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Carl Bowen
Blog
July 23, 2020

Top Industries Where AI Applications Are Creating Value (Part 2)

According to a recent Adweek survey, two-thirds of business executives say COVID-19 hasn’t slowed AI projects. Some 40% said that the pandemic even accelerated their efforts. While the economic activity and business sentiment has deteriorated over the past couple of months, the scope of AI has expanded, the biggest impetus being decreasing costs, improving performance, and increasing efficiencies. So which industry verticals are embracing AI and what are the top applications? In the previous post of this two-part blog series, we discussed how AI is transforming industries, enhancing performance across a wide range of applications in Banking, Fintech, Healthcare, and Industry 4.0.
In this final part, we look at the remaining industry verticals along with top enterprise applications:
Insurance: Mckinsey’s latest research report on the Insurance industry noted that in the wake of the global pandemic, the insurers should invest in digital and analytics capabilities to make them more customer-centric, simple, tech-driven, and competitive. The article outlined seven crucial digital and analytics imperatives and underscored the importance of AI-driven capabilities across the industry value chain. The insurance sector is gradually evolving and some of the biggest names in the industry are adopting AI. From Fraud detection using ML, chatbots with natural language processing capabilities to automated claims management, AI is reducing risk and improving operational effectiveness in this highly regulated industry. Our insurance customers have utilized dotData AutoML platform to effectively analyze customer data, predict needs, and recommend relevant services. We recently helped one of the largest Insurance customers improve the conversion rate by 250% by deploying hundreds of AI models. You can learn more about our insurance offering here.
Retail & Consumer Packaged Goods (CPG): The retail and CPG world has witnessed a profound change with increasing customer demand for personalized, engaging shopping experience, shortened product cycles, and competition from e-commerce platforms. And yet according to report on the Future of Retail from Bain, the world’s 10 largest traditional retailers are spending a much smaller proportion of their revenues on IT than Amazon, which views digital tools, data analytics and other technology as core to its mission to get ever closer to customers:

What can retailers do to better understand their customers, reduce complaints and decrease churn? As the Bain report highlighted, the solution to win and retain traffic, both online and in-store lies in predictive analytics and automation. As few savvy retailers have done, others must become proficient at using AI and ML to effectively forecast demand, streamline supply chain and manage operational efficiency. Whether it’s building recommendation engines, performing market basket analysis, optimizing pricing, leveraging AI and machine learning in the world of retail is becoming critical to maintaining market leadership.
Pharma & Life Sciences: AI has a huge potential in the Pharmaceutical and Life Sciences industry. IDC recently surveyed 120 pharmaceutical and biotech leaders about technology and data in their business. 94% of leaders said the ability to easily access, use, and apply advanced analytics and AI to data from across functional areas was important to achieving business strategy. AI can unleash a revolution in the pharmaceutical industry in multiple ways. AI can accelerate the time required to bring a drug to market and cut the costs of drug development by about 30%. Biopharma companies, by leveraging AI in the R&D process can fundamentally change the way research is conducted. Advanced analytics and end-to-end automation of R&D can dramatically reduce timelines. It should come as no surprise that today several pharmaceutical giants are using AI to develop better drugs and find faster ways for effective treatment by predicting treatment results.
Other Industries: Several other industries are increasingly using AI and ML in core operations to reduce operating costs, accelerate product development and enhance human performance. Predictive maintenance in the Utilities, Energy, and Power industry is becoming more common. Edge analytics with real-time processing capabilities for remote wind turbines and pipelines monitoring in midstream Oil & gas operations is delivering promising results. And technology vendors and distributors of electrical products are leveraging AutoML to predict inventory and delivery lead times.
Based on projections from Mckinsey Analytics, the potential total annual value of AI & Analytics across industries ranges between $9.5T to $15.4T. Clearly the best days of advanced analytics and AI are ahead of us.

Carl Bowen
Blog
July 16, 2020

Top Industries Where AI Applications Are Creating Value (Part 1)

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!