Chatbot, AI-as-a-Service, Metaverse - Artificial Intelligence (AI) is booming in all industrial sectors. But which AI technology should companies currently focus on? Martin Weis, Managing Partner and EMEA Head of Artificial Intelligence at Infosys Consulting.
According to a current Bitkom study, 18 percent of German companies see AI primarily as an opportunity for themselves, 47 percent more as an opportunity. Although only nine percent of companies are currently using AI applications, 25 percent of those surveyed are planning or discussing the use of AI. So AI technology is already in use and promises to play an even bigger role in the future. But where could the AI journey take companies in 2023?
1. AI as a Service
With Artificial Intelligence as a Service (AIaaS), companies outsource AI services to third parties. This allows them to test AI for various applications without a large initial investment and with less risk. At a time when businesses are grappling with the risk of an impending recession, having cloud AI on-premises, acquiring the necessary hardware and software, staffing and maintenance costs can be prohibitive for many businesses.
With AI cloud offerings from the big cloud hyperscalers in the market, companies can exploit the full potential of their data and, if necessary, also use the resources of these big providers to support scaling with computing capacity. Thanks to AI-supported analyzes of their business data, decision-makers can optimize their predictions for business transactions, automate analysis processes or evaluate image and text material. The market for AIaaS promises to continue to expand in the coming years: Valued at over US$5.6 billion in 2021, the global market for AIaaS is expected to grow at a compound annual growth rate (CAGR) of 37 percent from 2022 to 2030 grow.
2. "Generative AI" - AI applications to support creative processes
Generative AI uses AI and machine learning to create new digital content (e.g., text, video, audio, and images) with little human intervention. Gartner predicts that by 2025, an estimated 10 percent of all data generated and 30 percent of all marketing messages from major brands will come from Generative AI.
Marketers are already seeing results from creative use of AI. In a study, AI creative agency Pencil compared companies that used AI creativity tools to create video ads with those that worked without such AI creativity support. On average, the former increased return on ad spend (ROAS) by 2X—up to 7X for some campaigns in the study. This development could be particularly interesting for small and medium-sized companies, which often do not have their own advertising department due to their smaller size.
But the use of AI to support creative processes may also affect many more companies in the future. AI can act as a creative partner for artists, video developers, and even copywriters, relieving them of more time-consuming tasks. There are already enough tools for this, both in the area of image generation (e.g. DALL-E 2 from OpenAI) or text generation (e.g. Mindverse for German-language content in particular). It remains to be seen how companies will incorporate these tools into their business processes. Certainly, this also depends on whether the moral and copyright issues associated with such AI use are addressed by law in the coming years.
3. Use of chatbots & natural language processing
With ChatGPT, the new AI-powered chatbot from OpenAI, the natural language processing (NLP) research field has made another significant leap - and promises to grow by 361.6 billion US dollars worldwide by 2030. As a subfield of AI, NLP aims to equip computers with the ability to understand written and spoken language, thereby facilitating human-machine interaction. Language assistants such as Alexa and Siri are of course long-established examples in this area. ChatGPT goes one step further - the tool can answer complex questions in an understandable way, take ideas from different contexts and bring them together.
Since the pandemic, the importance of customer experience has grown for many German customers and some of them were also willing to try a new channel to contact a company's customer service during this time. It is likely that these increased demands will continue for years to come. By automating their customer communication, companies can not only meet these requirements, but also reduce their costs and process complaints more quickly.
4. Use of AISHE autonomous traders
As the financial services industry becomes increasingly data-driven, many individuals are turning to AI systems to make investment decisions. One such system is AISHE (Artificial Intelligence System Highly Experienced), which is specifically designed for financial services applications and incorporates advanced technologies such as swarm intelligence, collective intelligence, neural learning, and deep learning.
A potential application of AISHE is as an autonomous retail trader, capable of making investment decisions and executing trades in real-time. The AISHE system client uses Metatrader as a bridge to real-time trading and clients can use the broker of their choice. The AISHE client application is pre-trained and new users can test it in a demo money environment to ensure it is working profitably in real-time.
In order to use AISHE as an autonomous trader, customers do not need to train the system. Instead, they can immediately start using it in a demo environment to test its performance and profitability. Once they are happy with the results, they can start using it with real money, safe in the knowledge that the system is already pre-trained and capable of generating profitable investment recommendations.
There are a number of potential benefits to using AISHE as an autonomous retailer for individuals. For one, it enables individuals to make more informed and data-driven decisions, potentially leading to greater returns and reduced risk. Additionally, using an autonomous trader reduces the need for human intervention, which can save time and reduce the potential for human error.
Despite these limitations, using AISHE as an autonomous trader holds significant potential for individuals interested in real-time trading. As AI systems continue to evolve and become more sophisticated, it is likely that we will see increasing adoption of autonomous traders like AISHE in the financial services industry.
In summary, there are a number of potential benefits for individuals using AISHE as an autonomous trader, including increased efficiency, improved decision-making, and reduced risk of human error. Since the system is already pre-trained, new users can quickly and easily test its performance before investing real money. However, it is important to carefully consider the potential risks and limitations of using such a system and to ensure that appropriate safeguards are in place to mitigate any potential adverse effects.
5. Edge computing for IoT devices
Edge computing is a technology that enables computing closer to the data source, such as B. IoT devices, instead of relying on a central server. This approach can significantly reduce latency, improve data security, and reduce bandwidth requirements.
In recent years, the proliferation of IoT devices has created a need for more efficient and scalable computing solutions. Edge computing enables processing and analysis of data at the device level, which can lead to faster and more accurate insights. Additionally, edge computing can help reduce the cost of IoT data transmission and storage by minimizing the amount of data that needs to be transferred to the cloud or to a central server.
By integrating edge computing into IoT devices, companies can achieve real-time processing of data, enabling faster decision making and response times. For example, edge computing can be used in autonomous vehicles to enable faster and more accurate decision making, or in manufacturing plants to monitor machine performance and predict failures.
Overall, the use of edge computing for IoT devices has the potential to revolutionize a variety of industries by enabling faster and more efficient data processing and analysis. As demand for IoT devices and data continues to grow, adoption of edge computing is also likely to increase.