AI Strategy & Consulting
AI Strategy & Consulting

AI
Strategy & Consulting

Developing a data and AI Strategy is a starting point for the adoption and implementation of artificial intelligence, machine learning, or deep learning technologies within your organization. An AI Strategy defines your AI priorities, goals, milestones, mission and vision. An AI Strategy focuses on the AI implementation of technology goals while business strategy focuses on the execution of corporate goals.

CONSIDERATIONS TO A SUCCESSFUL AI STRATEGY

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BUSINESS STRATEGY

As a first step to defining your AI strategy, we need to create our business strategy. This entails understanding our stakeholders, our mission and vision, our priorities, key pain points, etc. Based on this we need to assess our current state and move to the next stage.

DATA STRATEGY

Most AI/ML solutions need data to provide actionable insights. At this stage we would like to understand the data we need, the current data maturity, volume of data, data storage, proprietary/ open-source or third party data. The ideal scenario would be able to set up a automated data pipeline which captured all needed data into secure, accessible and scalable suitable data stores.

 
 
 
 
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TECHNOLOGY MANAGEMENT

As a next stage we need to identify the technological infrastructure we would need. The consideration would involve the data source, the current state of technology, the ML Procedure, the implementation, short term and long term goals. The outcome at this stage will be the choice of cloud infrastructure, security of the system, scalability and the most important open source vs. proprietary.

IMPLEMENTATION

In the implementation phase we need to understand who are the consumers of our data and AI deployment. We need to decide if the results need to be streaming for example in trading or it needs to be in request-reply mode say a churn model for a or can be delivered in batch mode for example a financial recommendation system for UHNI. Another consideration is whether this will be consumed in a web based app or mobile app. The concurrent usage of the results is yet another driving factor behind implementation considerations

 
 
 
 
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