Top 10 areas of application of AI in companies

von | 18 March 2022 | Top 10

We celebrate the 10th anniversary of [at] - Alexander Thamm in 2022.  

In 2012, we were the first consultancy in the German-speaking world to take up the cause of Data & AI. Today, we can say that artificial intelligence has the potential to make an important contribution to some of the major economic and social challenges of our time: AI plays a role in the energy transition and climate change, in autonomous driving, in the detection and treatment of diseases or pandemic control. AI increases the efficiency of production processes and increases the adaptability of companies to market changes through real-time information as well as predictions.  

The economic significance of the technology is growing rapidly. More than two thirds of German companies now use artificial intelligence and machine learning.  

With #AITOP10 we show you what's hot right now in the field of Data & AI. Our TOP10 lists present podcast highlights, industry-specific AI trends, AI experts, tool recommendations and much more. Here you get a broad cross-section of the Data & AI universe that has been driving us for 10 years now.  

Enjoy reading - and feel warmly invited to add to the list! 

The top 10 areas of application for AI in companies 

Artificial intelligence is now finding its way into all industries and is playing an increasingly important role at all levels of the value chain. As a result, the technology is constantly opening up new opportunities to increase the efficiency of processes and build new business models. While some companies are already using AI intensively, many are lagging behind in exploiting its potential. Yet countless use cases prove that artificial intelligence offers real added value for companies. 

Rank 10: Quality control 

Whether car, semiconductor, smartphone or beverage manufacturers, production quality and yield are two of the industry's key performance indicators. Poor production quality results in high scrap rates, lower yields and high additional financial costs. The use of artificial intelligence can make a significant contribution to improving production quality at various points in the production process. AI-based solutions provide more accurate analysis of production data and can detect slight changes in real time. For example, AI-based visual inspection of parts, such as semiconductor panels or painted car doors, can improve quality and reliably detect defects. Another problem is slowly changing production processes. Unlike humans, AI does not "get used to" such slow changes, but can detect them reliably. 

Rank 9: Financial planning and forecasting 

Data-driven and automated planning of future revenues, expenses and other business parameters is now standard for companies. With the help of artificial intelligence, KPIs can be predicted and analysed more accurately. AI-based software tools can detect compliance breaches in real time, perform market analysis and calculate and display key performance indicators. As a result, companies can plan and deploy resources more accurately and act more quickly when any irregularities occur. See also: https://www.alexanderthamm.com/de/case-studies/liquiditaetsprognose/ 

Rank 8: Supply chain optimisation 

Today's large companies are constantly facing new challenges with their own supply chains: Amazon has to track and monitor 1.5 billion products every day. Wal-Mart processes over one million customer transactions every hour. Here, a supply chain strategy must be developed and, above all, monitored. By linking warehouse, supplier and production data, an AI-monitored supply chain can meet shorter delivery times, avoid penalties and optimise efficiency. See also: https://www.alexanderthamm.com/de/case-studies/optimierung-des-supply-chain-management/ 

Rank 7: Increasing long-term customer loyalty 

Only a satisfied customer is a good customer. But what exactly does "satisfied customer" mean? With the help of AI, the entire customer journey can be comprehensively analysed and clear problem areas identified. Personalised product, content and service offers in particular play a central role here.  

AI makes it possible to better classify customers and make statements about their future purchasing behaviour. Predictive analytics, chatbots and personalised offers via recommender systems can retain more customers and increase the probability of repeat purchases. The most important factor in any purchase decision is price. Here, a smart balance between profit and the customer's willingness to buy can be achieved through the use of AI. Self-learning algorithms suggest optimised prices based on past transactions as well as competitor prices and thus help the customer make a positive purchase decision. 

6th place: Logistics optimisation 

Logistics with its widely ramified networks is an ideal field of application for artificial intelligence. Through the intelligent evaluation of a wide range of data sources, from sales figures to supplier prices to production cycles, future production and transport volumes can be forecast, for example. Event-based and dynamic route planning ensures greater efficiency in transport and saves money. But also the reading of freight and delivery documents and their automated integration into existing systems should be mentioned here. This speeds up processes and reduces the susceptibility to errors. AI-supported warehouse planning can also help minimise storage costs.  

Rank 5: Fraud detection 

Fraud is a major problem in all financial services transactions. In 2020 alone, a total of $32.39 billion was lost globally through payment fraud, such as wire transfer fraud, card fraud and credit fraud - and the figure is rising. AI models can detect and block fraudulent transactions in real time. The AI learns from past cases, among other things, and becomes more accurate with each detected case of fraud. See also: https://www.alexanderthamm.com/de/case-studies/betrugsprophylaxe-fuer-fahrzeugfinanzierungen/ 

Rank 4: Customer interactions 

Chatbots already provide faster and direct communication with customers in many companies. Advanced ML language models such as GPT-3 make it possible to understand and better answer complex questions. The start-up Algolia, for example, uses GPT-3 to analyse the context of customer questions and provide precise answers. In this way, resources can be optimised in the customer service area and customer enquiries can be prioritised. 

3rd place: Process automation 

Robotic Process Automation (RPA) supported by AI is the accelerator for any business process. Repetitive processes and tasks are due in many areas of a company. Through RPA, these can be automated using "software robots", leaving more time for the really important tasks. Intelligent process automation thus opens up new business and revenue opportunities and forced process excellence. See also: https://www.alexanderthamm.com/de/case-studies/automatisierte-sofortregulierung-fuer-kleinschaeden-mit-nlp/ 

Rank 2: Gaining customer insights and knowledge  

If you know your customers, you know what their needs are. Social media has radically changed consumers in recent years. AI-powered customer orientation enables companies to respond much faster and better to their customers' needs. Machine learning software helps to monitor, scan and collect data, for example on social media channels. This can provide real-time insight into social sentiment and customer needs, and marketing strategies can be adjusted effectively. See also: https://www.alexanderthamm.com/de/case-studies/analyse-von-finanzprodukten-und-kundenaktivitaeten/ 

Rank 1: Improving customer experience and loyalty  

Helping customers make a purchase decision through a product tailored to them is a top priority for any business. 75 per cent of the content consumed by Netflix customers and 35 per cent of the products purchased on Amazon are suggested content and products, respectively, like a McKinsey Report shows. AI offers opportunities to provide a dynamic customer experience. Through customised offers and information, the customer receives the right decision support at every touchpoint of their customer journey. Virtual assistants, predictive personalisation and AI-assisted customer needs analysis are creating a dramatic shift in decision-making. Read also: https://www.alexanderthamm.com/de/case-studies/personalisierte-rezeptempfehlungen/

These are possible areas of application in which we make companies more competitive with artificial intelligence - researched and curated with the help of the [at] experiences from over 1300 AI use cases. 

 
What is your insider tip for taking the next step with AI? Let us know and tell us about your experience in using artificial intelligence. 

<a href="https://www.alexanderthamm.com/en/blog/author/lukaslux/" target="_self">Luke Lux</a>

Luke Lux

Lukas Lux is a working student in the Customer & Strategy department at Alexander Thamm GmbH. In addition to his studies in Sales Engineering & Product Management with a focus on IT Engineering, he is concerned with the latest trends and technologies in the field of Data & AI and compiles them for you in cooperation with our [at]experts.

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