Different approach for organisational decision making
Artificial Intelligence is enabling complex data processing at a scale larger than ever. This has drastically affected the process of decision making in the enterprises. During last few years, the shift of approach of decision making is moving towards data- driven from traditional methods. With advent of machine learning driven insights, the decision-making processes are set to get enhanced to a level where human intervention will become obsolete.
Equipped with advanced processing capabilities, AI based systems may possess a noteworthy ability to make decisions which are on par with human reasoning and intelligence. Despite the enormous capabilities of AI based systems, decisions are still prone to human biases and errors. Factors such as behavioral traits, lack of clarity of business goals as well as implementation errors can cause inconsistencies in the enterprise level decision making.
Here are few considerations that should be made before implementing AI based decision mechanism.
-Considerations of Domain Specific workflows
The goal of AI based automation needs to be defined accurately as there are two distinct insights which can emerge: automation of manual tasks such as data-entry or insights needed to make a decision such as a medical practitioner may benefit from tips about interpreting patients’ reports. However due to busy schedule of most practitioners, such insights can be of use only when delivered real time during the actual consultation. An ecommerce or retail customer may benefit from personalized product offerings. Based on domain specific requirements, the nature and features of the AI system may greatly diverge.
-Data-readiness of an Organization
Backbone of AI is data. AI based systems usually utilize past data to form patterns and customizable models which can update continuously with addition of new data. Organizations need to be prepared to provide the necessary data, at required frequency.
Many organizations possess legacy IT infrastructure developed gradually since years. This is usually manifested in the form of unorganized data frameworks. Intercommunication between data systems is often a challenge. Sometimes, organizational structures can also hinder seamless exchange of data between various teams/verticals.
Overcoming these challenges usually involves effective strategy and long-term investment of human and technological resources.
-Solving Expertise Conundrum through Collaboration
AI consumes large chunks of data but the data in question needs to be the correct and relevant data. Getting the right data requires collective expertise in domain knowledge, business processes, data science as well as data engineering.
In addition to above set of expertise, integration of cloud services may also be required which can turn out as an overwhelming exercise filled with attitude shifts and restructuring technological framework.
Key factor in tackling these issues lies in collaborating with experienced service providers who possess extensive expertise in conceptualizing and Implementing AI systems. A skilled partner can drive an AI related transition with ease and expertise. Investment of huge amount of time, money and resources are required to get started on the AI path, but with applying proficient know-how, the transition becomes smooth with definite business growth.