You probably read tens of articles on AI in business indicating numerous AI applications or exotic-sounding algorithms like deep learning or support vector machines. But you don’t know what you can do with AI for your own business today. We have a solution:
First, AI is a tool and writing in general about AI in business is like writing about computers in business. It helps to be a lot more specific so we will break down AI applications by industry and business function to give you an overview of what AI can achieve.
Everyone’s favorite villain after 2008, in reality, banks are more like a troubled child than a villain. In numerous markets, returns for a large number of banks trails below far less risky investments. However, this picture can change.
Banks are highly digitized, have the most skilled workforce in the market except pure tech players and already have experience automating their core capabilities. Some of the most exciting areas are:
Emerj claims that 316,810 complaints have been received by the Consumer Financial Protection Bureau (CFPB) in 2017. This corresponds to 39% of all complaints. While this is a prominent financial problem, companies can leverage AI to ensure an efficient debt collection process. In a case study, collectAI has improved Hanseatic Bank’s debt collection rate by 24%.
While banks already use complex models for credit decisions, increased availability of external data and new analytics approaches require rethinking models constantly. Using AI in this field would also help financial companies to reduce their costs.
ZestFinance claims to have helped Prestige Financial Services reduce lending losses and defaults without sacrificing credit approval ratings. According to this study, Prestige has reduced its credit losses by 33% and improved their borrower approval rates by 14%.
You can also read our AI credit scoring models article to gain more insights.
Fraud detection is a major challenge for merchants that accept electronic payments, acquirers that manage electronic payment networks and for banks that are exposed to various types of financial fraud including money laundering. AI agents can detect anomalies within companies’ systems and prevent these fraudulent behaviors and improve general regulatory compliance matters and workflows.
You can also read our related article to have more detailed information.
Through autonomous vehicles, the automotive industry is one of the most prominent areas where AI can impact. Owing to the incremental improvements in data-collecting sensors, edge compute resources and decision-making rules engines, autonomy is now within reach of the automotive industry.
From mining to manufacturing, self-driving cars/vehicles are increasing the efficiency and effectiveness of operations. With the recent improvements in image recognition and automation systems, the vehicles can perform longitudinal and lateral driving tasks and automate parking tasks.
As Tesla and Audi manufacture semi-autonomous vehicles today, they still require drivers to control. This technology rapidly continues to improve to reach a fully automated driving level. McKinsey predicts that roughly 15% of vehicles sold in 2030 will be fully autonomous.
Except for fully autonomous vehicles, AI can assist you while you drive your car. This assistance can include navigation services, reduced fuel consumption, and in-vehicle driver coaching tools. For example, Corrux Vision, a driving assistant startup, indicates that 90% of rear collisions can be avoided with the 1.5 seconds alert which its coaching tool provides.
Considering the transformative potential that AI could have on healthcare – both to optimize delivery systems and to reshape care practices – it’s no wonder there is so much buzz about the integration of AI tools in the industry. The healthcare industry can provide dramatic improvements in a wide range of areas with the support of AI tools.
According to 451 Research, patient monitoring is the most popular machine learning use case today at 45%. By applying AI to IoT medical devices, healthcare providers hope intelligent patient monitoring that will allow doctors to focus on the higher-level decision-making that improves patient outcomes and produces more personalized care.
Disease diagnosis is another area where AI will put its weight. Currently, medical professionals rely on their expertise and experience for diagnosis. However, AI can also suggest the best treatment based on the patient complaint and other data. It puts in place control mechanisms that detect and prevent possible diagnosis errors.
In complex cases, the diagnosis requires weighing several data points, or the data itself is more ambiguous. Using historical datasets to train models should lead to AI systems capable of making more accurate diagnoses by synthesizing more data points than a doctor or interpreting data with more accuracy.
Drug discovery solutions allow companies to develop innovative medicines in immuno-oncology, neuroscience, and rare diseases with high unmet need. Based on previous data and medical intelligence, AI tools can provide recommendations during research processes and support pharmaceutical companies to discover new drugs.
Human genes are still too complicated and there are lots of things to discover. AI can help doctors and scientists to understand genes and their components with lower research costs. It is also a powerful solution to predict the impact of gene edits while scientists work on editing genes for finding solutions to future diseases.
AI can support future mothers during pregnancy. It can monitor mother and fetus health to reduce mothers’ worries and enable early diagnosis. For example, Lucina Health claims that its AI solution can identify 85% of pregnant mothers within the first two trimesters.
For medical purposes, AI tools can provide advanced medical imaging to analyze and transform images and model possible situations. This helps the doctor to identify the medical situation more easily and accurately and improves the mortality rate in the end.
Human Resources (HR)
AI can be used for better insights into performance improvement and better candidate analysis. As companies get better in performance measurement, they can identify the problems more accurately and come up with more effective solutions. With that, AI can support companies to efficiently automate many back-office operations including reliable HR transactions and service delivery.
Companies can leverage to attract, screen, engage and hire the best candidates. After engaging with them, AI can save recruiters’ time by automating candidate sourcing and improve the hiring quality by standardizing job matching. This improves the employee quality within the companies. As a fact, Ideal states that AI recruiting increases company performance by 20% and saves 23 hours per hire.
Measuring employee performance is always a challenge in businesses. AI can be a solution as a better productivity measurement. Besides, companies can benefit from AI to predict which employees are likely to churn and improve their job satisfaction to retain them.
While retail apocalypse rages on, a new breed of retailers is raising from the bubble. Leveraging AI, digital and selective retail presences, brands are delighting customers.
Supply chain optimization
While Amazon is touted as a leader in automating supply chain management, other retail leaders are following. Optimizing inventory, markdowns, and logistics holistically, companies are reducing their supply chain-related costs.
As another example, Alibaba had benefited from AI algorithms to create more efficient delivery routes. This provided them with a 10%-decrease in-vehicle use and a 30% reduction in travel distances.
Companies can predict future sales with AI. This enables more accurate predictions and helps companies for better decision-making. With the improved accuracy, companies can reduce their inventory costs and improve their efficiency.
A houseware retailer in Australia had used AutoML tools to forecast the demand and adjust its prices accordingly. As the company had reach greater than 90% accurate forecasts, it achieved a 23% shelf gap reduction in its stores.
Self-checkout systems allow retail companies to serve customers in their physical stores without the need for cashiers. In these days, we witness AI-powered systems integrated into advanced sensors to identify purchased merchandise and charge customers automatically. This reduces waiting time and improves customer satisfaction significantly. You can read more about self-checkout from this article.
AI tools extract the data from the field, analyze the extracted data, and perform the required task during manufacturing. As these processes evolve, the machinery will inspect the whole production and be able to self-diagnose themselves. In the end, AI-powered processes perform continuous, faster, and cheaper production compared to human-based processes.
AI can be used to maintain machinery to minimize disruptions to operations. This would prevent unplanned downtime due to unexpected failure and the subsequent economic loss that it results in. Companies can also maximize their performance time, reduce energy consumption and strengthen the continuity of activities.
For example, Dragan Trivanovic, reliability manager in Mercer Celgar indicates that they have reduced catastrophic failures in their pulp mill from 50 to 4-5 per year after they introduced AI technologies to their manufacturing systems.
As AI techniques cover a greater portion of our lives, the product managers improve their capabilities during production processes. AI can assist designers in creating various unique products while maintaining the brand style and design integrity. It can co-create design solutions for a specific target and reduce the product design process drastically.
Telecommunication is a difficult field with stagnant average revenues per subscriber (ARPU) and constant need for network investment. For example, most telecommunication companies in the EU in the past 10 years have seen shrinking EBITDA-CAPEX values. This value is a good metric to evaluate investment heavy businesses like telecommunication.
While these companies are on the leading edge of delivering telecommunication technologies, their tech capabilities in other areas have lagged as network investments have been prioritized. This leads us to significant opportunities using AI in these areas:
While telecommunication companies always managed networks paying close attention to numerous network KPIs, the interaction between churn, upsell and network performance is quite complicated. An operator-specific model is needed to predict how that operator’s customers’ behaviors change with network performance. And only such a model can provide optimal investment guidelines.
Telecommunication operators, like other subscription service providers, have a monthly workflow of invoicing and collection. Most operators utilize multiple legacy systems in these processes that result in repetitive work that can be automated with solutions like RPA.
Infrastructure is an essential part of telecommunication companies to provide their services to their customers. AI can be used to analyze the infrastructure of certain areas and identify weaknesses for further improvement.
As usual, the pace of adoption of AI lags in the case of the public sector, where risk-taking is not rewarded. A 451 Research survey shows that only 40% of government respondents have plans to implement AI into government operations within the coming year.
According to 451 Research, 35% of government respondents indicate that they use AI for surveillance analysis. AI can strengthen video analysis and surveillance for governments. While humans need to monitor and analyze a handful of video feeds, AI can process and review an unlimited amount of video input and track people without missing anything out. It can also provide real-time insights from footages, which provides continuous security for the citizens.
Governments can implement AI technologies in transportation initiatives. With such implementations, AI can make recommendations for harmonizing public and private transportation and parking infrastructure. This will help mitigate urban congestion, reduce fuel consumption and commute time, and improve the quality of life for city residents.
This was a list of areas by industry where off-the-shelf AI solutions are available. However there are too many AI applications to list here. Companies can build custom AI solutions to solve their specific problems in areas where out-of-the-box solutions are not available. In cases where model performance is important, companies can get support from data science competition organizers to run competitions and build custom machine learning models for their specific business problems.
- To read about the current state of AI technology, feel free to read our related article.
- If you are curious about how AI will impact our lives in the future, take a look at this article.
- For general use cases of AI listed by business function, feel free to read our top AI use cases/applications article.
If problems specific to your business do not have off-the-shelf solutions, we can help you find the right partners to build custom AI solutions:
If you want to learn more about custom AI solutions, feel free to read our whitepaper on the topic:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
To stay up-to-date on B2B tech & accelerate your enterprise:Follow on
Next to Read
Your email address will not be published. All fields are required.