Skip to main content

Why is Robotic Automation different from Business Process Management Systems - BPMS?


BPMS is principally aimed at improving IT architecture to allow greater flexibility in automation and process management capability. Most often its aim is to support agent productivity through desktop acceleration, application connectivity, workflow management. As such BPMS is part of the core IT tool set, to which adjustments outside of configurable parameters to a solution normally require a traditional IT change-program. Most often connectivity between applications, and design work on how applications should be integrated against business requirements is a key skill that is required to operate BPMS effectively.

Robotic Automation is principally aimed at clerical staff replacement as opposed to clerical staff acceleration as with BMPS. The philosophy of the approach is therefore to target routine, repetitive, rules-based tasks (procedures as sub-tasks within a larger business processes). Such tasks can often tie clerical staff down for long stretches of time. Very often such tasks are small, possibly involving 5-10 people, and so do not justify large IT, or even BPMS, projects to automate. The difference for robot automation is that no IT is required, and business users can “show” the robot what to do. The capability is therefore distributed to operations staff so as to divide-and-conquer many mid-to-small automation initiatives that would otherwise require people.

Comments

Popular posts from this blog

What is the difference between Elastic and Enterprise Redis w.r.t "Hybrid Query" capabilities

  We'll explore scenarios involving nested queries, aggregations, custom scoring, and hybrid queries that combine multiple search criteria. 1. Nested Queries ElasticSearch Example: ElasticSearch supports nested documents, which allows for querying on nested fields with complex conditions. Query: Find products where the product has a review with a rating of 5 and the review text contains "excellent". { "query": { "nested": { "path": "reviews", "query": { "bool": { "must": [ { "match": { "reviews.rating": 5 } }, { "match": { "reviews.text": "excellent" } } ] } } } } } Redis Limitation: Redis does not support nested documents natively. While you can store nested structures in JSON documents using the RedisJSON module, querying these nested structures with complex condi...

Training LLM model requires more GPU RAM than storing same LLM

Storing an LLM model and training the same model both require memory, but the memory requirements for training are typically higher than just storing the model. Let's dive into the details: Memory Requirement for Storing the Model: When you store an LLM model, you need to save the weights of the model parameters. Each parameter is typically represented by a 32-bit float (4 bytes). The memory requirement for storing the model weights is calculated by multiplying the number of parameters by 4 bytes. For example, if you have a model with 1 billion parameters, the memory requirement for storing the model weights alone would be 4 GB (4 bytes * 1 billion parameters). Memory Requirement for Training: During the training process, additional components use GPU memory in addition to the model weights. These components include optimizer states, gradients, activations, and temporary variables needed by the training process. These components can require additional memory beyond just storing th...

How are vector databases used?

  Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.