Fintech and retail lead the fray in AI adoption by industry

Early AI adopters

Compared to other industries, the finance industry jumped quickly to finding value with artificial intelligence. Currently, AI is being used in a variety of ways within the financial services industry. The most prominent use case for AI is in fraud and anomaly detection. When fraud occurs, financial firms end up covering fraud prevention services for the impacted victims. In addition to having to manage funds lost through fraud, financial institutions often find themselves tangled in a variety of other issues pertaining to the loss of reputation. Through the use of machine learning's innate ability to spot patterns, and anomalies to those patterns, the use of AI is greatly helping to detect fraud as soon as it occurs -- and sometimes even while it is occurring.

The manufacturing sector is also seeing great efficiency from AI. AI-powered bots are becoming ubiquitous on factory floors, and computer vision and machine learning software implementations are improving quality control, safety and maintenance of manufacturing processes. AI-enabled predictive maintenance systems are able to self-monitor and report manufacturing issues in real time, preventing costly downtime and curbing delays in production. Additionally, AI is helping to increase workflow as well as supplement the manufacturing skills gap. AI systems can easily lower the production cost for companies by augmenting the current human workforce or significantly lessening the need for human employees.

From clothing design, to forecasting demands for future seasons, to the manufacturing and transport of goods, retailers are seeing some huge gains in AI collaboration. For customers, AI is enhancing the clothing buying experience through hyperpersonalization. AI-enabled shopping apps allow customers to take a screenshot of clothes, identify apparel and accessories in the photo, and then find the same outfit and shop similar product styles and looks.

Another industry that is seeing early success with AI is transportation. Many transportation companies are deploying AI around autonomous vehicles. Intelligent, self-driving systems have already been implemented in trains, boats, and aircraft. Companies such as Tesla, Ford, Toyota, Uber, Volvo and others have been working very heavily toward creating autonomous cars for consumers.

Pharma and healthcare warming up to AI

While many industries have already dived deep into realizing value from AI, some industries are just now warming up to the value that AI can bring their organizations. In particular, both the healthcare and pharmaceutical industries are now implementing AI and machine learning to valuable use. While it might seem likely that any data-intensive STEM field would be quick to jump on using AI, there are many factors to consider when it comes to actual adoption.

Some industries are more likely to adopt new technologies due to already being accustomed to rapid technology change. However, healthcare and pharma industry companies are notably much more cautious when it comes to taking risks. Pharma has been wary of AI as there is nothing that can be done to counteract a loss of life as a result of an AI mistake --  if an algorithm provided bad data to support drug discovery and clinical trials work over decades, it's a loss of money, many years of R&D development and loss of life. This high-stakes gamble is what has big pharma slowly edging into the AI race rather than jumping in headfirst.

Despite these concerns, some areas of pharma are beginning to embrace AI. Currently, GlaxoSmithKline, Pfizer and others are using machine learning for drug discovery and development. Additionally, Microsoft and AstraZeneca recently announced an AI collaboration for the benefit of healthcare patients. When used in as augmented intelligence with humans in the loop, AI and ML systems can show value to the pharmaceutical industry.

Moving forward with AI use cases

Though most industries are adopting AI in some capacity, that doesn't mean every company in the industry is taking advantage of AI. The biggest inhibitors when it comes to AI implementation are high initial cost and a lack of access to talent. While AI might be capable of improving a process and lowering costs, there is still a big initial investment that must be considered. When you factor in the relative newness of this technology, some companies are holding back for the time being.

Notably, the energy and utilities sector has been somewhat slow to adopt AI and machine learning broadly. So too have the construction and real estate industries. While the leaders in those markets are making big investments in AI to gain advantage over their competitors, as a whole, those industries are adopting a wait-and-see posture with regards to AI investment.

As we move towards a world where the differences in AI adoption by industry are negligent, we will begin to see widespread and equally strong adoption of AI across all sectors. The more use cases that we continue to find and the more that industries continue to invest, the more widespread AI will become. The key to widespread adoption is continued efforts to reduce the risk and cost for AI implementation, as well as simplifying tools and technology to enable organizations that don't have big seed money or talent to be able to achieve the same outcomes as leaders in their markets.