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enterprise strategy

Outsourcing Governance 101: Playbook | Sherpas in Blue Shirts

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Governance obligations are typically well covered in outsourcing contracts, and select governance documentation developed before the outset of the engagement. But even when both parties are committed to mutual success, conflicts can, and often do, arise between clients and providers.

To help ensure issues don’t raise their ugly heads, every outsourced relationship should develop, maintain, and bi-directionally enforce a governance playbook that is aligned to overall business goals. The playbook should include the governance framework, operating model, processes/procedures, contract, and documentation. A dedicated governance playbook repository for the outsourced relationship enables an overall view, inventory and access to all documentation related to governance processes, tools/templates, training for effective relationship management.

Critical components of the playbook include: 

  • A detailed outline of all key contractual obligations, such as reporting requirements, metrics, and documentation
    • Both the buyer and provider must clearly understand the contractual obligations and confirm a consistent understanding of their respective obligations
    • All the dashboards and reports in the world mean little if neither party understands the metrics being measured
    • To avoid misaligned expectations and continual delivery issues, ensure that both organizations have a clear understanding of the metrics and how each SLA will be captured, collected and monitored. SLAs are foundational to any services agreement.
  • Clear definition and alignment of problem resolution processes to business needs
    • Build governance processes that go beyond quality of services and contractual obligations
    • Focus on operationalizing joint governance to support accountability and oversight of the outsourced relationship, aligned to desired outcomes
    • Instate a process whereby unresolved issues are addressed at appropriate levels to preserve delivery continuity and prevent escalation of every item to the executive level for resolution.
  • Consider aligning metrics to documented processes
    • Develop process documents and metrics that matter most, and leverage the contract language, but also include concepts related to leadership objectives
    • Include these as part of the management review process to ensure alignment throughout the contract
    • Develop and maintain a joint Master Training Guide for all things governance to align expectations
  • Conduct training from the leadership level down to reinforce management commitment
  • Incorporate training to existing meeting agendas to complete training in real-time. 

What are the key components of your company’s governance playbook? 

For more insights on governance, please see the Proficiency and Partnership blogs in this three-part series.

Thinking about Robotic Process Automation (RPA) – What Can You Learn from Your Cloud Journey? | Sherpas in Blue Shirts

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Everyone is talking about the emerging disruptive technology that is the next transformational solution…

Tales of massive cost reductions and time-to-market improvements that will leave your competitors in the dust abound…

You are getting pressure from the C-Suite about what you’re doing about it…

The vendors have lots of slideware, but precious few production examples at scale…

Everyone is launching pilots or proofs of concept…

Hmmm…is this recounting the cloud services situation of 5 to 7 years ago, or today’s RPA situation?  Well, I think it is both. Our discussions in the market – encompassing both enterprises that are commencing their RPA journey and services and technology providers jockeying to deliver solution to those enterprises – suggest a picture that is eerily similar to a number of patterns we saw as the “last” disruptive trend was gaining its footing a few years ago. It got us thinking about what those wishing to capitalize on the emergence of RPA might learn from the trials and tribulations many firms went through as cloud services emerged.

  RPA
today
Cloud Services during emerging phase Fast forward: what happens next for RPA
Market maturity
  • Every conversation begins with “here is what we mean by RPA…”
  • Every conversation began with “here is how we define cloud…”
  • As enterprise-wide usage patterns emerge, nomenclature will converge to drive clarity on the value levers
Adoption patterns
  • Lots of pilots; slow to scale across processes/ enterprise
  • Focus on “no brainer” use cases
  • Lots of pilots; slow to scale to enterprise workloads
  • Early focus on “spiky” workloads where cloud yielded extraordinary benefit
  • Many “red herring” barriers (“cloud can’t be secure”)
  • Business users will continue to drive siloed initiatives until market reconciles buyer needs with provider business models (see below)
Solutions
  • Targeted at specific use cases with value enabling providers to extract maximum profit surplus
  • Not oriented toward enterprise value
  • Targeted at “low hanging fruit” use cases where benefits were unassailable
  • Broad enterprise-wide solutions limited to niche players (private cloud-led for most larger enterprises)
  • Solutions evolve from “bolt-on’s” to serving as a foundation/primary dimension of the environment
  • ROI driven by impact across processes and internal organizational boundaries
  • Enterprise software vendors build automation capabilities into their products
Market leading solution providers
  • Bifurcated market structure with leading software providers and business process services providers
  • Software firms struggling to align a software business model with market needs
  • BPO players conflicted with cannibalizing installed base
  • Tradeoffs embedded in software and BPO business models hinder adoption
  • “New entrants” exploited unique cloud business model
  • “Incumbents” that attempted to leverage their historical business model made compromises and fell behind
  • New RPA business model must emerge to fully align with market needs and drive enterprise-wide value
  • Solution providers will emerge with approaches that align interests (rather than create conflicts)
Value levers
  • Cost reduction with the ability to replace human capital with robots

 

  • Cost control by tying  cost structure to usage patterns
  • Value levers emerge that supplant cost reduction such as accuracy, flexibility, agility and compliance 

That’s not to say there aren’t some key differences in how these two disruptive trends are playing out. For example, while both sets of key early players created their business models from a blank sheet of paper, the cloud leaders (Amazon Web Services, Google, Microsoft Azure, etc.) clearly had deeper pockets than emerging RPA leaders and they leveraged that ability to invest ahead of demand to drive a market share-driven pricing strategy that secured and continues to protect distinct advantages.

Notwithstanding the differences, it sure feels like many enterprises (and service providers) have been down this path of pursuing the next disruptive technology before.

As you contemplate your RPA strategy, it probably makes sense to step back and gauge how your organization responded to the emergence of cloud services. The steps you took that worked for your cloud initiatives – and those that didn’t work so well – will provide a good path forward.


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Tales of Outsourcing Horror | The True Story Edition | Sherpas in Blue Shirts

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Despite all the successes in the marketplace, we all know there have been outsourcing arrangements that have gone terribly awry. So, in the spirit of Hallowe’en, I wanted to share some true outsourcing horror stories. But, be forewarned, and read on at your own risk…these true stories will send chills up and down your spine. 

Sales process | the secret in the lab

A service provider’s salesperson and solution architect promised to a large enterprise client a transformational technological solution that would save considerable amounts of money, enable realization of all its objectives, etc. The client was very happy with the promise of the solution, as it knew similar approaches provided by other service providers had been successful for the buyer organizations.

But when the engagement moved from transition to presumable steady state, and the results were supposed to start coming to fruition, the provider’s on the ground team had no idea what the client was talking about. The salesperson and solution architect knowingly and willingly sold a solution that their company did not have and had no intention of creating.

Sadly, the secret in the lab for the client was that there was no solution. And not at all surprisingly, the deal faltered and the provider was terminated. 

Transition | the monster under the bed

A client that had never outsourced before believed that transition management was the provider’s job, and thus chose to have no involvement in the process. Of course, without active participation from the client, things started to slide. The client began sensing things were going awry, but the provider consistently assured the client that all was fine. The client asked all the right questions, but because they weren’t actively involved, had no insight into what was lurking below.

When they got to the go live date, the provider listed a litany of things that weren’t yet ready, and in a real attempt to make the transition work, suggested alternatives. The client rightly questioned what impact the alternatives would have, but – looking at the situation from its own risk perspective, and truly wanting to fix the issues – the provider again assured the client there wouldn’t be any problems

Of course, there were massive problems. Missed deadlines, impossible turnaround times, finger pointing. The engagement became such a train wreck that no amount of corrective actions could recover the client’s original objectives.

Moral of the story? If you think there’s a monster hiding under your bed, don’t expect someone else to check for you. Actually, the real moral of the story is that it takes two parties to do the transition tango, and buyers must take management responsibility and accountability for their portions of the transition.

Governance | drinking the witches’ brew

For a number of years, a client was very happy with its ITO provider. It was productive, innovative, and collaborative. But, over time, the provider languished and lacked energy, and the initial objectives that everyone had been focused on seemed to die. Hard feelings grew, and eventually one person on the provider’s governance team developed an axe to grind with his client-side counterpart. Before anyone realized what was occurring, this influential person fed his witches’ brew to all his team members. The poison then spread to all the client’s governance team members. The bitter taste in everyone’s mouths grew until every meeting was a new, adversarial battle between the two separate factions. They could no longer work together toward a positive end result.

Ultimately, the only way the deal could be salvaged was by replacing enough people on both governance teams with new people who hadn’t sipped the poison.

On this day before All Hallows’ Eve, be aware that ghosts, ghouls, and goblins may be lurking in your deal. But also be aware that accountability, governance, and knowledge can help you spot and fight the bogeyman.


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Pervasive Artificial Intelligence in Software: Trends & Impact on Outsourcing | Sherpas in Blue Shirts

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One prediction I have made about the future of service delivery automation (SDA) is that increasingly enterprise software will have the technology embedded. This is particularly true of intelligent and cognitive type of tools. I expect these to become a common feature of enterprise software in the next 5-7 years.

We saw this kind of trend in the earlier days of business intelligence and reporting. The popularity of third-party tools saw the functionality built into enterprise software. As well as reports on activities, dashboards started to feature in applications giving instant views of what was going on in the enterprise. We do not have to look far to find such software today, for example, Blue Prism, includes analytics that report on operations and performance of its robots.

A current example of a more intelligent enterprise software is Oracle Policy Automation Cloud Service. This reads policies written in natural language. Then based on business rules and the policy, it decides what questions to ask the customer, performs eligibility checks, and produces a decision report.

Another example is HighSpot, an enterprise search tool that uses natural language processing for searches and machine learning for finding the most relevant information and ranking the results.

The availability of open source machine learning software libraries, such as Apache Mahout, and software tools from industry giants, such as Microsoft (Machine Learning Service on Azure), will accelerate the next generation of smarter enterprise software.

Some would say that intelligent enterprise software would be function-specific, but I believe some varieties will be able to do more than one thing within large software applications. The need for standardization of interfaces to these tools and the ability to interact with other intelligent applications will grow over time too. We could even see more automations crossing paths across workflows leading to more complex machine-based decision making.

The question is what impact will pervasive intelligence have on the outsourcing industry:

  • On the one hand, intelligent software will shrink the size of the workforce that is needed to fulfill many services and thus reducing the need for outsourcing
  • On the other, intelligent software will open up opportunities for outsourcing processes that have not been outsourced before:
    • These could be heavily document-centric processes, such as anything involving the administration and management of searching large volumes of content, for example, for legal discovery
    • The processes could also be the evolution of other processes. For example, in hospitals we might see the patient “meet-and-greet” services outsourced to service providers who can also run basic health checks supported by AI engines to produce first-pass health assessments, before the patient is ushered to see a doctor
  • Another outcome will be higher expectations of artificially intelligent outsourcing services; upping the ante for smarter outsourced processes – this is inevitable as those on the buy-side of the market become more and more accustomed to intelligent software.

Intelligent enterprise software is here. And we are on the brink of it becoming pervasive and commonplace. As it does, I’ll continue to share my insights on its evolution.


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The Internet of Things and the March of the As-A-Service Economy | Sherpas in Blue Shirts

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The irresistible force paradox asks, “What happens when an unstoppable force meets an immovable object?” I think it’s the opposite when it comes to the Internet of Things (IoT) and the already booming as-a-service economy: “What happens when an unstoppable force befriends an unstoppable object?”

Most of the discussion to date around the as-a-service economy has been focused on cloud services, SaaS, and the likes of Uber. At the heart of this economy are the fundamental premises that customers – either business or consumer – can “rent” rather than own the product or service, and can do so, on demand, when they need it, paying as they go.

Although wishing for the utopian as-a-service model may be a futile exercise, the IoT can initiate meaningful models for heavy investment industries and quite a few consumer-focused businesses, and as technologists we should continue to push the envelope.

Let’s step back and think about how the IoT can push the sharing economy to its potential. Can product manufacturers leverage IoT principles, and create a viable technical and commercial model where idle assets are not priced, or are priced at a lower rate, thus saving customers millions of dollars? This would, of course, require collaboration between customers and product manufacturers to enable insight into how, when, and how much a customer consumes the product. But consider the possibilities!

One example is the car-for-hire market. Could a customer’s wearable device communicate with a reserved car, notifying it of approximate wait time until it’s required, enabling the vehicle to be productively deployed somewhere else, in turn enabling the business to offer lower prices to the customer and reduce the driver’s idle time? I think the technology is there, and although the task is humongous and with uncertain returns, I am sure someone, (ZipCar?) will experiment with this model at scale in the near future.

Another example is the thousands of small healthcare labs that cannot afford to own a blood analyzer. Innovative manufacturers of these machines could leverage IoT principles to analyze the blood test patterns of individual labs, and offer them a subscription model by which they are charged per blood test executed, or offered a bundled price of $X per 100 blood tests (much like HP’s Instant Ink offering.)

The IoT has the potential to really bring upon us the power of a sharing economy. In the near-term, businesses face challenges in developing a viable commercial and support model. However, they must overcome this in order for society at-large to truly benefit from this once-in-a-lifetime opportunity. They must remember that most industry disruption these days comes from outside the industry. If they don’t cannibalize themselves, someone else will. Thus, as the traditional competitive strategy levers are fast losing relevance, the IoT most definitely should be an integral part of their strategy.


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Trekking New PEAKs in the BFSI Sector | Sherpas in Blue Shirts

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With no rest for the weary, a wave of regulatory overhaul and technological disruptions made the first half of 2015 very busy for enterprises in the banking, financial services, and insurance (BFSI) sector. Indeed, rather than being an enabler of efficiencies and operations, technology is now the fundamental differentiator for banks to grow their revenue and increase market share.

To keep up with all the activity, Everest Group in the past six months published a number of research reports examining the health of the market, the service provider landscape, and the digital effectiveness of BFSI organizations.

Following are some key insights and highlights from our research.

  • Overall BFSI ITO sector
    • The global BFSI ITO market size was estimated to be US$110-130 billion in 2014
    • Increasing regulatory scrutiny placed higher cost pressures on BFSI buyers, leading to a reduction in the total ITO spend. This in turn resulted in a decline of 5 percent in the number of transactions, and a 43 percent decrease in total value of BFSI ITO contracts signed in 2014
  • Banking
    • Banking organizations globally are focusing on a triple mandate: run, manage, and change. This focus translates into efficiency, compliance, and transformation initiatives
    • Our ITO in Banking Annual Report: Riding the Digital Wave report found that investment in digital channels (mobile, online, and social), disruption in the payments landscape, and the emergence of small and medium enterprises (SMEs) as a focus segment have raised demand in retail banking, cards and payments, and the lending lines of business. Against the backdrop of banking market characteristics, the report also identifies key initiatives banks are undertaking to address the industry headwinds
    • Dell Services, HCL, IGATE, Infosys, and Virtusa were the 2015 Banking AO Market Star Performers in our ITO in Global Banking PEAK Matrix™ report: Rise of the Challengers, based on their Year-on-Year (YoY) movement in our annual assessmentBanking ITO PEAK Matrix 2015
    • Retail banks are making significant investments to stay relevant to digital natives and the millennial generation. A seamless transaction experience, stronger customer engagement through higher penetration of digital channels, posting of richer content, and larger breadth of value-added services are some of the key attributes of digital leaders in the retail banking space, per our first-ever APEX Matrix™ that assesses leading retail banks in the United States and United Kingdom on their digital functionality and the business impact it generates

So what is in store for the next few months? Lots! Our upcoming reports through the end of 2015 include:

  • Insurance – We’ll be exploring industry trends in our upcoming ITO annual report on the global insurance market (Life, P&C, and Re-insurance), and evaluating global insurance service providers in our global Insurance AO PEAK Matrix report
    • BFSI in Europe – Europe is driving the financial services market in terms of new deal signings. Our upcoming Europe-focused PEAK Matrix assessments on Banking and Capital markets in Europe and Insurance in Europe will explore the European service provider landscape
  • Digital PEAK Matrix assessments – Service providers’ offerings within the digital technologies umbrella are rapidly maturing. To cover the evolving excitement in the industry, we are significantly expanding our portfolio of published PEAK Matrix evaluations in 2015. New reports we will be publishing before the end of year are:
    • Mobility in banking
    • Mobility in insurance
    • Big data analytics in banking
    • Big data analytics in insurance

Everest Group’s goal is to help ensure enterprises and service providers achieve maximum success from their sourcing initiatives. Thus, we encourage you to reach out to us directly with your questions and comments.

Jimit Arora, VP and Global Head of IT Services Practice, [email protected]

Aaditya Jain, Senior Analyst, [email protected]

Archit Mishra, Senior Analyst, [email protected]

Ronak Doshi, Senior Analyst, [email protected]

Demand Management Is Made Possible through as-a-Service Model | Sherpas in Blue Shirts

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Demand management has been the unicorn of enterprise IT – something frequently talked about but rarely seen and never captured. Every centralized IT organization would love the ability to accurately manage user demand. It would provide tremendous return if it were possible; but to date it has been largely or completely thwarted in large enterprise IT organizations. But there’s good news, thanks to the as-a-service model.

The reason demand management has been thwarted is that IT is organized on a functional basis; the data center, servers, network, purchasing, and security app development and maintenance are all defined functionally. IT leaders are held responsible for driving out cost and building capability that is shared by multiple departments. The problem is there is no relationship between demand and supply capability because business users don’t understand how to measure their usage/demand.

When IT asks business users how many servers they you use, their response is often “How many did we use last time?” Or when IT asks how many programmers will you use and why, business user typically respond with “We need 10 percent more than we had last time.” There is no relationship between actual demand and the actual demand drivers and the estimates they must provide to IT. This is a hopeless and fruitless exercise. It’s like a broken clock that is right only twice a day. This demand estimate is doomed to be wrong every day.

So what’s the answer? We need a service construct where IT is organized into service models. This construct gives business users a way to understand their usage or demand. For example, a healthcare payer understands how many people it expects to enroll. This is a metric the business can use to predict usage and the time frame in which they will need the service. IT can then manage the demand for the service it provides to the payer based on the number of enrollments.

When companies organize IT along service lines, they can translate business activities into technical consumption. The as-a-service construct attempts to make as much of its service chain or supply chain as elastic as possible. It adjusts each part of the supply chain to the usage demand. So unlike the traditional functional IT structure, business users only pay for what they use.

There are three mechanisms to make a technology or service elastic:

  • Share it (such as AWS); when you’re not using it, someone else is
  • Automate it; spin it up, do the work, and shut it down
  • Buy it on a consumption basis

Typically, as-a-service providers use all three of these techniques to allow them to use their full service stack with the business metrics that the technique serves.

The as-a-service model achieves one of the Holy Grails of centralized IT – it provides a realistic demand management vehicle where the business can make accurate estimates. It also provides paying for the services only when they are used; this is the consumption-based model that the services industry is moving to.

Demand management to date has been completely illusive to centralized IT because of the take-or-pay nature of IT. This method for building capability – and business users sharing the cost to be able to use it – has no connection to business metrics that the business can control and understand how to estimate their technology capacity/demand. But the good news is the as-a-service model puts a rope around the unicorn. It creates the ultimate answer to demand management.

Automation Bias | Sherpas in Blue Shirts

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We’re at an inflection point in the ITO and BPO services world where we’re about to see a new level of technology: automation. On the whole, automation is a good thing. But there are some significant aspects we should be aware of. One is automation bias. And it’s dangerous.

When we move to automation, whether it’s cognitive computing or replacement of repetitive tasks, the people who are in the process become dependent on the automation. In fact, not only do they become dependent, they start to believe that whatever comes from the computer is truth. They take it for granted that the results are accurate. This is automation bias.

As a simple example, when you use a calculator, you quickly start to trust whatever the calculator results are. We have blind trust in automated tools.

Why is automation bias so dangerous?

A computer will slavishly do what it’s told to do or will run down the same cognitive analysis it has done in the past. When the world changes, the computer may not recognize that the world has changed. Change can come from one of the data sources having made a change. Or it could be an upstream or downstream change in a business process. Although people in the business process should recognize the change, automation bias may cause them not to recognize it because they believe that everything coming out of the computer is correct. This is a significant business risk.

The fact is automated tools are fallible. We all know that the world constantly changes, and automation bias presents the risk that the computer won’t recognize the change.

We’re on the verge of taking robotics and automation at a scale we have never done before. This will dramatically change how we perform business processes and how we run data centers. Organizations going down the automation path need to be aware of automation bias and build safeguards against it.


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How Are Automation Services like Christmas? | Sherpas in Blue Shirts

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The industry is abuzz with enthusiastic discussions around the potential for robotics, cognitive computing, and robotic process automation (RPA). You can’t go to a conference – whether it’s IT, BPO, or shared services – without hearing a vigorous and spirited discussion around service delivery automation (SDA). Given the promise of SDA for people replacement, dramatic improvement to productivity, significant cost savings, and improvement of cycle time, why haven’t we seen more adoption?

The answer is awareness is still building. When we look at the actual adoption of SDA (which encompasses cognitive computing and RPA), we see this to be in a very early stage.

Large enterprises rarely adopt technologies without pilots, and results are coming in on a daily basis. The early adopters are just now finishing their early pilots. The services industry is grappling with how to industrialize the technology. From the results coming in on a daily basis, it’s very clear that the services industry will be greatly affected by SDA.

For those who are in disbelief, I advise further research.

For those of us who wonder why SDA is slow in coming, I caution you that it’s like Christmas – it will be here before you know it. I believe it is only a matter of time.

And just like Christmas, as SDA starts to take hold, it will feel like it comes with a rush. As any parent knows when dealing with their children before Christmas, it seems to be slow but then comes in an all-consuming giant rush. Look out – it will be overwhelming.


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So You Think You’re Digital? | Sherpas in Blue Shirts

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These days it seems as if every enterprise is talking about “going digital,” and service providers are adding to the noise with hyperbolic promises about digital solutions that will re-imagine the workplace as we know it. However, each stakeholder in the ecosystem, from service providers to enterprises, industry shapers to investors, is using a different definition of digital adoption. So in the interest of industry cohesion, we will attempt to bust some prominent myths surrounding digital adoption and offer a workable definition of digital.

First, a few myths

Myth 1: Standalone implementation of a single digital technology theme counts as “digital adoption”

The true power of digital adoption is realized when enterprises leverage and integrate a variety of digital technology themes across the enterprise. Putting some data in the cloud or creating a nifty mobile customer interface tool is not digital adoption.

Myth 2: Digital adoption is solely about digital marketing and/or enabling online/mobile channels

While most of the hype around digital services and solutions refers to its use in marketing, the reality is that digital is much more inclusive and pervasive. In fact, our research shows that almost half of North American enterprises are concentrating their digital investment on back- and core mid-office efficiency, rather than market-facing business processes.

Myth 3: Digital is just another name for SMAC

Another myth being perpetuated is what we call “digital-washing,” pulling a bait-and-switch with terms like SMAC (social, mobile, analytics, and cloud) or BYOD (bring your own device). Digital is much more comprehensive than any of these existing terms, encompassing an array of technologies to support and augment digital functionality that touches every aspect of back-,
mid-, and front-office business processes.

So how does Everest Group define digital?

Enterprises are spoiled for choices in adopting next-generation solutions and services. Possibly for the first time in history, enterprises are challenged not by the lack of technology, but by its overwhelming abundance.

But that abundance creates its own difficulties. Enterprises that are looking to ride the digital wave to improve operations and grab greater market share need to look at digital solutions with a more holistic view. The greatest benefits of digital solutions come from the development and implementation of a comprehensive digital strategy, not a piecemeal adoption of a particular next-generation technology for a siloed business process.

In other words, digital adoption is the converged use of emerging technology themes to drive efficiencies across back-office and core mid-office business processes, as well as to enhance competitive advantage by impacting market-facing front-office processes.

Let’s focus on two key aspects of this definition.

  1. Digital is about technology convergence: In more than one way, digital adoption perfectly represents the concept “the sum is greater than its parts.” The combination of multiple technology themes ‒ SMAC, Internet of Things (IoT), artificial intelligence (AI), etc.‒ is more powerful in resolving real business challenges than is employing each of them separately.

    In other words, enterprises achieve the true power of digital adoption when they develop strategies that leverage and link the benefits of a broad number of digital technology solutions, e.g., engaging analytics using social and mobile data stored on a cloud infrastructure.

  2. Digital adoption encompasses multiple layers of functionality and technology enablers across enterprise value chains and business processes: Our research indicates that enterprises are investing in – and, more importantly, gaining significant value from – digital technology themes across the enterprise value chain and throughout various business processes. Far more than fancy marketing gimmicks, true digital adoption touches nearly every aspect of a business, with use cases ranging from employee engagement to supply chain transformation.

Digital Adoption Definition

Finally, as the plethora of digital solutions, services, and developments indicates, the opportunities for digital adoption are ever-changing; the range of digital-enabling technologies and corresponding interfaces in the interaction layers is not a static concept, but instead is dynamic in nature. As such, the collection of available technologies across the interaction and enablement layers can change over time, creating new opportunities…and new challenges.

Have you been bitten by the digital bug? Keep your eyes on this space for findings from our soon-to-be-released report, North American Digital Adoption Survey – How pervasive is your digital strategy.