Leveraging Applied AI to Sustainable Business Growth

With exciting solutions in critical business operations, AI is gaining momentum and observing considerable company adoption to accelerate business outcomes.

FREMONT, CA: Applied AI has been gaining traction in the market in recent times. It is moving away from the laboratory and getting integrated into business processes. AI has provided significant solutions for crucial business processes like talent acquisition, cybersecurity, market prediction, customer needs analysis and more. It is revolutionising industries and solving real-world challenges at a higher scale. Artificial intelligence is widely recognised as the greatest modern-day enabler for businesses worldwide, allowing organisations to automate minor tasks requiring manual labour intervention. This helps the workforce to focus on business-critical tasks that drive optimised outcomes.

In the customer services, lending, and credit industries, AI offers various applications with several use cases. Optimising AI to identify borrowers will become a mainstream practice for lending customer services. Banks and credit industry players are also harnessing AI to amass the digital footprints of people to recognise potential borrowers. AI can also help lenders find customers' creditworthiness and reduce default risks. Thus, banks and lending businesses can alleviate the overall financial threat by leveraging AI.

Irrespective of the scale of their operations, any organisation can use AI models effectively to derive sustainable business outcomes. However, there are certain preconditions for manoeuvring AI. This includes AI alignment across organisations and their different departments, acquiring quality data from reliable sources, identifying techniques, matrices, and algorithms to model data effectively, and building an agile culture to cope with the changing AI landscape.

Ensuring the appropriate amount of AI alignment across companies, obtaining data engineers and data science specialists through external recruitment or internal mobility who can introduce quality data facilities for organisations, leveraging the right tactics and algorithms to model the data, and bringing people together to improve the AI adoption agenda will accelerate AI adoption within the enterprise. Although there is a serious lack of technical expertise needed to understand AI, businesses should nevertheless use it because technological advances have made complex AI and ML algorithms far more interpretable and flexible.

To make responsible and ethical use of AI, every AI project must run through system checks that scrutinise whether the initiative is socially beneficial and examine if it creates or reinforces any unfair biases. Companies have instituted several normative principles that define the AI applications or projects businesses will achieve and the ones they will abandon. A few broad dimensions that will determine and drive projects under responsible and ethical AI applications include safety, security, transparency, robusticity, and explainability.

With the steady growth of global AI adoption, artificial intelligence is expected to emerge as the fundamental driver of world GDP growth in the near future. The world will witness incredible AI and ML innovations, accelerating the adoption of contemporary technologies. Existing data assets can be processed by optimising advanced analytics and AI tools to build actionable insights and enhance business results.

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