The Most Common Challenges:

1) Technology-First Mindset

Instead of defining business goals and evaluating potential technologies based on a business case to achieve those goals, AI is introduced for its own sake. The result: unclear use cases, little to no adoption within the company.

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2) The Prototype Trap

Technical solutions deliver the desired results in PoCs, but the data infrastructure, data governance, and data culture are not yet mature enough to provide a fertile ground for scaling AI solutions. The result: each new prototype promises the breakthrough. Sustainable, industrialized solutions never materialize.

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3) Completely New Economics

In traditional IT projects, the initial investment is depreciated over time. AI solutions, however, require ongoing computing resources both for use and for continuous training of the models. Once AI solutions are scaled, it often proves difficult to maintain a positive ROI over time.

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4) Organizational and Process Bottlenecks

The organization and leadership are not aligned with technology-driven work models. AI solutions are technically efficient, but the communication and coordination efforts around the solution increase significantly. The result: costs are not reduced – they are merely shifted elsewhere.

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The Solution
To contribute sustainably to value creation with AI and data, a structured approach is a critical success factor. The steps:

1. Evaluation of the current state (Maturity Assessment), potential, and definition of the target picture

2. Development of business models, targeted design and validation of scalable PoCs and MVPs

3. Adjustment of organizational and leadership structures in relevant teams and departments

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At Gueth.com we support companies throughout these three phases – business-oriented, hands-on, and pragmatic.