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Global Business Services Senior Leaders Forum

Title GBS Forum: Digital in Shared Services

May 5, 2016

|McKinsey & Company 11

Key messages

Digital is dramatically changing how shared service leaders think about efficiency and effectiveness. Most processes have significant untapped digital potential

Several barriers stand in the way of capturing the digital opportunity in shared services, but companies are finding innovative workarounds

Leaders need to incorporate digital into core capabilities –COEs are becoming an important part of the future operating model

What steps will you take to harness the digital opportunities in your organization?

McKinsey & Company 2|

Digital advancement is changing how companies think about driving efficiency and effectiveness

SOURCE: McKinsey analysis

2

3

5

6

4

1From … …To

Lean and continuous improvement programs to make people more efficient

Automation and analytics programs to substitute manual work with technology

Improvement within functions organized current organization structures

End-to-end approaches designed to fully incorporate technological capabilities

Focus on “home run” technologies that drive a step changes in expenses

Programs designed around “singles and doubles” that generate scale over time

“Wait and see” approach to adopting innovative solutions

“Skunk works” that adopt solutions early in the development cycle

Investment in specific technologies for a limited number of use cases

Investment in a portfolio of technologies to acquire a wide range of capabilities

Incremental 4-6% year-over-year productivity across all areas

Digitally-enabled productivity of 50%+ in select areas


Digital solutions already exist to automate a large portion of work in the economy…

5.8

2.2

3.6

Automatable wages

Total wages2 Not automatable wages

FTEs3

Percent (100%= 130M)

44 56

Annual wages by ease of automation1

US $ Trillion

SOURCE: BLS 2014, O-Net, Automatability index, Global Automation Impact Model, McKinsey Analysis

1 We define automation potential by the work activities that can be automated by adapting currently demonstrated technology2 Based on median wages and FTE (130 Million) from BLS; 2,080 hrs/year; excludes occupations without DWAs ($0.1T in wages, 2 Million FTEs)3 Ratios are not equal due to FTE weighting


ILLUSTRATIVE…with even more share of typical shared services processes being automatable

Potential for automation using currently demonstrated technologies

Fully automatable

Difficult to automate

Somewhat automatable

Mostly automatable

Total tasks

20%

100%

12%

20%

48%

No human intervention

Most work managed by technology

Most work managed by

humans

Few technology applications

Record to report

Percent of tasks

SOURCE: McKinsey Analysis

How does this resonate with your experience?


More complex

Less complex

Classical levers▪ Deploy simple IT enablement levers (e.g., desktop analytics, workflow tools)▪ Work with existing vendors to make full use of existing feature sets

Off-the-shelf solutions▪ Leverage add-ons to current platforms (e.g., ERP modules)▪ Introduce technologies from vendors with proven use cases (e.g., procurement

SaaS platforms, reconciliation engines, analytics packages)

Clean sheet transaction automation▪ Convert unstructured data (e.g., scanned invoices) into structured data that can

be managed by automated systems (e.g., XML files)▪ Deploy robotic process automation and basic rules engines to eliminate manual

processing and approvals

Clean sheet cogitative automation▪ Build learning algorithms to uncover predictive factors for use in workflows▪ Use cognitive agents to automate human/machine interface and knowledge

management

Companies are employing a wide range of automation solutions in shared service organizations


Robotic process automation is emerging as one of the primary levers currently being deployed to capture the opportunity in shared services

SOURCE: Press reports, web search, RPA software providers

Company Examples of RPA ImpactRetail and institutional banking▪ Transaction investigations▪ Tracing funds▪ Recalling funds▪ Audit certificates▪ Funds disbursement for construction

loan mortgages

▪ 100 bots deployed in first 6 months▪ Cost savings are 40% or more and

significant reduction in end to end customer service delivery

▪ No need to first re-engineer a process –sub-optimized processes can also be automated – bots can work 24x7

Financial shared services▪ Shell used 70 global hubs to control

its close across 650 legal entities and required a total of 5,000 people

▪ Closing and analyzing the books took 15 days and accuracy checks were inefficient

▪ RPA tool and SAP’s Financial Closing cockpit used to automate and coordinate close

▪ Cut 4 days off its close, improved close quality and reduced FTE’s effort

▪ By the time the rollout is complete, Shell will have over 10,000 closing tasks automated

What has been your experience with RPA and other technologies?


Sizing and prioritizing the automation opportunity requires an understanding of how technologies map to specific tasks

ILLUSTRATIVE

Social and emotional reasoning

Fine motor skills/ dexterityGross motor skillsNavigation

Sensory perception

Mobility

Highly automatableAutomation potential Difficult to automate

CapabilitiesMatch candidates to job requisition

Define contingent workforce policiesand strategies

Tasks

Social

Cognitive

Physical

Generating novel patterns / categories

Optimization and planning

Natural Language generationRecognizing known patterns/ category

Logical reasoning / problem solving

CreativityInformation retrievalCoordination with multiple agents

Natural language understanding

Output articulation/ display

Emotional and social output

Social and emotional sensing

Capabilities hindering automation 1 5

SOURCE: Global Automation Impact Model

Low

Medium

High

Hindrance

Capabilityrequirement

▪ Processes and roles can be broken into specific tasks, each requiring a unique set of capabilities

▪ Automation opportunity is a function of two factors– Level of

capability required

– Availability of technol-ogy for the required capabilities

McKinsey & Company 8|SOURCE: McKinsey analysis

Applying the full range of automation levers has the potential to dramatically reduce costs

Supplier management

Invoice receipt, pre-processing

Processing

Payments

Disputes andreporting

205

20

30

FTEs▪ Supplier enrollment▪ Supplier data maintenance

▪ Match invoices with PO▪ Non-PO invoice approval

▪ Query management▪ Dispute resolution

Nature of work (pre digitization)

▪ Scanning and indexing▪ Acknowledgements▪ Invoice validation

▪ Auto invoice routing with tolerance limits▪ Automated validation and

acknowledgement

▪ Automated 3-way match▪ Exception handling for <1% of volume

▪ Check printing▪ Remittance advice▪ Reconciliation

▪ Automated interlink bank transfers

40 ▪ Duplicate supplier analysis▪ Liaison with strategic vendor▪ Vendor relationship managers

▪ Electronic report distribution▪ On-demand supplier self-service▪ Cross-functional team for disputes

Nature of work (post digitization)

23

3

3

7

2

8

FTEs

Interventions deployed▪ Customer back redesign of end-to-end process to fully leverage existing technologies▪ Enabling automation to eliminate all manual interventions, including those initiated by vendors (e.g., payments)▪ Cross-functional and expert teams for value-added services (e.g., relationship managers, disputes managers)

CASE EXAMPLE

115


ADVANCED ANALYTICS

TASK AUTOMATION

PROCESS AUTOMATION

NLP + COGNITIVE

▪ Goes beyond ‘statistical’ learning

▪ Utilizes natural language to build neural network

▪ Integrated offering utilizing real time data and processes

▪ Learns, creates real-time “mental maps”

▪ Advanced algorithms beyond human capabilities

▪ Utilizes unstructured data

▪ Insights vs. task driven

▪ Performance improves over time

▪ Rules based process automation

▪ Structured data sources

▪ Static performance level

▪ Point solutions▪ “Plug-in” apps▪ Easily scalable

Further technology advancements will unlock new automation opportunities in the coming years

DEEPMIND


There are 5 emerging use cases for GBS organizations to drive value from cognitive computing

SOURCE: McKinsey Global Institute analysis

ILLUSTRATIVE

DescriptionSectors impacted▪ Finance▪ Education▪ Health care▪ Media and

communications▪ Government and

social sectors▪ Infrastructure

and utilities▪ Transportation▪ Retail

Knowledge services

▪ Increasing consistency of tasks such as searching and analyzing information (e.g., marketing analytics, supply chain allocations)

Content creation and analysis

▪ Automatic content creation and synthesis (e.g., operational and financial reporting)

▪ Automated data consolidation and translation

Natural language interfaces to systems

▪ Access to advanced IT tools and other information systems through language interfaces (HR benefits eligibility, product / service technical support)

Professional services

▪ Professional judgment with machine-learning –spot connections humans would miss (legal, insurance eligibility check, etc.)

Contact center▪ Automating tasks – answering customer calls

or dispatching assistance (e.g., IT, HR, Purchasing, Finance)

What other use cases have you seen?


Several barriers exist to capturing the digital opportunity

Presence of autonomous and automatic technology is a recent development with uncertain effects, which can create discomfort and distrust among humans towards new technology

Working with new technologies will not only require reengineering of core processes, but also a significant shift is the beliefs, attitudes, and behaviors of employee. The entire organization structure will evolve as it adopts automation technologies

Although the technology may exist to enable automation, linking different automation capabilities to each other, to other systems, and data will be challenging and requires significant effort

Automation holds huge potential to improve enterprise and work, but organizations must balance feasibility of investments against expected performance gains and savings to avoid making expensive mistakes

Thoughtful regulation will be a crucial element in ensuring that AI and robotics are beneficial for society. However, the limitations placed on technology makers can slow down the space of innovation and automation

Human preferences Organization Technology Economics Policy and law

Beyond the availability of technologies, enterprises must address 5 major barriers

What other barriers are you facing?


Conversations with a number of companies have uncovered implementation challenges and potential workarounds

Challenges Workarounds

Savings are captured as “fingers and toes” rather than whole roles

Org structures need to be redesigned in parallel with process changes

1

Technology providers bring “hammers” and search for “nails”

Processes need to be “clean sheeted” to fully incorporate a range of technologies

2

Processes and systems are highly fragmented

Sub-optimized processes can be automated to generate quick wins and refined over time

3

Frontline employees resistant to support automation efforts

Engage high potentials in design efforts, with opportunity to build new capabilities

4

IT capacity, funding, and resources are limited due to cost pressures

Fully leverage existing feature sets and low cost solutions (e.g., desktop RPA)

5

Existing skillsets are insufficient to support digital transformations

Build automation and analytics CoEs to support digital initiatives

6


Organizationally, many models can work and thinking through key questions helps identify the best option

Guiding principlesDimensions Key questions

Organization

What is the right level centralization for a digital CoE?

▪ Create a critical mass of centralized digital experts▪ Degree of centralization depends on

– Stakeholder needs and functional specialization of digital SMEs1

– Organization size and budget for digital resources ▪ Balance the need for business domain consultative expertise with deep

functional digital expertise▪ Hybrid models with digital CoE as a shared capability with business

domain experienced stakeholder relationship managers

Governance

How should the CoE engage with the business?

▪ Initiatives owned by business stakeholders, leveraging the CoE for tools and expertise required

▪ Cross functional Business subject matter experts paired with digital teams to pressure test real business use cases

People and change

How to you foster the right mindset?

▪ Startup atmosphere that facilitates creativity, pilots, and “failing fast”▪ Change from continuous improvement to ‘Lean Digital’ mindset▪ Step-level impact of new technology to accelerate business impact

Sourcing & partner mgmt.

How much work should be outsourced vs. done in-house?

▪ Degree of outsourcing driven by– Importance of maintaining proprietary knowledge/specialized skills– Availability of capable providers – Sustainability of size of in-house digital group– Brand/ scale to attract top talent


Companies are beginning to setup digital CoEs, leveraging a range of organizational models

Digital capabilities

Hybrid Distributed Centralized

Description

Suitable for

Pros & cons

More difficult to spread digital adoption in rest of organization

Economies of scale

Large group is more likely to reach high sophistication level

Standardization and high reuse of resourcesHarder to generate impact due to weaker connection with stakeholders

Close connection with stakeholders

Hard to obtain critical mass of skills, requires stakeholders to develop own capabilitiesLimited reuse of skills, learnings, data and developmentRequires more FTEs

Enables synergies and knowledge distribution between areasCan adapt to specific needs of stakeholders

Enables both functional and deep digital expertiseRequires careful prioritization and balancing of resources

▪ Organizations with no significant cross-domain requirements – large variation in stakeholder needs

▪ Organizations where most people are technically skilled/ numerate

▪ Diversified lines of businesses▪ Large cross-domain priorities

▪ Single dominant "business“ type ▪ Service oriented organizations with no

strong digital background

▪ No dedicated central digital organization

▪ Capabilities are spread across organization

▪ Central team with capabilities acting as specialized delivery org or CoE

▪ Smaller, specialized capabilities in key area s (e.g. marketing, finance, operations)

▪ Central team for digital development▪ May be complemented by “power

users” across organization

What model(s) are you pursuing?


Many shared services organizations have already started the automation journey, but long term success depends on an integrated approach

Sequence automation initiatives, balancing potential impact with ability to scale approach and solutions from initial use cases

Map the opportunity

Pilot 1-2 use cases

Build a roadmap

Establish partnerships

Rethink org structures

1

2

3

4

5

Estimate the automation potential of each major process, surfacing tasks that map well to existing technologies and consume substantial resources today

Select a process or sub-process to clean sheet using a portfolio of technologies (e.g., RPA, machine learning, natural language generation) and initiate proof of concept pilots to validate designs before scale-up

Select technology providers to supply platforms for each of the major technology solutions required to execute the roadmap

Create blueprints for future org structures to understand how to capture impact and embed new capabilities (e.g., Automation CoE)

Last Modified 5/4/2016 8:48 PM

Eastern Standard Time

Printed


Discussion questions

Which digital opportunities are you already pursuing?

What steps are you taking to accelerate digital in your operations?

What would 40%+ automation mean for your org designs, career paths, and footprint?

How are you keeping up with the evolving provider landscape and constructing a digital roadmap?


Appendix


Our analytical framework assesses the potential for incremental automation at the activity level


Occupations (>800 in total)

1Business Operations Specialists

▪ ...▪ …▪ …

Technical capability assessment (18 factors)

▪ ...▪ …▪ …

Activities: Business Oper-ations Specialist example(>2,000 for all occupations)

Submit financial applications

▪ ...▪ …▪ …

2Personal Financial Advisors

3

First-Line Supervisors of Retail Sales Workers

4

Market Research Analysts and Marketing Specialists

Advise others on business or operational matters

Develop business relationships

Oversee business processes


Output articulation/display

Optimizing and planning

Logical reasoning

We determined capabilities needed for each activity based on the way humans currently perform activities. Some activities will be performed differently if processes are transformed, and some capabilities will no longer be needed. For example, no physical capabilities are needed to “connect calls” today, but telephone operators in the 1950s would need to physically connect switchboard plugs.

▪ Robust model that can be leveraged to assess the technical potential for automation across a number of use cases, including internationally

▪ Estimation of time that occupations spend on various work activities

▪ Level of automation impact on occupations

▪ Aggregation across occupations to assess the impact on industries

Perspective on automation impact

Work activity assessment▪ Performance level required of humans

to complete the work activity▪ Ability of currently demonstrated

technology relative to humans

INTERIM FINDINGS


We developed a framework of 18 capabilities to understandthe performance requirements of work activities


Cognitive capabilities

Social and emotional capabilities

Physical capabilities

Description (Ability to…)Automation capabilitySensory perception ▪ Autonomously infer and integrate complex external perception using sensorsSensory perception

▪ Solve problems in an organized way using contextual information and increasingly complex input variables other than optimization and planning

Logical reasoning/problem solving

▪ Create diverse and novel ideas, or novel combinations of ideasCreativity

▪ Optimize and plan for objective outcomes across various constraintsOptimization and planning

▪ Search and retrieve information from a large scale of sources (breadth, depth, and degree of integration)

Information retrieval

▪ Interact with others, incl. humans, to coordinate group activityCoordination with multiple agents

▪ Deliver outputs/visualizations across a variety of mediums other than natural language

Output articulation/presentation

▪ Deliver messages in natural language, including nuanced human interaction and some quasi language (e.g., gestures)

Natural language generation

▪ Recognition of simple/complex known patterns and categories other than sensory perception

Recognizing known patterns/ categories (supervised learning)

▪ Comprehend language, including nuanced human interactionNatural language understanding

▪ Identify social and emotional stateSocial and emotional sensing

▪ Produce emotionally appropriate output (e.g., speech, body language)Emotional and social output

▪ Autonomously navigate in various environmentsNavigation

▪ Move objects with multi-dimensional motor skillsGross motor skills

▪ Move within and across various environments and terrainMobility

▪ Manipulate objects with dexterity and sensitivityFine motor skills/dexterity

▪ Accurately draw conclusions about social and emotional state, and determine appropriate response/action


▪ Creation and recognition of new patterns/categories (e.g., hypothesized categories)

Generating novel patterns/ categories ▪ Each of these

capabilities is evaluated across a 4-level continuum of performance:– Not required– Basic skills

required– Median

human capabilities required

– Top quartile human abilities required

▪ The description is intended to give a high level summary of the capabilities we have evaluated

INTERIM FINDINGS


Technology has advanced to match or exceed human abilities for some capabilities

SOURCE: Nature Publishing Group; Amazon; Finovate, press search

INTERIM FINDINGS

Capability

Recognizing known patterns/categories (supervised learning)

Sensory perception

Optimization and planningCoordination with multiple agentsNavigation

Natural language generation

Recognizing known patterns/categories

Fine motor control

Sensory perception

Challenge addressed

▪ Training sufficiently complex models can be extremely slow and data-intensive

▪ Coordination of multiple robots in a fast-paced and dense environment is very difficult

▪ Generating human-sounding text reports (even if technical) from data has eluded researchers for decades

▪ Surgery has long been considered too risky area to use of automated systems

Example breakthrough technologies

▪ Google’s DeepMind– Takes only a few hours to

train visual cortex like N.N.– Beats humans in many

Atari video games

▪ Amazon’s Kiva fleet– 4x faster order fulfillment– 2-4x more placements per

employee

▪ Narrative Science’s Quill– Performs numerical

analysis and generates reports in seconds

▪ Mako’s robotic surgery– Disallows MD cutting out

of 3D region▪ McGills McSleepy and J&J’s

Sedasys deliver anesthesia automatically


Several key gaps remain between technologies and human capabilities

1 “Meet Pepper, the ‘love-powered’ humanoid robot that knows how you’re feeling” PC World (2014);2 “Computers can't read your mind yet, but they're getting closer” Fortune (2015); 3 “Amelia” IPSoft;4 “Computational Creativity: The Final Frontier?” Colton and Wiggins; 5 “Painting Fool’s portfolio reveals artificial artist” New Scientist (2012);6 “Distilling Free-Form Natural Laws from Experimental Data” Science (2009); 7 “Wonderful widgets” Economist (2015)

SOURCE: Press search, McKinsey analysis

INTERIM FINDINGS

Discipline

Creativity

Logical reasoning/ problem solving

Key gaps to human performance

▪ Algorithms are unable to justify or describe the meaning of the formulas that they derive6

▪ Automated design can only optimize parts of a system based on human-defined design criteria

Current state and example

▪ Compose symphonies, poetry, visual arts4

▪ Cornell researchers developed a program that derived Newton’s second law from empirical evidence6

▪ Engineering consultancy Arup used a computer optimization program to design rigging that is 75% lighter than human-designed components7


Social and emotional sensing

Emotional and social output

▪ Predict emotional response and determine underlying issue, e.g.– How would customer react to phrasing?– Is the customer upset at hospital bill or

family death?

▪ Does not yet address different societal norms or individual mannerisms

▪ Displaying subtler emotions, such as sarcasm

▪ Robots use only limited body language▪ Nuanced intonation and inflection

▪ IPSoft’s Amelia can respond appropriately to statements like “I resent you”3

▪ Aldebaran Robotic’s Pepper detects human state through tone and facial expressions1

▪ Affetiva analyzes video of human faces to identify emotion and can tell the difference between a smile and a smirk2

▪ Amelia’s tone and facial expression show combinations of pleasure, arousal, dominance, etc.3

Pepper holding hands on stage1

Amelia’s visual representation of a neutral expression3

Artwork generated by AI “The Painting Fool”5

▪ Responding to cultural and artistic trends▪ Handling loosely-defined problems, e.g.

– How can our company improve profits?– Write an interesting mathematical proof

Computer designedcable support from Arup7

Soci

al a

nd e

mot

iona

l cap

abili

ties

Cog

nitiv

e ca

pabi

litie

s


Automation potential based on demonstrated technology of job titles in the US (cumulative)

Automation potential1 based on demonstrated technology by US FTE (cumulative)

While few jobs are 100% automatable, 60% of all jobs have at least 30% technically automatable activities

SOURCE: BLS 2014; O*Net; McKinsey analysis

Example occupa-tions

Sewing machine operators

Logging equipment operators

Stock clerks Travel agents Dental lab technicians

Bus drivers Nursing assistants Web developers

Fashion designers Chief executives Statisticians

8771605041342619101

100

% of roles(100% = 775 roles)

>30>70 >60 >10>20

Percent of automation potential

>80 >50 >40>90

9072

54423426201371

>20

Percent of automation potential

% of FTE(100%=129million)

>10>40>50>60 >30>90100 >70>80

INTERIM FINDINGS

1 We define automation potential according to the work activities that can be automated by adapting currently demonstrated technology


5646

5039

40

34

49

35

5548

4364

4043

52

3635

60

24

35

MiningUtilities

Healthcare/social assistance

Manufacturing

Federal, state, and local governmentProfessional, scientific, and technical services

Educational services

Agriculture, forestry, fishing, and hunting

Administrative/support/waste management

Management of companies/enterprises

Information

Finance/insurance

Retail trade

Real estate/rental and leasing

Other services

Arts/entertainment/recreation

Transportation/warehousing

Construction

Wholesale trade

Accommodation/food services

Automation potential across US industries varies between 24% and 64%

Impact of automation by industry in the US

SOURCE: BLS 2014; O*Net; McKinsey analysis

Time weighted percent of technically automatable1 activities by industryPercent

Preliminary results show 43% of work activities in Singapore have the technical potential to be automated (compared to 44% in the US)

All industries likely to be impacted by advances in automation technologies

The potential for automation varies across industries –24% in education vs. 64% in accommodation and food services

Total economic wages by industry100%= $5.8 Trillion

15%10%9%9%8%8%5%5%5%5%5%4%3%3%2%1%1%1%1%0%

INTERIM FINDINGS

1 We define automation potential by the work activities that can be automated by adapting currently demonstrated technology


Machine learning is poised to significantly change the finance industry with multiple applications being pursued by established players

Leading FIG firms have initiated machine learning (ML) projects to increase predictive capabilities and improve consumer security and experience

Insur-ance

Banking

IBM Research collaborates with Bank of America to develop online fraud protection software to accurately detect phishing websites

Citibank and IBM Watson team up to augment mobile banking services and security, and to work to perfect cloud-based data development

Strands has partnered with Barclays, ING, and BMO to use machine learning to calculate the probability of customers buying from a particular merchant, generating personalized recommendations

Capital One uses a “deal optimization engine” to automatically analyze customer spending patterns and demographics, and continuously-improving ML algorithms to match customers with offers

Allstate employs ML to rapidly analyze customer policies, claims, and quotes and revises calculations to best predict what customers prefer

Liberty Mutual uses advanced analytics to more accurately identify high-risk homes that require additional examination to confirm their insurability, ensuring that its home insurance portfolio aligns with its business goals

State Farm uses big data to understand huge volumes of information to act upon the needs and preferences of customers across all access points

Progressive’s Snapshot device monitors and catalogues customer driving habits, allowing them to use predictive algorithms to adjust policy pricingaccording to driver’s risk

Incumbent initiatives

SOURCE: Press search

INTERIM FINDINGS


Startups are reimagining financial practices using machine learning, adding to the potential for significant changes to the finance industry

Selected startup initiatives

Produces 40% more accurate credit scoring using an advanced analytics underwriting ML model that leverages consumer metrics

Uses advanced ML algorithms to automatically update and improve user profiles for its online banking and money management clients

Uses natural language technology to help financial industries analyze “unstructured data” such as documents and transcripts

Uses ML algorithms to develop a platform that creates a web of comparable firms to aid private equity and venture capital decision making

Combines optical character recognition and human verification to transform hand-written forms into accessible data for firms like Humana and Veri-Tax

Uses behavioral algorithms and statistical data to best match hedge funds and investors

Assesses the repayment rate of individual borrowers by using ML in combination with the borrower’s digital footprint to create a credit rating for bitcoin exchanges in their peer lending platform

Helps homeowners refinance using ML algorithms and big data to provide users real-time credit checks and precise quotes

SOURCE: Press search

INTERIM FINDINGS

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