How to automate internal processes like risk assessments


5 Project costs (digits) 4 Running costs (per month, digits) How to automate internal processes like risk assessments Rationalization Plug and Play Project costs (digits) 5 Running costs (per month, digits) 4 Adam Friedmann The problem Qualified employees should solve qualified problems and not waste a huge portion of their capacity with pre-qualification of respective problems. A company that starts seriously with digital transformation will face the challenge that there will “pop-up” a lot of projects. In this case described here, due to strategic goals like implementing AI and Big Data Analysis, there was high demand to pre-qualify projects e.g. regarding risk assessment. So, the customer needed to transform their legal process and find a cost-effective solution, that is scalable on demand to pre-qualify new projects. Cost pressure Digital transformation awareness New technological possibilities No DevOps resources for own implementation The solution Necessary pre-qualification is optimized by a Conversational AI guiding the employee through the process The solution provider used the existing questions and evaluation logic structure to create a Conversational AI survey and evaluation chatbot that is now used to pre-qualify projects e.g. regarding risk assessment. The chatbot collects all the data in a conversational manner and reacts to the user input. The chatbot is capable of analyzing the data and providing a unique response/evaluation. For this solution there was no coding necessary and all necessary features were out of the box ready. As a result, productivity gains of qualified employees in this process by a factor of 10. Insights Stumbling blocks Evaluation logic is set up as a second instance technically. From user perspective it is one chatbot. In order to scale change management content and logic wise as well as new built-outs, we created the system in a very modular and variable driven approach. What had the customer tried before? The customer conducted a qualified market and provider scouting Tried establishing the process without chatbot, however, faced limitations on the implementation and flexibility of the system Business Benefits Enabled automatic assessment of intended Data Analysis projects without necessary clearance by consulting legal team Changes in content and logic can now be done 10x faster due to modularization and massive use of variables End-to-end automation No need to wait for legal professionals to continue with the implementation of the use The project schedule use cases description with questions (content) and content/sub-question/evaluation logic by customer create MVP by supplier test and provide feedback on UX by customer re-work and add more content by supplier re-test by customer add evaluation logic by supplier final test by customer push to production by supplier Project maturity level Used company-wide Project duration 8 weeks Project cost (digits) 5 Running costs (per month, digits) 4 Involved employees (Operating phase, FTE) 0 Involved employees (Project phase, FTE) 0.5 Technical The project schedule create MVP to demo the flow and UX tune functionalities enhance content/question scope modularize approach to better cope with future changes build evaluation logic as system integrate into customer’s intranet Technical Specs in productive internal global use NLP and chatbot technology proprietary to Passage AI enabled by a user friendly platform and console hosting required Where is the data stored? customer data is located in customer´s databases and systems solution is compliant to National and European data protection laws Providers Adam Friedmann "If a customer lacks the content and evaluation logic or APIs to their systems , then we are not the perfect match, but if it comes to executing based on a solid logical structure of a survey to free employees’ productivity , we're number one." Adam Friedmann The information may of course vary in individual cases. Please contact the provider for an assessment of your project.

Project costs (digits)

Running costs
(per month, digits)

How to automate internal processes like risk assessments

Rationalization

Plug and Play

Project costs (digits)

Running costs
(per month, digits)

Adam Friedmann

The problem

Qualified employees should solve qualified problems and not waste a huge portion of their capacity with pre-qualification of respective problems.

A company that starts seriously with digital transformation will face the challenge that there will “pop-up” a lot of projects. In this case described here, due to strategic goals like implementing AI and Big Data Analysis, there was high demand to pre-qualify projects e.g. regarding risk assessment. So, the customer needed to transform their legal process and find a cost-effective solution, that is scalable on demand to pre-qualify new projects.

Cost pressure
Digital transformation awareness
New technological possibilities
No DevOps resources for own implementation

The solution

Necessary pre-qualification is optimized by a Conversational AI guiding the employee through the process

The solution provider used the existing questions and evaluation logic structure to create a Conversational AI survey and evaluation chatbot that is now used to pre-qualify projects e.g. regarding risk assessment. The chatbot collects all the data in a conversational manner and reacts to the user input. The chatbot is capable of analyzing the data and providing a unique response/evaluation. For this solution there was no coding necessary and all necessary features were out of the box ready.

As a result, productivity gains of qualified employees in this process by a factor of 10.

Insights

Stumbling blocks

Evaluation logic is set up as a second instance technically. From user perspective it is one chatbot. In order to scale change management content and logic wise as well as new built-outs, we created the system in a very modular and variable driven approach.

What had the customer tried before?

The customer conducted a qualified market and provider scouting
Tried establishing the process without chatbot, however, faced limitations on the implementation and flexibility of the system

Business

Benefits

Enabled automatic assessment of intended Data Analysis projects without necessary clearance by consulting legal team
Changes in content and logic can now be done 10x faster due to modularization and massive use of variables
End-to-end automation
No need to wait for legal professionals to continue with the implementation of the use

The project schedule

use cases description with questions (content) and content/sub-question/evaluation logic by customer
create MVP by supplier
test and provide feedback on UX by customer
re-work and add more content by supplier
re-test by customer
add evaluation logic by supplier
final test by customer
push to production by supplier

Project maturity level

Used company-wide

Project duration

8 weeks

Project cost
(digits)

Running costs
(per month, digits)

Involved employees
(Operating phase, FTE)

Involved employees
(Project phase, FTE)

0.5

Technical

The project schedule

create MVP to demo the flow and UX
tune functionalities
enhance content/question scope
modularize approach to better cope with future changes
build evaluation logic as system
integrate into customer’s intranet

Technical Specs

in productive internal global use
NLP and chatbot technology proprietary to Passage AI enabled by a user friendly platform and console
hosting required

Where is the data stored?

customer data is located in customer´s databases and systems
solution is compliant to National and European data protection laws

Providers

Adam Friedmann

"If a customer lacks the content and evaluation logic or APIs to their systems , then we are not the perfect match, but if it comes to executing based on a solid logical structure of a survey to free employees’ productivity , we're number one."

Adam Friedmann

The information may of course vary in individual cases. Please contact the provider for an assessment of your project.