Pathways to Exploiting Value from AIecrisponsor.org/presentations/KPMG AI Presentation ECRI...2 Document Classification: KPMG Confidential © 2020 KPMG LLP, a UK limited liability

Pathways to Exploiting Value from AI

Getting to Business Results

Adrian Bradley KPMG Digital

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Document Classification: KPMG Confidential

© 2020 KPMG LLP, a UK limited liability partnership and a member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved.

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We believe that AI will pervade every aspect of the enterprise in the 21st Century.

Organizations must rise to the challenge or be disadvantaged relative to traditional and non-traditional competitors.

© 2019 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved.

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Valu

e

Time

It’s a Journey

Many organizations take a step-by-step approach, with discrete projects that demonstrate business value building AI capabilities along the way.

Step 1

Step 2

Step 3

Step 4

Experiment / prototype

Repeatable value

Integrate into operations

Transform

Initial focus on Data Expertise & AI Technology

Add Business Process and Workforce elements

Consider Risk and Reputation implications

Achieve the business visionAI-Enabled Transformation Journey


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What does KPMG do with AI?

KPMG Ignite


Drive growth Manage risk Optimize cost

KPMG Ignite (Artificial intelligence platform)

Cognitive Vouching

AI-enabled extraction and comparison of key attributes from various document types for audit procedures

Cognitive Contract Management

AI-enabled contract management provides a enhanced approach for contract review, assessment and management

Cognitive Transfer Pricing

AI powered identification of comparable companies for benchmarking

LIBOR Analytics

AI-enabled interpretation and amendment of LIBOR based agreements

Procurement

Compliance

Finance

Financial Services

Compliance

Procurement

Tax

Finance

AI enabled extraction and comparison of key attributes from various document types for analysis procedures

KPMG Intelligent Interactions

Customer

Enable the integrated, personalized & enabled, and proactive approach to customer

AdvancedData Management

(incubation)

Regulatory Mapping

(incubation)

Regulatory Compliance

Financial Services

AI enabled regulatory mapping that automates and enhances parsing, and mapping of regulations

AI-enabled tax transfer pricing to surface the most promising companies for benchmarking

AI enabled offering for the next generation of digital data management and governance

AI enablement for the enterprise, through tackling tooling, IT, production, and data scientist user challenges

Enterprise

AI Platform Design and Implementation

Enterprise

In the past 5 years we’ve moved from experimentation to using AI at the heart of both consulting and audit.

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Why is it hard to start?Data Science initiatives differ from traditional technology-driven initiatives. The challenge starts with a search for high value challenges that are a good fit for data science.

Finding high value challenges that are a good fit for data science is tough.

Operating environments are complex, interconnected and difficult to codify.

Even of large volumes of data exist, it is hard to unlock their potential

There are business problems that are assumed to be too costly to solve.

A lot of digital initiatives are getting stuck in proof of concept phase, and are hard to productionise at scale.

There are multiple pain points when trying to implement data science projects and scale value, such as:

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KPMG’s perspective

We take a performance led, user centric approach to identify the highest value use cases, and ensure the team remains focussed on the real world challenge to realise the most value from the solution.

Driving BAU impact in a complex data science world is hard, but can have great impact

How we start

Start with performance. Identify performance challenges closely linked to the client’s business. For example, better reporting and forecasting in finance; or better performance management in manufacturing; or predictive maintenance in insurance.

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Stay focused. We ensure the conversations stay focused on well-defined data science archetypes, that are high value, resonating with our clients and the market.

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3

4 Validate assumptions early. We model the size of the monetary prize and validate the assumptions as early as possible in the process for value realisation planning.

Challenge. We challenge the searchlights as we move up the ledger when designing use cases and searching for hypothesis and value pools.

Sensor technologyWhat additional data do I

need?

Data & systemsWhat insights can I gain from

my operational data?

Models/SimulationsWhat tools support process

automation and decision making?

People and personasWho operates the processes

& decisions that affect performance?

InterventionsWhat levers can I pull in to

influence performance?

PerformanceWhat is the performance

challenge?1

2

3

4

5

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Starting with Value

Case Study 1

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This Data Science CoE used a very structured ideation process to drive quality in the ideas then explored with AI and wider data science techniques

Starting with Value

A: Is this an idea that could drive value?

• How will value be generated?

• Who in the organisation will benefit?

• What is involved in creating the solution?

• Does it meet broad investment criteria?

B: Is there a value case, scope and plan to deliver demonstrable value?

• What is the payback period?

• What is the MVP and roadmap?

• Will the solution re-use or enhance business capability?

• What is the detailed development plan?

Initiative-specific checkpoints as part of Incubate process:

• B1: Have we demonstrated that there is value in developing an MVP to test in our operations?

*Note: PoC optional step prior to MVP

• B2: Have the pilot features delivered repeatable value in our operations?

C: Do we have a solution which is ready to scale in our operations?

• Has a scalable business model been defined and agreed?

• Is there a technical and business change roadmap to scale the solution?

• Have handover criteria been agreed?

D: Is the solution sufficiently mature to be managed as BAU by business and IT?

• Have the agreed thresholds been met to trigger handover?

• Have all of the breakthrough features been delivered?

• Is an ongoing support and maintenance plan in place?

• Are the relevant accepting business capabilities in place?

DCBAnalysis & feature

definitionA

Discover Incubate Scale Finish

Scale solution / Features

Continuous assessment of value

case

B1

Establish operating model changes and manage change

Rapid mobilisation

Build PoC

Develop MVP B2

Prepare to scalePilot

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Exploring characteristics of use cases driving real value

Data science helps us explore complex problems. Here we look at types of problems which benefit from this, and use a worked example from the use of AI in the P2P process for contract audits.

Value Is this a high value area? It is likely that your biggest contracts will be subject to the most leakage

Variety Is there are wide variety of things in the data set? The more different types of spend that exist within one invoice, the easier it will be for errors to occur

Volume Is there a high volume and frequency of spend activity in this category?

The more frequently you receive invoices, the more difficult it is to reconcile credit notes, amendments,

and variations

Variability Is there a lot of variability between invoice documentation and supplier formats?

The less uniform supplier formats are over time, the more difficult it becomes to verify if they are accurate

Value at Scale

Case Study 2

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Expanding on the P2P exampleInvoice leakage is when you pay your supply chain more than they are contractually entitled to due to overbilling

$20bn

Global Spend in Supply Chain

Leakage of 4.5%

Findings in Pilot + Release 1

$10-30m per month

Scaled value of this finding globally

Key outcomes

Automation

Control and governance

Cost saving

Ultimate goal is prevention before invoices are paid.

Getting the right talent mix

Case Study 3

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Successful teams –

Using this proven squad structure de-risks our work and enables standardisation, reducing KPMG delivery costs for our clients.

Data Science ConsultantI use my experience in developing and deploying data science solutions to guide and mentor the product owner in the kinds of digital methods and capabilities that will best suit their needs.

Scrum MasterI set up and oversee Agile delivery process and ensure smooth

facilitation between disciplines and timely delivery.

Data Scientist x3I build the models underpinning the solution and develop the algorithms that give the product predictive and prescriptive

capabilities.

Data EngineerI design and implement the data flows through the solution, making

sure we are managing our needs efficiently, and optimising the code deployed.

Cloud EngineerI develop the underlying architecture and backbone of the product.

I also take care of provisioning services and ensuring smooth running of all of the cloud components.

Client Product Owner I use my experience as a subject matter expert to prioritise the

development of initiative features that drive the highest value for my business.

Business analystI ensure that all business and user requirements are captured an

implemented correctly. I populate, update and help to prioritise the list of user stories in JIRA or similar Agile planning tools.

ERP data specialistI ensure rapid and accurate extraction of complex ERP data tables, and use my experience to quickly transform this into a form that the

data scientists can rapidly navigate.

The secret sauce is to combine data science and business expertise within a framework for success

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Using a leading edge data science platform - Ignite

Who What WhyA platform for Data Scientists and Engineers.

A global AI platform with a modular component architecture.

Unlocks the value of unstructured data with surgical precision on complex problems.

• Designed to enable Data Scientists and Engineers to build AI services from prototype to industrialization ready

• Designed to integrate with SME and business knowledge expertise

• A development platform that creates the ability to dynamically leverage the best “AI-tool” for the job

• A modern ML architecture that supports API and container based deployments

• Enables the reuse of previously built models and collected domain expertise to maximize data science time

• Handles challenging data sources –documents, images, voice, etc.

• Enables KPMG to build custom, quality, production AI services

Encourages and enables reuse for scale.


Questions



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Pathways to Exploiting Value from AIecrisponsor.org/presentations/KPMG AI Presentation ECRI...2 Document Classification: KPMG Confidential © 2020 KPMG LLP, a UK limited liability