We often hear about AI in consumer applications such as Alexa voice service (Natural Language Processing), Netflix recommendation engine (Machine Learning), and Facebook Facial Recognition (Deep Learning). However, we don’t hear enough about enterprise AI applications. Mckinsey Global Institute had predicted in 2018 that AI will transform the enterprise world. Today AI is generating tremendous cost savings and improving business operations across several industries. AI’s significance and impact will get even more dramatic in the future. In this two-part blog series, we look at the top industries where enterprise AI applications are being deployed and how AI is adding value.
Let’s start with the top four industries and enterprise applications where AI has moved from PoC to production at scale:
- Banking: Banks are constantly facing pressure from competitors, growing governance, and regulatory requirements. The banking industry must manage financial and operational risk, prevent fraud, reduce customer defaults while keeping costs under control. To overcome these challenges, banks are leveraging the power of AI across the entire value chain – retail banking services, middle, and back-office to improve customer experience, detect suspicious activity/fraud, and optimize underwriting respectively. Leading banks are using AI to process large volumes of data, automating decision making to prevent financial crime, and leveraging algorithm-driven trading at large investment banks. A common machine learning use case for banks is predicting which customers are best suited for first-time mortgage loans and automatically coming up with ideal product offerings based on the historical data. You can learn more about how one of the largest Japanese banking giant leveraged AutoML solution here.
- Financial Services: AI in the global Fintech market is projected to reach $22.6 Billion by 2025. AI and ML are accelerating the digital revolution and improving the Fintech industry by enabling the ecosystem players to act on real-time information, improving accuracy, and reducing risks. AI is increasingly being used these days in credit risk assessment, automated loan workflow, underwriting and debt collection, etc. Predictive analytics models are assisting fintech customers to identify use cases that can deliver the highest potential value to their organizations. Many customers are using AI to get accurate cash flow projections, liquidity management, payment processing completely changing corporate treasury management.
- Healthcare: The healthcare industry is facing a multitude of challenges on multiple fronts – the ever-increasing cost of healthcare services, demand for better patient experience, talent shortages, regulatory and compliance issues, integrating the latest in medicinal and technological advancements without compromising drug efficacy and safety, etc. In fact, a growing concern is the use of a reactive approach in healthcare due to a lack of insurance coverage of preventative care. That’s why AI has the potential to transform the healthcare industry. From processing the wealth of data from clinical trials to leveraging patient data in order to improve decision making, AI is delivering a huge impact on healthcare. According to a recent McKinsey AI in healthcare report, diagnostics and clinical decision making are the top applications of AI in healthcare today:
- Industry 4.0: Industrial companies are enhancing their time series analysis capabilities and the trend of applying machine learning to time series analysis is gathering momentum. Analytics professionals at Manufacturing, Energy, and Oil & Gas are realizing the limitations of traditional statistical techniques – parametric (static) models, inability to handle multivariate input, and poor prediction capability. Industrial practitioners and SME’s are deploying AutoML solutions to address use cases such as monitoring quality, predictive maintenance, and inventory & supply chain optimization.
Edge analytics is increasingly becoming important for industrial companies. For use cases where data needs to be analyzed in real-time to drive decisions, it makes sense to perform data processing at the edge of the network near the source of data. The manufacturing industry has several use cases where processing data in the cloud is challenging from a latency, cost, and bandwidth perspective. In this scenario, you need streaming analytics at the edge. However, cloud-only or edge-only approach is not ideal and end-users should think about an edge-to-cloud integration and choose the architecture that best meets their requirements.
We will discuss the remaining four verticals and applications in part 2 of this blog, scheduled for publication on July 23rd, 2020. Stay tuned!