Category: Automation

Fragmented DevOps = Minimal Value | Blog

September 13, 2019

Enterprises are increasingly embracing DevOps to enhance their business performance by accelerating their software time-to-market. In principle, DevOps covers the entire spectrum of Software Development Life Cycle, SDLC, activities from design through operation. But, in practice, only ~ 20 percent of enterprises are leveraging DevOps end-to-end, according to our recent research, DevOps Services PEAK Matrix™ Assessment and Market Trends 2019 – Siloed DevOps is No DevOps!

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That means the remaining ~ 80 percent that are taking a siloed approach to DevOps are missing out on the many types of values it can deliver.

Types of DevOps fragmentation

Instead of adopting DevOps in its intended end-to-end fashion, many enterprises in different verticals and at different stages of maturity are tailoring it to focus on siloed, distinct portions of the SDLC. The most common types of fragmentation are: 1) Apps DevOps, applying DevOps principles only across the application development cycle; 2) Test Ops, using DevOps principles in testing; and 3) Infra Ops, applying DevOps principles only to infrastructure.

Why fragmentation delivers minimal value

Pocketed adoption makes it tough to realize the full value that DevOps can deliver. The primary reason is bottlenecks. First, workflow throughout the SDLC is impeded when DevOps principles and automation are only applied to certain phases of it. Second, lack of end-to-end adoption makes it more difficult for enterprises to gain a full view of their applications portfolio, spot bottlenecks, incorporate stakeholder feedback in real-time, and make the entire process more efficient.

Additionally, when DevOps is used in a siloed manner, it focuses primarily on increasing the technical efficiency of processes. This means that DevOps’ ability to support the enterprise’s broader business-oriented objectives is severely restricted.

Finally, fragmented DevOps adoption creates a disintegrated culture in which teams work independently of each other, in turn leading to further conflicts, dependencies, and stretched timelines. All this, of course, defeats DevOps’ main purpose.

Moving to end-to-end DevOps adoption

To successfully adopt DevOps end-to-end, enterprises should place automation, culture, and infrastructure at the heart of their strategy.

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  • Automation: Automating various elements of the SDLC is extremely beneficial; doing so helps reduce implementation timelines and increase team productivity by standardizing processes and diminishing the scope of errors
  • Culture: A collaborative culture is essential to a successful DevOps implementation as it involves the development, operations, and business teams working together in an iterative fashion to achieve cross-team and business-oriented KPIs
  • Infrastructure: Increasing adoption of cloud-native technologies like as infra-as-code, microservices, serverless, and containers helps maintain configuration consistency in deployment, eventually leads to an increase in developer productivity, and saves on cloud computing costs.

DevOps has the ability to deliver significant value to enterprises. But implementing it in a siloed manner quickly dilutes a lot of that potential value. To realize all DevOps’ benefits, enterprises should implement it end-to-end, invest in automation, robust and modular infrastructure, and tools and technologies to ensure agility, and develop a culture that helps them improve cross-team collaboration.

What has been your experience in your DevOps adoption journey? Please share with us at [email protected] and [email protected].

Artificial Intelligence: Why It’s Essential For Digital Platforms | Blog

September 10, 2019

Companies widely recognize the potential power of artificial intelligence (AI). They instinctively understand that it feels like we’re on the cusp of something that will change our lives and our businesses in a profound way. Yet, many struggle with where to apply it. Executives can’t shake the feeling that they should have use cases for AI and use it productively today, even recognizing that AI is not mature yet and will be far more powerful tomorrow and in the future. If you’re looking for how and where your company should use AI, let me give you a perspective on a great application of AI today: your digital platforms.

Read my blog on Forbes

Aware Automation: An Enabler of Business-Centric Infrastructure | Blog

September 10, 2019

In today’s digital world, enterprise success is all about speed, agility, and flexibility in order to adapt to market and competitor dynamics. It is no surprise that 62% of enterprises view IT services agility and flexibility as a primary focus of their IT services strategy1, with cost reduction seen as a derivative.

The digital businesses of today require a business-centric IT infrastructure that is agile, flexible, scalable and cost-effective. For a long time, IT infrastructure has taken up an inordinate amount of time and the lion’s share of precious resources (particularly financial). However, with new cloud delivery models gaining prominence and advancements in the underlying technology, business leaders now view IT infrastructure as an enabler of digital transformation — or at the very least, want to ensure that their IT infrastructure evolves to such a state.

Read the blog on IPSoft

 

Aware Automation: How Enterprises Can Capture Value | Blog

September 10, 2019

In a previous blog post, we explored the evolution of enterprise IT infrastructures from a cost-center positioning to one that enables digital transformation through a concept known as aware automation  — a combination of intelligent automation and cognitive/Artificial Intelligence (AI)-driven automation. In this post, we’ll explore some potential use cases and best practices for aware automation within the enterprise.

Read more in our blog on IPSoft

Are the Automation Savings Numbers You Hear Real? | Blog

July 24, 2019

While today’s enterprises turn to automation for a multitude of competitive advantages, cost savings is at the top of their list. Through their marketing initiatives, often backed by client case studies and references, third-party service providers often boast automation-driven FTE reductions that save their clients millions of dollars.

Indeed, we’ve seen claims of savings to the tune of 30-70 percent FTE reductions. But our own data, culled from BPO deals on which we advise, show FTE reductions that are one-third to two-thirds lower.

Why is there such a significant gap? It’s because the service providers are calculating the reduction at the project level, instead of at the process level. While the numbers show well using a project level calculation, they’re very misleading, and often lead to disappointment.

Let’s take a quick look at an invoice processing example to see the glaring differences.

invoice processing example

As you see, an automation-driven invoice data extraction project in North America results in a 60 percent FTE reduction. Yet, when you expand the calculation to include invoice coding and exception handling in all operating regions – i.e., the enterprise-wide end-to-end invoicing process – the number drops to 10 percent. A 60 percent FTE reduction is highly enticing, and technically it’s correct. But it doesn’t show you the whole picture.

In order to properly assess the value of automation and develop your business case, you need to look at the percentage savings for the entire process. This is the only way you’ll obtain objective, realizable benefits data.

How can you find the automation savings data you need?

Your first thought might be to try and collect it from similar enterprises that have already implemented automation. But the numbers won’t be particularly reliable, as most enterprises are in the early days of their automation journey.

The most practical and valuable approach is to look at the BPO deal-based data for the entire process to be automated. Doing so gives you a realistic view of the automation-driven FTE savings for a couple of reasons. First, the FTE base for automation benefit calculation in deals is clearly defined in the baselining/RFP phase as the total number of FTEs in the process. And second, the FTE benefit numbers within deals are slightly more aggressive than the current norm, but because providers are continually refining their capabilities, they are comfortable with contractually committing to the higher numbers.

And remember that your BPO and/or RPA implementation provider should present this data to you to set realistic expectations. If they don’t, you’ll be armed with the ammunition you need.

Automation has the potential to greatly reduce your expenses. But before you leap, you need to carefully evaluate how the savings are being calculated. Your satisfaction depends on it.

If you’d like detailed insights on the FTE reduction numbers across different BPO processes within live BPO deals, please connect with us at [email protected] or visit https://www.everestgrp.com/research/domain-expertise/benchmarking/.

Digital Investments are Helping Offshore Service Providers Reinvent Themselves | Blog

July 19, 2019

Just a short five or so years ago, digital capabilities were a competitive differentiator for major service providers. Today, they’re a competitive must. As a result, global and offshore-heritage service providers alike are making significant investments in digital technologies such as blockchain, Artificial Intelligence (AI), Robotic Process Automation (RPA), and Internet of Things (IoT).

While the global players took the lead in building what is now a billion-dollar digital landscape, offshore-heritage service providers such as Infosys, TCS, and Wipro are quickly catching up. And their strategies to build and deliver greater value through digital-driven productivity and IP are clearly paying off. For example, our research found that their digital revenue jumped from 20 percent to 30 percent of their total revenue between Q1 2018 and Q1 2019.

Let’s look at how offshore-heritage service providers are upping their game with digital investments.

Internal digital-based capabilities

One of their strategies is to enhance the customer experience and improve efficiency through internal development of digital-based capabilities. For example:

  • Infosys launched AssistEdge Discover to increase the rate of automation implementations at the enterprise level through process discovery
  • TCS launched the connected intelligence data lake software on Amazon Web Services (AWS) to help clients build their own analytics services
  • Wipro made its AI and Machine learning (ML) solutions available on AWS to govern supply chain processes and enhance productivity and customer experience
  • Tech Mahindra launched NetOps.ai, its network automation and managed services framework, to speed up 5G network adoption
  • HCL launched iCE.X, an IoT device management platform, to bring intelligent IoT device management to telecom and media services, and increase IoT use case adoption.

Digital-focused acquisitions

As their initial reskill/upskill approach left them far behind global service providers’ inorganic approach, offshore-heritage service providers have taken the leap and started acquiring companies to obtain direct access to already-trained talent. For example:

  • Genpact acquired riskCanvas to access its suite of anti-money laundering (AML) solutions
  • Tech Mahindra acquired Dynacommerce, a computer software provider, to support its digital transformation strategy and enable a future-proof and future-ready digital experience for its customers
  • HCL acquired Strong-Bridge Envision (SBE), a digital transformation consulting firm, to leverage its capabilities in digital strategy development, agile program management, business transformation, and organizational change management. With this acquisition, SBE will become part of HCL’s global digital and analytics business
  • Tech Mahindra acquired K-Vision, a provider of mobile network solutions, for US$1.5 million to build and support the roll-out of a 4G and 5G telecom network in Japan. The acquisition will leverage the local presence and expertise of K-Vision to build its network services business in the country.

Partnership with startups

In order to develop skills and knowledge about these next-generation digital technologies in the general workforce, offshore-heritage service providers are partnering with niche start-ups to improve their agility/flexibility, reduce costs, and access stronger and better insights. For example:

  • Wipro partnered with RiskLens, a provider of cyber risk software and management solutions, to deliver quantitative cyber risk assessments to enterprise customers and government organizations
  • Tech Mahindra partnered with Rakuten Mobile Network to open a next-generation (4G and 5G) software-defined network lab. The partnership will help both the firms drive innovation and disruption in the 5G space
  • TCS partnered with JDA software to build next-generation cognitive solutions to optimize supply chains for customers. The partnership will develop joint, interoperable technology solutions for supply chains of the future, and accelerate human-machine collaboration to solve complex business problems
  • HCL partnered with Kneat.com, a software firm, to provide and implement next-generation digital solutions for facilities, equipment, and computer systems validation processes leveraging Kneat’s paperless software platform.

Future outlook

With digitalization on the rise across industries and product segments, and a bearish economy outlook in key markets such as the United States and Europe, offshore-heritage service providers will continue to invest heavily in next-gen technologies. This will help them to emerge as strong partners for global organizations to wade through their economic pressures.

To learn more about offshore providers’ digital strategies, key market trends, global locations activity, and service provider activity in Q2 2019, please see our Market Vista™ : Q2 2019 report.

Should You Scale Agile/DevOps? | Blog

July 1, 2019

Scaling in an application development environment can take many different shapes and forms. For the purposes of this blog, let’s agree that scaling implies:

  • From one team to a project
  • From one project to a program
  • From a program to multiple programs
  • From multiple programs to a portfolio
  • From a portfolio to a business
  • From a business to the entire enterprise.

Now that we’ve set the stage…our research suggests that over 90 percent of enterprises have adopted some form of Agile, and 63 percent believe DevOps is becoming a de facto delivery model. Having tasted initial success, most enterprises plan to scale their Agile/DevOps adoption.

The first thing we need to address here is the confusion. Does increasing adoption imply scaling?

Purists may argue that scaling across different projects isn’t really scaling unless they are part of the same program. This is because scaling by its very nature creates resource constraints, planning issues, increased overhead, and entropy. However, the resource constraints primarily relate to shared assets, not individual teams. So, if team A on one program and team B on another both adopt Agile/DevOps, neither team will be meaningfully impacted. Both can have their owns tools, processes, talent, and governance models. This all implies that this type of scaling isn’t really challenging. But, such a technical definition of scaling is of no value to enterprises. If different projects/programs within the organization adopt Agile and DevOps, they should just call it scaling. Doing so makes it easier and more straightforward.

The big question is, can – and should — Agile/DevOps be scaled?

Some people argue that scaling these delivery models negates the core reasons that Agile was developed in the first place: that they should thrive on micro teams’ freedom to have their own rhythm and velocity to release code as fast as they can, instead of getting bogged down in non-core tasks like documentation and meetings overload.

While this argument is solid in some respects, it doesn’t consider broader negative enterprise impacts. The increasingly complex nature of software requires multiple teams to collaborate. If they don’t, the “Agile/DevOps islands” that work at their own pace, with their own methods and KPIs, cannot deliver against the required cost, quality, or consistent user experience. For example, talent fungibility is the first challenge. Enterprises end up buying many software licenses, using various open source tools, and building custom pipelines. But because each team defines its own customization to tools and processes, it’s difficult to hand over to new employees when needed.

So, why is scaling important?

Scaling delivers higher efficiency and outcome predictability, especially when the software is complex. It also tells the enterprise whether it is, or isn’t, doing Agile/DevOps right. The teams take pride in measuring themselves on the outcomes they deliver. But they often are poorly run and hide their inefficiencies through short cuts. This ends up impacting employees’ work-life balance, dents technical and managerial skill development, increases overall software delivery costs, and may cause regulatory and compliance issues.

What’s our verdict on scaling Agile/DevOps?

We think it makes sense most of the time. But most large enterprises should approach it in a methodical manner and follow a clear transitioning and measurement process. The caveat is that enterprise-wide scale may not always be appropriate or advantageous. Enterprises must consider the talent model, tools investments, service delivery methods, the existence of a platform that provides common services (e.g., authentication, APIs, provisioning, and templates,) and flexibility for the teams to leverage tool sets they are comfortable with.

Scaling is not about driving standardization across Agile/DevOps. It’s about building a broader framework to help Agile/DevOps teams drive consistency where and when possible. Our research on how to scale Agile/DevOps without complicating may help you drive the outcomes you expect.

What has been your experience scaling Agile/DevOps adoption? Please contact me to share your thoughts.

Blue Prism’s Acquisition of Thoughtonomy: Does 1+1 =3? | Blog

June 21, 2019

As a reader of this blog, you likely know that we’ve been researching and analyzing the RPA market in-depth for more than five years and have conducted multiple RPA technology vendor PEAK Matrix® evaluations in the same time frame.

Starting in 2015, Blue Prism earned a Leader’s spot in our assessment because of its extensive features and strong market presence. Thoughtonomy made it into our Leader’s group starting in 2016 for its Software-as-a-Service (SaaS) offering, and for combining RPA and AI for unstructured data processing.

Because it is a public company, Blue Prism’s strong growth over the years is a matter of public record. Thoughtonomy has also grown strongly, gaining around 77 direct clients and another 200 indirect through its service provider partners.

Against that backdrop, we believe that Blue Prism’s announcement earlier this week that it is acquiring Thoughtonomy for a total consideration of £80 million is a positive move for three reasons.

First, Blue Prism gains several hundred mid-sized direct clients in an instant. Second, and more importantly, its ability to deliver intelligent automation through a SaaS delivery model gives it the opportunity to much more easily sell into the mid-market. Third, this is a strategic move by Blue Prism. Right now, the adoption of RPA on the cloud is in the early stages. At the same time, many AI solutions are offered on the cloud to enable access to computing power on demand, and many work with RPA in combination when needed. Having both RPA and AI on the cloud could help companies realize the full potential of intelligent automation and achieve higher scalability. Blue Prism is becoming cloud-ready with this acquisition.

But there is more.Blue Prism Acquires Thoughtonomy

What Thoughtonomy Brings to Blue Prism

Thoughtonomy was set up in 2013 to provide a cloud-based intelligent automation platform. At its core, it is a cloud version of Blue Prism’s RPA, combined with other capabilities that Thoughtonomy has developed over the years, including:

  • Features for human-in-the-loop automation (Self-Serve), including next-best-action recommendation – These features will help Blue Prism with attended automation that is typically used in the front office. Currently, Blue Prism offers human-in-the-loop through its technology partner, TrustPortal, which provides the UI for this capability
  • Built-in AI / machine learning within the platform to optimize workload distribution and robot performance
  • Natural Language Processing (NLP), sentiment analysis, and chat interface to automate processes using chat as a channel
  • A web-based interface for controlling and monitoring robots – While Blue Prism offers a central console for controlling and monitoring robots, it is not web-based. This will help improve the accessibility of its console
  • Wireframer, an intelligent coding quality tool – Blue Prism currently has an automation methodology, but not a coding quality tool
  • Use cases in IT process automation – This will help improve Blue Prism’s value proposition for IT use cases, which are growing in demand

In addition, Thoughtonomy will help enhance Blue Prism’s presence in some verticals, such as healthcare and government & public sector, where it currently has limited market share.

With Blue Prism at the heart of Thoughtonomy’s SaaS platform, the job of integrating the two product sets should be relatively straightforward.

All in all, we believe in this case that 1+1 does add up to more than 2. Is it a 3? Maybe not, but it is a solid 2.5.

The challenges of SaaS, selling to the mid-market, and targeting the front-office market

Blue Prism’s model includes a minimum licensing requirement that can make it expensive for smaller companies to get started with its RPA offering. Thoughtonomy was absorbing these requirements. Blue Prism will no doubt clarify how it will handle licensing for its SaaS offering.

The addition of Thoughtonomy’s human-in-the-loop interface will help boost Blue Prism’s attended automation value proposition. But if it intends to target this segment – which primarily consists of front-office and contact center use cases where thousands of robots might be required – it will need to adjust its pricing to reflect large orders. Additionally, it will need to deliver more desktop-based features in order to outshine established attended automation vendors such as NICE and Pega. As this doesn’t appear to be a high-priority segment for Blue Prism, we may not see those additional features in the near future.

The market outlook

With this move into SaaS, Blue Prism has captured a competitive edge. We expect other companies will quickly follow suit. Several RPA vendors are cash-rich thanks to recent private equity investments, as well as good organic growth, and they may well have their eyes trained on potential acquisitions. Other RPA technology vendors and other companies that provide complementary technologies, like chatbots, could well be either acquirers or acquisition targets. AI-based automation vendors, e.g., those with NLP or intelligent virtual agents, could make acquisitions of their own to complement their products. And we wouldn’t be surprised to see large software vendors acquiring RPA vendors, just like SAP did last year with its acquisition of Contextor, an RPA vendor that we positioned as an Aspirant in our 2018 RPA Technology Vendor PEAK Matrix® Assessment several months before SAP made its move.

This is just the beginning of the consolidation phase of this expanding market, and we have no doubt there is more to come.

Everest Group will be publishing its 2019 RPA Technology Vendor PEAK Matrix® Assessment in the next few weeks. In the meantime, please check out our recent service optimization technology-focused publications, including Intelligent Document Processing (IDP) Annual Report 2019 – Let AI Do the Reading. 

Do We Really Need a Robot Per Employee? | Blog

June 10, 2019

When I started researching the RPA space five years ago, vendors were working hard to position themselves in the unattended automation space, where robots ran on servers in the data center, according to schedules, typically delivering back-office functions.

This was a departure from attended automation that for some years had boosted (and still does) agent efficiency in the contact center.

Today, the market has come full circle, with a focus on helping other office workers, not just contact center agents, increase their productivity. A robot per employee is a marketing message we are hearing increasingly frequently, boosted by the concepts of lo-code software and citizen developers who can build their own robots with little help from tech developers.

Examples of automation vendor activity in this space include:

  • NICE’s NEVA, an avatar for NICE’s attended automation, to help all office workers automate their repetitive tasks
  • Softomotive’s People First approach, which intends to democratize automation in the enterprise. This applies to both attended and unattended automations, but puts the power in the hands of employees
  • UiPath, which is putting out a robot per employee messages in addition to its Automation First campaign. It has even showcased robot-based consumer apps at its event.

One could argue that going full circle back to attended is because unattended automation is proving tough to scale. That does not diminish the potential opportunities that the concept brings to the enterprise and its employees. But it is not immediately obvious what attended robots could do for the average office worker.

Here are a couple of examples.

At the recent Pegaworld event in Las Vegas, a healthcare payer company showcased several examples of how it is using attended automation, including logging employees in to half a dozen systems, a task they need to perform every morning, through what the company calls “start my day,” and changing passwords on those systems on behalf of the employees, at the frequency dictated by the corporate IT policy. Another is helping with repetitive sales administration tasks, e.g., the robots update daily sales information for reporting purposes.

The big question is, do these kinds of examples, good as they are, justify the investment in desktop/attended automation robots by the thousands? True that attended robot licenses typically cost much less than unattended ones, and vendors are likely to offer good rates for bulk orders. But overhead costs, such as training employees to code their own robots and for the enterprise to support them, also come into play, as do robot performance: how fast can they run on those desktops, and can employees get on with other work while the robots are running?

It is early days for a robot per employee model, but it is high time that we boosted office worker productivity again. It has been decades since the advent of personal office software led to the last productivity revolution.

Personally, I am looking forward to seeing attended automation evolve and become really useful. I cannot wait to “robot-source” some of my daily routine work. First though, we (office workers) have to try attended automation for ourselves and see what works and what doesn’t. Lessons learned in the contact center can help us with this, but hands-on and trial and error is the best way forward.

Race to Reality: Full Contact Center Automation vs Fully Automated Cars | Blog

April 15, 2019

The contact center industry is changing considerably due to technology enablement. Contact center automation is rapidly becoming a priority as centers increasingly embrace technologies such as artificial intelligence (AI), chatbots, robotic process automation (RPA), and robotic desktop automation (RDA) to handle customer interactions on rote queries like account balances, package tracking, and reservation confirmations.

A similar transformation is also taking place in personal transportation. Advancing technologies and intense competition are driving amazing strides in the autonomous vehicle industry. While cars aren’t yet 100 percent self-driving, companies like Tesla are already offering advanced driver assistance solutions that can pretty much take control of driving, albeit with human supervision.

With the perceived nature of each of these two industries, it’s easy to assume that contact centers will be fully automated in far less time than the two to three years some believe it will take for autonomous driving solutions to get you from one point to another without human intervention.

However, this is an incorrect assumption.

Indeed, counter-intuitive as it seems, it’s much more difficult to completely automate contact centers than it is to automate driving. Why?

Driving involves a large, but still finite, number of scenarios that need to be programmed for. But a contact center environment can throw up potentially infinite unique problem statements and challenges that enterprises cannot possibly predict and program for in advance. Yes, AI helps, but even that can only get you so far. At the end of the day, the human mind’s problem-solving ability far exceeds anything that the current or foreseeable technology can offer. And while most people would be more than happy to let robots take over the wheels on the road, they still expect and require human touch, expertise, and judgment for the more complex pieces that usually make or break the customer experience. Technology just isn’t sophisticated enough to handle these yet.

The degree of contact center automation that can be leveraged within an industry varies by process complexity

Race to Reality blog image

Although technology use in contact centers is in the early stages, we are already witnessing higher agent satisfaction and lower attrition rates in an industry that has one of the highest churns globally. And as robots increasingly take care of customers’ simple, straightforward asks, we certainly expect agents’ satisfaction to increase.

Of course, agent profiles will continue to evolve as they are required to deal with more challenging and complex issues leveraging machine assistance. This will, in turn, demand greater investments into talent acquisition and upskilling programs.

It will be interesting to see how all of this plays out in the next few years as technology becomes increasingly advanced and capable. The only thing we can say with certainty is that the customer experience of the future will be much more pleasant as irritations like long wait times, inept IVR responses, and repetitive conversations with agents who hold incomplete information become issues of the past…or, shall we say, smaller and smaller objects in our rearview mirrors?

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