Bringing AI into your Enterprise


This blog is authored by Piyush Chowhan, VP and CIO, Arvind Lifestyle Brands. He is a speaker at the Nasscom Big Data and Analytics Summit

Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. AI is real and the early implementers are already tasting success but the adoption has predominantly been seen success in the digital native companies while other large organizations are still just doing POC’s to understand the use cases. There are inherent challenges which any large organizations will face while democratizing AI and it’s important that a clear cut strategy is drawn which can help them adopt it to get the full benefit. This article will help put a very simple high level construct to evolve a Framework / Roadmap for adoption of AI in your organization.


Identify the opportunity for AI – It’s quite common for CXO to get lost in the noise around AI to identify the correct use cases. A simple framework can help identify clear use cases around AI are:

  • Identify – Is this task for application data driven? – AI problems will work only when task are data driven. If you would like to find out how customers will perceive a new product launch.
  • Assess – Is the data available? – It’s no fun applying AI to problems for which data is not readily available. For e.g. If the data is not available in a data lake / WH then it would not make sense to apply AI since the results may not be effective. Also use of IOT bases solutions should be good use cases for AI application.
  • Measure – Is problem to be solved at scale? – The application of AI will be relevant if the scale of problem is big. For e.g. if a small team of 4-5 analysts are looking a sales data and creating forecast it may not be very effective to apply AI to solve the problem. The idea would be get the use of that forecast and solve that problem.


A simple 3-point assessment as mentioned above will help in identification of appropriate use case for application of AI by the business.


Build vs Buy – This is a question which organizations are always wondering and this is quite relevant for AI. There might not be a straight answer either side and hence it must be a middle path which needs to be adopted. AI solutions have three core elements i.e. sensing, Analyzing, and Proposing. Sensing is all about creating large IOT / Big Data Platform which curates and stores large amount of relevant data. This data needs to be analyzed at scale and in real time in most of the real life AI solutions. These may require large scale implementation of Speech / NLP / Vision recognition technologies which would be easier to buy rather to build as these maybe platform services in the days to come. The real differentiation would be in the proposing where the use case needs to be enterprise specific. The use of the sensing and analyzing of this large data would be where the real benefits for the org lie hence it would be wise for enterprises to work on building in-house capability to identify those competencies to apply these data to the correct AI use-case. Use freely available open source software to quickly develop solutions. Google, Microsoft, Facebook, Amazon and Yahoo have all released open source machine learning or deep learning algorithm libraries.



Pre-requisites for starting AI – It would be essential that you are looking at application of AI only after proper modernization of necessary Technology landscape. A few pointers for the same are:

  1. There should be a Service Based Architecture available for easy access the various data elements to be used by AI engine.
  2. The data should be clean and accessible in Data lake / non-relational database.
  3. The organization should have built basic capability in IT / Business Team to understand AI / Machine Learning algorithm.
  4. Build necessary highly scalable infrastructure on-prem or cloud for application of AI solutions. Look at GPU / TPU infra structure on cloud for the best use case.
  5. Ensure that Data Security and Protection is covered before you venture into large scale AI. It would disastrous to control large scale solutions once basic security policies are not in place.
  6. The organization should have adopted agile ways of working and there is enough appreciation of design thinking and modern ways of working in the organization.
  7. The enterprise should also be working on small agile projects which are not very duration for assessing early success and / or failure of implementation to course correct.


There is no perfect algorithm available but it needs to be built for each enterprise and its use case. Hence don’t look at perfect solution on day one. Being the AI journey and take small steps to evolve your AI solution to reap the real benefits.


In the coming years, artificial intelligence will change the way we interact with our colleagues, family and the world around us. We will expand our capabilities and understanding of the way we interact with others. AI will drive growth for the companies that embrace this new change. In this new AI era, we will be able to automate processes that will allow our associates to embrace new challenges while freeing them from time-consuming repeatable tasks. By bringing together AI and the world of digital, we can connect and expand the capabilities of entire industries to push human knowledge to previously unknown heights.


Happy AI – Piyush Chowhan


The Nasscom Big Data and Analytics Summit will touch on all the above and much more in detail.

About the Author

Piyush Chowhan

VP and CIO, Arvind Lifestyle Brands

As the CIO for Arvind Brands, he is responsible for IT strategy and execution of technology for all its brands business.

He possesses a strong domain knowledge in retail, e-commerce and supply chain management while working for global retailers like WalmartTargetCircuit CityTescoBest Buy etc. He has set up and managed competency centers/ teams for retail and supply chain as shared service or captive units.

Piyush Chowhan has strong expertise in Data Analytics, Business Intelligence, Customer Relationship Management and IT business strategy. With various publications under his name, Piyush is also proficient in Program Management, P&L Management, Business Consulting, PMO setup, ERP Implementation.


He has been featuring in many IT related events and published his views in many magazines like ETCIO, AIM

Piyush Kumar Chowhan has an MBA in Finance and Operations from Xavier Institute of Management (XIMB), Bhubaneswar. 


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