Artificial intelligence (AI) has rapidly gone from a trend to an integral element of many business models and is set to gain further importance. Studies, for example, by McKinsey show that 60 to 70 percent of day-to-day work could be automated and 30 percent of new medications and materials could be developed using AI technologies by 2025. This shows that AI is no longer just a nice to have for forward-thinking companies.
AI as a service: efficient, easy access
Platforms like Microsoft Azure provide AI models and services, allowing organizations to put advanced AI solutions to work for them without having to use their own processing capacity. Pre-made models also facilitate rapid integration and can be adjusted to specific requirements and seamlessly integrated into existing business processes.
Large Language Models (LLMs) like OpenAI's GPT are revolutionizing data processing, decision making and the way we can use information. They reduce workloads, enhance efficiency and productivity, lower costs, and make it possible to focus on core competencies. Through in-depth data analyses and by strengthening human creativity, they contribute to improved quality while reducing dependency on experts and optimizing knowledge sharing and personnel resources within the company.
Example: AI in the legal sector
Let’s take a look at some concrete examples. AI is bringing numerous advantages in the complex world of the legal sector and corporate regulatory departments. These areas deal with mountains of legal regulations, detailed requirements, and complex issues on a daily basis. Beyond that, they also have to cope with huge volumes of textual data, from local laws to international regulations and court judgments. These huge volumes of data offer an ideal field of application for LLMs such as the GPT series from OpenAI.
Three stages in the expansion of AI
Artificial intelligence can provide support in three progressive expansion stages, each building on the one before and allowing for ever-deeper data penetration. Here are two potential use cases, associated with the three expansion stages:
Internal perspective: corporate departments
Finance, controlling, and legal departments all need a clear big-picture view of regulatory requirements and how they affect the organization, especially in regulated industries such as pharmaceuticals, aviation, and IT security.
External perspective: legal sector
Law firms face the challenge of arriving at solidly reasoned assessments quickly and identifying successful lines of argument in legal disputes, which can be supported by LLMs such as OpenAI’s GPT series.
Stage 1: information retrieval
First, AI makes it possible to retrieve information in precise detail. At this stage, it acts as an advanced search tool that is able to filter relevant information out of the mass of available data. In our two use cases, that means:
Stage 2: information extraction
The next stage is information extraction, which is concerned with filtering specific data points out of the information gathered. Let’s look at our two examples here as well:
Stage 3: generative AI
The third and most advanced stage is generative AI, which is able to create new knowledge based on the information extracted. This can range from preparing detailed reports to drafting documents containing well-founded forecasts. It makes sense to take a look at real-world applications here, too:
Cross-industry uses for AI
These examples help to clarify the many advantages of AI. However, artificial intelligence can be used in an even wider range of applications, which are not limited to specific industries. Companies and departments that regularly process large volumes of text can benefit significantly from AI. These include areas such as knowledge management, legal departments, HR, marketing, customer service, and research. AI systems provide effective support to these departments in seeking out information, consolidating data, and preparing text content, which in turn contributes to efficiency and productivity gains. Ultimately, however, the possibilities of generative AI are not limited to text. Multimodal media such as spoken language, images or videos can also be processed.
Challenges in AI integration
Integrating AI into corporate processes requires a structured basis in data. Ideally, it should be stored in the cloud and accessible via standard interfaces. However, many companies have not yet reached that point. Another major challenge, one of the biggest, is training staff. Employees need to be empowered to master the new AI-supported workflows while contributing their unique human skills – such as critical thinking, empathy, and creative problem solving, all of which go beyond pure data processing.
Using artificial intelligence also brings changes in project management. Conventional methods need to be adjusted to accommodate the particular requirements of AI-based initiatives. Many companies do not have the necessary expertise for this internally. External specialists such as Campana & Schott can help companies to navigate this transitional phase and optimize the synergy between human intuition and the efficiency of machines.
AI integration and data maturity analysis
Our experts at Campana & Schott offer full-spectrum support in analyzing the maturity of your organization’s data and subsequently implementing AI solutions. Our objective is to answer one key question: “Where can artificial intelligence have the biggest impact within your organization?”
Following this approach, we offer holistic advising and support to facilitate the transition to intelligent data-based processes and guide your organization into the future of the AI-powered business world.
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