Mastering the AI landscape: Making AI applicable

⏰ Estimated reading time: 2 minutes

Be ready to discover how AI serves as a catalyst for innovation, efficiency, and personalised user experiences, all of which are crucial for success in the digital age. This blog series consists of three parts:

1. Making AI Applicable
2. AI-Driven Marketing Organisations
3. AI-Driven User Experience Analytics‘

Each blog highlights how companies can leverage AI to not only increase operational efficiency, but also form deeper, more personalised connections with their customers. From practical implementation to creating unparalleled customer experiences, we explore the multifaceted impact of AI on digital transformation within organisations.

AI in Companies – Bridging the gap between theory and practice

Artificial intelligence (AI) has evolved from a futuristic concept to a reality in daily business practices. Its adoption is reshaping enterprises by optimizing crucial aspects such as pricing, customer engagement, and the preventive resolution of possible machine malfunctions. valantic stands at the forefront of incorporating AI across various areas, unlocking substantial business value and fostering a competitive edge.

The agility of AI – particularly in automation via robotic process automation (RPA) and generative AI – delivers swift, cost-efficient solutions that help to exponentially scale business operations. AI facilitates the pragmatic shift from theoretical models to profitable, real-world applications. This not only
ensures companies’ survival but helps them to thrive and evolve in an AI augmented future. Consequently, the implementation of AI projects must be part of any comprehensive, long-term business strategy.

The 4 phases of AI integration

Achieving a data-driven company that uses intelligent machine support across all areas is not a quick process; rather, it typically unfolds in four distinct phases:

1. Launch AI initiative
2. Conduct targeted experiments
3. Establish company-wide AI expertise
4. Embed AI know-how in the company’s DNA

Two pivotal core competencies play a critical role in each of these phases
and need to be successively refined:
1. The organization’s analytical core competence: This encompasses excellent data quality, skilled employees (such as data scientists and AI experts), and AI tools precisely aligned with business objectives and application scenarios.
2. Business expertise complementing analytical components: The AI strategy must receive support and endorsement from the C-level executives and all business units.

Challenges and strategies in AI implementation

Uwe Tüben, Partner & Managing Director

“Technical feasibility, ethical considerations, customer acceptance, and innovation potential: The introduction of AI presents unique hurdles in these areas, demanding a well-crafted strategy and evaluation framework for effective utilization. Companies must assess their technical readiness, data
quality, and computing resources to ensure they can adequately support AI initiatives.”

Additionally, evaluating AI applications regarding ethical implications and ensuring compliance with rigorous data protection laws is crucial to prevent bias and discrimination. Understanding customer expectations and fostering trust in AI-generated results are also pivotal for successful implementation. That’s why companies need to conduct user testing and gather feedback to establish AI as a catalyst for innovation and to identify new growth opportunities.

In the next blog, we will take a closer look at AI-driven marketing organisations.

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