proposed by SAE International is becoming an inspiration for technology developers. Not surprisingly, they want to leverage AI to build intelligent applications that can have autonomous environments and release. Some are even pushing AI to build the software itself. While this is still in its infancy, our research suggests that developers’ productivity will improve by 40 percent if AI systems are meaningfully leveraged to build software.

The Open Source Ecosystem is Becoming Indispensable

Although enterprises used to take pride in building boundary walls to protect their IP and using best of breed tools, open source changed all that. Most enterprises realize that their developers cannot build an AI system on their own, and now rely on open source repositories. And our research shows that 20-30 percent of an AI system can be developed by leveraging already available code. However, scanning these repositories and zeroing in on the needed pieces of code aren’t tasks for the faint hearted given their massive size. Indeed, even the smartest developers need help from an AI intelligent system.

There’s little question that using AI systems to build, test, and fight AI systems is disconcerting. That’s one of the key reasons that enterprises that have already adopted AI systems haven’t yet adopted AI to build, test, and secure them. But it’s an inevitability that’s already knocking at their doors. And they will quickly realize that reliance on a “human in the loop” model, though well intentioned, has severe limitations not only around the cost of governance, but also around the sheer intelligence, bandwidth, and foresight required by humans to govern AI systems.

Rather than debating its merit or becoming overwhelmed with the associated risks, enterprises need to build a governing framework for this new reality. They must work closely with technology vendors, cloud providers, and AI companies to ensure their business does not suffer in this new, albeit uncomfortable, environment.

Has your enterprise started leveraging AI to build, test, or fight AI systems? If so, please share your experiences with me at [email protected].

Telematics in Insurance – A Big Opportunity yet to be Fully Explored | Sherpas in Blue Shirts

November 14, 2018

Price competition used to define the competitive dynamics of the P&C insurance industry. However, as margins started squeezing with low interest rates and rising claims costs, it became imperative for insurers to focus on product differentiation in order to attract new customers and drive premium growth.

This is when usage-based insurance (UBI), an insurance product model where the premium varies according to the risk of claims that the insured’s policy-related behavior poses, started gaining traction. UBI is noteworthy as it offers a remarkable opportunity for insurers to deliver hyper-personalization and evolve from a product-centric to a customer-centric business mindset.

To date, the auto insurance segment has been the most aggressive adopter of the UBI model, which is enabled by the underlying telematics infrastructure. Telematics technology enables insurers to capture each customer’s driving data, which is then used to continually update the customer’s risk profile and compute the payable premium. Data collection devices have evolved from black-box to OBD-II dongles to in-built telematics units in automobiles and smartphones.

UBI’s Business Case is Strong; however, Sourcing Gets Complicated for Insurers

We expect the market for UBI to grow substantially at a CAGR of ~40 percent during 2018-2020, with an estimated 35-40 million UBI policies in force by the end of 2020. This is certainly impressive growth.

However, to launch UBI products, insurers must make substantial investments in connected devices and data infrastructure. Moreover, not all insurers have the scale, risk-appetite, investable capital, or technology expertise to make significant inroads into UBI. Thus, insurers are leveraging third-party vendors to support their telematics journey.

Yet, the vendor ecosystem is fragmented, making it challenging for insurers to determine what organization to partner with.

Here’s the breakdown of the three major categories of telematics vendors:

Telematics Service Providers (TSPs)

These have the capability to manage the entire value-chain, from telematics device sourcing to device deployment and maintenance to end-customer engagement to telematics data management. However, as a single TSP might not be able to provide access to all the underlying connected devices, insurers must pre-strategize their requirements for data depth and breadth. There have been cases where insurers have entered into partnerships with multiple vendors with varying competency to leverage connected devices and technology maturity.

Data exchanges

The core value proposition of this class of vendors lies in their access to huge volume of data and their data handling capabilities, which reduces the burden of data management at the insurer’s end. Players that have entered this market also have developed a modest understanding of the insurance sector, which enables them to provide risk assessment support to insurers. However, while data exchanges typically can augment insurers’ telematics journey, they cannot provide end-to-end support.

OEMs

OEMs have emerged as significant competitors to the other classes of vendors due to their direct control of the point-of-sale. As the telematics unit is prebuilt into the automobile, insurers do not have to worry about the entire infrastructure management of telematics devices. However, partnering with an OEM could also mean loss of revenue from value-added services.

Service Providers as the Orchestrator – Big Opportunity Waiting to be Capitalized

With each of the categories of vendors specializing in specific parts of the telematics value-chain, insurers face a big challenge in connecting with different parties for different values, and in managing the multi-vendor ecosystem.

This is where IT/BP service providers can enter the picture. To date, they have failed to establish a competitive differentiation for themselves in this market. However, considering they have a sound understanding of insurers’ businesses, operations, and IT systems, they could provide significant value as the orchestrator of this branched ecosystem.

They could look to source the best value from different classes of vendors by tying partnerships with select technology vendors across the ecosystem. Then, they could serve as a specialist to help insurer wrap their operations around telematics technology to drive product differentiation.

In this model, service providers could – potentially – offer an integrated value proposition that would involve: owning the implementation risk; providing value-added services such as risk assessment and customer management support; managing the complexity involved in coordinating with multiple classes of vendors; and assuming responsibility for the risks (e.g., business risk, technology lock-in, etc.) associated with engaging with niche firms.

This could be a win-win-win scenario, for insurers, end-customers, and providers.

How service providers ultimately decide to capitalize on the telematics opportunity remains to be seen. However, they should be cognizant of not frivolously trying to compete where their expertise does not lie, and instead leverage their strengths to make themselves most relev

How to Drive Alignment with Your Service Provider in Implementing Digital Technologies | Sherpas in Blue Shirts

September 28, 2018

Companies are on the horns of a dilemma. They signed long-term, managed service contracts for IT or business processes, which took advantage of the savings from labor arbitrage. But now they find that there is significant potential to leverage the new suite of digital technologies that promise improved performance and lower cost. The problem is that that their incumbent service providers often actively resist implementing these technologies, using delaying and obviation tactics, refusing to pass on the savings and/or demanding additional work or other concessions in return for complying. Now that I’ve identified this major issue that many companies face today, let’s look at how they handle this non-alignment situation.

Investing Big in RPA is Not a Fool’s Game | Sherpas in Blue Shirts

September 27, 2018

The news of another big round of funding for UiPath, US$225 million series C, and a valuation of US$3 billion created a lot of excitement and amazement in the market. It followed on from Automation Anywhere’s whopping series A funding round of US$250 million in July, which valued the company at US$1.8 billion, and which surpassed UiPath’s earlier series B funding of US$153 million and a valuation of US$1 billion in Q1 2018.

These valuations are phenomenal. In UiPath’s case, the rise from US$1 billion to US$3 billion in less than six months is, I believe, unprecedented. You might think that investors are living on a different planet than us ordinary folks, and that this kind of valuation is plain wrong. I beg to differ.

Investing in the Future of RPA

My case rests on the rapid increase in market adoption and the huge investments that vendors are making in their platforms. As much has already been said about the fast rate of enterprise adoption, there’s no need for me to repeat it again here. Jumping to the second part of my case: RPA today is not the RPA that launched this market three to four years ago. The original developments lacked many of the features that we see today, e.g., computer vision to pick objects on the screen and robust control panels. Similarly, tomorrow’s RPA will be superior to today’s.

As someone who assesses RPA technology on an annual basis, I see a fast rate of product development, not just year on year, but in some cases quarter by quarter.

Everest Group’s “RPA Virtuous Circle” highlights the continuous cycle of developments in the market.

Much has been said of organizations struggling to scale their deployments. I completely agree with this, and for a while I’ve been asking vendors to do something about this issue. I am delighted to see that they have been listening and are investing in features for scaling. These include enhanced robot run time control and management features including intelligent control systems for dynamic workload balancing, auto-scaling, and even identifying processes for further automation. Another major stream of development is turning RPA platforms into the glue that holds together business process management systems (BPMS), different varieties of machine learning, and narrow artificial intelligence. These will ultimately be integrated and will combine seamlessly to provide end-to-end process automation.

While vendors do their bit for scale, organizations should also examine their deployment models for RPA and take a more programmatic approach. Automation is going to be a serious competitive differentiator, and a programmatic approach would significantly speed up organizations’ adoption and realization of desired outcomes. Everest Group’s RPA Pinnacle study highlights some of the approaches that organizations have taken to achieve excellence in RPA.

Related: 2018 RPA Vendor Technology Landscape PEAK Matrix™ Preview

Of course, these enormous investments in RPA do carry some risks. There is the possibility of tech giants bringing their own RPA solutions to market, in turn pushing out the current RPA vendors. But that wouldn’t be easy to do, as the existing vendors have gained a lot of hard to emulate know how in the past few years. And any one of the existing RPA vendors could be acquired in a major acquisition, but then the investors would get the handsome returns they anticipated…just in a different way.

Taking the Manufacturing Model to Business Processes

Another reason for my optimism about the recent investments in RPA and vendor valuations is that I recently got a glimpse into the future of business automation by looking at manufacturing. On a visit to Siemens Digital, I saw how the concept of digital twin and simulation of manufacturing processes is helping speed up production times and efficiency, even in manual/human processes.

For years, corporate global services functions have attempted to copy manufacturing principles, e.g., adopting Lean and Six Sigma methodologies. Today, they have moved on to automation, which manufacturing adopted decades ago. Having started on automation of global services, enterprises are not going to turn back. They will continue to follow manufacturing’s lead.

Leading organizations are already giving their processes version numbers with supporting documentation, having taken each step through a rigorous Lean Six Sigma methodology. On the automation front, while the focus has been primarily on tactical needs, it will increasingly move to outcomes and the finished “product,” as in manufacturing.

We will see enterprises develop digital twins of their processes or robots, and run complex functions end-to-end in virtual reality before committing to the final model for deployment in the real world. Future versions of RPA will have to support these requirements, and that is where some of the millions of funding will be spent; on product development and advanced features.

Today’s RPA products are paving the way for a far bigger change in automation of global services than we have seen to date. They are the building blocks of the platforms of the future for an inevitable automation journey that every organization will have to take sooner or later. That is why the current group of vendors are so attractive to investors. They are betting not just on today’s growing revenues, but what is to come.

RPA’s Virtuous Circle Story | Sherpas in Blue Shirts

August 7, 2018

How hot has Summer 2018 been around the globe? Red hot…but not as hot as the RPA marketplace. The speed of evolution in this industry segment is almost without precedent. Firms that had revenues worth tens of millions of U.S. dollars just a couple of years ago are talking about reaching a billion in revenue in just a couple of more years.

So why all the excitement? Some chalk it up to Robotic Process Automation being a clever product idea and others to the even cleverer marketing of sexy robots.

But the reality is that it’s the perfect storm – or heat wave – of innovation and capital intersecting at just the right time.

Related: Five Keys to Unlocking the Benefits of RPA for Enterprises

Of course, it doesn’t hurt that enterprises have already captured most of the potential value from offshore labor arbitrage. But when you combine the need for a new source of cost savings with the acute shortage of labor in the U.S. and Europe, you have a market condition in which enterprises are screaming for automation that allows continued productivity improvements for less money, with less human labor-based effort.

The RPA Virtuous Circle Story

These four keys make up the RPA virtuous circle: More sophisticated software platforms, real value propositions, significant capital infusion, and aggressive buy/build decisions. Let’s unpack each one to get the full story.

More sophisticated software platforms – the software platforms underlying RPA are not new; some of them have been around for many years. But as interest and revenues in the segment grow, the vendors are investing in better software and getting invaluable real-life implementation experience. And great use cases and robust feedback loops will drive enhanced software innovation.

Real value propositions – while a great idea is always fun to talk about, the story quickly fades if the economics are insufficient. In RPA’s case, enterprises are finding real savings and, probably most important, operational improvement. What makes this such an exciting story is that RPA doesn’t apply to just one aspect of the enterprise – it applies anywhere human resources are being deployed for labor-intensive services. So not just G&A functions, but also core business operations.

Significant capital being infused – where there is monetary value creation, Wall Street and Silicon Valley will certainly be found nearby. In the RPA segment, multiple investments in excess of US$100 million have been made. In total, we have seen more than a half billion dollars in investments in just the past six months. These are huge flows of capital, especially considering that in many cases they far exceed current revenues.

Aggressive buy/build decisions – of course, when that much capital is deployed, there’s tremendous pressure to take action to generate real, quantifiable results. The most obvious is to deploy larger sales/account teams to support the growth. But, there will be also significant development needs as use cases expand. We also anticipate that RPA firms will go on a buying spree of niche competitors or companies that increase automation functionality for items like OCR, machine learning, artificial intelligence, and natural language processing.

Right now, the velocity of the Virtuous Circle is increasing…better software, increased enterprise value propositions, and another round of investments.

To learn more about Everest Group’s take on RPA, view the replay of our popular August 8 webinar on the latest developments and implications for enterprises. By registering, you will also receive a a copy of the presentation and deck for download after the webinar.

Five Keys to Unlocking the Benefits of RPA for Enterprises | Sherpas in Blue Shirts

July 27, 2018

My recent meetings with the top RPA vendors made it clear that RPA is shifting into new gears of adoption and implementation. But the vendors also made it clear that the true promise of RPA is getting lost in flashy headlines and hype-ridden marketing messages.

Here are my five recommendations for how enterprises can drown out the noise and harness RPA’s real benefits.

Experimentation is Over – The Value is Real

The question “Should I pursue RPA?” has been answered and is widely being replaced with “How can I leverage RPA to gain the most value?” As you see in the graphic below, the benefits of RPA are very real. Nearly every enterprise we have spoken with is seeing real savings – typically around 30 percent lower cost and 30-50 percent improvement in accuracy, cycle time, staff productivity, etc.

Forget about RPA Vendors’ Pitches

Despite all the hype, enterprises must remember that RPA vendors are not selling a digital workforce; they are selling software that can speed up, improve, and support many processes currently performed by staff members. While this is sophisticated software, it’s not a physical entity or an army of robots. It can be tempting to get lost in the imagery, but enterprises need to be careful not to lose sight of what they are getting. Otherwise, they can be left with the feeling that vendors have overpromised and underdelivered.

Ignore the Buzz Words

From OCR to NLP to Intelligent Automation, there’s no shortage of RPA buzzwords. But the labels themselves don’t really matter. What does matter is the ability to identify processes that are using precious staff resources, limiting operational improvement, or diminishing the customer or employee experience. Enterprises should start with the process they want to improve and then approach the vendor with that specific need as the starting point in the context of their overall automation – including and beyond RPA – journey.

Focus on the Operating Fundamentals

The basics of building an enterprise automation capability can seem amazingly easily…until it becomes obvious that it’s not. Some enterprises undoubtedly acquire robots for simple plug-and-play automation. But when mission critical processes come into play, serious and complex issues – like enterprise-grade security and business continuity – come into play and must be carefully and thoughtfully addressed. Don’t allow these issues to become barriers to RPA adoption (as many enterprises do), because, if well implemented, the benefits far outweigh the risks.

Automation Tools are a Must for Business Growth

Automation tools can help enterprises tackle the labor shortage challenge by making their existing teams more productive and retaining key employees by offering opportunities to perform higher-value work. Although cost savings are important, an automation-augmented workforce is key to competing and excelling in the marketplace.

To help you avoid getting caught up in the industry hype around RPA, we’ve created a simple graphic that describes the four key dimensions you should be thinking about. This enterprise automation analysis framework looks beyond vendors’ marketing pitches and addresses questions based on opportunities from your point of view, including:

Business problem complexity – how big and complex is the business process?
Rate of operational improvement – how much of a business process improvement do we want to see?
Solution/technology investment – which of the many different automation solutions should we deploy (considering investment and benefit)?
Operational execution – how do we best implement in your organization?

At the end of the day, however you choose to move forward with RPA technology, start by considering your enterprise’s use cases and business requirements. Then, build the business cases to support them. And then set your automation team loose on an increasingly exciting new set of capabilities.

Click here to read more of our RPA thought leadership

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Category: Automation