The document provides an introduction to digital decisioning, emphasizing its role in leveraging AI to enhance organizational decision-making and business value. It outlines critical success factors, principles, and examples of digital decisioning applications, highlighting the gap between AI ambitions and actual deployment. The emphasis is on operationalizing machine learning and continuous improvement to drive better decision outcomes, particularly in operational contexts.

1. © 2019 Decision Management Solutions
James Taylor
An Introduction To Digital Decisioning

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Your Goals
 Get some basic information on
digital decisioning
 See how digital decisioning will
help your organization
 Identify your next steps to get
started

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Our Goals In This webinar
 Show you how digital decisioning delivers value from AI
 Outline the most important drivers of success
 Give you some examples
 Suggest some steps to get started
James
Taylor

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AGENDA
© 2019 Decision Management Solutions
AI Opportunities and Challenges
Digital Decisioning
 A Definition
 Principles of Digital Decisioning
 Delivering Digital Decisioning
Getting Started
Q&A

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Quick Payoff for listening
It’s easy to spend money on AI and Machine Learning
Much harder to get value
Digital Decisioning delivers the value of Machine Learning
Critical Success Factors
 Decision Modeling
 Machine Learning AND Business Rules
 Continuous Improvement

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AI Opportunities And Challenges

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AI will add $13T to the global economy
97% are investing in AI
75% AI will enable new businesses
85% AI will allow a competitive advantage
76% AI will “substantially transform” companies
AI will add $13T to the global economy over the next decade
—Building the AI Powered Organization, HBR July-2019
97% of firms are investing in big data and artificial intelligence (AI)
—2019 survey by New Vantage Partners
Three-quarters of executives believe AI will enable their companies to move into new businesses.
Almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage.
—Reshaping Business With Artificial Intelligence, MIT Sloan Management Review September 06, 2017
76% [believed AI] will “substantially transform” their companies within the next 3 years
—Tom Davenport, The AI Advantage

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Gartner 2019:
 86% have initiated AI projects
 4% have AI deployed
Gartner's 2019 CIO survey points to the fact that,
although 86% of respondents indicate that they either
have AI on their radar, or have initiated projects, only
4% have projects currently deployed.
In a 2017 McKinsey survey with 3,000+ respondents,
only 20% had adopted one AI technology in one part of
their business
The gap between ambition and execution is large at
most companies… only about one in five companies has
incorporated AI in some offerings or processes. Only
one in 20 companies has extensively incorporated AI in
offerings or processes. Across all organizations, only
14% of respondents believe that AI is currently having a
large effect on their organization’s offerings.
—Susan Athey, Economics of Technology Professor at
Stanford Graduate School of Business, quoted in MIT
Sloan Management Review September 06, 2017
Many organizations’ efforts with [AI] are falling short.
Most firms have run only adhoc pilots of are applying AI
in just a single business process… Firms struggle to
move from the pilots to companywide programs
—Building the AI Powered Organization, HBR July 2019
Gap between ambition and execution
 20% AI in some processes
 5% Extensive AI
Most firms
 Ad-hoc AI pilots
or AI in one business process
 Struggle to move beyond pilots

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Enterprises waste time and money on
unactionable analytics
Digital decisioning can stop this insanity
The Dawn Of Digital Decisioning: New Software Automates Immediate Insight-To-Action Cycles Crucial For Digital Business John R. Rymer and Mike Gualtieri

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Critical Success Factors
Institute a culture of
digital decisions-first
design thinking
Think of digital
decisioning as the nexus
of business rules, data,
analytics, and machine
learning models
Decisions
Matter
ML Does Not
Stand Alone
Experience is that AI
and ML projects must
be focused on a
specific decision from
the beginning. You
cannot simply develop
an AI and then try to
figure out how to
improve a decision
with it.
Core AI algorithms are
machine learning-
based. ML is often
presented as
“replacing” other
decision-making
technologies like
business rules, data
mining or
optimization. However,
not all problems are
best solved by machine
learning.

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Decisions Matter Particularly
Operational Decisions
Especially
Digital Decisions
Focusing AI on decision-making dramatically increases the odds of success.
Because operational decisions are made repeatedly and in large numbers, they generate the data you need for AI and repay an investment in an AI Algorithm.
It also turns out that embedding AI into an automated, digital decision is more effective than presenting the results to a human user, so digital decisions are key.
One smarter, automated decision
can be worth millions in terms of
customer acquisition, retention,
and/or operational efficiency

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Different Kinds of AI
Interface AI
Natural Language Processing
Image Recognition
Transcription
Search
Decision-Making AI
Decision Logic
Optimizing Algorithms
Probabilistic Algorithms

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Digital Decisioning

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Digital Decisioning
 Uses Decision Management to Deliver Business Value from AI
 Operationalizes machine learning and artificial intelligence
 Uses business rules to guarantee agility, transparency and compliance
 Supports continuous learning and improvement
 Precise
 Consistent
 Real-time decisions
 … at every touch point

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The Value of Digital Decisioning
Improves customer experience
Reduces fraud
Manages risk
Targets individual customers
Increases agility
Drives business growth

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Digital Decisioning
Medical Claims
 Business Problem
 Deliver 20%+ STP of medical claims across
chatbot, mobile, online, direct from hospital
 Results
 50% Straight Through Processing
 Weekly updates
 Business User Control
 Solution
 Manage risk with cautious as-is rollout
 Business-led continuous improvement
 Simulation and impact analysis
 8% STP on day 1, 28% on day 100, 50% on day 250
0%
10%
20%
30%
40%
50%
0 50 100 150 200 250
Days
STP Rate

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The Value of Digital Decisioning
Improves customer experience
Reduces fraud
Manages risk
Targets individual customers
Increases agility
Drives business growth

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Digital Decisioning
Up-Sell and Cross-Sell in agent POS
 Business problem
 How to increase the Annual Premium
Equivalent (APE) of sales made by agents
using mobile app to work with new clients
 Results
 $6,000,000 in APE uplift
 >98% Agent adoption
 14-24%+ Acceptance
 Solution
 Decision service used financial data captured
from client and proposed purchase to suggest
intelligent up-sell or cross-sell
 Seamless integration into sales conversation
 Also integrated into customer portal
“It’s like finding dollar bills lying in the
street. The ROI is enormous and the
effort tiny.”—CFO

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Four Principles of Digital Decisioning
 Operational, transactional decisions
 Not process, not data, not functions
Decisions First
 Design and execution transparency
 Safe business agility
Transparent and
agile
 Predict risk, fraud and opportunity
 Predict decision impact
Predictive not
reactive
 Not a one-time effort
 Continuous improvement & experimentation
Test, learn, and
improve

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Digital Decisioning Architecture
Performance
Management
Enterprise Platform
Business
Intelligence
Data
Infrastructure
Application Context
Decision Service
Decision
Monitoring
Business Process
Management
Legacy
Applications
Business
Rules
Decision Modeling
Machine
Learning
Optimization
A decision service encapsulates business rules, advanced analytics and even AI technology to deliver
automated decisions to your application context. Leveraging your data, its behavior is defined by a
decision model that also connects its results to business performance for effective decision monitoring.
Packaged
Applications
Apps and Mobile
Applications

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Decision Management
Lifecycle for Digital Decisioning

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Decision Discovery and Modeling
Decision Models Show What’s Involved In Digital Decisions
Knowledge
required
Structure of
decision-making
Data required
• Decision models are best
developed using the
Decision Model and
Notation (DMN) standard.
• This defines a notation
showing decisions, their
decomposition into
reusable sub-decisions, the
data each decision needs
and the knowledge
required to define business
rules or analytics for each.
Decision to be
made

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External Data
Big Data
Decision Service Definition and Implementation
Analytics,
ML and AI
Business
Rules
• Business Rules are quick
to change
• Good for regulations,
policies, flash updates
• Less insight-rich than
analytics
• Analytics are insight-rich
but often opaque,
especially ML and AI
• Good for patterns,
trends, categorization
• Must be fed new data and
continuously improved
A decision service encapsulates
business rules, analytics, ML and
AI to deliver automated decisions
to your application context.
Data about business outcomes and decisions
made is integrated with external data to
close the loop and improve both rules and
analytics

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Decision Measurement and Improvement
Continuously Improve by Capturing Decision Outcomes
 Gather data
 What was decided
 Why was that decided
 How did that work out?
 Change the way you decide
Good Machine Learning platforms keep models learning
as new data is gathered. Add data about the decisions
you made, and how they worked out in business terms,
and you can understand your decision-making and turn
your machine learning into business learning.

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Getting Started

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Three Critical Success Factors For Digital Decisioning
DecisionsFirst
Design Thinking
Mix and Match
Technology
Continuous
Improvement
• DecisionsFirst Thinking - Think about decision design first to build a decision model and drive practical innovation
• Mix and Match Technology – Business rules, analytics and AI under a decision umbrella, deployed as a decision service
• Continuous Improvement – Analyze decision-making to drive business learning and focus on gradual improvement

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Get Everyone On The Bus
 Business
 IT
 Operations
 Analytics

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There’s Something For Everyone
Business Owner
Control
Analytic Insights in
Production
Operational
Transparency Clean Integration with
Legacy Systems
Continuous
Improvement

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Digital Decisioning
Using Decision Management To Deliver Business Impact From AI
 A completely updated version of an established and popular book
“…the only approach that has actually allowed me to
operationalize predictive models and deliver real ROI!”
“Essential reading for COOs looking to rigorously
improve automation through AI.”
“Anyone trying to automate and embed analytics
to support decisions should read this book.”
“Nothing but solid knowledge, sage advice, and great
examples without an ounce of hyperbole or fluff.”
 Forewords by Tom Davenport and Eric Siegel.
 Buy: mkpress.com/DD

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Your Questions
Enter questions
for the Q&A here
James Taylor, CEO
james@decisionmanagementsolutions.com

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Thank You
 More resources for boosting
insurance productivity on our
website
 decisionmanagementsolutions.com
 Decision Management Solutions
works with clients to improve
their business by applying
business rules and analytic
technology to automate and
improve decisions
 We can help you improve your
productivity with our
DecisionsFirst approach
 info@decisionmanagementsolutions.com

32. Thank You
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Decision Management, go to:
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© 2019 Decision Management Solutions