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Automation/RPA/AI

Are the Automation Savings Numbers You Hear Real? | Blog

By | Automation/RPA/AI, Blog

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

By | Automation/RPA/AI, Blog

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

By | Automation/RPA/AI, Blog

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

By | Automation/RPA/AI, Blog

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 MatrixTM evaluations in the same time frame.

Starting in 2015, Blue Prism earned a Leader’s spot in 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, 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 MatrixTM 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 MatrixTM 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

By | Automation/RPA/AI, Blog

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

By | Automation/RPA/AI, Blog

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

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?

Four Reasons Enterprises Aren’t Getting Full Value from their Automation CoEs | Blog

By | Automation/RPA/AI, Blog

As we mentioned in our Enterprise RPA Pinnacle report, there are multiple benefits of having an automation CoE:

  • Facilitates best practices and skill development, which eventually help in faster scaling-up
  • Provides structure and governance to the automation program, e.g., clarifies roles and responsibilities of various teams involved
  • Enables optimization of software and license costs
  • Fosters sharing and pooling of resources
  • Develops strong cross-functional collaboration among stakeholders.

While many enterprises have established CoEs to overcome challenges and accelerate their automation journeys, not all have been able to extract value from them. Here are some common pitfalls that limit the true potential of an automation CoE.

4 reasons not getting full value automation coe

No Enterprise-level Mandate to Drive all Automation Initiatives through the CoE

Automation CoEs work when there is a clear mandate from enterprise leadership. This directive helps ensure that the operating procedures and governance mechanisms are standard across the enterprise. Lack of it could result in the enterprise driving automation initiatives in pockets, leading to potential cost increases, lower license utilization, lower reusability of automation assets, and increased governance challenges.

Lack of Relevant Capabilities within the CoE

In its true sense, a CoE represents an entity with the capability to drive automation initiatives across the enterprise independently, with minimum oversight from outside. In many enterprises, however, automation CoEs lack the relevant skill sets – such as developers, project managers, solution architects, and infrastructure support staff – that are critical to driving these initiatives. Successful CoEs typically have three focus areas: day-to-day delivery, operational/tactical decision making, and strategic decision making/providing direction. Each layer, or focus area, has a unique set of roles and responsibilities that are critical to a smooth functioning CoE. Successful CoEs have an intentional focus on developing and nurturing in-house talent to strengthen the capabilities across the three layers. Many CoEs also bring in third-party specialists to accelerate learning. Our blog titled Driving Success in Your Automation Center of Excellence provides more details.

Loosely Defined CoE Roles and Responsibilities

The role of an automation CoE goes beyond just deploying bots into production. CoEs in best-in-class adopters of automation have evolved from executing solutions to empowering businesses across locations to drive initiatives on their own. For instance, the automation CoE in a financial services firm has established standard operating procedures (SOPs) for driving automation initiatives which include a well-defined approach for process selection, evaluation of ROI, talent impact, access to a library of reusable assets, etc. The CoE has created a platform through which business leaders can access these SOPs to evaluate opportunities on their own, and provides necessary governance and execution support, including talent and infrastructure.

As highlighted in Everest Group’s Smart RPA Playbook, the typical roles and responsibilities of automation CoEs include:

  • Providing training and education to develop talent
  • Approving all automation procedures before they are put into production
  • Assessing suitability of Smart RPA versus other Smart IT tools for use cases
  • Ensuring quality and compliance through well-defined standards, procedures, and guidelines owned and developed by the CoE
  • Driving the roll-out and implementation of Smart RPA projects, and ensuring coordinated communication with relevant stakeholders
  • Defining the roles, responsibilities, and skills sets required for driving automation across the enterprise, and regularly reviewing and optimizing them
  • Tracking success/outcomes in collaboration with operational teams so they can build, review, and refine the business case for scaling up.

Disconnect between Automation Strategy and Broader Digital Transformation Objectives

Outcomes achieved through automation initiatives are best realized when these investments are in line with the enterprise’s broader digital transformation objectives. Factors such as investments in other digital technologies (e.g., ERP transformation), changes in leverage of Global In-house Centers (GICs) or shared service centers, changes in vendor roles, etc., can have an impact on automation strategy. Automation CoEs need to closely align with enterprise strategy to realize maximum value.

Pinnacle EnterprisesTM – which we define as companies that are achieving superior business outcomes because of their advanced capabilities – have mastered each of these four requirements for successful automation CoEs. To learn more about how they’re maximizing value from their automation CoEs, please see our Enterprise RPA Pinnacle report.

Feel free to share your opinions and stories on how your automation CoE is evolving in its journey directly with me at [email protected].

Process Mining for Automation Gold | Blog

By | Automation/RPA/AI, Blog

The process automation market is evolving in more ways than one. Many organizations are taking the next step of complementing Robotic Process Automation (RPA) with Artificial Intelligence (AI) solutions such as virtual agents and intelligent document capture. Others are looking deeper into their business functions with process mining and discovery software to scale automation and capture more returns from them.

Process mining and discovery solutions automate a part of automation itself. This is effectively mining processes for elusive gold opportunities for automation.

Process Miners

Process mining software has been around for a while and can be used for many purposes, but several vendors have made a name for themselves in the automation space, e.g., Celonis and Minit. These types of solutions use application logs to reconstruct a virtual view of processes. They discover business process flows and models, and provide process intelligence analytics. They can even suggest how to change a process using smart capabilities. The result is information that allows organizations to decide what process to automate next.

Some service providers have developed their own capabilities in this space as well. An example is Accenture, which uses process mining for automation as a competitive differentiator.

Valuable as it is, however, process mining also has its drawbacks. For example, it requires a lot of data. And if you want to find opportunities among processes that go across enterprise systems, you need to integrate the logs from these systems, e.g., build a data warehouse. Those of you who have built data warehouses know what a massive pain this can be.

Process Discoverers

While process miners can also do process discovery, several RPA vendors – including EdgeVerve, Kryon, and Nice – are offering new solutions. They’re using their desktop automation and action recording capabilities, complemented with AI, to capture and reconstruct what the human worker does, and then map and analyze the actions to identify opportunities for automation. Process discoverers do not require a load of application data, but they do come with their own challenges. For example, a recording may not capture the full set of relevant steps. And employees may have concerns around privacy.

The Art of the Possible

So, is it worth it to use process mining and discovering solutions despite their downsides and flaws? Yes, absolutely. But curb your enthusiasm, set expectations at the right level, and go for the art of the possible.

For example, there are many opportunities for automation within individual applications, without having to include processes that go across systems. And, you can use human intelligence to manually fill in the gaps and augment the findings of an automation discovery tool, even though doing so is going out of fashion.

With yet another category of software coming to the fore, enterprises would be right to feel that they are on a technology investment hamster wheel – there is no end to the cycle. After all, in recent years we have had the huge wave of RPA adoption. And today, in addition to competitive pressure to invest in AI-based automation, enterprises are having to evaluate process mining and discovery as well.

The good news is that automation can generate significant returns on investment. Our research and interactions with enterprises have shown this to be the case time and again. Process mining is another piece of the jigsaw, and it can help you find more automation gold.

Everest Group will be publishing a detailed viewpoint on process mining and discovery very soon. Be sure to keep an eye out for it, so you can mine it for gold.

Thanks to RPA, “Integration” is No Longer a Dreaded Word | Blog

By | Automation/RPA/AI, Blog

Many enterprises that have used Robotic Process Automation (RPA) have seen the power of digital transformation, even if only in a small way through a few automated processes. The transformational value they experience is often a tipping point that whets their appetite for even more automation and deeper levels of application integration. But, this creates a quandary about how to maintain the array of automations. Ultimately, their success depends on the scope of the centers of excellence (COEs) that maintain their automations. Let’s explore further.

Getting the Wheel Spinning – Getting that Old-time Integration Religion

I believe that RPA has helped companies that previously held back from adopting newer technology solutions see the value of a digital mindset. These converts are now finding more opportunities for automation, and greater conviction in moving to digital-first operating models.

In short, something comparatively simple like RPA helps inspire confidence and vision.

The Ironic Corner to Turn – Moving beyond what Initially Made RPA so Enticing

Once this passion is unleashed, organizations come to fully appreciate that RPA is only one tool for automating operations. Many desire to transform their high volume, fast processes, and must confront the reality that surface-level RPA integrations are often not sufficient. The next steps towards more powerful automations often include integration via connectors and APIs.

The following exhibit reflects the diversity of systems which may now need to be integrated in a digital-first operating model world. (Spoiler alert: we’ll be writing a lot more about the Digital Capability Platform in the upcoming months.) And there are many ways to go about creating the needed integrations.

 

Digital Capability Platform

 

Some enterprises have cast aside the promise of surface-level RPAs, and now use their RPAs more through APIs. This is a bit ironic and worthy of a discussion by itself, but let’s get back to what happens as the types of automations proliferate.

Holding it Together – not Firing and Forgetting

One thing that all integrations – surface, APIs, or connectors – have in common is that they need maintenance. With surface-level RPA, you need to do a lot of robot maintenance when application layouts change. But all integrations, RPA included, require maintenance for other reasons as well. The biggest is the need to resolve data ambiguities, e.g., common customer names (think Jane Smith) with similar account types requesting a temporary address change. Which record should be updated? How can this correctly propagate across all the relevant systems and processes?

This is why a COE should be responsible for all types of automations, whether through surface or other integration methods. By looking across all automations, a COE can not only more accurately maintain the automations, but also identify anomalies and conceive new ways to structure interdependent automations. Of course, adding AI-based tools into the mix adds even more API connections to manage. But AI connections are far from the only ones that will need to be managed; the landscape will become more complicated before it simplifies (yes, I’m trying to be optimistic here.)

I can hear some of you saying that the COE should be an overall digital center of excellence. My answer is a big “no.” Digital is a far broader field that often involves major legacy transformation projects. Automation is clearly a part of digital, but it is operationally focused on the practical realities that come from modernizing processes that still primarily run on legacy systems.

This is a different mindset and a different set of competencies. As a result, it is best to keep a separate automation COE focused on the details of operational processes, while separately working towards the corporate digital objectives in a broader digital office. And that automation COE’s remit should be bigger than just RPA – it must deal with the combination of all types of automations that are enabling the operating processes.

Stop Trivializing AI: It Is Not Just Automation | Blog

By | Automation/RPA/AI, Blog

AI is certainly being used to attempt to solve many of the world’s big problems, such as health treatment, societal security, and the water shortage crisis. But Everest Group research suggests that 53 percent of enterprises do not – or are not able to – differentiate between AI and intelligent automation and what they can do to help them compete and grow. This trivialization of AI is both eye opening and frustrating.

While it’s true that automation of back-office services is one strong case for AI adoption, there are many more that can deliver considerable value to enterprises. Examples we’ve researched and written about in the past year include intelligent architecture, front-to-back office transformation, talent strategies, and AI in SDLC.

It’s been said that “audacious goals create progress.”  So, how should enterprises think more creatively and aspirationally in their leverage of artificial intelligence to extract real value? There are three ingredients to success.

Think Beyond Efficiency

Enterprises are experimenting with AI-driven IT infrastructure, applications, and business services to enhance the operational efficiency of their internal operations. We have extensively written about how AI-led automation can drive 10-20 percent more savings over traditional models. But enterprises have far more to gain by experimenting with AI to fundamentally transform the entire landscape, including product design customer experience, employee engagement, and stakeholder management.

Think Beyond CX

Most enterprises are confusing putting bots in their contact center with AI adoption. We discussed in an earlier post that enterprises need to get over  their CX fixation and drive an ecosystem experience with AI at the core. Our research suggests that while 63 percent of enterprises rank CX improvement as one of their top three expectations of artificial intelligence, only 43 percent put newer business model among their top three. We believe there are two factors behind this discouraging lack of aspiration: market hype-driven reality checks (which are largely untrue), and enterprises’ inability to truly grasp the power of AI.

Think Beyond Bots

While seemingly paradoxical, humans must be central to any AI adoption strategy. However, most enterprises believe bot adoption is core to their AI journey. Even within the “botsphere,” they narrow it down to Robotic Process Automation (RPA), which is just one small part of the broader ecosystem. At the same time, our research shows that 65 percent of enterprises believe that AI will not materially impact their employment numbers, and that bodes well for their realization of the importance of human involvement.

And, what do enterprises need to do?

Be Patient

Our research suggests that 84 percent of enterprises believe AI initiatives have a long gestation period, which undoubtedly leads to the business losing interest. However, given the nature of these technologies, enterprises need to become more patient in their ROI expectation from such initiatives. Though agility to drive quick business impact is welcome, a short-sighted approach may straight jacket initiatives to the lowest hanging fruits, where immediate ROI outweighs longer term business transformation.

Have Dedicated AI Teams

Enterprises need AI champions within each working unit, in appropriate size alignment. These champions should be tech savvy people who understand where the AI market is going, and are able to contextualize the impact to their business. This team needs to have evangelization experts in who can talk the language of technology as well as business.

Hold Technology Partners Accountable

Our research suggests that ~80 percent of enterprises believe their service partners lack the capabilities to truly leverage artificial intelligence for transformation. Most of the companies complained about the disconnect between the rapid development of AI technologies and the slowness of their service partners to adopt. Indeed, most of these partners sit on the fence waiting for the technologies to mature and become enterprise-grade. And by then, it is too late to help their clients gain first-mover advantage.

As AI technologies span their wings across different facets of our lives, enterprises will have to become more aspirational and demanding. They need to ask their service partners tough questions around AI initiatives. These questions need to go far beyond leveraging AI for automating mundane human tasks, and should focus on fundamentally transforming the business and even creating newer business models.

Let’s create audacious goals for artificial intelligence in enterprises.

What has been your experience adopting AI beyond mundane automation? Please share with me at [email protected].