Successful Digital Transformation strategy driven by Big Data and Artificial Intelligence (AI) in large enterprises

Developments in Artificial Intelligence (AI) mainly powered by advancements in Machine/Deep Learning in collaboration with data-driven technologies, pushed large digital enterprises toward becoming intelligent enterprises. Those companies either evolved alongside the technology itself or were themselves driving innovations and enabling technologies due to their internal growth needs, combined with the unavailability of solutions in the market. AI technologies are already revolutionizing not just how we perceive and do business, but also the business environment and its overall landscape. In this sense, I believe we can safely conclude that AI has come to be the greatest enabler for businesses undergoing digital transformation and that any digital transformation strategy should use AI as its major driver.

The question that arises is how legacy organizations or those that are not natively digital can adopt the AI revolution through a successful digital transformation strategy. The answer to this question is neither straightforward, nor can there be an immediate answer.  A recent NewVantage survey revealed that 77% of businesses reported that business adoption of Big Data and AI initiatives continues to represent a big challenge for business. Moreover, another survey from VentureBeat reported that 87% of AI projects never made it into production. Those numbers are very disappointing, considering that most big tech companies describe AI as the forth industrial revolution.

Α successful digital transformation presupposes that business leaders and executives of an organization have a vision and an understanding of how the world is changing and how their enterprise should evolve in response. This evolution must first of all be a top-down process into the organization based on that vision and all members have to embrace it. Secondly, and most important is the existence of a plan for AI adoption. In order to design such a strategy, businesses must understand what AI is, its complexity and most importantly that data is to AI “what food is to humans”.

The following graph is an excellent representation of that complexity by showing all the components that fall under the term AI and the degree to which data is related to AI. 

Ingesting, processing and analyzing Big Data is at the heart of AI. The following graph shows the five basic characteristics (5V) of Big Data. 

Large enterprises that produce huge amounts of data, need proper Big Data infrastructure and management tools. Unfortunately, most organizations are struggling to build a proper infrastructure and find it too complex to work with it.

Building a Big Data infrastructure is the first step of AI digital transformation, but not the only one. In order to achieve the true potential of AI, organizations must first understand the end-to-end process of an AI application, from development to production. The following graph, published by Google some years ago, gives a good visualization of all the different processes that must be completed in order for an AI project to be ready for production.

It is immediately understood that one person or one team cannot complete the whole process. An end-to-end AI solution needs different teams with different skills. Data engineers, data scientists, machine learning engineers, data analysts and business analysts must cooperate all together in order to have a successful result.

This is exactly the key element of a successful AI digital transformation strategy. Organizations should avoid building complex bid data infrastructures with silos between different teams and adopt a unified solution that will combine data engineering, data science and business intelligence. It must be a design of cutting edge technologies capable of:

·        High quality data storage and performance

·        Collaborative workspaces between different teams

·        Data governance and security

·        Be an easily scalable infrastructure

At the same time, such a design should provide all the functionality and tools within the common workspace to:

·        Build ETL data pipelines from all data storage systems of the organization

·        Give the ability to extract insights from data with Business Intelligence applications

·        Prepare data for use in AI models

·        Put those models from development to production easily

·        Monitor the whole process

·        Present and communicate the result of the AI models to the business users through interactive applications

Thankfully many big tech companies, especially the big cloud providers, have realized the need for a unified AI solution and have started proposing their own solutions in order to fill that gap. Now that technology is more mature, it is the ideal time for large enterprises to start embracing Big Data and AI in their digital transformation strategy.