Introduction :

Most organizations, from Manufacturing to Healthcare to Financial sectors have realized the importance of modern technology to scale up their operations. So all massive organizations are going through it's Digital Transformation journey post 2010 and make most of the latest technology. This involves transformation of broadly two key components:

1. The internal experience of the organization

2. The external experience of the organization

1. The internal experience talks about empowering the internal employees of the organization through modern tools and technologies so as to increase the overall business productivity.

2. The external experience focuses mainly on the reducing the overall time to market for the external products yet keeping quality as the top priority and improving the quality of the client services provided by the organization.

On a technology aspect, both internal and external experience depends on use of modern hardware, software and IT infrastructure. But since such a major breakthrough can not be always smooth for all organizations, the main question is, what can possibility go wrong in such a journey, considering time as a constraint since all organizations would want to reach there as quickly as possible?

The major issue is related to the decisions that the organization takes. Decision about the overall budget to invest, decision about moving certain set of operations to the cloud, decisions about the security, decisions about the infrastructure cost, decisions about the ideal team size and etc.

But all these decisions can be made more impactful when these are backed by data and supported by artificial intelligence.


In this session, I would talk about how data science and AI can help such organizations take such critical decisions to improve user experience and scale their operations.

In the session I would talk about certain use cases in which most companies end up taking wrong decisions while in the process of digital transformation and eventually end up taking a step back.

Use Case 1: Move operations to the cloud for scaling up existing operations

When we talk about digital transformation, moving some of the existing infrastructure to cloud or automating existing operations and managing operations remotely with the help of cloud platforms is a very common strategy. But the question is what can possibly go wrong in such cases?

Although most cloud platforms provide pay as you use subscription models, but what the users really fail to estimate "what to use" ? The most probable decisions that go wrong is the "SKU" of the cloud resources, like what is the most optimal specifications of the cloud based resources. For example, if I am working on a machine learning algorithm on a cloud based VM, I won't be using a GPU and instead a CPU core will be sufficient and hence saving a lot of cost. Hence I propose for an AI system that will help the organization take such decisions and plan their budget accordingly.

To start with, the user interface can be a simple chat-bot, which will ask the user about his requirements. Using Natural Language Processing (NLP) from the user requirements and using certain ML algorithms ( it is based on the complexity of the problem, and can be as simple as a decision tree ) the user will get the exact specifications and the suggestion about which cloud platform provider to go for, considering the cost in the long run. The system will also help to estimate, that if they go for such a cloud based approach, will it actually help the organization to improve their productivity and operations or not.

Use Case 2: Planning the hiring strategy and finding the optimal team size

Hiring strategy is something, which I have felt from my experience, that the companies often go wrong. If the organizations hire more people than required, in the long run, it can be a huge problem, both for the employees as well as the employers. In such a case, for the employees , they will get lesser opportunities to learn and grow and work on new and challenging products and services, which indirectly impacts their job satisfaction. If the hiring strength is lesser than what is required, it results in slower throughput, lower output and more work pressure for the employees.

Now, how can the proposed AI system help organizations for such a use case?

I believe the recruiting strategy has alot to do with the vision and mission of the company, along with capability of the organization to invest. So, the proposed system can have a similar chat-bot like UI to make these requirements, like to what scale the company wants to increase it's operations and how many end-point users that the company would like to reach.

Now, employee churn rate, average employment period, geo-location and average employee salary depends on the specific job role. So, based on this parameter, the system can come up with a predictive model to tell what should be the number of people hired for a particular job role for the next two years based on the organization's demand and recruitment.

Use Case 3: Securing the existing technology

Every organization from Manufacturing, to supply chain, to typical software companies, invest a lot on security, may it be cyber security, may it be employee safety and security or it can be related to securing the infrastructures. Like the other two use cases, the proposed system can help to come up with better options in choosing the right specifications and investment budget for improving the overall security and detect and report anomaly whenever possible.

So, these three use cases can be three key areas in which artificial intelligence backed by relevant data can help organizations to complete their journey of digital transformation very easily. There are plenty of other use cases as well, but from my experience the above three use cases are the major ones in which organizations struggle the most.

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Outline/Structure of the Talk

Introduction : Discussion on the problem statement

Objectives : Discussion on the target that can be achieved


Social Impact

Sustainability and Future Scope

AI for ALL

Learning Outcome

The audience is expected to receive overview on the following topic:

1. Use of AI and data science for structuring investment budget plans

2. Use of AI and data science for coming up with a better recruitment strategy

3. Use of AI and data science in improving the overall security of an organization

4. NLP based chat-bots

5. Predictive analysis on structured data

Target Audience

Any one interested in Data Science and AI

Prerequisites for Attendees

  1. Basic knowledge on statistics
  2. Basic knowledge on Machine Learning
  3. Basic Knowledge on Deep Learning
schedule Submitted 1 week ago

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