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.