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Global Intelligent Automation Market Report (H2 2017)

Inside!! COE Models – Pro/Con; Governance; and #AutomationAnxiety

PLUS a special section on future technologies featuring Internet of Things, Blockchain, 3-D Printing, Machine Learning and Artificial Intelligence

SSON’S GLOBAL INTELLIGENT AUTOMATION MARKET REPORT H2 20172

EXECUTIVE SUMMARY

This report serves to support you in guiding your planning for optimal implementation. There are a number of prerequisites to getting it right. First and foremost, providing a robust framework in the form of operating model and governance procedures; and subsequently a carefully thought out change management program to support transformation and alleviate inevitable anxiety resulting from digital ‘colleagues’ joining the workforce. Failure in the latter is all too common, and the 'anxiety' highlighted in this report an all too obvious, and avoidable, consequence.

We also include a special section on future technologies featuring introductions to IoT, blockchain, additive manufacturing, machine learning and artificial intelligence.

Once again, SSON is delighted to partner with Lee Coulter, Chief Intelligent Automation Officer at SSON and CEO of Ascension Services, as well as EY’s global IA team, led by Weston “Indiana” Jones, Partner, Global and Americas Robotic and Intelligent Automation Leader, EY Advisory Services.

Barbara Hodge Global Editor Shared Services and Outsourcing Network (SSON)

In tracking the market over the past six months since SSON’s Global Intelligent Automation (IA) Market Report H1 was published, we have witnessed a significant uptick in enterprise adoption of Intelligent Automation across its various formats, although still predominantly in the realm of robotic desktop automation and robotic process automation.

In the H1 report, we introduced the Intelligent Automation continuum, highlighting the ‘fit for purpose’ characteristics of the many tools on offer, and providing guidance on how to make the right selections.

In this, part 2, of our 2017 IA report series, we look at operating models, governance frameworks, and the anxiety that is being unleashed as a result of robotic implementations. Despite the fact that these tools ‘work’ and improve performance, the path is, nevertheless, littered with casualties as a result of insufficient planning and incorrect implementation. A clearly dawning realization is that despite the fact that Intelligent Process Automation is not a traditional ERP-type transformation, these capabilities are transformational. An analysis of failures typically reveals root causes consistent with any significant business process transformation.

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https://www.ssonetwork.com/global-intelligent-automation-market-reportH12017


GUEST EDITORS AND CONTRIBUTORS

Lee Coulter SVP, Ascension and CEO, Ascension Service Center Chair, IEEE Working Group on Standards in Intelligent Process Automation Chief Intelligent Automation Officer, SSONLee Coulter is a change leader with expertise in shared services, outsourcing, and technology developed over nearly 30 years in leadership positions in companies including General Electric, AON, Kraft Foods, and Ascension, the United States’ largest non-profit health system. Ascension tasked Lee with creating an enterprise-wide shared services center as part of its landmark transformation initiative. Previously, Lee was SVP of Global Shared Services at Kraft Foods, implementing more than 100 shared services. While there, he received Chairman Roger Deromedi’s CEO Award. Prior to that, he served as VP of Infrastructure Services at AON Corporation, where he implemented ITIL and shared services, outsourced IT, and reduced costs by more than 35%. Coulter served as a VP in GE’s IT outsourcing business. He received the President’s Award from GE CEO Jeff Immelt. www.Ascension.org/IPA

Weston A. Jones Partner, Global and Americas Robotic and Intelligent Automation Leader, EY Advisory Services [email protected] A. Jones is a member of the Advisory Practice within EY, a futurist and a recognized global authority on Robotics and Intelligent Automation. Weston is a board member of the Institute of Robotic Process Automation, and a founding member of the IEEE working group on IPA standards. He has led the development of EY’s Robotic and Intelligent Automation group and set up the Robotics Innovation COE, developing strategy and execution across EY’s 60,000+ member advisory practice. Offerings include automation strategy, robotic business transformation, robotic managed services, and robotic arbitrage. Weston also completed Robotic Proof of Concepts and Value for Finance, HR, Supply Chain, IT, and Customer, utilizing different robotics software vendors. He leads the relationships with all major software vendors as well as product evaluations.

Barbara Hodge Global Editor Shared Services & Outsourcing Network (SSON) [email protected] Hodge is the Global Editor of the Shared Services & Outsourcing Network (SSON), the largest and most respected forum for executives tasked with promoting and delivering optimized business services. Barbara joined SSON in 2000, having started her career in capital markets, before joining Armstrong Information, a specialist publishing group, where she spent seven years. She is responsible for sharing best practices and thought leadership with SSON’s 120,000+ member base, as well as promoting the value of the network worldwide. In her editorial role, Barbara ensures SSON’s content reflects current trends and opportunities, both in terms of technology as well as best practices, that help SSO leaders continuously drive their own teams’ performance upwards.

Rochelle A. Hood PMP, Global Head of Customer Analytics and Research Shared Services & Outsourcing Network (SSON) [email protected] Hood is responsible for enhancing member value and experiences at SSON. Rochelle has managed operations and led projects in multiple disciplines including shared services, call center operations and information services. She is a certified Project Management Professional (PMP) and is a Lean Six Sigma Green Belt. Rochelle’s professional experience includes project management, process excellence and service delivery roles at IBM, QVC and companies in the defense contracting industry. She has served in multiple board leadership roles for the Project Management Institute (PMI) Space Coast Chapter Board of Directors and is a member of the PMI UK chapter. She is a member of the American Business Women’s Association (ABWA).

CO-AUTHOR


TABLE OF CONTENTI. Global IA Market Report 2017 H2

1. Intelligent Automation Operating Models – Pros, Cons and Considerations • In-House • Hybrid • Outsourced

2. Governance • Purpose • Structure • Activities • Measures of success

3. #AutomationAnxiety • Change Management • IT • Control • Public Relations • Customer • Employees • Management • C-Suite

II. The Intelligent Automation Universe H2 Update

III. The Future of Automation: Tales from Leading Edge Adopters of Advanced Technology

• Internet of Things • Machine Learning • Blockchain • Additive Manufacturing (3-D Printing) • Artificial Intelligence


I. GLOBAL IA MARKET REPORT 2017 H2IA OPERATING MODELS – PROS, CONS AND CONSIDERATIONS

In this model, the enterprise has decided to invest in the internal capabilities, skills and resources to set up, manage, and develop an Intelligent Automation COE and delivery capability in-house, with its own resources.

The features below are common to the in-house model:

n Executive sponsorshipThe key factor here is senior executive sponsorship, which is significant in an in-house model, and acts as a core driver and mentor for the implementation. Because support is required from multiple parts of the organization, to be successful, sponsorship typically is by a member of the C-Suite or at a minimum a direct report to a member of the senior executive team.

n InvestmentLaunching an in-house model for IA requires investment: new jobs, new roles, new tools, education and awareness of what automation is and what it can do. Many organizations

aspire to this model – in other words, would like to run the model in-house, but it’s only in digging deeper into various requirements that the reality of its implications, and its requirements, sink in. For many practical purposes (time, politics, funding, or even simple interest), the in-house COE may not be possible.

n Existing organizational groundingOne of the key questions to address right from the start is whether IA is going to be run as an ‘IT’ capability – and if so, how? Anchoring Intelligent Automation in the IT organization exposes it to traditional IT processes and management, which are often lengthy, complex, and expensive. Wherever the model sits, it’s crucial that IT understands the role it has to play – as enabler, supporter and participant. [It’s also worth pointing out that many studies of failed Intelligent Process Automation initiatives reveal that up to half of the automation initiatives that fail do so primarily because they were IT led.]

The Intelligent Automation operating model describes the organizational and behavioral structures that create policy and process, guide use case selection and business cases, and often manage the rollout, implementation, and administration of IA driven processing.

There are three common options which we address here: in-house; hybrid; and outsourced.

IN-HOUSE MODEL


n StrategyStrategy is key. There are two strategies relevant here: One to create the COE, and one that will create the actual Intelligent Automation strategy governed by the COE. The first is often where IT and business leadership disagree. In the end, this first strategy will set the stage for whether it is a business led initiative or not. An important distinction is worth noting and that is that while technology is used, Intelligent Automation occupies a new space between operations and IT. If Intelligent Automation is subjected to traditional processes, failure is nearly twice as likely.

“It’s not unusual for the entire RFP process for an IT tool to run up to a couple of hundred thousand dollars, and take 15 months,” says Lee Coulter, Chief Intelligent Automation Officer for SSON, and CEO of Ascension Services Center. “When you consider that the end investments in the actual product might just represent a fraction of this cost, it doesn’t make sense to approach it in this way. The core mainstream Intelligent Automation tools work. The problem is that IT is very good at using a hammer so often, everything looks like a nail.”

n Controls & auditAudit and controls will be impacted by IA processing in ways that are not generally recognized nor expected at the outset of these programs. It’s important to bring both audit and controls groups into the conversation early to avoid complex rework later on.

“Some of the most regulated industries, like financial services, have gotten comfortable with the controls environment for RPA,” says George Kaczmarskj, Partner, Americas Financial Services, Robotics and Intelligent Automation Leader, EY Advisory. “However, IA requires a review of a company’s control and risk management framework in a whole new light because of the learning and decisioning impacts, not only to internal controls, but also for reputation and other regulatory risks.”

Failing to bring in your audit partners early can create costly and time consuming efforts after the fact. This should be a high priority item for the COE, regardless of operating model.

n PlanningCOE planning is key in terms of identifying the roles, responsibilities, resources, processes and decision rights of stakeholders early on. Today, there are many successful IA programs to reference – something that was not the case even just two years ago. Some fundamental elements of COE design are now better understood: Things like how many Business Analysts (BAs) are needed as a percentage of automation specialists; or how many ‘run and maintain’ automation resources you need for X number of automated production processes; or how should your Continuous Improvement teams and analytics teams interact with the automation teams?

“At Ascension, we created seven brand-new jobs that the organization never had before, as a result of introducing Intelligent Automation and running it in-house,” explains Lee. “If you don’t get this right at the beginning you’ll struggle later on.”


“Some of the new roles included Sales Engineers, Solution Architects, Business Analysts, Bot developers, Bot Controllers, Bot Testers, Cognitive Developers, etc. – to name a few,” adds Weston “Indiana” Jones, Partner, Global and Americas Robotic and Intelligent Automation Leader, EY Advisory Services.

In addition, many jobs we think we know today change significantly under an automation transformation. The role and work of a Business Analyst for automation differs significantly from that of a traditional BA. Continuous Improvement and Lean Six Sigma methodologies or practices are not easily transferable nor even applicable to automation. Other methodologies can be put to powerful use when married with their automation counterparts. Many of the service providers and integrators are running educational programs to highlight automation’s unique requirements. “The COE should make this analysis and change plan a priority,” suggests Lee.

Companies need to realize that today training needs to be ‘pushed’ to employees as they need it, explains Weston. “The use cases, products, etc. are constantly changing and require a much different approach to ensure employees are receiving the right type, and amount, of training. Some vendors have commented that they update their training daily,” he says.

n StaffingHow you build and staff your model is as important as how you decide to pay for it. As a captive, one option is to self-fund, which means you quickly go after some major low hanging fruit, free up some capacity, and you hire new types of resources in the same fiscal period – in other words, transferring the savings into resource investment. Alternatively, you can get the funding up front and spend the money based on a business case that identifies the returns. Regardless of the approach, there is an up front investment to get the program underway.

If you time it so that you drive performance before your fiscal period starts, and you execute well within the timelines, you can end up net-neutral or positive within that fiscal period, explains Lee: “If you build up a strong automation team that includes all the primary skill types required before any level of execution, you may be looking at a considerable investment. If you are considering an in-house COE, the best course of action is to quickly implement some low hanging fruit automations with a light touch CEO, to fund the investment to advance the program to enterprise levels.”

“Lee’s approach aligns well with the advice many analysts provide to customers who reach out for external expertise, due to the fast moving IA market, to help set up a self-sustaining model,” agrees Weston.


CONS

n Start-up costDepending on your organizational culture and politics, and the extensive learning and knowledge acquisition implied in an in-house model, it may be preferable to do some level of outsourcing or augmentation of your program. “I use the analogy, do you want to buy a fishing rod, and learn to hook, bait, and catch a fish – or do you just want to buy a fish sandwich?” explains Lee. “Both are ok and can have satisfying results, but know your organization’s appetite first. You can fly below the radar without making big ripples if you buy the sandwich, as people may not notice. But it is hard to start an enterprise automation production capacity without being highly visible,” he warns. “The in-house model often means a lot more scrutiny around every part of the program: the financials, strategy, IT and security, ownership, measurement of savings, etc., in your business case.”

PROS

n LearningWhen you build up your own IA capability in-house it becomes a strong foundation for transformation. There are plenty of opportunities to internalize learnings from the providers you work with. Indeed, many enterprises are working with multiple providers for precisely this reason. There is no substitute for learning by doing.

“We have worked with a number of different providers in assessing their offerings and every conversation has taught us something new,” says Lee. “Today, understanding seven different products help us understand what each product can do to optimize our performance.”

“Where the knowledge about processes and uses of Intelligent Automation reside are key decision points for companies,” adds Weston.


Choosing a hybrid model allows you to take a step towards automation, but less visibly and more rapidly. This model requires less executive sponsorship and lets you leverage your existing resources to take on some of the roles while retaining their original responsibilities. A hybrid model can start at a senior leadership level, targeted at a specific process area under that managements’ control. In-house leadership is still a key factor, as is a robust understanding of technology enablement to recognize what Intelligent Process Automation can do – and what it cannot. A business lead is preferable, but where a core BPO relationship is being leveraged to deliver that service, IT can sometimes successfully take a lead on the partnership with the BPO process experts and the provider’s automation capabilities. In addition, where there is a strong internal executive sponsor, but this role is not

proactive and doesn’t engage significantly with the BPO, front-line procurement could take a lead with the provider.

Sometimes it takes significant time and effort to raise executive awareness and support. The hybrid model is a good choice when your starting point can be pretty well isolated and your provider is ready to do their part; and that part will need to include contract restructuring to reflect changes in the cost to deliver.

The process management component can be outsourced to the provider where the program is flying ‘under the radar’, or, for example, where the BPO approaches the client directly and acts as the primary driver of robotics. That way, the nascent automation COE work can be tucked into the existing BPO CRM team.

HYBRID MODEL


There are three key components to consider in a hybrid model:

1. strategy and governance, which drives prioritization and benefits;

2. choice and implementation of technology; and

3. management of production automation.

Whether you are using a BPO as the ‘other part’, or your automation COE, or you are using a third party automation services consulting group, get roles and responsibilities nailed down early on to prevent lengthy pauses as the group works to deal with this in the midst of active automation implementation.

In a fully captive model all the above reside in the enterprise. In a hybrid model, however, the makeup can take many forms. One common scenario is having all COE roles and authority internal except for the implementation of production automation, which is done by the third party. In this case, the partner would provide the business analysis, data capture to build the configuration of tools license provisioning; and runtime environment for automation.

In this case, the enterprise performs the key strategy COE functions and the provider performs a larger share of actual implementation. Regardless, the enterprise must drive strategy and change management, particularly when processes span internal and external teams.

In a hybrid or fully outsourced delivery model, the big difference is that you are not in charge of your process anymore. You often don’t have the knowledge to drive insights, nor the people with that knowledge or insight. Over the years, your BPO provider became your process expert. The choice becomes the extent to which you are a hybrid – closer to in-house, or closer to fully outsourced? And that depends on which work is being held back. The hybrid does contain a continuum of the distribution of duties between you and your partner.


CONS

n Not developing your knowledge In a hybrid model you are unlikely to be close enough to the activity to imagine what else could be done. This is an opportunity cost in terms of building up a knowledge resource that delivers value to the enterprise. Likewise, the ability to translate this capability to another process area is reduced and you may become reliant on your partner’s choices of tools and techniques, and give up some advantage in strategic automation moving your company into the world of more sophisticated cognitive systems.

n Contracting challengesAnother challenge that may arise under a hybrid scenario is how to manage FTEs: How do you design new commercial models based on outcomes, etc.? How do you account for the work taken out of the system? Who owns the automation? Who runs it? To some extent you are at the mercy of the provider, or the provider’s capability. If the provider is partnering with only one solution developer, you are not going to be exposed to other solutions that could be useful, and which might be a better fit. Your awareness regarding alternative tools will naturally be lower. And, of course, the provider wants some of the benefits, too.

PROS

n Fewer political repercussionsWithin a hybrid model there will be fewer political optics, as enterprise awareness regarding the use of robotics will be much lower. No one cares that automation is going on, as it’s not obvious, and you will be less accountable to the sensitivity around robots entering the workplace (at least for a while). But if you work in a place that needs to ‘see it to believe it’, this might be a good option.

n Self-containedWith the span of people involved in the robotic implementation narrowed, this model is more self-contained and therefore easier to manage, navigate and control. Hybrid models usually sprout in an SSO or a transactional part of a function with at least one moderately senior leader who is a believer and can provide air cover while the proof needed for the larger organization is developed.

n Faster implementationAs you are leveraging the expertise of a third party provider, your speed to start up is higher.

There is the risk that your chosen provider may not be interested in spending more money on developing innovative solutions, however. It will fall to you to maintain a strong sense of your strategic objectives, and ensure you are exposed to market developments and stay up to speed.


In the case of an outsourced model, you pay more to tap into robotics, but the advantage is that you are ‘instantly on’. Statements of work are more quickly established, and drive the commercial relationship to start very quickly. Risks are mainly linked to the commercial aspects of the relationship, for example if an existing service provider relationship is discontinued or changed for any reason.

An outsourced model is an accessible solution if there’s an immediate business pressure that needs to be addressed – for example, cost. Savings can be driven quickly, within a relatively short timeframe, because you ‘hit the ground running’. This model can also arise in businesses with a very keen focus, which opt to hire partners for things they consider non-core.

In addition, it’s easier to attain sponsorship as, in an outsourced arrangement, this generally rests at a middle management level. Senior leadership does not need to be included, and the resulting savings or improved metrics speak for themselves. In some cases, outsource providers will even give you up front savings if you agree to an extended automation program that will deliver significant provider benefits.

CONS

n Wins are not integratedIn an outsourcing model there is no single comprehensive saving that can be measured, nor is there the option of leveraging learnings to deliver more value. Such value as does accrue is strictly compartmentalized, as are the strategies that drive them, in part of a function or service line, with little bleed over.

n IT governanceIn some cases, organizations are surprised by subsequent IT ‘audits’ in terms of being accountable for some of the commitments entered into. When operations are fully outsourced it is easy to overlook things, with potentially serious consequences.

n Knowledge retention and expansionAs IA advances, companies can experience a hollowing out of their process and IA technology expertise. This expertise is seen by many as their strategic advantage, which they seek to retain. This is not a new issue but something that takes on a high level of priority.

PROS

n SpeedThe most critical advantage of outsourcing is that you get rapid tactical results – but there is a price to pay. The quid pro quo is that other aspects are ‘overlooked’ – for example, questions around business continuity failure or audit. As outsourcing does not drive stakeholder inclusion at the early stages, questions may arise at a later stage.

n ResponsibilityThe responsibility for the success of the implementation is largely in the hands of the outsource provider.

CONS

n Resistance Very often, greater automation gains are to be had when the end-to-end process is transformed. In this case, your provider will expect you to manage your internal organization to support the overall business case. You run the ‘not invented here’ risk when you use a provider for the entire program. Your provider may be put at risk with ‘what right do they have to force us to change our processes?’ There are inevitably significant change management issues as the workforce pushes back on expected redundancies, and anxiety spreads through the impacted teams. Expect active resistance. Change management becomes crucial, especially as the perception will be that a third party is driving the change – it’s one thing to initiate robotics yourself but quite another to be ‘done to’.

n Price

Outsourcing comes with a premium to pay – or at least a benefit split more favorable to the provider. To counter that, internally, you need strong governance and a self-motivated leader to develop the strategic direction of the program. It is very important that although the implementation is owned by the provider, the strategy should be owned by the customer. Procurement and contracting should be a key participant in this kind of approach.

OUTSOURCED MODEL


GOVERNANCE

1. Strategic alignment The purpose of governance is primarily to achieve strategic alignment. Intelligent automation is not a strategy, in and of itself, but is an enabler for business strategies. This cannot be understated. Many IA initiatives are started with an eye on taking out costs, but as the organization progresses and awareness builds, new ideas emerge. Similar to the early days of Shared Services, where the objectives could be cost, scope, scale, quality control, or speed, in Intelligent Automation you can focus on efficiency, effectiveness, quality, speed, and experience, as strategic objectives; all of which may or may not have a cost element attached to them. The potentially much bigger win is the shift in customer experience that robotic automation can deliver.

2. Organizational change management By ensuring that leadership and messaging is part of the charter from the beginning, governance supports organizational change management. All of the basic essentials of OCM apply here. Overcoming resistance is key. Answering the WIFM – or what’s in it for me – to various stakeholder groups will become more important. Overcoming the fear of job loss and displacement is always a very real emotion at play in automation programs. A full scale enterprise automation program should have the same level of OCM planning and execution.

3. Visible identification of executive sponsorship With so many short-lived initiatives being launched across an enterprise, it’s often hard to know which ones to acknowledge and support and which ones to sit out. Governance presents a verifiable alignment and support of executive sponsorship.

4. Clarification of roles Governance offers guidance on decision-making roles as opposed to recommendation oles, effectively clarifying and supporting how decisions are made.

5. Risk management Automation introduces new and unexpected risks into operations, and it is the role of governance to identify, evaluate and manage these. For example, a piece of automation can easily show up in an audit or be at fault in a continuity failure. These things are ‘whens’ not ‘ifs’. Being proactive is a key responsibility of the COE.

6. Sustainability of the program Intelligent automation is not a ‘fix’, but a new way of doing things, and as such will become embedded in the enterprise just as Lean, Six Sigma, etc., have become embedded. The purpose of governance in this case, therefore, is to highlight the sustainability of robotic automation.

7. Prioritization Automation is a strategy accelerator, but governance guides prioritization of projects. As programs mature, the backlog will become an emotional topic. Clear methods for prioritization need to be a feature of good COE behavior.

Governance serves a very important role in managing an implementation on a number of different levels. First and foremost, governance establishes the rules of engagement, accountability, and responsibility. More specifically, however, it agrees purpose, structure, membership, activities, and measures of success. Depending on how your organization defines a COE, this could simply be a description of the role of the COE. In other companies, a COE may also have or direct delivery resources.

PURPOSE


The structure of a governance model is similar to that of a Center of Excellence/Expertise but participation is generally broader. A typical governance team consists of an executive sponsor, who is the key decision-maker and reflects the C-suite’s priorities. This should be an EVP role, though it can be delegated to an SVP or VP for primary management of the team. This role must be operational, not IT-led. It’s the business that needs to be leading Intelligent Automation’s deployment.

STRUCTURE

In terms of representation, governance teams generally consist of up to a dozen people, and reflect a broad policy perspective. The roles and expertise vary by sector but generally reflect a combination of the skill sets below:

n Audit – for control managementn IT – for security and infrastructure enablementn HR – for staffing, new jobs, comp, career development and talent acquisitionn Marketing and Communications – to support change management activitiesn Operations – as key drivers of the programn Process Solution Architect – to bring design thinking and the ‘what’s possible’n System Solutions Architect – enabling effective use of legacy systems of record and interfacen Legal – for contractingn Taxn Global Process Owners – to drive prioritizationn SSC or GBS Leadershipn Customer – to drive greater performance from the COEn IA leader

HR is important as a representative of associate relations, and to champion feedback on the change management components of governance. Marketing and communication also play a strong role in effectively guiding the change management component through well-written communications and a branding plan. Integrating IA effectively adds a new, permanent, major feature into the corporate operating model. As such, it is crucial that the structure of the governance model reflects the wide-reaching impact and implication of this change.


The dichotomy of governance’s role is whether its purpose is to listen, or to enable; to shape or to form; to critically evaluate or to drive.

The answer depends to a large extent on the function or process represented. The activities, in other words, are connected to the objectives of that position’s role in governance.

Internal controls audit, for example, is responsible for critically evaluating. For IT, a key challenge is to distinguish between human labor and digital labor in applying group policies to establish what can and cannot be done within a digital system.

A real example: in the case of a human operator, IT has put a screensaver in place that appears after a given time to secure the content on the PC when the operator walks away. Robots do not normally work with monitors, nor would they walk away from a live screen, but they do cease access to a given system for a time. If a screensaver kicks in, therefore, it can lead the robot to ‘strike’ or it can lock the robot out, preventing it from doing further activities. So, digital labor needs a different group policy on the network than human associates. Enabling digital labor is a complex endeavor from an HR perspective, and requires the same focus on needs, policies, potential conflicts, goals, and objectives and measures, as human labor.

MEMBERSHIP


In terms of ‘activities’, governance’s role is to establish automation program-specific strategies and the linkage to overall corporate strategies. This includes establishing measures of success and proving that it has, in fact, delivered on expected results. These results are far wider than simply cost, and will become even broader as the program matures.

The measure of success is not necessarily in terms of hours saved, however, but often in terms of experience, risk reduction, quality, speed, etc. A common measure being used today is ‘hours returned to the business’. This is a good way of simply saying that this time was returned to ‘do something else with’ – something, that could contribute to any number of strategic objectives.

ACTIVITIESGovernance supports prioritization through its objectives and activities. Deciding on resource investment and its allocation, qualifications, and career advancement are also important. Education falls into this category, in terms of what automation is capable of doing for an enterprise, and how. Finally, communication [see above] supports governance activities by ensuring the correct messages are communicated and heard.

What does not fall under governance’s activities is issue management – that sits one level below, within management.

It is dangerous not to have clear lines of demarcation between governance and operations.


The milestones of success measures are, at the start, often based on those measures that are easiest to track, but an overall master plan needs to be developed with the relevant milestones clearly listed and project management instituted to close out major parts when necessary.

Two key aspects here are participation and compliance.

In terms of participation, it’s important to measure whether you have the right people on board, whether they understand their role, and whether they show up. And by ‘showing up’, it’s not just physically that’s important but also professionally. While this may seem overly simple, many programs and projects fail just because the key executives don’t show up, either physically or professionally.

For compliance, it’s important to link this to the objectives of the governance group. It can be as easy as numerically capturing action items after each meeting and then evaluating the progress at subsequent meetings to ensure people are doing what they took ownership for. Keeping an eye on status trackers and meeting minutes, and checking how often stuff “slides” are good measures of compliance, too.

KPIs must be aligned to the major strategic objectives of the program, in other words connected to the targeted drive for change in customers, financial return, or employee

MEASURES OF COE OR GOVERNANCE SUCCESSengagement. “Although fewer is better”, concedes Lee, “it’s important to establish and highlight relevant KPIs that track the journey, and to consider cost in the total return on investment.”

Another measure that is often overlooked relates to organizational skills and capabilities. In rolling out change management, for example, you have to ensure you have enough change agents and people are adequately trained in tools and methods. But the measure is whether you are holding the sessions, doing stakeholder analysis, resistance analysis, etc. So, in terms of governance fulfilling its higher order level purpose – it is based on a combination of the above.

“If capacity creation is your goal, it is important to focus on benefits realization. Otherwise any capacity created can be absorbed back into the enterprise, leaving questions surrounding the true benefits of IA,” says EY’s Weston Jones.

This cannot be understated. What choices the business makes with the capacity returned is key. The program must track those hours and chase the business on what it has done with those hours returned. Has it taken them as productivity? New services? New analytics? New projects? Those hours of returned capacity are your stock in trade, and you should track them.


#AUTOMATION ANXIETY

Any change releases anxiety across an enterprise, and the relatively unknown factors still surrounding Intelligent Automation are causing a lot of this to spread across operations.

Let’s remind ourselves how anxiety is defined, according to Merriam Webster:

• apprehensive uneasiness or nervousness, usually over an impending or anticipated ill; a state of being anxious • an abnormal and overwhelming sense of apprehension and fear often marked by physical signs (such as tension, sweating, and increased pulse rate), by doubt concerning the reality and nature of the threat, and by self-doubt about one’s capacity to cope with it • mentally distressing concern or interest • a strong desire sometimes mixed with doubt, fear, or uneasiness

Anxiety is counter-productive in the work environment so countering its effects should be a change management priority.

In acknowledging the change in operating models as a result of Intelligent Automation, enterprise executives are having to move quickly to establish an infrastructure for change management. In some instances, the capability already exists, perhaps as part of a Shared Services rollout; in others, it might be necessary to hire it in. Anxiety can be paralyzing to an enterprise so it’s key to move fast to outline where change management needs to be applied, how, which tools are required, and to identify potential failure points.

A good example to reference is the change management surrounding an ERP deployment. Intelligent automation is, potentially, as significant a change (albeit on a longer baseline) as the general assumption is that you will move from task automation to

knowledge worker automation and beyond. So, how do you train people for a future in which human and digital labor are both embedded in the enterprise?

“For us, it required a spherical review to identify stakeholder resistance and build up an appropriate change management strategy,” explains Lee. “What’s absolutely clear is you cannot do it alone – you have to collaborate. Without others, you won’t succeed in the long term, certainly not in your strategic implementation, as there is a natural upper limit that you will quickly hit with the tactical stuff alone.” And while you can ‘push’ some automation into the organization, he adds, to truly transform, the organization must ‘pull’. But the extent of pull will depend on how people feel about automation.

CHANGE MANAGEMENT


Anxiety is diffuse and exists in almost every part of the organization in one form or another. For the uninitiated, Skynet’s [think Terminator series] analogies prevail. Those who won’t come on board with IA already expect a disaster, warns Lee. “With many CEOs over the past six months being quoted in the press as saying there are too many human employees in their business, and publicly targeting 10% of their total labor force as digital, it’s no wonder that anxiety is cascading across enterprises,” he explains.

Many executives are still struggling with whether IA is real and here to stay, or whether it’s a passing fad that will be replaced and made redundant by the new features ASP providers are promising to build into their solutions in the next couple of years. So, many are asking themselves, can I wait this out?

In addition, in the near future machine learning (ML) solutions will be making judgments and decisions. Auditors who have traditionally separated IT General Controls from business risk or process risk are trying to figure out how they will audit an ML data model. There is still much to be discovered and developed in terms of how that will happen.

In the short term, this is a largely untested method of getting the work of an enterprise done. How should we think about risk? How does it impact audit? Audit firms at the same time are thinking about how to use Intelligent Process Automation to transform their own services using ML and Intelligent Process Automation.

CONTROL

IT departments are facing a different kind of anxiety. They are used to controlling technology as masters of everything technological. Many perceive robotic automation, frequently driven by the business, as a real threat to their position and role. IT is specifically being told not to own robotic

automation – and yet is required to enable it with a light touch. Remember, too, that IT is often not intimately familiar with what robotics is, and how to reflect this emerging digital labor in group policies. IT is certainly not comfortable with technology being driven by the business.

IT


Associates are primarily concerned about losing their job, and whether the enterprise will invest in them sufficiently so that their new skill sets will still be of value.

Many companies have used IA to increase employee job satisfaction by eliminating the ‘robot’ inside each of us, Weston explains. “However, that doesn’t get as many headlines as the ‘job losses’ narrative”.

Anxiety around seeing colleagues and friends disappear, or the loss of a team, can

spread across an organization like wildfire, with employees not knowing how to resist gracefully or voice concerns in the right forum. “Change management is key to employee acceptance,” emphasizes Lee. The issues to address include: Is a digital FTE a partner or do they work for their human employees? Are they part of the team – or a scapegoat? “These questions will be increasingly tested over the course of the next few years,” predicts Ascension’s Lee Coulter.

EMPLOYEES

One of the issues enterprises need to be clear on is whether they owe it to their customers to communicate the fact that digital labor is providing some of the service. Does the customer need to know that he or she is talking to an automated piece of software? Will they attribute mistakes to digital labor as opposed to human labor? “The interesting thing is that we see significant differences in service expectation based on whether it’s being driven through automation or a human,” says Lee. “Generally, expectations are significantly higher from automated service – at a nine Sigma level, and with

zero forgiveness for mistakes. We expect far less from humans, because we humans can connect with the foibles of humanity. We forgive honest mistakes a person makes. A single mistake in automation (think self driving car accident), is simply unforgivable.

The Wall Street Journal published an article this year predicting voice recognition technology getting to 99% accuracy in the near future, which will be a game changer. “The mainstream consumer will go from rarely using voice recognition to not knowing how they lived without it,” comments Weston.

CUSTOMERS

One of the more obvious externally driven anxieties, from a marketing or public relations perspective, concerns letting go of employees and replacing them with robots. Investor relations, on the other hand, is concerned about the potential risk of a public spotlight shining on a piece of faulty processing. There are numerous examples of this kind of risk. For example, Twitter was recently a case in point of what can happen when chatbots go wrong, when inflammatory language was used in a highly public forum.

The potential for mistakes that are either rolled up exponentially or cause ripples across external stakeholders is driving a lot of PR anxiety around a brand’s reputation and its impact on an enterprise. While Twitter is a spectacular example, more common risks relate to labor relations, automation performing mission-critical processes, and data models making predictions. While developments are currently still fuzzy, they will become real in the next two years.

PUBLIC RELATIONS


The C-suite is experiencing an unusual bifurcated anxiety. Most business magazines are leading with robotic automation – and every airline magazine is currently featuring it. C-suite executives are reading about IA everywhere and yet are not sure that their organizations are actually doing it. The pressure is on, therefore, to get caught up, and quickly. But who should lead the initiative? Where should one start? What can

actually be done? How will it reflect on the board? Dare we wait it out for a year to see what it’s doing in other organizations?

On top of this, CEOs are now concerned about being publicly questioned on their enterprise robotic automation strategy. What kind of questions will they face? And how to answer without a proper risk management evaluation?

C-SUITE

The management layer is currently the weakest link in supporting and driving Intelligent Automation, and sometimes displays the greatest level of resistance. “We’ve seen our leadership and even associates buying into robotics surprisingly well, but management is a key layer that is still a hurdle for us,” says Lee. “We have more to learn about why some resistance remains, but we believe it to be related to fear of job loss and the fact that management in future will need to be comfortable managing both digital and human labor – with all that it implies.”

For management, a key concern is whether they are equipped to manage a workforce that now consists of digital workers alongside humans, where traditional employee relations simply do not apply. “I have seen a trend of managers taking evening or weekend technology classes to enhance their skills in order to stay relevant in their positions”

says Weston. “Integrating automation or digital labor into their overall functional area of responsibility is something that is causing a loss of anxiety.” In addition, with the number of staff within their area potentially dropping significantly, as automation takes on more work, a manager’s confidence is easily undermined. ‘Do I still hold the same responsibility and role in the organization with a staff of only 12 compared to 30?’ is a question many are currently asking themselves.

Added to this is the fact that discontinuous performance objectives are suddenly changing. Whereas in the past, a 3% year-on-year cost reduction was the norm, today the talk is of taking out 30% costs in a few years. “The time to deliver has suddenly shrunk from days and weeks to just hours,” says Lee. “It’s a lot of pressure to take on. In a world in which the mantra is disrupt yourself before someone else does, this is becoming the new normal.”

FRONT LINE MANAGEMENT


II. INTELLIGENT AUTOMATION UNIVERSE – H2 UPDATEAs the intelligent automation provider market continues to mature and grow, SSON’s Data Analytics centre has analyzed tool implementations via the IA Universe tool. Of the more than 2200 pilots and implementations tracked to date, Banking, Financial Services and Insurance is the industry most heavily represented, and the finance

function leads with roughly half of all implementations and pilots, whereby the APAC region shows an even greater percentage of activity accruing to finance.

It’s interesting to compare the extent of regional tendency towards BFSI in the charts below.

GLOBALWhich industries have the most pilots & implementations? (GLOBAL)

Implementations Pilots

Banking, Financial

Services & Insurance

Manufacturing - CPG

Transport & Logistics

Utilities Mining, Oil & Gas

BPO Technology (IT, Internet,

SaaS)

Other Media, Publishing & Advertising

Healthcare

AMERICASWhich industries have the most pilots & implementations? (AMERICAS)


Banking, Financial


BPO Manufacturing - CPG


Pharmaceutical & Life Science

Technology (IT, Internet,

SaaS)

Healthcare Other Manufacturing - Industrial

Professional & Legal

https://goo.gl/fPHJoF



Banking, Financial


Utilities Other Government (Local, State)

BPO Manufacturing - CPG

Professional Legal

EMEAWhich industries have the most pilots & implementations? (EMEA)



SaaS)


Mining, Oil & Gas

APACWhich industries have the most pilots & implementations? (APAC)


Banking, Financial


BPO Mining, Oil & Gas

Other UtilitiesMedia, Publishing

Manufacturing - CPG

Manufacturing - Industrial


SaaS)


THE US AND INDIA ACCOUNT FOR ROUGHLY 40% OF ALL GLOBAL IMPLEMENTATIONS AND PILOTS TO DATE, SPLIT EVENLY BETWEEN THE TWO COUNTRIES.

WHAT ARE THE TOP 5 COUNTRIES WITH IA PILOTS & IMPLEMENTATIONS? (GLOBAL)

% of pilots/implementations

United States

United Kingdom

Philippines

Germany

India

21%

20%

11%

5%

4%


MARKET CONFIRMATIONPractitioner testimonials are heavily in favor of the positive impact of intelligent automation, for example:

“$3 million of FTE savings through RPA by removing 65% manual no value activities through the GL reconciliation process of extracting, loading, and matching."

"Attended RPA robots accelerate and improve reliability of the complaints treatment on invoicing – provide 360°

customer view, automated situation analysis, and reinforcement of the exchange with the customer."

"Reduced matching [a highly manual repetitive tasks] by 99% to focus on managing only the important items – the exceptions. Also helps to identify core issues to improve the process over time."

A number of implementations reference the benefits of extracting data from legacy terminal systems and moving it into web interfaces. Similarly, the "synchronization" of data between

heterogeneous applications when managing international support files is a noteworthy result.

WHY DOES IA FAIL? LEADING CAUSESOn the other hand, although the tools certainly work, IA frequently fails to deliver. One of the questions that SSON Analytics has addressed over the past month is why this happens. All of the vendors currently listing in the IA Universe were polled and the results shown below.

Nearly 40% of failures, according to vendors, are due to “poor choice of process for initial pilot” whereby the process is generally characterized as being too complex, showing too many exceptions, etc. The absence of subject matter expertise during the pilot or implementation stage is also a leading cause of failure.

Most vendors complain of their job being made harder through “long and complicated procurement processes” that lead to delays that causes loss of attention or priority; in addition, where people running the pilot are too far

removed from business sponsorship this tends to undermine the success of the project. Finally, where intelligent automation is not part of the overall automation strategy, success can also be hindered.

COGNITIVE CAPABILITIES ON THE RISEThere has been a marked growth in cognitive capabilities across the IA provider community, in response to customer demand. Nearly 3/4 of the vendors represented in the IA Universe now offer cognitive capabilities.

LEADING ROOT CAUSE FOR IA FAILURESOver the past quarter, what was the LEADING root cause IA implementations failing to match clients’ expectations?(Responses from software vendors in the IAU. Vendors could select one option only)

38%

19%

14%

10%

10%

Poor choice of process for initial pilot (too complex, too many exception paths, etc.)

Lack of subject matter expertise during implementation

No executive business sponsorship

Poor change management

IT led as opposed to business led initiative

Over complicated the initial process with ful blown RF(x) for tool selection

Approached the project in a typical IT waterfall approach

5%

5%




HOW ARE IA TOOLS MOST COMMONLY PROCURED?The reseller network is one of the strongest channels for vendors to promote their solutions to the customer market. More than half of the vendors in SSON Analytics’ IA Universe list a balance of internal sales and reseller network as their primary channel for sales. However,

more than a quarter of vendors sell their licenses directly with limited use of a reseller network.

With the significant growth in sales of this sector, it's interesting to note that 27% of vendors are seeing an increase in sales through reseller networks in the second half of 2017, with minimal growth in the number of licenses sold directly.

71% OF SOFTWARE VENDORS REPRESENTED IN THE IAU

OFFER COGNITIVE CAPABILITIES

WHAT COGNITIVE CAPABILITIES DO THESE SOFTWARE VENDORS OFFER?

95% 95%

68%

50%

> Accenture <> Another Monday <

> AntWorks <> Autologyx <

> Automation Anywhere <> Ayehu <

> Celaton <> Edgeverve <

> EXL <> GleematicIkarus <

> Intelligent Automation by NORIAN <> JidokaKofax <

> Kryon Systems <> NICE <

> Redwood Software <> Softomotive <

> Thoughtonomy <> UiPath <> Verint <

> Workfusion <

Natural Language Processing

(NLP)

Machine-learning algorithm built into every bot

Solution has the capability to act & interpret situation

by imitating human judgement

& reasoning

Solution has the

capability to act & interpret emotions

COGNITIVE CAPABILITIES OF IA SOFTWARE VENDORS

4TH QUARTER TRENDS IN PROVIDER SALES CHANNELS*

WHAT BEST DESCRIBES THE SALES CHANNEL FOR YOUR SOFTWARE AND IA SOLUTIONS?

*Based on feedback from software vendors in the IA Universe

Balance of internal sales team and reseller/partner network channelsPrimarily sell licenses directly, with limited use of reseller network or partner allianceWe sell all licenses directly, no resellers or partnersMainly through a reseller network, with select account licenses sold directly

47%

No change, our sales strategy has remained the same

Increase in sales from and/ or expansion of number

of licensed resellers

Initiating the use of licensed resellers or preferred partners

59%

35%

6%29%

12%

12%



CONSULTANTS AND INTEGRATORSAs the IA market has increased, so has the consultant or ‘integrator’ market grown to meet the needs of enterprises anxious to adopt intelligent automation within their

support services, whereby a number of consultants are aligning themselves with select vendors to build upon these partnerships. Based on the consultant data in the IA Universe, more than half the consultants are engaged in a reseller arrangement with one or more IA vendors.

In addition, consultants are leveraging a strong background and expertise in certain areas that are of great value to their enterprise clients. Assessing proof of concept, establishing a business case, and project management, for example, are skills that are readily supplied.

Areas of weakness, today, include providing configured IA solutions via software as a service from the consultant's own cloud. Checkbox and virtual agents are also areas

in which consultants are still building up their value add.

WHAT AREAS IN IA DO CONSULTANTS/ INTEGRATORS HAVE DIRECT APPLIED EXPERTISE IN?

SNAPSHOT OF IA CONSULTANCIES’ RELATIONSHIPS WITH SOFTWARE VENDORS

55% of Consultants are Licensed Resellers

Sell IA product licenses in a reseller arrangement from

one or more IA software manufacturers

44% of Consultants are Vendor Agnostic/

Independent

22% of Consultants have a Partnershipor formal alliance with one or more IA software

manufacturers but not currently a licensed reseller

PARTNERSHIPS

22%VENDOR

AGNOSTIC ONLY

22%

VENDOR AGNOSTIC

& LICENSED RESELLERS

22%

LICENSED RESELLERS ONLY

33%

100%Assessment and

Proof of Concept Pilot

Direct/Lead Change Management Services

100%

Strategies for the application of IA technologies

89%

Build and configure IA products for client’s use

100%

100%Project Management

100%Business Case

100%Demonstrate applied expertise

in the continuum of IA technologies

56%Provide configured IA

solutions in a SaaS model from consultant’s own cloud

89%Vendor Selection

89%Provide support in

data integration



Given the relative “youth” of the intelligent automation consulting market (the largest segment of consultants represented in the IA Universe launched their first customer engagement only two years ago) growth is happening at a fairly rapid pace. With the trend toward partnering with one or more solution

providers gaining momentum, we would expect enterprises to benefit from the greater understanding consultants have all the tools, albeit there is concern around the potential “limitation” of exposing clients to a broad set of tools, given consultants tendency to align themselves with select vendors.

WHERE DO YOU HAVE APPLIED EXPERTISE IN IA TECHNOLOGIES?

YEAR OF FIRST IA CUSTOMER ENGAGEMENT

Robotic Process Automation (RPA)

Artificial Intelligence (AI)

Machine Learning

Cognitive

Robotic Desktop Automation (RDA)

Chat Bot

Virtual Agents

100%

89%

89%

78%

78%

67%

56%

44%

2015 2016

22%

2014

22%

2017

11%


https://www.sson-analytics.com/ia-universe


III. SETTING THE FOUNDATION FOR FUTURE OPPORTUNITIES

The discussions in 2018 and beyond will increasingly include topics around scaling, integration and data. In other words: How do we scale intelligent automation across the enterprise? How do we integrate technologies like RDA, RPA, ML, AI, IOT, Blockchain and 3D Printing into an intelligent automation program to achieve greater value? And let’s not forget data. The success of any intelligent automation initiative hinges on the 5R’s of data, according to David Shrier from MIT. If you start with the 5R’s (relevancy, recency, range, robustness and reliability) you are off to a good start in unlocking the value companies are striving to achieve.”Weston Jones, Partner, Global and Americas Robotic and Intelligent Automation Leader, EY Advisory Services

THE INTERNET OF THINGS“Today computers—and, therefore, the Internet—are almost wholly dependent on human beings for information. Nearly all of the … data available on the Internet were first captured and created by human beings—by typing, pressing a record button, taking a digital picture or scanning a bar code. Conventional diagrams of the Internet include servers and routers and so on, but they leave out the most numerous and important routers of all: people. The problem is, people have limited time, attention and accuracy—all of which means they are not very good at capturing data about things in the real world.

If we had computers that knew everything there was to know about things—using data they gathered without any help from us—we would be able to track and count everything, and greatly reduce waste, loss and cost. We would know when things needed replacing, repairing or recalling, and whether they were fresh or past their best.”

Kevin Ashton, That ‘Internet of Things’ Thing, RFiD Journal, 2009

Kevin Ashton is credited with first mentioning the Internet of Things in a presentation he made to Procter & Gamble in 1999. He may have been ahead of his time but we are rapidly catching up. Today, the Internet of Things refers to the ability to be globally connected, by tapping, and feeding into, a global nervous system of sorts. Modern products and services increasingly contain sensors that send and receive data, and digital data stacks are continuously fed information through all manner of channels, which they relay or share through a raft of modern sensor transmissions. The idea of data, sensors, connectivity, people and process are all interlinked in the IOT.

INTERCONNECTIVITYA key characteristic of the digital enterprise is interconnectivity alongside integration. The Internet of Things (IOT) provides both a concept and a tool to leverage as a performance driver, explains Dr. Aleksander Poniewierski, Partner, Global IoT Leader, EY Advisory Services. it can be leveraged to boost the business as a growth strategy; or to optimize it.

The ‘growth’ element derives from the

Internet of Things providing a value-adding, enabling conduit between a product and a customer, and building on the notion of human-to-human interactions being replaced by, first, human-to-machine and then, machine-to-machine interactions. A key factor driving its growth has been the concept of ‘asymmetry of information’.

“Simply put, what this means is that one party has access to significantly or substantially more or better information than the other, which leads to an opportunity to transact,” explains Aleksander. “Where significant asymmetry exists there is also the potential for significant profit margins. As the gap decreases, however, so does the potential to derive profit from it.”

As an example, Aleksander cites that 20 years ago, tax advisors knew significantly more than their clients and therefore commanded large fees. Over time, and as clients gain knowledge themselves, the size of the fees charged drops accordingly.

IOT: A BRIDGEThe digital era has given rise to a third party that inserts itself between the product and the customer: a bridge or

INTERNET OF THINGSThe pressure to stay ahead and remain relevant requires enterprises not just to prioritize customer facing digital strategies, but to reflect these practices across their business support operations, as well.

CONTINUED ON THE NEXT PAGE n


platform – the Internet of Things – that allows the synchronization of real-time data. An example is Uber’s mobile app that connects drivers and their cars (the product) with customers in need of a ride. Without the platform, enabled by the IOT the two parties could not transact.

OPTIMIZATION OF ASSETS“Again it’s about asymmetry of information: we don’t know who wants to order a ride and we don’t know who has the car; leveraging this platform allows us to monetize the data from both parties. This platform leverages information asymmetry by enabling the synchronization of data,” explains Aleksander.

The second opportunity currently driven by IOT it around optimization, or better asset utilization, Aleksander explains. The more an asset is utilized, the lower its cost. This idea underpins predictive maintenance, which improves the usability of an asset by optimizing its use, predicting failures, or changing the logic under which it is consumed. Asset performance management is closely connected, and can be optimized by developing a digital ‘twin’.

An easy example is an airplane engine, which is monitored continuously

via hundreds of sensors. However, the engine can also be simulated, to experiment on a digital representation as opposed to a physical engine.

DIGITAL TWIN ENVIRONMENT “We are talking about avatars of course,” says Aleksander. The IOT enables you to manage or measure asset performance via a digital representation. The potential for testing out new process flows or alternative workflows within a digital twin environment promises time and money saving alternatives to traditional, more limited options.

“If we consider production process performance management, for example, we can imagine building a real-time representation of chemical/physical processes that is difficult, perhaps even impossible, to describe using mathematical formulas. The simulation costs nothing compared to the physical rendering, so IOT drives significant benefits and makes it possible to predict things like quality of product in real time,” says Aleksander.

The value of IOT is the ability to leverage machine-to-machine interactions, guided by Artificial Intelligence, to predict the future, which may dramatically change how processes can be supported remotely. In

future, explains Aleksander, process maintenance will be managed, measured and analyzed by machines (or robots); powered by Artificial Intelligence; run on an IOT platform, and accounted for, or tracked, by blockchain-type distributed ledgers.

An easy example to illustrate this is safety inspections in manufacturing environments, where hundreds of different documents, assets, and parameters are continuously being checked – traditionally by people, using their sense of vision, sound, or even smell, which are an expensive and time-consuming resource. IOT enables assets to be measured without using people, just as the digital dashboards in modern cars tell you everything you need to know about the state of the vehicle.

REVOLUTION IN DIAGNOSTIC CONTROLS IOT is the most important single source of data relating to corporate assets, and Business Services will be able to leverage it to significantly improve its performance. The IOT represents nothing short of a revolution in diagnostic controls in addition to providing a connectivity between assets, which can be further leverage through AI.

Dr. Aleksander Poniewierski, Partner, Global IoT Leader, EY Advisory Services


HOW TO GET FROM RPA “2015” TO COGNITIVE MACHINE LEARNING 2020One of the biggest challenges in expanding an Intelligent Automation (IA) program is trust – or rather, lack of it. The point at which many executives get stuck is in trusting the ability of machines to codify human intelligence into non-standard execution – so, at least, says Matt Alexander, Principal, EY Advisory Services. While most practitioners are, by now, comfortable with technology automation for two-way matches – for example, for reconciliation – this is a “very 2015” strategy, says Matt. “Bot farms to automate transactions and workflow – that’s old hat. The real value comes in the ‘2020’ version of automation, which redefines processes through Intelligent Automation, changing how services are delivered and reducing the risk to the enterprise.”

But that, in turn requires a much greater leap of confidence on the part of the enterprise, and there is still significant concern around assessing risk in contracts driven by cognitive automation. “There’s still a lot of skepticism around IA’s ability to operate as a human would, particularly in terms of it getting a decision wrong – and in the wrong direction,” says Matt.

To shift to the ‘2020’ automation, however, takes money and interactions on modeling the intelligence, he explains: “I realize this is different from how we can approach simple process automation. The tools work, we know that, and while to get to the 2015 version of transactional automation you can skip the proof of concept stage you really need to examine the ROI through a pilot to take advantage of cognitive automation. It’s at this level that you generally face the greatest skepticism,” he says, “as you augment the capabilities of engineers, lawyers, accountants etc. – all highly qualified and often with more than a decade’s worth of experience.”

The truth is, however, that you can take certain parts of those jobs and automate them by codifying and developing rubrics that allow machines to learn how to execute those rules. One of the challenges, however, is that while people apply subjectivity, and do so constantly, machines only apply rules.

“It’s tougher to get comfortable with this kind of a risk model,” concedes Matt. One result is that companies often adopt very conservative approaches to rules interpretation to minimize risk.

“The higher ROI of cognitive driven automation is matched by a higher risk model,” explains Matt. “The best way to overcome that skepticism is through a phased approach. Given that the investments are not insignificant, with the introduction of Cortana and Watson level technology, a phased approach provides the customer with the chance to adjust, to gain trust in the system’s ability to codify rule sets into machines,” he adds. But it also means balancing the investment cost of that process up front with returns later on.

“While economically the ROI exists to take on the 2020 version of automation, the bigger challenge is helping executives get comfortable. What we see is that trust in their business partner is absolutely paramount,” says Matt. “

A robust roadmap highlighting milestones and risk as well as the realization of ROI is a key tool in practitioners gaining trust in more advanced IA implementations. Where the strategic goals – whether cost, risk, growth, etc. – are clearly highlighted and balanced against risk, enterprises are more willing to take on operational transformation.

“Challenges can be overcome if change management is a part of the transformation,” says Matt. “No one wants to put their career on the line for an uncertain project.”

NOT SUSTAINABLEWhile not everyone has the appetite for change around bringing cognitive automation into their operations right now, sticking to the 2015 version is not really sustainable, believes Matt. “There are limits to automation and the ROI of automation if you don’t add in a cognitive element,” he warns. “You don’t need cognitive capabilities for bank account reconciliations but if you’re looking at contract execution in procurement, finance revenue recognition, and global regulatory compliance within a fragmented technical infrastructure… then simple RPA just cannot adequately reduce risk or even take out enough manual

activities to support adoption.”

These kinds of objectives require a broader understanding of what machine learning and cognitive automation can do. “It’s about the art of the possible,” says Matt. “Basically, show me what you do and I bet we can make it better through cognitive systems processing. But you have to weigh up the business imperatives against the risk.”

One trend that limits enterprises is that of ROI timelines shrinking. “Today, most companies are looking for one year paybacks,” he says. “These are fairly aggressive timelines that we are seeing in the current climate. There’s a huge appetite for automation and standardization around Centers of Excellence, but there is not always the necessary budget being allocated in advance. This is where we often see a mismatch between demand and the funds available to implement or execute.”

In addition, many organizations are not taking into account the total, true cost of implementation when they first look at automation, Matt says. “Beyond just license fees you need to take into account servers, visualization business processes and engineering costs etc.”

All of these costs, combined, are not insignificant, and also weigh in favor of a phased approach; but that means pushing ROI timelines out, he adds.

Building a bot is not the hard part, Matt reminds us. It takes time, cost and effort to get the internal environment ready for bot-driven activities, however. This includes information security requirements, IT requirements, control requirements, etc. “You hear about quick ROI timelines and that message resonates well with clients, but they often forget what it takes, internally, to prepare for automation. Where we see failures, it’s often on account of not seeing the whole picture up front.”

MACHINE LEARNING

Matt Alexander, Principal, EY Advisory Services


HOW WILL BLOCKCHAIN AFFECT SERVICES?While many Shared Services leaders are still scratching their heads about this new ‘tech kid’ on the block, in essence, what blockchain offers is the same level of visibility, control and efficiency that is today assumed inside the enterprise, to the extended business network, i.e., to external transactions and partners. As performance deficiencies often originate outside the enterprise, the impact of blockchain will be not just significant, but the transparency and efficiencies it guarantees mean lower administrative costs across the organization.

SO, HOW DOES IT WORK? Blockchain represents a batch transaction process system that takes digital assets and treats them like physical assets, explains Paul Brody, Partner/Principal & Global Innovation Leader, Blockchain Technology, EY Advisory Services; meaning, you cannot easily duplicate them or move them around arbitrarily but you have to follow rules. This means they are reliable.

Another characteristic of blockchain is that you can embed the rules in business processes applying to any interaction between two parties, and they will be run consistently and universally across the network.

Blockchain’s value is best leveraged across the ecosystem that connects enterprises, not necessarily purely internal environments, says Paul, because it uses lots of technology capital to ensure information is reliable and trustworthy – much of which is redundant in internal transactions, where the other party is implicitly trustworthy. Blockchain effectively mirrors what ERP does so well internally, i.e., store data and initiate actions on the basis of business rules triggered by certain events, and applies this functionality across the multi-enterprise ecosystem. Within highly decentralized enterprises running on dozens, even hundreds, of ERP systems, blockchain can also play an important role in enabling cross-system transactions.

As a transaction processing infrastructure at the ecosystem level, blockchain has enormous promise.

“Really, we should be calling blockchain an Ecosystem Resource Planning tool,” says Paul. “Its real value is in extending the same transparency and accountability we have come to expect internally, to external transactions.”

Much of this ‘trust’ hinges on the governance that is embedded in the code itself, which, being open sourced, is highly transparent.

“What this means is that it’s possible to mathematically verify that you are being treated fairly,” says Paul. “There is nothing arbitrary or subjective about it.”

In addition, changes in the underlying algorithms can only be implemented by a majority of users, so it’s a highly democratic system. What’s going on in bitcoin right now – a perfect example of blockchain in action – is a case in point, explains Paul: “The bitcoin market is currently facing lots of internal disagreements on how it is governed. Because of the democratic nature of the infrastructure, however, it’s going to take the majority of parties coming together in agreement. Within a corporate environment you would simply have a centralized edict.”

There are three main categories to blockchain, the most recognized perhaps is ‘public and permissionless’, for which bitcoin is an example. The second category, which is proving highly interesting to enterprises, is a ‘private’ blockchain, where participation within the network is limited and based on application and acceptance (examples here are Hyperledger and Corda). The third category is hybrid, based on semi-public permissions, but this is currently far less common.

“We are seeing the most business activity on private networks right now, but in the long run I would assume that public networks will take over,” says Paul. “It will just become too complex and data intensive for each supplier to join dozens of networks.”

To illustrate: If today a large retailer is using blockchain to track food freshness, for example, it will require each of its suppliers to join its blockchain group. Now, if you extrapolate this thinking to other retailers, a supplier will soon be overwhelmed by the requirements to join

different retailers’ groups. “It just makes sense, from an efficiency point of view, to do this private kind of business on a public network,” says Paul, “whereby we will need to improve the scalability and the maturity of the kind of encryption used in this type of shared public network, known as Zero Knowledge Proofs, which are relatively new and not yet thoroughly tested.”

In addition, a limiting factor to this development is that as more data is shared within a blockchain and the number of transactions ratchets up, the amount of data storage required could become unmanageable. As a result, believes Paul, the most efficient model will be one where companies consolidate around shared blockchains, but limit them to particular products or regions to keep size and transaction volume manageable. Brody thinks those kinds of shared models will mature and gain acceptance over the next 12-18 months.

SEAMLESS AND FLUIDThe advent of blockchain presages a world in which businesses seamlessly and fluidly interact with their counterparts outside the enterprise, pushing both volume and scope beyond what is currently feasible. The enormity of implication is such that those of us who thought humans were restricted to earth, cars limited to petroleum-based fuel, and phones defined by audio, find ourselves, once again, having to reconsider the realm of what is possible.

The real hurdle right now might be corporate inertia, as companies that hesitate to adopt an open-sourced business network risk being left behind. Such is the potential of blockchain, however, that leading academics and tech experts describe its impact as a “game changer”, sure to change the nature of business. Given the vast amount of research and development happening in private groups, it is certain to be only a matter of time.

BLOCKCHAIN

Paul Brody, Partner/Principal & Global Innovation Leader, Blockchain Technology, EY Advisory Services


VALUE CHAIN IMPACTED END-TO-END 3-D printing will impact the entire value chain from end-to-end, including product development, manufacturing, supply chain, logistics, procurement, sales and finance.

With digitalization impacting the enterprise across all areas, we already see 3-D printing, or Additive Manufacturing as it is more correctly known, having a strong impact on the whole value chain. While this technology is not as new as many think – it has been in the making for the past 30 years – we are starting to hear more about it today, as advancements in printing systems (speed, size and material choices), computing power and transfer speeds to move large data segments across networks as well as cheaper solutions with expiry of early patents and rising competition, impact its adoption.

As a result, the opportunities offered through real-time, on-demand print ‘production’, based on the latest specs, are exponentially improved and the impact on the manufacturing end of business is undergoing significant transformation.

“Today’s technologies don’t just print ‘faster and better’,” says Frank Thewihsen, Partner - Global 3DP/Additive Manufacturing leader, EY Advisory Services, “but also offer an overwhelming amount of innovation opportunities, mainly as a result of the manifold developments in the computing and materials area. 3D Printing is not just another way of producing a part. It is more about changing the way of thinking how to produce parts and about adding value like functionality or free form production.”

The rapid developments around Additive Manufacturing are changing business models, too. Adidas, for example, is producing the mid-soles of its latest sneakers in Bavaria- not known as a low cost country – via 3-D printing. it is just a matter of short time (and price) when individualization will be the standard.

Similarly, in the oil and gas industry there will no longer be the need to wait on missing or replacement spare parts, but digital files can be transmitted securely by using latest technologies to the nearest metal 3D-printer to deliver the parts in hours. “To some extent, manufacturing is therefore shifting from delivering product to delivering Intellectual Property,” Frank says.

Global Business Services or Shared Services may come to play a significant role in supporting innovative developments like 3-D printing across an enterprise value chain. Procurement, for example, will no longer focus primarily on stock management and planning, but will manage Intellectual Property. That trend will, eventually, eliminate many issues around logistics, stock, and working capital.

“The ability to print what you want, how you want it, and where you need it, over secure processes, is a game changer,” Frank explains, and promises to revise the entire value chain – from product development over rapid prototyping, to production/printing on demand. Already, companies like Siemens’ are refurbishing gas turbine burner in only 4 weeks – instead of 44 weeks.

LOCALIZED SERVICE PROVIDERSThis trend will also give rise to trusted, integrated, localized service providers who will partner with multiple

enterprises, even in the same industry, to produce products on demand, right where they are needed, via 3-D printing. Concerns around the protection or risk exposure of valuable IP are being met with security measures, mainly driven by the manufacturers of the printing systems and related 3DP software / platform providers that protect digital files from being read by third parties, whereby Blockchain will play a growing role.

SKILLS REQUIREDWhile printing technology and available printing materials are on a fast innovation track, one of the hurdles for faster spread of 3DP/AM is the current lack of 3DP experienced or trained engineers to design and develop 3-D capabilities. It is taking the market a while to integrate the capability as it emerges. Nowadays more and more strong talent is being developed across the US and Europe, specifically Germany, Italy, the Nordic countries and Eastern Europe, building on the classical engineering education already existing in these countries.

The developments in Additive Manufacturing promise to positively ‘disrupt’ the entire E2E value chain, from product design to full execution over a product’s life cycle, incorporating procurement, supply chain, logistics and finance – particularly for business cases.

ADDITIVE CHAIN MANUFACTURING: 3-D PRINTING’S IMPACT ON ORGANIZATIONAL VALUE

Frank Thewihsen, Partner - Global 3DP/Additive Manufacturing leader, EY Advisory Services


WHAT CAN ARTIFICIAL INTELLIGENCE DO FOR SHARED SERVICES?While robotic processing has made steady headway within service delivery organizations, the big headline this year has been artificial intelligence (AI), and what it might do for the same. Although actual case studies are still few and far between, the conversation across the consulting and provider landscape around AI has been strengthening. Opportunities are now crystallizing and becoming more clearly understood.

What AI is most definitely not, is a magic ingredient that will change everything. However, it offers an extension of what could be called traditional robotic process automation (RPA) across the scope of work, because AI extends the conditions under which automation can occur. It does this by leveraging machine learning based on complex algorithms that replicate human outputs under various scenarios.

Artificial intelligence is not about creating knowledge; it’s about training machines to understand how to react. The beauty of this is that it pushes the envelope of quality criteria.

So, let’s take a closer look.

THE OPPORTUNITYProcess automation is driven by three desirable objectives: consistency, scalability, and cost. These considerations apply across the Intelligent Automation continuum, so also to artificial intelligence, with the key differentiator being ‘scope’.

“Process automation generally covers 40% of processing in a shared services environment,” explains Gavin Seewooruttun, Partner, AI and Advanced Analytics, EYC3, EY Advisory Services. “But that still leaves 60% untouched. Artificial intelligence can push into that 60%, by relaxing some of the qualifying criteria.”

Traditional robotics methodology follows the path of, first, targeting a given business process; then identifying relevant qualifying criteria; and finally establishing a clear logical sequence

of events. Matching criteria is crucial, but difficult where well-defined logic does not apply – for example, where a judgment-based call needs to be made within a process. Traditional RPA cannot support these types of decisions, but in many cases, crucially, artificial intelligence can. Today, we can train machines (tools) to replicate the judgment of experts, explains Gavin: “Artificial intelligence can be used to automate another 10% processing activity. And while that may sound small in percentage terms, it can translate into tens of millions of dollars within a shared services environment.”

WHAT’S PREVENTING PROGRESS?A number of hindrances are slowing down the adoption of AI within services delivery models.

Subpar information management practicesArtificial intelligence works by training machines, i.e., feeding scenarios into a machine and then ranking its outputs, from low to high. In this way, machines ‘learn’ to target the [correct] highly ranked results.

“We are not actually creating knowledge,” explains Gavin. “Rather, we are teaching machines to use existing knowledge to make judgment calls that are are similar to an experienced person’s,” he says.

In this respect, the ability to identify high-quality data to be fed into an artificial intelligence engine, for training, is crucial. Gavin cites the example of an enterprise using AI to replicate the answers senior engineers would give junior engineers. “The driver is time saved and performance, as junior engineers have to rely on either their seniors or a number of complex documents to source the right answers,” he says. Through AI, the company is now training a cognitive system to answer junior engineers’ questions on anything, in exactly the same way a senior engineer would. “The challenge is finding the gold standard for this knowledge base,” concedes Gavin. “There are lots of different sources of truth and an AI engine cannot

discriminate between them. So identifying the right knowledge assets and managing them is critical to success.”

Machine learning is always easier to teach in an environment guided by policies and procedures. Anything defined by one version of the truth is a good candidate for a high automation, says Gavin, citing routine HR transactions as an example. But the problem is that where subjective understanding is required, like dealing with exceptional employee circumstances, there is no single ‘gold standard.’

Generally, routine HR processes are more easily robotized because they tend to follow a well-defined ‘decision tree’, Gavin explains. “A request for a pay slip, or reference, or name change, is characterized simple procedures and outcomes. Contrast this to the highly unstructured discussions around performance management or personnel grievance, though, which require context. So, HR offers a big bucket of opportunity for RPA, but relatively less for artificial intellige nce. Finance on the other hand had a more diverse mix of opportunities because activities often involved an element of judgment.

Technology concernsArtificial intelligence technology sits in a completely different category to robotic process automation. Right now, the market is dominated Amazon’s Alexa, Microsoft’s Cortana, IBM’s Watson, and Google’s DeepMind, and most large organizations are participating in pilots with one or more of these developers.

What’s important to understand about AI, say the experts, is that it’s not about writing code, but rather about configuring and then getting experts to train the machines. “Training is done on a question and answer basis, where answers are rated, similar to how Amazon provides feedback on online purchases,” Gavin says. “The engine learns to improve its answers based on this training, and running on organizational information.”

While this may sound easy, there are a number of features that are still causing concern.

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Transmitting of sensitive data overseas What corporations don’t realize is that many technology providers operate by transmitting data to overseas centers. While some functionality may be hosted in-country, many of the popular AI “Application Programming Interfaces” or APIs are hosted in the US or India. This may inadvertently create data privacy compliance issues, explains Gavin, breaching restrictions on what data can be transmitted across sovereign boundaries. “Not every AI provider has data centers in Australia, for example. That means that some are running their Australian clients’ AI services out of the US or India. And that may violate data privacy regulations, presenting a serious compliance risk.”

This issue generally arises during the design phase, where data privacy is addressed. It can cause serious setbacks because organizations may find that only 50 to 75% of the functionality provided by their AI technology of choice is available in country, throwing a spanner in the works when discussions are already well underway. “It’s a key objective for technology vendors to develop functionality in all of the key regions but it takes time and money, so homegrown alternatives are being developed to plug the gap in the meantime,” says Gavin.

Lack of skillsets The key to robotic automation’s success is usability, i.e., developing solutions that people like to use and know how to use. This is also one of the key stumbling blocks for artificial intelligence right now.

“There are three core skills that are needed to develop and leverage AI,” explains Gavin. “The first is data science, a scarce resource that serves to fine-tune the performance of AI solutions via a collection of algorithms. An example is tweaking voice recognition for regional accents and terminology.”

“The second is Human Factors Design experience, which is about usability, and manages the user interface design to optimize the user experience. This is a specific branch of engineering that focuses on the interaction with machines, leveraging behavioral psychology techniques to optimize the look, feel, flow, even mix of shapes and colors, that are deployed when humans interact with AI. Many organizations that have launched sophisticated AI solutions fall down on the user experience design. “Particularly troublesome are exceptions, where AI cannot provide reliable answers because of information or training gaps, and the conversational management flow does not address these cases,” explains Gavin.

The third skill challenge relates to relevant talent, generally. While India and China are rapidly scaling their AI talent pool, other regions are struggling to match quality with required volumes.

“Data science is a highly academic skill incorporating mathematics and statistics”, explains Gavin. “We are finding that China and India have a relatively large supply of labor to develop these skills at scale. Elsewhere in the world, however, there are still significant gaps in supply.”

KEY SUCCESS FACTORSThere are three factors that will support the success of an artificial intelligence implementation.

A solid information management strategy Organizations are effectively information processing machines. Information tends to get ‘better’ as it moves from the point of origin to the point of use. Identifying where the information is most mature along its journey is is not easy. However, that location will mark the entry point of your AI program.

Most organizations contain far too many information assets to manage easily. And yet, the success of artificial intelligence hinges on knowing where the source of truth is stored, and accessing it. Identification, maintenance, and management of information, and setting appropriate ‘gold standards’, are key. Many teams are in a rush to ‘do’ AI, and don’t necessarily want to be sidelined for months as they collect and classify the necessary information assets. What you get out of any automation program, however, depends entirely on the quality of information you put in it.

A consistent technology implementation strategy As AI is making headlines and gaining traction, there is a danger that different parts of the organization will make their own decisions in procuring and implementing solutions. To optimize and leverage capability across the enterprise you need to develop a technical strategy for AI from the start. “I’m already seeing organizations spend money twice or even three times on similar technologies,” says Gavin. “In addition, many are not taking the time to compare different AI solutions’ performances – which vary widely, based on our research, in terms of speed of response, quality, and cost. Speed of response for conversational AI is particularly important and can be the ‘make or break’ factor.”

Every solution excels in one area but is a compromise in another, he explains. “You need to decide what’s right for you, not based on average cases but on your specific case.”

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Quality of trainers Artificial intelligence solutions depend on quality trainers to learn how to emulate best practices. By asking questions of the system and ranking its answers, the machine ‘learns’ which are desirable responses. However, what’s really crucial is to get top quality trainers and deploy them consistently. If your trainers are not experts, then you will be teaching the system the wrong behaviors. This is especially key when the judgment is subjective and mixed messaging, via different levels of expertise, deliver poor results. “Training machines is just like training a child, in that inconsistencies will create confusion,” explains Gavin. “It’s really important to tap into the very best resources for machine training. The most successful outcomes are based on high-quality trainers whose level of expertise can be evaluated objectively.”

Gavin advises choosing trainers on the basis of staff performance rankings – for example, where service is delivered on the basis of tickets raised, and customers issue satisfaction ratings. “We would look for a consistent rating of four-stars and

above. There needs to be an objective way of identifying the best quality trainers to work with machines.”

EARLY STAGESWhile there is a lot of excitement around artificial intelligence, and our world and work will undoubtedly be enormously impacted as a result of it in future, we are still in the early stages, at least within service delivery. And while 80-90% process automation sounds amazing, we are not there yet. Today, 50% process automation within finance is probably a realistic benchmark, where 10% accrues to AI. In HR, which is more ‘binary’ in its actions, we see higher overall percentages of process automation, but AI has lesser applicability.

Today, money is still being left on the table because of information technology gaps that we are not yet able to bridge. As organizations evolve, privacy concerns are addressed, legal constraints are more easily overcome, and human appetite for using AI solutions increases, we may experience a significant step change in its adoption.

Gavin Seewooruttun, Partner, AI and Advanced Analytics, EYC3, EY Advisory Services

GLOBAL INTELLIGENT AUTOMATION MARKET REPORT29www.sson-analytics.com | [email protected]

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Explore the global landscape of intelligent automationactivity in the Intelligent Automation Universe

This interactive data tool showcases a comprehensive A-Z directory of IA software vendors, as well as customer implementations and pilots around the world. The Intelligent Automation Universe includes:

• CUSTOMER ADOPTION LANDSCAPE: Explore this interactive map to discover where all the intelligent automation implementations and pilots are happening around the world. Drill down by region and country and filter by industry, company size, functions and processes.

• SOFTWARE VENDOR DIRECTORY: Interrogate the data to understand who and where all the vendors are, including their product capabilities, type and geo-footprint of their customers.

• CASE STUDY CATALOGUE: Get real-life use-cases of implementations of RPA, Cognitive, and AI software in business support functions, including transparency of actual process, method, timelines and results that end users have experienced when implementing the software.

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