Today, AI to most businesses is a technology that aids in the automation of tasks and augments decision making by providing insights to key leaders and decision takers. There has been a rise in the use of AI technologies for data-driven decision making for numerous business goals revolving around customer experience and operational efficiency. For instance, profiling of customers for personalized marketing campaigns, faster and accurate detection of tumors in millions of EMRs, efficient maintenance of inventory and manufacturing plants, early detection of defaulters of loans, and development of smart cities and smarter infrastructures.
In all of these use cases, the role that insights play is crucial; be it in better campaign designs, quicker and timely detection of tumors to prevent late-stage complexities, huge amount of savings due to smart plants, or connected citizens for better citizen services. Companies are trying to become insights-driven organizations that can think better and act faster. Two technologies – Cloud and AI have risen to the fore as the go-to technologies for such organizational transformation.
Insights-driven organizations ‘Sense’ data from disparate data sources and assimilate that data into valuable data sets. They ‘Think’ and contextualize the problem statement to derive an understanding of the environment. They then ‘Respond’ to the business problem with reports, actionable insights and recommendations, automatic interventions and finally, ‘Learn’ to optimize the entire journey based on feedback from the various units along the journey and the end-user reactions to the response provided. Consequently, by preparing the right kind of data and then driving insights through technology, these organizations are transformed into intelligent enterprises that can function autonomously or semi-autonomously for better benefits to key tracks such as customer experience, operational efficiency, governance and trust.
While for many people, AI is playing the role of an enigma that creates magic with everything it touches and promises an intelligent insights-driven solution, it is important to note that companies must work on certain pre-requisites and competencies for AI to fulfil this promise. The reality is that AI is one foundational element of the enterprise transformation journey apart from other elements such as data, talent, or ecosystem. It is imperative to address many of these elements, pre-requisites or competencies for successful AI implementations.
Barriers for successful AI
The adoption of AI has seen growth largely due to its ability to automate repeatable tasks and to process information of massive volumes in a matter of seconds but organizations have not quite grasped how to implement AI successfully for enterprise-wide effects. AI is comparable to a machine that needs maintenance at certain time intervals and has every reason to fail. The reasons for slow or unsuccessful AI implementations range from obvious to the ignored. Four key reasons are -
Understanding these reasons for failure, and bringing together several components of Data and Model Management requires a well-thought AI Strategy and Journey blueprint. The AI Journey blueprint will provide a high-level view of steps that an organization experimenting with AI can follow as a checklist to ensure all competencies needed for AI to fulfil the enterprise transformation promise.
What steps must an organization follow for successful AI implementation?
Organizations must develop an AI Journey blueprint that will call out the approach that they would follow for solution development. The journey blueprint could include a variant of the below steps that is best suited to that particular organization.
Succeeding with an AI Journey blueprint
The challenge for business leaders is to ensure that the business has the right strategy to enable and support AI capabilities along with the right infrastructure to support AI implementation. Both AI innovators and adopters need to develop and use AI technologies in their business processes, as it will enable businesses to work smarter and faster.
AI blueprint is a template for businesses that allows them to systematically create the right infrastructure and ecosystem to build, train, and deploy machine-learning models for use cases while minimizing side effects on existing processes. While data is one of the most valuable assets for a business, when coupled with the power of AI, it can offer organizations a unique competitive advantage via AI-driven analytics.
The approach AI blueprint advocates is to observe, learn and experiment with AI, evaluate benefits and build core processes to scale up and drive efficiency – fuel AI engine with innovative pipeline, measure the impact, and, extend the scope as and when organizations mature for further transformation and thus, enable new offerings and change landscape.
Managing Consultant, Data Analytics & AI Consulting, Wipro
Saurabh is a data scientist involved in driving business value by using AI/ML techniques in diverse industry use cases. He is passionate about solving real business problems using data science and AI/ML techniques. Saurabh holds Professional Doctorate in Engineering from TU/e, Netherlands and Masters of Technology from IIT Kanpur, India.
Sravya Bharani M
Senior Consultant for Strategy & Planning, Data, Analytics & AI, Wipro
Sravya provides advisory and thought leadership strategies with an understanding of the digital and analytics industry including market view, competition landscape and related ecosystems. She holds a Master’s in Business Administration from Indian Institute of Technology (Madras) and B.E. in Electronics & Communication Engineering.