Artificial intelligence (AI) offers companies a wide range of opportunities to optimize processes, reduce costs, and make data-driven decisions more efficiently. However, many medium-sized and larger companies face significant challenges when implementing AI solutions. This article highlights the five biggest hurdles and shows practical strategies on how companies can successfully overcome them.

Maximilian Hahnenkamp and Felix Beissel, Co-Founders of Scavenger AI:

"We have seen how great the wow effect is for our customers as soon as they can not only get to know Scavenger theoretically but also try it out themselves. We wanted to make this experience available to everyone interested in the topic."

Hurdle 1: Lack of Technical Expertise

One of the central barriers to the introduction of AI is the lack of technical expertise within the company. According to a study by Bitkom, 84% of companies state that the lack of technical know-how hinders the implementation of AI.

Solution Approaches:

• Training and Workshops: Investments in the education and training of employees can help build internal AI knowledge.

• External Partnerships: Collaborating with specialized consulting firms or research institutions can facilitate access to necessary expertise.

Hurdle 2: Lack of Data Quality and Availability

AI systems rely on high-quality and comprehensive data. However, many companies struggle with incomplete or unstructured datasets, which hinders the development of effective AI solutions.

Solution Approaches:

• Develop Data Strategy: Implement clear processes for data collection, storage, and maintenance to ensure data quality.

• Establish Data Partnerships: Collaborations with other companies or institutions can enable access to additional relevant datasets.

Hurdle 3: Legal Uncertainties and Regulations

The legal framework for the use of AI is often complex and can cause uncertainties. With the EU AI Act coming into effect on August 1, 2024, new regulations have been introduced that companies must consider.

Solution Approaches:

• Seek Legal Advice: Specialized lawyers can help interpret current laws and ensure compliance.

• Develop Internal Policies: Create company-specific guidelines for the ethical and legal use of AI.

Hurdle 4: Resistance within the Workforce

The introduction of AI can evoke fears and resistance among employees, especially if it is unclear how their roles will change.

Solution Approaches:

• Transparent Communication: Early and open information about planned AI initiatives and their impact on the workforce.

• Involve Employees: Actively involve employees in the implementation process and offer training to reduce apprehension.

Hurdle 5: Integration into Existing Systems

The seamless integration of AI solutions into existing IT infrastructures can be technically challenging and requires careful planning.

Solution Approaches:

• Gradual Implementation: Start with pilot projects to test integration and gradually expand.

• Use Specialized Tools: Utilize established MLOps tools and platforms that support the development, deployment, and maintenance of AI models.

Conclusion

The successful implementation of AI requires a holistic approach that considers both technical and organizational aspects. Through proactive measures and the involvement of all stakeholders, companies can overcome the mentioned hurdles and harness the full potential of AI.

Sources:

• Bitkom: German economy accelerates in artificial intelligence

• activeMind.legal: AI Act or AI Regulation (explained for companies)

• Wikipedia: Regulation on Artificial Intelligence