How to automate internal processes like risk assessments
How to automate internal processes like risk assessments
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.
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
Additional challenges at the customer
- Cost pressure
- Digital transformation awareness
- New technological possibilities
- No DevOps resources for own implementation
What criteria were important to him?
- Technological feasibility
- Scalability in terms of costs to enable business case
- Scalability in terms of language reach to act global
- Educational support from vendor to understand further possibilities
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
Project duration
Project cost
(digits)
Involved employees
(Operating phase, FTE)
Running costs
(per month, digits)
Involved employees
(Project phase, FTE)
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