Artificial intelligence (AI) is developing apace at the moment with generative AI and Large Language Models (LLM) dominating public discourse. Yet, a much more sustainable development is emerging in the background, as more and more organisations rely on personalised AI solutions to provide tailored responses to their specific challenges.
What is often overlooked, however, is that the success of these solutions does not depend on AI itself, but on the quality, availability and structure of the data it uses. A sound data strategy is therefore not an option for a company – it is a basic requirement.
1. AI needs more than models – it needs context
Many decision makers believe that simply using a powerful model is enough. But that is only partially true. AI needs context. It is only when the data reflects the company’s reality that truly useful results can be achieved.
An AI system can only perform as well as the data available to it. This data has to:
- Correctly replicate the processes and structures within the company
- Be consistent and up to date in terms of its content
- Be correctly linked and accessible
Methods for ensuring the quality of data include:
- Data Profiling: Analysis of data to understand its structure and content
- Data Cleansing: Removing errors and inconsistencies in data
- Data Enrichment: Supplementing data with additional information to enhance its value
If these requirements are not met, systems emerge that are either inefficient or even dangerous. This leads to misconceptions, overlooked dependencies and unclear responsibilities. The impact can be severe, especially in sensitive environments such as healthcare or medical technology. Holistic data management is therefore critical. It ensures that the data context is not only understood, but also maintained on an ongoing basis.
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2. Personalisation ≠ isolated solution
Many companies quickly develop an AI tool for a specific use case – a chatbot for customer service, a forecasting function for production or a smart analytics dashboard for management. Such solutions can provide initial insights. But without strategic anchoring, they remain isolated solutions.
What’s typically missing is:
- Seamless integration into existing systems and workflows
- A consistent data life cycle – from entry to archiving
- Clearly defined processes for data governance and sustainable scalability
An AI use case without a sound data strategy is like a house built on sand: As soon as requirements change, markets shift or new systems are added, the architecture collapses.
The topic of data integration is especially relevant in this context, connecting different systems and data silos and therefore creating the basis for comprehensive company-wide AI solutions.
Some of the technical and organisational challenges posed by data integration include:
- Middleware solutions: Software that connects different applications and data sources
- API management: Management of interfaces for exchanging data between systems
- ETL processes (Extract, Transform, Load): Procedures for extracting data from different sources, transforming it to a standard format and loading it into a target system
3. Data quality creates trust
Whether in public administration, hospital operations or in medium-sized manufacturing, trust is the basis for all use of AI. However, this does not stem from technical promises, but from verifiable data quality.
To trust an AI decision, you need to know:
- Where the data comes from
- How it was processed and
- Who is responsible for it
Well-thought-out data governance defines roles, responsibilities and processes for data management within the company. It creates structures that ensure that personalised AI applications are not based on black box data, but on clear, verifiable principles.
Transparent data flows, consistent metadata standards and clearly defined responsibilities not only strengthen user trust – they also make personalised AI auditable and scalable.
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Personalised AI is not a goal – it is a result
Many companies define AI as a strategic goal. But, in reality, personalised AI is a result – the result of solid data management. Anyone working with AI today should start defining their data strategy tomorrow.
Without a robust data foundation, artificial intelligence remains a silo solution – no connectivity, no relevance and no sustainability.
Our conviction
Personalised AI will only become economically relevant if investments are made today in data quality, data integration and governance.
Organisations that view data as a strategic asset lay the foundation for true innovation. They move beyond the world of proof-of-concepts and achieve the next level: operationalised AI – integrated, trustworthy and value-adding.
Why data integration is the key to successful projects
A personalised AI solution is only as powerful as the data flows on which it is based. Drawing on concrete practical examples, this article shows how closely linked systems, consistent interfaces and standardised data models make the difference:
Connecting BIM, ERP and others:
Data integration as the key to construction projects
Lack of data integration between BIM, ERP and others leads to media discontinuity, delays and rising costs. This has to change.
Data strategy is critical to the success of AI initiatives
Personalised AI is not a plug-and-play product – it is the result of systematic data management and strategic decision making. To reap the full benefits of AI applications, it is crucial to ensure the right data structure, quality and responsibility within the company.
Without a clear data strategy, AI remains piecemeal. With a solid basis, on the other hand, it becomes a real competitive advantage: personalised, scalable and economically relevant.
Organisations that invest now in a corporate data strategy, data governance and data integration not only gain a technological edge, they also create the conditions for trust, transparency and sustainable business success in the AI age.
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