Category: Automation/RPA/AI

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

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

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.

Telematics in Insurance – A Big Opportunity yet to be Fully Explored - potential impact

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

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

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.

Virtuous Circle w title - Investing in RPA blog

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

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

The RPA virtuous circle for business

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

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.

RPA Value blog image

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?

RPA Framework blog image

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

View a complimentary abstract of the Enterprise RPA Adoption | Pinnacle Model™ Analysis

Insurers and AI InsurTech Partnerships | Sherpas in Blue Shirts

Insurers are increasingly investing in AI to enhance the customer experience with automated personalized services, faster claims handling, and individual risk-based underwriting processes by empowering agents, brokers, and employees. Our recently released Insurance IT Services Annual Report 2018 found that more than half of insurers are opting to build in-house AI capabilities through hiring, internal training, hackathons, acquisitions, and partnerships with InsurTech companies, while the rest are turning to IT service providers.

Increased InsurTech Investments

The appetite for change within the insurance industry is certainly there. To make that change happen quickly, insurers have been investing in InsurTechs, firms offering technology innovations designed to squeeze out savings and efficiency from the current insurance industry model, to align data and integrate backend systems. Total InsurTech funding reached US$2.3 billion in 2017, a 36 percent increase from the US$1.7 billion recorded in 2016. In 2016, AI and IoT accounted for almost half of the total investment in InsurTech startups globally.

AI InsurTech investment has increased multi-fold since 2016. Seeking access to talent pools, innovative ideas, high speed, and lower cost of innovation, leading insurers have invested in startups including Betterview, Captricity, CognitiveScale, Lemonade, Mnubo, and Uniphore.

And 2018 appears to be spurring even more investments. Indeed, some of the top insurers have created dedicated venture capital arms – e.g., Allianz Corporate Ventures, MetLife Digital Venture, and XL Innovate – to invest in technologies such as voice biometrics, cognitive virtual assistants, speech analytics, telematics, drone imagery, and machine learning.

Strategic Decisions

Research we conducted on 24 leading insurance firms’ investment model suggested that more than 70 percent of their investments in AI InsurTechs are not just from a funding perspective. Rather, they are entering into partnerships with the InsurTechs as a more strategic decision to fulfill their long-term vision of digitalization.

Insurers and AI InsurTech Partnerships blog - Overview

Significant Impact across Insurers’ Value Chain

  • Process optimization: The majority of the AI InsurTech investments are for automating underwriting policy administration and policy administration, resulting in increased process efficiency. For instance, AXA partnered with TensorFlow to use machine learning to optimize pricing
  • Product innovation: In addition to fixing processes, insurance companies are partnering with InsurTechs to develop new customized policies and pricing, per user demand through usage-based information. For example, in 2018, Munich Re’s HSB Ventures led a US$16.5 million venture financing for Mnubo, an IoT, data analytics, and AI startup, to build tailored financial solutions to improve the company’s business and facilitate new business models
  • Customer experience: AI is making traditional claims processing a thing of the past. Companies are pioneering new cognitive solutions that are making the claims process faster, smarter, and more efficient. For instance, in 2018, GENERALI implemented Expert System’s Cogito® technology to focus on registration and claims processing, and to automate the customer email classification, resulting in a swift and smooth claims process and better customer service.

We believe these partnerships create a win-win situation. They give insurers access to the necessary talent pool, latest technology, innovation, and speed they need to thrive, not just survive. And they provide vital to insurers’ ability to compete, and provide InsurTechs with the guidance, infrastructure, funding, and customer base they need to grow.

If you’d like insights on leading InsurTechs and how they’re changing the insurance industry, please feel free to reach out to [email protected] and [email protected].

IT Modernization Investments to Dominate 2018 | Sherpas in Blue Shirts

What are the major areas where companies will focus their spend on technology or third-party services this year? What challenges will impact those investments? In reviewing the trends in 2017, I believe we’ll see more of the same this year and an increase in digital adoption. However, I believe we’re at the beginning stages of a megatrend for the next five years, and I’m calling the start of this phenomenon: I believe 2018 will be the year of IT modernization.

Over the next five years, large enterprises will drive relentlessly to modernize their IT environment. This activity will range from moving workloads out of legacy environments into the cloud, adopting agile and DevOps and investing much more deeply and thoroughly in world-class security.

I differentiate modernization from digital transformation. I see a different set of initiatives occurring often in the same companies, which I characterize as digital transformation. These initiatives often use some of the same technologies; however, they arise from the business and are focused on achieving competitive advantage. The funding, project management, and impact on change management are different in kind and scope. The rise of IT modernization will not slow the need and velocity of digital transformation, which I believe will continue to grow as well.

With respect to digital transformation,  we can expect the 2017 trend of digital pilots moving to much bigger programs to continue. However, change management and business model redesign will be a major constraining factor for successful digital transformation, and I believe we’ll see companies start focusing more on managing digital change.

As IT organizations prepare for modernization, they increasingly focus on three main journeys:

  • The journey to cloud resulting in establishing cloud as the infrastructure of choice
  • The journey from waterfall to agile
  • The journey to implement adequate security.

IT modernization will sweep across an organization’s entire IT portfolio, rethinking and restructuring infrastructure, networks, applications, and the process and policies that govern them. I expect IT modernization to drive a profound rethink of the enterprise IT structure as it will both collapse the IT stack and cause organizations to align services by end-to-end functions rather than horizontal functions. In contrast, digital transformation goes end to end and integrates the portfolio. In digital transformation, a company considers pulling workloads and activity out of the enterprise IT function or segmenting it into a different organization that is run end to end.

The results of this modernization will lead to a dramatic decrease in IT costs, while significantly increasing the speed and agility of IT’s ability to react in a timely fashion to business demand. This sudden increase in efficiency will have a dramatic effect on the service provider community, shrinking their existing revenue streams while demanding new skills and capabilities.

The new business models that emerge from this transformation are unlikely, at least at first, to be as profitable as the existing business models based on labor arbitrage. The combination of reduced revenues and lowered margins will place the incumbent service providers in a dilemma with very substantial conflicts of interest. The necessity to protect revenues and keep margins high is likely to make the incumbent service providers poor partners in the emerging digital marketplace.

One potential bright spot for the imcumbents, at least in the short run: although the overall legacy services segment will shrink, I believe IT modernization will result in a set of workloads with new workloads for service providers. For legacy workloads that have not been outsourced and are not ready to be modernized, companies will need to put them into a stable environment. I believe some of those workloads will move to the services market so companies can focus on modernization rather than legacy. This new work for service providers will partially offset some of the runoff that is happening because of IT modernization.

As I look forward to spending trends and challenges for this year, I think Robotic Process Automation (RPA) is hot and will continue to grow in adoption. Artificial Intelligence (AI) is starting to build momentum, and I think it will be red hot in 2018. I see AI being more disruptive than RPA and, therefore, causing greater change management and business model changes than RPA. RPA adoption already was constrained by change management issues in 2017, and I believe AI will be even more constrained by these issues because of its deeply disruptive nature.

We will also see blockchain technology grow in adoption. Although blockchain is truly a disruptive technology, its disruption will focus on specific areas where a distributed ledger can be applied (in comparison to AI, which has a broader set of uses than blockchain). 2018 will see a greater number of blockchain pilots, and some pilots will become programs. However, like AI, RPA and other new technologies, disruptive business model changes will be a major constraint to adoption.

Can Devops Be Delivered in a Distributed Labor Model? | Sherpas in Blue Shirts

Companies frequently ask us at Everest Group if the benefits a devops team can be delivered in a distributed labor model. In other words, can a company configure a devops team to operate with part of the team in one onshore location and other part of the offshore or in a different onshore location? To be clear, there is currently a significant debate around this question. Many tech companies and new service providers emphatically say devops can’t work in a distributed model. But legacy service providers with large investments in offshore talent factories argue that It absolutely can work and point to examples in which they are utilizing components of a devops model in an offshore and distributed manner.

Legacy service providers have a strong vested interest in maintaining their current offshore factories, which are highly leveraged with cheap junior resources and are working hard to persuade their customers that the offshore models only need an injection of devops technology. However, none of them appear to be running at the productivity level – or even close to the level – of devops teams that are not in a distributed model.

Could RPA and AI Save GDPR Laggards from Hefty Fines? | Sherpas in Blue Shirts

With just seven months to go to the General Data Protection Regulation (GDPR) compliance deadline, many companies still have wholly inadequate data management capabilities. Strict requirements for personal data security, privacy, and the right to erase, among other things, will cause severe headaches for many CIOs not only in the EU but in all regions, as organizations will have to know which data is and is not subject to the regulation, and where in the world it is stored.

Download our special complimentary report: EU GDPR: Is There a Silver Lining to the Disruption?

No doubt many complex and conflicting scenarios will arise out of GDPR. For example, consider the following data-related issues:

  • When a request to be forgotten comes in from a customer, how will the organization find all the occurrences of the same data across the vast enterprise IT estate?
  • Will public and private cloud and other infrastructure providers be able to handle the requirements in a timely manner?
  • What would be the knock-on effect of a customer asking for his/her data to be erased? What systems will be affected and how would that effect audit trails and other regulatory requirements, such as maintaining company-related data for audit purposes for several years?

These and a multitude of others will take many more years to understand, get guidance on, and resolve. In the meantime, companies must be compliant, or face fines that are the greater of €20 million or 4 percent of global annual turnover.

For those organizations that have not yet prepared for GDPR, the overheads of data management are increasing significantly. For example, they must figure out how to best obtain and maintain personal consent, handle access requests, process revocation of consent and requests to be forgotten, train personnel to know what they can and cannot do with data under GDPR, ensure outsourced services, cloud providers, other suppliers, e.g. in the supply chain, and partners are compliant, and run audits to check the readiness and effectiveness of the provider/supplier/partner ecosystem.

Enter RPA

This is where, with its rules-based bots, Robotic Process Automation (RPA) could prove to be God’s gift to the laggards. Scenarios where RPA could be ideal include, but are not limited to:

  • Running audits of data against consent and revocation databases for compliance
  • Checking a queue of in-coming consent or revocation requests, and acting upon them, e.g., setting the right flags in systems or actively deleting data while maintaining an audit trail
  • Producing audit reports
  • Propagating changes of personal data and related consent across all the systems that hold that data, by cutting and pasting updates and maintaining consent-related databases

The role of AI

As organizations collect more and more GDPR-related data, Artificial Intelligence (AI) solutions could come into their own by helping with risk and impact analysis and reporting:

  • How many systems will be affected by a GDPR consent and access related change?
  • What is the knock-on effect on workloads and audits trails? How do these affect other regulatory requirements of data retention?
  • How many systems will be affected, and what would be the impact on operations and other legal and regulatory requirements?
  • What is the data security threat level of the day? What is the likelihood of data breaches on a daily/hourly basis, and what preventative measures could be taken?
  • What security breach has happened and what actions have been taken? Who has been affected by it and must be notified?
    Additionally, good governance is an imperative for GDPR. RPA and AI can be used to embed governance in daily operations for enforcing and monitoring purposes.

A new era of data protection is upon us. It is coming at a time when, some would say, that companies have taken far too many liberties with their customers’ data. The full implications for businesses are yet to be understood. But we believe that all organizations that hold or process personal data will experience some disruption in service delivery as a direct result of GDPR. For more on Everest Group’s point of view, please see our latest free publication: “EU GDPR: Is There a Silver Lining to the Disruption?

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