Increased efficiency through Artificial Intelligence

Introduction to Artificial Intelligence

Artificial intelligence (AI) is one of the fastest growing and most talked about technologies at the moment. The current global AI market, estimated at more than $136 billion, is expected to increase significantly over the next seven years and is expected to grow more than 13-fold to an impressive $299.64 billion by 2026. This growth trend will continue with an impressive compound annual growth rate (CAGR) of 38.1 % until 2030.

Why is AI so popular? AI is transforming businesses by making better and faster decisions. It works with predictive modelling and provides arguments for fast decision making. AI takes into account a large amount of customer comments and reviews that were previously difficult to analyse and can transform a business regardless of industry or company size.

The different types of AI solutions range from very simple and inexpensive to very complex:

• Cognitive computing processes the data input but does not draw human-like conclusions. It is often used in data analysis and is the “cheapest” solution
• Computer vision is an area of artificial intelligence that enables computers to interpret and understand visual information from the real world, similar to how humans perceive and analyse images and videos
• Deep learning uses existing external neural networks with multiple layers (deep neural networks) to process and learn from data, allowing the model to automatically extract complex features and patterns from complex data sets

Figure 1: Main characteristics of AI types (1/2)

• Neural network is a computational model inspired by the structure and functioning of the human brain. It learns patterns and relationships in data through training
• Generative AI involves creating or generating new content such as images, text, music or even entire scenarios using algorithms and models. It is based on limited data pools. The learning processes of Generative AI are very costly and time-consuming
• Machine learning includes various techniques for recognising patterns or predicting and extracting insights from large data sets. It requires a large amount of well-prepared data

Figure 2: Main characteristics of AI types (2/2)

The main focus of AI use is to optimise routine tasks and increase efficiency in business operations. Organisations should clearly understand why they need AI and what outcome they want to achieve with it. It is crucial that each organisation defines its business goals and understands the capabilities of AI in this direction. According to recent surveys, AI has already gained a reputation for increasing business efficiency, bringing economic benefits and being instrumental in customer engagement and profitability.

Industry landscape

The introduction of artificial intelligence is taking place at different speeds within different industries. Industries that rely heavily on data – such as banking, healthcare, telecommunications and the media – have been processing big data for a long time, which forms a solid basis for AI products. There is intense competition for solutions in these industries.

There are 4 groups of industries categorised according to the level of AI adoption and the diversity of their applications (Strong and Early Digital Adopters, Very Strong AI Adopters, Moderate AI Adopters, Laggards).

Figure 3: Industry landscape in relation to the presence of AI

• Strong and early digital adopters are pioneers in the development and creation of AI solutions. Their business strategy is fundamentally linked to AI in all operational processes. The range of use cases in this group is the broadest
• Moderate AI users strive for transparency to improve their information procurement and search and use AI selectively in operational workflows
• Very strong AI users: These industry representatives integrate AI deeply into their workflows and are very dependent on AI in decision-making processes. They are looking for more productive and efficient AI solutions, but do not invest much in developing their own products, instead increasingly favouring the outsourcing of AI
• Laggards: The comparatively slow introduction of AI is a result of the complexity of the industry and its resistance to change. But progress is underway. In some of the laggard industries, limited technical familiarity, cost considerations and more traditional approaches (e.g. in certain areas of construction) may lead to a rather hesitant introduction of AI

Figure 4: AI maturity curve and the most common use cases of AI solutions

Impact on the value chain

Artificial intelligence (AI) is changing the value chain of companies in all sectors. It is reshaping the way companies work, create value and remain competitive. The role of AI in the value chain is diverse and is becoming increasingly crucial. At the beginning of the value chain, AI makes an important contribution to product design and development. Machine learning algorithms analyse extensive data sets, customer preferences and market trends to develop product ideas. This not only improves the innovation process, but also minimises the risk of developing products that do not meet market requirements.

In the supply chain, AI optimises logistics, inventory management and demand forecasting. Predictive analytics supported by AI help companies reduce costs, minimise waste and ensure that products are available when and where customers want them. In addition, AI-driven robotics and automation streamline manufacturing and distribution processes. In marketing and sales, AI enables highly personalised customer experiences. Recommendation engines, chatbots and natural language processing facilitate customer interaction and increase sales. AI-supported insights from customer data enable companies to deploy their marketing measures in a more targeted manner. AI plays a crucial role in customer support and service. Chatbots and virtual assistants offer 24/7 support and deal with enquiries and problems immediately. With the help of sentiment analyses, companies can measure customer satisfaction and adapt their services accordingly. In the aftersales phase, AI improves maintenance and support by predicting when devices need servicing or when products might fail. This proactive approach minimises downtime and ensures customer satisfaction.

Figure 5: Impact of AI on the value chain

AI improves the efficiency, accuracy and cost-effectiveness of supporting activities in the value chain. It enables companies to better manage their resources, reduce risks and offer their customers higher quality products and services.

Figure 6: Impact of AI on the value chain (supporting activities)

In essence, AI is an integral part of the value chain of modern organisations, driving efficiency, innovation and competitiveness. Its influence will continue to grow as companies utilise its capabilities to create value and meet changing customer expectations.

Case studies on the impact of AI on the efficiency of the value chain

AI drives cost-saving potential at every stage of the manufacturing value chain. Each phase can be evaluated based on the applications of existing AI solutions or approaches. The cost of AI implementation can vary greatly (routine tasks can be easily automated with AI, but complex processes cannot). Each phase also differs in terms of the chance of achieving rapid success after AI implementation. There are many use cases that are already heavily integrated into business operations, both internally developed and specifically designed by technology companies for outsourcing.

Figure 7: AI in inbound and outbound logistics

Figure 8: AI in marketing

For further use cases, you can download our dossier ‘Increasing efficiency in the value chain with the help of AI’.

Challenges and pitfalls in the introduction of AI

The S-curve for AI adoption illustrates how a new technology, in this case artificial intelligence (AI), is introduced and integrated into different industries and sectors over time. It usually follows an ‘S’ shape when plotted on a graph. Explanation of the key phases:

1. Initial phase (slow introduction): In the initial phase, the introduction of AI is relatively slow. Companies are cautious and awareness and understanding of the potential of AI is limited. Pilot projects and research are common, but widespread implementation is rare.
2. Acceleration phase (rapid adoption): As AI technologies mature, their adoption accelerates. Companies recognise the competitive advantages and potential efficiency gains that AI can offer. Investment in AI increases, leading to the development of AI applications, tools and platforms.
3. Turning point (steep growth): In this phase, the use of AI reaches critical mass. More and more industries and companies are using AI for various areas, from marketing and customer service to work processes and decision-making. AI becomes a strategic priority.
4. Maturity phase (slower growth): After initial rapid growth, AI adoption may slow down in certain areas as the technology matures and saturates some markets. However, new use cases and industries continue to introduce AI, ensuring continuous growth.
5. Plateau phase (saturation): In the final phase, AI adoption is approaching saturation in many sectors and the technology is becoming ubiquitous. Most companies have integrated AI into their operations and innovation shifts to optimising and refining existing AI applications.

It is important to note that different industries and sectors may be at different stages of the S-curve and the curve itself may vary depending on specific AI technologies and applications. The S-curve of AI adoption reflects the typical trajectory of how transformative technologies are integrated into society and the economy, ultimately reshaping industries and economies.

Figure 9: Timeline of the S-curve of AI introduction

What should you consider when introducing AI?

• Legislation for AI solutions: The EU AI Act defines dos and don’ts for AI practices. Even though the law is still in the final negotiation phase and will not come into force until 2025, AI developers should be aware of the possible legal framework to minimise future regulatory risks.
• Rigorous risk management: Companies have concerns about the impact of AI and recognise the need for internal risk policies and legal clarity. There are many social and economic aspects that need to be considered when using AI.
• Resource consumption: AI consumes enormous amounts of electricity and causes a high network load. Many highly qualified specialists with in-depth knowledge of maths and economics are needed.

FOSTEC & Company’s approach to the strategic integration of AI into your business processes

FOSTEC & Company supports clients throughout the entire AI implementation process, from defining the business requirements to assessing the data and operational requirements for AI. After successful implementation, the client needs a regular AI health check: is the deployed AI delivering the promised profits or significant cost savings, or should it be replaced by other AI solutions.

At every stage of the value chain, FOSTEC can support the client in implementing the AI rollout with fewer friction points and provide top management with a strategically tailored catalogue of measures. FOSTEC adapts the project approach to the client’s business requirements. The client and consulting team work together to develop a vision of future AI-supported processes.

Figure 10: Project approach: AI in your value chain. Comprehensive proof-of-concept help for companies

Eine hervorragende Lösung, die bereits bei einem Mandanten implementiert wurde, ist das KI-Content-Powerhouse. FOSTEC & Company bietet den Mandanten eine KI-gestützte Content-Automatisierung für eine verbesserte Marketingwirkung. Es löst viele Effizienzprobleme innerhalb der Marketingprozesse und spart dadurch Zeit und Kosten ein.

Figure 11: F&C AI content powerhouse

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