The document discusses best practices for successful automation projects based on common failures. It recommends having a strategic vision for automation that aligns with corporate goals. When designing automation projects, metrics should be created that align with goals, input should be gathered from various levels of an organization, and care should be taken not to automate valuable human tasks. The methodology should focus on getting a minimum viable product launched quickly and being prepared to pivot based on feedback. Examples of failed insurance claims automation projects are provided to illustrate best practices.
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Process
automation
Business
process
management
Case
management
Task/decision
automation
Robotic
process
automation
Service APIs
Decision
management
Intelligent
analysis
Artificial
intelligence
Machine
learning
Process
mining
Content/
capture
Optical
character
recognition
Natural
language
processing
Content
management
Interaction
Chatbots
Intelligent
agents
7. Best practices for automation success
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30. What is possible now?
• Existing business
• Automate repetitive tasks
– more efficient
• Decision guardrails
– less risk
- faster training
• Fast, fair claims
– more competitive
• New business models
• Adjacent low-value markets
• Micro-insurance
• Parametric insurance
• Usage-based pricing with telematics
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31. Summing it up
• Understand the automation imperative
• A lot of companies are failing at business automation, but
you don’t have to be one of them
• Align automation metrics with corporate goals
• Design the right thing the right way
• Get something into production fast, then prepare to pivot
• Watch for #fail indicators
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The success of business automation projects is measured by the degree of business improvement or transformation that is achieved, relative to the business goals. As simple as this seems, why do so many automation projects fail?
Join Sandy Kemsley as she delves into some of the reasons for automation project failure, and what steps you can take to make your project a success. As a technology and business architect who helps her clients create large-scale automation solutions, as well as an industry analyst researching the application of technology to improve business, she offers a unique perspective on the factors leading to success or failure of these projects. You’ll learn about:
What business leaders need to know about key automation technologies and their application to business operations
Methodologies that can align the outcome of automation projects with your operational business goals
Indicators that a business automation project is off course – and how you can steer it back in the right direction
What’s possible with business transformation through automation, including a use case for how automation can drive maximum business benefit
Pandemic plus technology advances have ramped up automation initiatives
In the old days, it was (just) a waste of money
Failure to successfully automate is a survival issue
Michael Lewis “Against The Rules” podcast about Athena Health
This is why it’s complex:
Claims manager needs to figure out what is missing, and request it
Each iteration, CM reviews file, figures out if they can make a decision, and makes it
Too much manual decisioning and ad hoc processes
Expertise is buried in the knowledge workers
Goal: knowledge workers should be focused on handling decisions and customer interactions that are better managed by people – true for most business process automation
Process to drive overall flow and interact with workers
Decisions to determine auto versus manual tasks (e.g., auto-adjudication)
ML to learn parameters of auto-adjudication, know when to ask for more information – learn from knowledge workers – explainable decisions based on past decisions and regulatory guidelines
Usage-based pricing: Allianz BonusDrive for UBI using telematics, more competitive for different market segments
Algorithmic: New Paradigm’s parametric insurance pays out automatically based on parameters, e.g., weather instead of handling individual claims