Item: Joule
Rating: 8
Author: Verified User

Overall Satisfaction with Joule

Use Cases and Deployment Scope

Since its inception, adoption techniques for Generative AI, especially in the context of Enterprise Applications, have been changing at an unprecedented rate. Joule is a great help to get insight e.g. in HR area, executing Task in complex task in vendor handling where it needs autonomous decision , build forecast in demand planning areas with solution like IBP and SAP Analytics cloud. It also help in resolving complex problem in industry like for CPG industry , direct store delivery is very complex area , specially route planning and optimization , end of day settlement. Joule bring tremendous value here and bring both efficiency , real time data driven decision making here.

Pros and Cons

Pros

taking autonomous decision
provide good insight
navigate though SAP transaction

Cons

remembering context
better feedback collection
reduce hallucination

Return on Investment

30% efficiency in the team
20% error reduction
10% elimination of manual task

Usability

Joule is based on chatbot co-pilot based on natural language processing which is very easy to use. On top of it Joule is embedded into SAP skill , which delight the user when they use it and can avoid to navigate screen to screen , which brings lot of efficiency. Also it handle real time data and provide a easy to understandable output.

Alternatives Considered

Joule has better integration and context of SAP knowledge. It is grounded with SAP documents and can access with SAP data on real time. For Microsoft co-pilot it will be a custom build which is expensive and time consuming and not very efficient. Joule is also LLM agnostic , which give better edge and value for Joule.

Key Insights

Do you think Joule delivers good value for the price?

Yes

Are you happy with Joule's feature set?

Yes

Did Joule live up to sales and marketing promises?

Yes

Did implementation of Joule go as expected?

Yes

Would you buy Joule again?

Yes

Other Software Used

Microsoft Copilot Studio, AWS Amplify, Snowflake

Likelihood to Recommend

optimizes high-volume, high-line-item sales
orders within the CPG industry, which are processed through EDI with minimal
human intervention, by leveraging SAP S/4HANA, SAP Joule Studio,
SAP BTP CAPM, and Agentic AI.
Current business challenges include: The ability to identify orders
exceeding the capacity of a Full Truckload (FTL), calculate the volume and
weight of items to ensure they fit into the truck’s capacity, Group products
based on the default ship from location for this customer and product, Create a
proposal for a multiple of FTLs, meeting the order fulfillment requirements (Order
Logistics rules, ie: Minimum Order Quantity), Freshness check, etc. This solution solves
the challenges of manual or semi-automated activities and bring process efficiency

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