It is important to note that things are changing very fast. With technology evolving at breakneck speed, we're likely to see most of the stated barriers being breached. AI has been one of the biggest buzzwords in the technology industry over the past few years, given its immense potential to transform our world. With more tasks being performed with AI, the enterprise adoption of this nascent technology is rapidly evolving. From business planning and forecasting to predictive maintenance and customer service, AI is now an intrinsic part of an enterprise ecosystem.
The potential of AI is limitless, but there are certain barriers holding traditional large enterprises back from embracing AI in a big way. These include factors such as the absence of a clear strategy, lack of data, skills shortage, and functional silos within the organisation. While a few companies have mapped out potential AI opportunities across their infrastructures, only a handful have a clear strategy in place for sourcing the data that enables AI work.
That said, data synthesis methodologies are now available to combat data challenges in AI. With the emergence of meta-learning techniques, AI is becoming far less data hungry. Aspects like explainability of AI, elimination of bias, and ethical use of AI need to be addressed for mainstream adoption of AI in the enterprise context.
As enterprises mitigate the challenges around AI adoption, we can see a plethora of new applications and use-cases opening up across industries:
In the financial services industry, AI can play a role in data extraction, data validation, breach detection, and customer risk profiling. AI finds its application in the banking sector for fraud detection, anti-money laundering, regulatory reporting, document extraction, payment reminder follow-ups, and real-time user authentication.
The insurance industry can benefit greatly from AI, especially in areas such as claim data extraction, claim management, regulatory compliance, risk evaluation, adjudication, and match to issued policy.
AI can help streamline distributed marketplaces, food auditing, inventory control, loyalty programs, procurement optimization, and drive supply chain traceability.
On the media and telecom front, AI can help significantly enhance network operations and improve fraud detection, predictive maintenance, and customer service.
In the services and utilities industries, AI can help achieve better load forecasting, demand management, predictive maintenance, energy trading, consumption insights, and analysis.
Industry agnostic enterprise processes
In addition to these industry-specific use cases, AI can ameliorate a plethora of use cases such as customer service, finance and accounting, HR, marketing and sales, and procurement. For instance, AI can help streamline customer enquiry routing, offer customer self-service support in the form of chatbots or voice assistants, and run customer feedback and surveys. That's not all, AI can support the HR team through resume screening, candidate profiling, performance management, and employee virtual assistant.
Enterprises can leverage AI to further enhance their marketing and sales function such as price optimisation, shelf audits, social media marketing, lead management, and customer data management. In procurement, AI can enable better demand forecasting, payment processing, goods receipt and confirmation, e-auctions, and contract management.
Once AI truly comes into its own, it could lead to gross GDP growth of around 26 per cent or $22 trillion by 2030. Much of this growth in revenue will be derived from automation of labour, which could add up to 11 per cent or around $9 trillion to global GDP by 2030.
Innovations in products and services could increase GDP by about 7 per cent or around $6 trillion by 2030. At full potential, AI can achieve impressive results while serving customers, improving business and efficiency metrics, scaling without adding headcount, and above all, providing the deep-level insights into an organisation's ocean of data.
By all indications, we will see the golden age of enterprise AI adoption in 2020 and beyond.(The author is Senior Vice President, Service Offering Head - Energy, Communications, Services and AI & Automation Services)