AI for Business – Opportunities, Challenges, and Best Practices for Implementation

In this article, you will learn

• how your business can benefit from artificial intelligence (AI),
• which use cases are emerging and
• how to build your AI skills and implement a solution in just one month.

Why wait? The benefits of AI for businesses are clear. Companies that invest in AI now can gain a competitive edge, especially while many others remain hesitant. AI reduces the burden of repetitive tasks, increases productivity, and offers a powerful response to the growing shortage of skilled workers. It also opens up entirely new opportunities to automate and autonomously optimize business processes.

Artificial intelligence (AI) is capturing the attention of businesses, as confirmed by the 2024 Bitkom study Artificial Intelligence in Germany. According to the study, 73% of German companies view AI as the most important technology of the future, and a similar percentage see it as an opportunity. In theory, three out of four companies are planning to adopt AI – at some point.

AI has advanced rapidly in recent months. With the emergence of pretrained, freely available large language models (LLMs), the barriers to adoption have decreased significantly. These models offer a powerful new foundation for AI in business. Rather than investing time and resources into training custom models, companies can now delegate standard knowledge management tasks to AI with just a few clicks. This allows employees to focus on higher-value, strategic activities.

AI excels at helping businesses manage and make sense of large volumes of data. It can perform tasks such as conducting research, generating executive summaries, and retrieving relevant information. When these tasks are performed routinely, AI can automate them entirely.

Closely tied to research on unstructured and constantly evolving data is data analysis. The right AI tools can also process and evaluate numerical data. While many analyses are retrospective, AI is driving new momentum in predictive modeling. These deeper insights enable managers to make more informed, data-driven decisions.

Artificial intelligence also supports innovation by helping companies develop new products and services. In the automotive industry, for example, AI is used to generate market and competitor analyses. Both internal and external data sources are evaluated to answer key product development questions: What services do car users want? What prices are acceptable? What innovations are competitors pursuing?

AI-based service bots are also becoming more common. They offer a compelling alternative to traditional customer service hotlines. While human support often involves long wait times, digital assistants are available 24/7 and can respond instantly. With the right contextual information, they can handle specific customer inquiries and even initiate internal company processes. For example, health insurance providers use AI chatbots to fully automate routine tasks like replacing lost insurance cards. The result is a better user experience – and a clear competitive edge.

“There are various ways to introduce AI into a company,” says Marco Bensch, Partner at the technology consultancy Detecon. “But implementation requires expertise, experience, and a clear strategic direction.” Many companies – especially those with limited internal know-how – recognize AI’s potential but struggle to find a practical entry point.

Detecon identifies four key challenges businesses face when adopting AI:

Is AI just hype? What can it actually do? Can it already create value today? Many businesses view AI as a complex and unfamiliar field and don’t know where to start. Two factors are essential for getting started: identifying relevant use cases and building a clear understanding of their potential impact.

How quickly can AI be implemented? How long will the solution remain relevant? How can companies ensure a strong return on investment? One thing is clear: implementing AI requires significant resources – development, operations, tools, and talent. But which tools are the right fit? Which will still be relevant in the company’s domain in a few years – and which might disappear from the market?

Which vendors and offerings are right for us? How do we get the best value for our investment? The market is difficult to navigate. Some vendors offer end-to-end solutions; others provide only partial components. Some tools are broad and flexible, while others are highly specialized, but potentially more effective. Navigating this fragmented landscape is critical to success. In addition to choosing the right solution, companies must consider how AI will integrate into their existing IT environment and where it will be operated. Media discontinuities and multi-cloud setups increase complexity and reduce scalability.

How can we ensure data protection when implementing AI? How do we stay compliant with legal requirements? Emerging technologies, especially those that are difficult to understand, introduce legal uncertainty. Even the most advanced AI solution is useless if it can’t be deployed due to data privacy violations. Models must be trained on high-quality, unbiased data, while sensitive internal data must remain protected. Effective AI governance includes audits, compliance teams, and human oversight.

The Bitkom study highlights the significant role regulation plays in slowing AI adoption. Top barriers cited by users include legal restrictions, data protection concerns, and legal ambiguity. One particularly notable finding: 84% of companies using generative AI say the provider’s origin matters. Among them, 86% prefer a German provider.

The easiest way to begin using AI, even with limited in-house expertise, is to partner with experienced professionals who understand the entire ecosystem: the technology, the business potential, and the provider landscape. That’s exactly what the AI Fast-Track Program from Detecon and the Open Telekom Cloud offers – a streamlined, targeted path to AI adoption.

The AI Fast-Track Program supports businesses from the initial idea through to real-world implementation. It provides not only strategic guidance but also end-to-end support, turning concepts into operational AI services. The program delivers a clear, actionable roadmap that prioritizes opportunities based on business impact, cost, and risk – making it an ideal way for companies to gain hands-on AI experience. The result is a minimum viable product (MVP) that demonstrates technical feasibility on a scalable, sovereign cloud platform while showcasing the business value of a relevant AI use case.

This approach turns a concept into a functional prototype, allowing companies to test feasibility before committing to full implementation. The MVP is deployed on the Open Telekom Cloud, which ensures GDPR compliance, EU-based operations, robust security standards, open-source flexibility, and enterprise-grade AI/ML capabilities. By leveraging a sovereign cloud, companies can eliminate compliance concerns and launch their own AI service within just a few weeks.

Detecon has already supported numerous clients in implementing AI solutions using this methodology. One example from the telecommunications sector illustrates the approach: the Fast-Track customer designs and installs telecom products at client sites and collaborates closely with general contractors.

The process involved multiple stakeholders, including clients, contractors, and internal employees, each with distinct roles and questions. As projects advanced, all parties regularly faced highly specific questions related to planning, installation, and coordination. Finding answers required navigating a wide range of technical data sources, many of which were inconsistently structured and filled with domain-specific terminology.

The result was significant inefficiency. Information retrieval was slow, heavily reliant on individual expertise or manual support. The absence of a centralized, user-friendly interface made the process error-prone and difficult to scale. The customer needed a way to standardize access to information and deliver it in a simple, intuitive format – ideally through natural language interaction.

Detecon applied its structured, end-to-end AI deployment process to support the customer in implementing an AI solution. Together, the project team identified high-impact use cases, engaged key stakeholders, and defined both technical and business requirements.

At the core of the solution was an initial prototype built on a large language model, functioning as an autonomous agent. This AI agent can interpret natural language input, perform API-based queries across multiple source systems, and deliver clear, user-friendly summaries. It enables practical use cases for diverse user groups by automating information retrieval while preserving contextual accuracy and relevance.

The initial prototype allows participants in telecom projects to ask complex technical and operational questions using natural language. The system responds in real time by pulling information from multiple sources, eliminating the need to search through scattered documents and databases.

By centralizing all process-relevant data in a single interface, the AI agent establishes a solid operational foundation. It handles complex queries quickly, reliably, and with minimal manual effort. The fully automated architecture streamlines support, eliminates repetitive tasks, and significantly reduces the time required to access critical project data.

The first prototype was delivered in just ten weeks. It now automates routine information retrieval and significantly reduces manual workload. Both internal teams and external partners benefit from a user-friendly tool that supports their day-to-day tasks with ease. This initial step toward AI-driven operations has laid the foundation for broader automation, enhanced knowledge management, and more effective collaboration across the customer’s entire ecosystem.