Identifying Technological Requirements Prior to AI Implementation

Kumar Srivastava, VP, Product and Strategy, Machine Learning, Artificial Intelligence, BNY Mellon
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Kumar Srivastava, VP, Product and Strategy, Machine Learning, Artificial Intelligence, BNY Mellon

Kumar Srivastava, VP, Product and Strategy, Machine Learning, Artificial Intelligence, BNY Mellon

The Artificial Intelligence (AI) hype is driven by the growing emergence of neural networks and deep learning capabilities that deliver real results in terms of voice enabled interfaces, natural language generation and virtual agents. The increased adoption of AI among customers can be attributed to their eagerness to seek advantage of this smart technology and the experience that accompanies it. Therefore, the impact of AI is perceived across various industries to satisfy this consumer-generated demand, while helping businesses tackle their pressing challenges.

Encompassing a vast domain of evolving technologies such as machine learning, robotics, and automation, the rapid development in AI technology is possible due to the increase in accessibility to computing power, quality data and mature algorithms. AI has enabled software implementations in the form of bots that have the ability to interact with applications to produce substantial results from repetitive tasks, saving time and labour. However, all enterprises should assess the requirements for AI in their products or services before employing this technology.

  All enterprises should understand the requirements for AI in their products or services before employing this trend  

Finding the Right Technology

In case of any technological integration, it is wiser to assume that success stories of other enterprises’ are not applicable to your own business. Instead of being distracted by the hype, non-tech companies should perform a thoughtful analysis of their key problems. The ideal solution is deducible once these problems are identified, allowing the organization to discover the technology necessary to make that solution functional. If the technology required to fix the key issues is AI, then it is crucial to remember that no AI solution can be built without customization. Out-of-the-box AI solutions are unable to work satisfactorily, as application capability is highly dependent on the specification of the data set used to create it. The AI solution provider should have the ability to customize their offering as per the enterprise’s data set, delivering apt solution for their specific problems.

AI Built on In-house Expertise

Alternatively, the AI solution could be built in-house. This way the staff would have a deeper understanding of the operating method of the solution, making it easier for them to address any issues regarding its performance. Furthermore, unlike generic AI solutions, customized ones focusing on a specific data set provide you with the necessary edge over your competitors.

During the AI integration, non-tech enterprises are required to find the underlying insights in the massive amount of data accumulated from various external and internal sources. Since data is their main value driver, unorganized data in the form of legacy data and application stacks lead to dealing with high workload within the organization. Hence, modernization of infrastructure in terms of data and application is crucial in creation and deployment of AI enabled applications.

To make the integration process easy, it is necessary to perform an audit of customer touch points. This will help the company identify the interaction points similar to existing consumer products. This provides the organization with the capability to modify their domestic interface with respect to the ones consumers are already comfortable with, for instance, Facebook and Twitter. The consequence is that the organization is able to recognize problems in their AI solution related to functionality, undelivered values, or customer dissatisfaction.

AI integration assists organizations to leverage information in the best possible way. The benefits of utilizing this data include predicting failures, problems or anomalies in the expected technological disruptions. From virtual personal assistants to online customer support, the impact of AI and machine learning is quite evident in our daily lives. Hence, it is beneficial for enterprises to act toward integrating this technology in their business processes as well as products and services. Indifference towards AI is not an option, as going beyond the role of productivity enhancer, AI holds the capability to transform the way organizations think, perform and grow.

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