3 Minutes Read By Christian Brugger, Dr. Mihail Minev

AI in Action: Success Factors and Challenges in 2025

#Artificial Intelligence#Digital Execution#Digital Strategy#Digital Transformation

Artificial intelligence (AI) continues to redefine industries by driving innovation, boosting efficiency, and enabling personalized experiences. Yet, its adoption presents significant challenges, from aligning AI initiatives with strategic goals to navigating technical, cultural, and regulatory hurdles.

In this interview, OMMAX Partners Christian Brugger and Dr. Mihail Minev address some of the most pressing questions about AI in 2025: Can companies afford to forgo AI without risking their competitive edge? Why do so many AI projects fall short of expectations, and how can organizations sidestep these pitfalls? The conversation also explores actionable strategies for launching successful AI initiatives, including prioritizing use cases, selecting the right tools, and weighing custom versus standard solutions. Finally, the discussion highlights emerging trends, such as ethical AI, and their growing impact on businesses seeking to leverage AI to stay ahead of the curve.

1. Can companies afford to forgo artificial intelligence in 2025?

AI has become a critical competitive factor across many industries already in 2024. Companies that forgo AI risk falling behind in areas like efficiency, innovation, and meeting customer expectations, given that AI drives automation, personalization, and data-driven decision-making. Sectors such as finance, healthcare, logistics, and e-commerce can especially benefit from AI-powered solutions. Without AI, companies face the risk of losing market share, operating inefficiently, and maintaining a competitive disadvantage.

2. Why do AI projects in companies often fail to deliver the desired results or fail altogether?

Several factors contribute to this issue. A lack of clear goal definition often leads to project failure — when realistic, measurable objectives are not set, projects falter. Data problems, such as poor quality, insufficient data, or lack of integration, can render AI models ineffective. Poor integration with existing systems and processes can also limit the utility of AI solutions. Another element to be considered is human factors, such as insufficient expertise in AI and data analytics, make implementation difficult or error-prone. Unrealistic expectations about AI capabilities and resistance to new technologies within company culture further hinder success.

3. How should AI projects be approached to ensure successful completion? What are the first steps?

A structured approach is essential. It begins with use-case ideation, prioritization, and conceptualization. The first steps are to identify challenges and inefficiencies along the value chain and define use cases for automation and optimization. Then, these cases should be evaluated and prioritized using strategic criteria such as feasibility, impact, and alignment with the business strategy. then, the most promising use cases should be developed further by analyzing costs and benefits and defining measurable goals. Assess data quality, availability, and integrity, and involve all relevant stakeholders. Finally, the implementation should start with a pilot project: begin small, test the solution, and optimize iteratively based on user and stakeholder feedback. Throughout the process, it's crucial to involve experts in AI, data analytics, and change management.

4. How can companies choose the right AI tools from the thousands available on the market?

Start by focusing on the problem, not the tool. Conduct a process and needs analysis to define requirements and goals. Based on these requirements, decide whether to "make or buy," i.e., choose between commercial tools or custom-built solutions. Opt for tools tailored to your industry, ensuring they are flexible, scalable, and compatible with existing systems. Look for tools supported by robust documentation, updates, and customer service. Thoroughly test tools before implementation.

5. Custom solution or standard approach: What are the pros and cons?

The choice between a custom solution and a standard approach depends on the business problem and requirements. A custom solution offers perfect alignment with specific needs and can provide a competitive edge, but it often comes with higher costs and longer development times. On the other hand, a standard approach is faster and cheaper to implement but may require compromises in functionality and being tailored to the company's unique needs.

6. What challenges are associated with implementing and adapting AI tools?

There are five main categories to be considered: technical challenges include integration with existing systems, data migration, and model training. Cultural challenges involve gaining employee acceptance and trust in the technology. Regulatory requirements, such as data protection and ethical standards, must also be addressed. Costs, including initial investment and ongoing maintenance, must be considered. Finally, ensuring scalability for long-term use is crucial.

7. What role do external partners and internal employees play in the successful implementation of AI solutions?

External partners are often indispensable accelerators for AI implementation and financial success, as they bring expertise, guidance, and proven processes to help select and deploy the right technology. On the other hand, internal employees ensure long-term success by integrating AI solutions into daily business operations. They need to be trained and involved throughout the process, and their core competencies, acceptance, and feedback are vital to establishing AI solutions sustainably in the organization. This is part of the "democratization" of AI use cases.

8. What developments do you foresee in AI usage for 2025?

AI has entered the age of agents — for example, holistic agent systems will automate complex, repetitive tasks and task clusters. Another development is Edge AI, where more AI applications run directly on devices rather than in the cloud. Explainable AI will gain prominence, focusing on the transparency and traceability of models. Personalization will advance, delivering tailored solutions. Additionally, with increased regulation and emphasis on fair, non-discriminatory algorithms, ethical AI will also play a critical role.

Do you want to know more about OMMAX’s experience in artificial intelligence? Get in touch with Christian Brugger (christian.brugger@ommax.de) or Dr. Mihail Minev (mihail.minev@ommax.de).

About OMMAX

We believe everyone can become a digital leader.

OMMAX is a fast-growing digital strategy consultancy specializing in transaction advisory, strategy, and end-to-end execution of digital initiatives. It’s our vision to build digital leaders worldwide to foster innovation and accelerate digital growth and profitability. Over the past 14 years, we realized 300+ M&A deals with >€20B deal value and 2,000+ international value creation projects in various industries for leading private equity firms in commercial strategy, digital operational excellence, advanced data strategy, analytics, tech, and automation. As a front-runner for holistic data-driven strategy consulting and end-to-end execution, we are the leading consultancy within the global private equity sphere, designing and delivering best-in-class digital strategy and value creation.

By Christian Brugger

By Dr. Mihail Minev

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