You probably read tens of articles on AI in business mentioning 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. Or just take the shortcut, go to appliedAI.com and choose the industry of your business and the business function you work in to identify all AI use cases for your business.
Understand how AI is shaping your industry
Though we are not covering every industry yet, you can find most major industries here. While we provide summaries below, you can find more detailed overviews of impact of AI in different industries on appliedAI’s industry section.
Everyone is talking about self-driving cars but other important use cases are
- Industrial analytics: As one of the largest manufacturing companies, automotive companies are spending billions on manufacturing. Even small improvements can be impactful.
- Predictive maintenance: Automotive companies manage the largest robot parks in the world. Predictive maintenance can help automative manufacturers reduce maintenance costs and minimize manufacturing disruptions
- Digital assistants: Though KITT and most James Bond movies feature talking smart cars, they are hard to come by in the real world. However, it is unlikely to continue like that as cars drive themselves and will need much higher level input from us such as feedback on speed or the address we want to get to.
For more, explore our automotive section including numerous use cases and case studies on the industry.
Everyone’s favorite villain after 2008, in reality banks are more like a troubled child than villain. In numerous markets, returns for a large number of banks trails below far less riskier 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:
- Sales: Predictive scoring systems: Banks have a large number of sales reps making calls to clients. Focusing them on the right clients can offer immense rewards. A bank was able to reduce its calls to upsell credit cards by 50% while retaining 95% of the sales.
- Loans: Predictive scoring systems: While banks already use complex models for credit decisions, increased availability of external data and new analytics approaches require rethinking models constantly
- Customer Service: Digital assistants can help banks improve self service and increase upsell.
- Operations: Robotic Process Automation (RPA) is a huge lever for banks with bank CEOs regularly boasting about how they are using it reduce menial work. While most RPA bots do not use AI or only solve basic problems like Optical Character Recognition (OCR), bots’ capabilities are evolving to include more complex tasks.
For more, explore our financial services section.
While retail apocalypse rages on, a new breed of retailers are raising from the bubble. Leveraging AI, digital and selective retail presences, brands are delighting customers.
- Marketing optimization: For retailers, marketing spend is one of the most significant spending items that can be optimized with relative ease. Using machine learning to personalize marketing communication, companies can improve the ratio of customers they reach at the right time, through the right channel with the right message and offer.
- 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 in a holistic manner, companies are reducing their supply chain related costs.
We have more details on these usecases in our retail section.
Telecom is a difficult business with stagnant average revenues per subscriber (ARPU) and constant need for network investment. For example, most telecom companies in EU in the past 10 years have seen shrinking EBITDA-CAPEX values which is a good metric to evaluate investment heavy businesses like telecom.
While telecom companies are on the leading edge of delivering telecom technologies, their tech capabilities in other areas have lagged behind as network investments have been prioritized, leading to significant opportunities for telcos using AI in these areas:
- Sales & marketing: As discussed in banking and retail, predictive scoring and AI powered marketing optimizations are crucial to improving both costs and revenues of telcos.
- Network management: While telcos 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.
- Operations: Telecom 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.
- Customer Service: Most inquiries by telecom customers are relatively simple inquiries that can be automated if such automation is permissible by regulation.
Understand how AI is shaping your business function
While it is interesting to know how different industries are being shaped by AI, it is also interesting to see how your business function is impacted by AI. Read our detailed & comprehensive list of AI use cases by different business functions to explore how AI can improve marketing, sales, customer service, operations, IT, analytics and other business functions.
If you have a question you couldn’t get answered here, feel free to reach out to us.