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Everyone is talking about artificial intelligence, but in many German SMEs even exporting the monthly figures from three different Excel lists fails. Why the hype about AI is dangerous when the foundation is missing and why real digitalization means craftsmanship before it becomes art.
The AI dream and the Excel nightmare
Hardly any topic dominates the headlines as much as artificial intelligence. New tools, Chatbots and Automation solutions are intended to revolutionize business processes and multiply productivity. But while large corporations are investing in pilot projects, many small and medium-sized companies (SMEs) are already failing at a simple task: exporting consistent monthly figures from multiple Excel files.
The is no cliché, rather Everyday. In countless Companies exist ten or more from each other separate data silos:
Emails overflow Outlookcustomer appointments are over Calendly booked, contacts in CRM system maintained, for example in HubSpot or Pipedrive. The orders end up in the online shop, stocks end up in a separate merchandise management system (WaWi) or even in another Excel table.
The Gross Disadvantage:
None of these systems typically communicate consistently with the others. Reliable interfaces and synergy effects are missing. The Online shop don’t know what that is Warehouse reports. The CRM has no access to open ones Bills. The accounting is the same Evidence manually because there is no automatic data flow.
Yesterday’s Excel solutions become a patchwork quilt Island solutionswhich have grown over the years, are common On-premise and present IT managers with a maintenance-intensive and hermetically sealed challenge.
Why AI doesn’t work on garbage data
“Garbage in, garbage out” – No artificial intelligence can work meaningfully in this environment of fragmented data.
Because AI trained on incomplete, contradictory or incorrect data produces exactly that: incorrect results. The well-known phrase “Garbage in, garbage out” becomes visible here in practice. So anyone who tries to apply state-of-the-art AI tools to data garbage can only expect expensive, automated data garbage as a result.
This is the real dilemma of many medium-sized companies: the desire for efficiency meets established structures that do not appear digital, but only digitalized. And this is exactly where the real work begins: the craft behind digitization.
Digitalization is not a buzzword, it is a craft
The term “digitalization” has become a buzzword in recent years. But anyone who reduces it to simply introducing new software is missing the point. Digitalization does not mean licensing another SaaS tool or adding a new interface to an existing system. It means eliminating the existing chaos of individual solutions and creating a uniform, central database.
The foundation: Single Source of Truth
This basis is known in technical language as “Single Source of Truth” (SSoT) designated. She stands for one central, reliable data pointto which all employees and systems in the company relate. Instead of individual data sets, which are used separately by sales, accounting, warehouse and human resources management, for example, there is a common structure sensible interfaces, synergies and real gains in efficiency enabled.
This structural standardization is the basis of digital craftsmanship: the clean, comprehensible organization of information flows makes efficient digitalization possible. Only when all areas of the company are on one common platform work, transparency, efficiency and the possibility of thinking about automation in a meaningful way arise.
Haufe X360
A practical solution is the cloud ERP platform Haufe X360which is based on Acumatica’s technology. It combines finance, inventory management, CRM, project controlling and HR in a single system.
The aim is not to replace individual tools, but rather to integrate them one common Data architecture to embed. This means that digitalization becomes structural work on the foundation, instead of a collection of digital gadgets. Only when the database is correct can a company automate processes, make data-based decisions and provide AI applications with meaningful information.
The 66% ROI case: What a modern cloud ERP can achieve today
The reluctance of many medium-sized companies to change to an ERP is understandable. The focus is on concerns about high investments, lengthy adjustment processes and disruptions to day-to-day business. But reality shows: the costs of non-digitization are significantly higher.
Manual work steps, duplicate data maintenance, a lack of transparency and inaccurate reports slow down companies in their day-to-day business. At the same time, they lead to rising personnel costs, unused potential and low scalability.
The hidden costs
- Manual work steps
- Double data maintenance
- Lack of transparency
- Inaccurate reports
- Rising personnel costs
- Unused potential
- Low scalability
The danger of waiting
This is particularly dangerous in economically uncertain times:
Many companies have adapted to this the crisis to wait and seeand thus lose touch. When the upswing returns and all resources are tied up in operational business again the moment for structural renewal has already passed in most sectors.
Scientific data shows that investing in a modern cloud ERP pays off. A ‘Total Economic Impact’ study conducted by Forrester on Acumatica, the technology basis of Haufe X360, puts the ROI at 60% with a payback of 16 months. [Quelle: The Total Economic Impact of Acumatica, 2020]
The Pay show:
Digitalization is not a cost factor, but rather a return project – provided it is implemented structurally. And that is exactly the difference between short-term activism and sustainable corporate development.
First the ERP system Haufe X360, then the AI - this is how medium-sized businesses become truly future-proof
Artificial intelligence is not a replacement for structure. It is its logical continuation. Anyone who tries to start AI projects on a fragile database today is investing in a black box with no foundation. The smart IT manager and the forward-looking CEO therefore first invest in what often remains invisible: in the structure of their data.
A modern one Cloud ERP How Haufe X360 creates the necessary “Single Source of “Truth” – a central, clean and real-time database on which intelligent systems can function. As a specialized cloud ERP, it is not “old” technology, but rather the prerequisite for “new”. It delivers the clean, real-time data that makes AI applications possible and valuable.
IT responsible and CEOs, the her Pursue strategically and with Foresight set up want, invest for this reason first in one clean Data structure as Foundation.
Is this foundation laid, becomes AI from the Buzzword for the real Tool:
Digitalization does not mean running behind technology. It means mastering them and designing the foundations in such a way that innovation becomes viable. Digitize comes before automating. If you pour the foundation correctly, you won’t build a digital house of cards, but rather a sustainable company.
Sources: The Total Economic Impact of Acumatica, Forrester research, 2020; Haufe X360 product information, www.haufe-x360.de
Image sources: AI generated, Ideogram 3.0 Turbo model
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The article AI hype in medium-sized companies? Why we first have to solve the data problem appeared first on BASIC thinking. Follow us too Google News and Flipboard or subscribe to our newsletter UPDATE.
As a Tech Industry expert, I believe that the hype around AI in medium-sized companies is both understandable and necessary. AI has the potential to revolutionize business operations, improve efficiency, and drive growth in ways that were previously unimaginable. However, in order for medium-sized companies to truly unlock the benefits of AI, they must first address the data problem.
Data is the fuel that powers AI algorithms and enables them to make accurate predictions and decisions. Without high-quality, clean, and abundant data, AI systems will struggle to perform effectively. Medium-sized companies often face challenges when it comes to collecting, managing, and utilizing their data. They may not have the resources or expertise to properly structure and analyze their data, which can hinder the success of their AI initiatives.
In order to harness the full potential of AI, medium-sized companies must prioritize solving the data problem. This may involve investing in data management tools and technologies, hiring data scientists and analysts, and implementing data governance policies. By building a strong foundation of data infrastructure, medium-sized companies can ensure that their AI projects are successful and deliver tangible results.
In conclusion, while the hype around AI in medium-sized companies is justified, it is crucial that they first address the data problem in order to fully capitalize on the benefits of AI technology. By investing in data management and governance, medium-sized companies can position themselves for success in the AI-driven future.
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