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May 10, 2024
AI as an accelerator for digitalization: Why wait until you are "ready"?
Table of Contents
Introduction: The Fallacy of the "Perfect Timing"
AI as a Catalyst Rather than an Endpoint of Digitalization
Accelerating Digital Transformation with AI
Five Concrete AI Applications as Digitalization Accelerators
Comparison: Traditional vs. AI-Supported Digitalization Approach
Case Study: LAUDA-GPT
Industry-Specific AI Potential for Accelerating Digitalization
Cost-Benefit Comparison: Conventional vs. AI-Supported Digitalization
The Four Steps to Successful AI Integration
Common Objections and Their Rebuttal
Conclusion: AI as a Strategic Competitive Advantage
Introduction: The Fallacy of the "Perfect Timing"
“We are not yet ready for Artificial Intelligence. First, we must complete our digitalization.” Such or similar statements are often heard in German companies, especially in the SME sector. However, this thought is based on a fundamental misunderstanding: Digitalization is not a linear process with a defined endpoint, but a continuous transformation.
Even more problematic: The assumption that AI should only be used at the end of this process overlooks the enormous potential that Artificial Intelligence offers as an accelerator of digitalization. According to a recent study by the Digital Association Bitkom, 78% of German companies plan to increase their investments in AI technologies in the next two years – a clear sign that AI is understood not as an endpoint but as a catalyst for digital transformation.
"Common Misunderstandings in AI Implementation"
The societal change of our time is strongly characterized by the digitalization of almost all areas of life and work. Like no other development, it offers great economic and social potentials that can sustainably change our coexistence. This is where AI comes into play – not as the crowning achievement of completed digitalization but as a powerful tool to accelerate this transformation.
This blog post shows why you do not need to be "perfectly digitalized" to benefit from AI and how AI technologies can help you implement your digitalization projects faster and more efficiently.
AI as a Catalyst Rather than an Endpoint of Digitalization
The Myth of Completed Digitalization
Many companies mistakenly believe that they must first digitize all processes, maintain structured data, and thoroughly train their employees before they can introduce AI technologies. However, this perspective overlooks that AI systems can actually help create these supposed prerequisites:
Data Preparation: Modern AI systems can help organize and categorize unstructured data – without the need for manual processing beforehand.
Process Optimization: AI can identify inefficient processes and make improvement suggestions, rather than waiting for these processes to already be optimally digitized.
Knowledge Transfer: AI tools can capture individual knowledge within the company and make it accessible to all, without implementing complex knowledge management systems in advance.
Experience shows that companies that integrate AI early into their digitalization strategy make faster progress and achieve better results than those that wait for a "complete" digitalization.
AI Has Already Arrived in the Present
McKinsey states in its Global Survey on AI that 55% of companies are already using AI in at least one business function – with annual growth rates averaging 25%. Particularly interesting: Many of these companies use AI specifically to drive their digitalization forward, not vice versa.
The PwC study "Artificial Intelligence Drives Growth" predicts that AI will increase Germany's gross domestic product by 11.3% by 2030. This enormous economic potential results not least from the fact that AI acts as a digitalization accelerator.
Accelerating Digital Transformation with AI
The digital transformation accelerates our life and provides immense amounts of information and opportunities to every individual. It connects things, automates processes, and thereby makes human intervention unnecessary or even impossible in many cases.
AI plays a dual role in this transformation:
As Part of Digitalization: AI applications are themselves an important component of digital solutions.
As an Accelerator of Digitalization: AI helps optimize and accelerate digital transformation processes.
"AI as an Accelerator of Digital Transformation"
The Deloitte AI Study shows that companies that see AI as an integral part of their digitalization strategy achieve 40% faster digital maturity on average than companies with separate AI and digitalization strategies.
Five Concrete AI Applications as Digitalization Accelerators
If you want to advance your digitalization, AI can support you in various areas:
1. Automating Manual Data Transfers
One of the biggest obstacles to digitalization is media disruptions and manual data transfers between systems. AI solutions can significantly help here:
Document and Text Recognition: OCR (Optical Character Recognition) in combination with AI can automatically extract information from paper documents and transfer it to digital systems.
Intelligent Data Integration: AI-based tools can analyze data from various sources, harmonize it, and convert it into uniform formats.
Case Study: A medium-sized machine manufacturer used AI tools to digitalize daily production reports that were previously recorded manually. The time savings of 15 hours per week were directly reinvested into further digitalization projects.
2. Accelerating System Integration
Connecting various IT systems is often a time-consuming and costly process. AI can serve as an "intelligent bridge":
Intelligent Data Mapping Tools: AI systems can analyze the data structures of different systems and automatically identify connection points.
Bridging Solutions: Instead of waiting for full integration, AI tools can act as an interim solution by transferring and transforming data between not fully compatible systems.
Case Study: A trading company used AI-supported integration tools to connect its warehouse management with the ERP system. What was originally planned as a months-long project was realized with AI support in three weeks.
3. Systematizing Unstructured Information
Many companies have large amounts of unstructured data – from emails to documentation and customer notes. AI can help structure and make this information usable:
Semantic Analysis: AI systems can analyze unstructured texts for topics, sentiments, and calls to action.
Automatic Categorization: Documents can be automatically classified and integrated into existing system structures.
Practical Example: An insurance company implemented AI to analyze and categorize hundreds of thousands of customer emails. This not only led to faster response times but also provided valuable insights into customer needs that were used for further digitalization.
"The Five Key AI Applications for Digitalization"
4. Intelligent Process Analysis and Optimization
Before digitalizing processes, they should be optimized – a principle of process management. AI can play a crucial role here:
Process Mining with AI Support: AI systems can reconstruct existing processes from log data and identify optimization potentials.
Intelligent Process Modeling: Based on descriptions and examples, AI can create and refine process models.
Case Study: A medium-sized supplier was able to accelerate its order processing by 30% through the use of AI-supported process mining while simultaneously identifying numerous manual steps that were prioritized for digitalization.
5. Knowledge Management and Decision Support
Digitalization often requires enterprise-wide knowledge and quick, informed decisions:
Intelligent Knowledge Databases: AI can make business knowledge accessible and provide it contextually.
Decision Support Systems: AI systems can provide decision-makers with relevant information and forecasts.
Case Study: An industrial company implemented an AI-based knowledge database that assisted employees in digitalization by providing best practices and solutions for common problems. The implementation speed of new digital tools increased by 45%.
Comparison: Traditional vs. AI-Supported Digitalization Approach
Aspect | Traditional Approach | AI-Supported Approach | Advantage of AI Approach |
|---|---|---|---|
Prerequisites | Complete digitalization as a prerequisite for AI | AI as a tool to accelerate digitalization | Faster start, parallel development |
Data Quality | High data quality must be ensured before AI use | AI helps identify and cleanse data quality issues | Continuous improvement instead of blockage |
Process Optimization | Processes are optimized first, then digitized | AI identifies optimization potentials during digitalization | More efficient processes from the start |
Investment Sequence | First invest in digitalization, then in AI | Parallel investment in digitalization and AI | Better ROI through synergy effects |
Need for Skilled Workers | High demand for digitalization experts | Combination of AI and less specialized staff | Mitigation of skilled labor shortages |
Implementation Time | Longer implementation time due to sequential approach | Accelerated implementation through parallel processes | Time-to-value significantly shortened |
Future Security | Risk of technological obsolescence | Continuous adaptation through self-learning systems | Higher adaptability to market changes |
Case Study: LAUDA-GPT
An illustrative example of the successful integration of AI into an ongoing digitalization process comes from LAUDA, a leading manufacturer of temperature control devices. The company implemented a personalized AI platform with "LAUDA.GPT" for over 600 employees – although digitalization was not yet fully completed in some areas.
"LAUDA-GPT: Successful AI Implementation Despite Ongoing Digitalization"
The implementation took place in three phases:
Setup and employee training (3 months)
Key user activation (100+ users)
Global rollout in 5 languages
The results speak for themselves:
76% usage rate among all employees
€1.62 million in savings per year
1,400+ hours saved per month
300+ daily prompts for process optimization
Particularly noteworthy is that the AI implementation has even contributed to identifying and closing digital gaps. LAUDA used the insights gained through AI to target and accelerate further digitalization projects.
Transparency Note: The LAUDA case study is based on a client of meinGPT. The presented figures were derived from concrete measurements and customer surveys and are fully documented. For an independent assessment of AI platforms, we additionally recommend the reports from the Fraunhofer Institute.
Industry-Specific AI Potential for Accelerating Digitalization
Different industries benefit to varying degrees from the use of AI as a digitalization accelerator:
Industry | Digitalization Challenges | AI Solution Approaches | Acceleration Potential |
|---|---|---|---|
Manufacturing Industry | Paper-based processes, silo solutions | Predictive Maintenance, intelligent quality control | ★★★★★ |
Trade & E-Commerce | Omnichannel integration, inventory management | Personalization, intelligent demand forecasting | ★★★★☆ |
Banks & Financial Services | Regulatory requirements, legacy systems | Automated compliance checks, fraud detection | ★★★★☆ |
Healthcare | Sensitive data, complex documentation | Intelligent document analysis, assisted diagnosis | ★★★★☆ |
Logistics & Transport | Complex supply chains, route optimization | AI-based route planning, shipment tracking | ★★★★★ |
Insurance | Paper-intensive processes, fraud risks | Automated claims processing, risk assessment | ★★★★☆ |
Construction | Project planning, decentralized work processes | BIM integration, construction progress analysis | ★★★☆☆ |
Education | Diverse learning needs, complex administration | Personalized learning paths, automated assessment | ★★★☆☆ |
Energy Supply | Network management, consumption forecasting | Smart grid optimization, consumption analysis | ★★★★☆ |
Public Sector | Bureaucracy, paper-based administration | Automated application processing, intelligent forms | ★★★★★ |
Note: The evaluation of acceleration potential is based on an analysis of available case studies and industry reports. Actual results may vary depending on the company, initial situation, and implementation approach.
Cost-Benefit Comparison: Conventional vs. AI-Supported Digitalization
The integration of AI into digitalization projects offers significant economic advantages:
Cost Aspect | Conventional Digitalization | AI-Supported Digitalization | Savings Potential |
|---|---|---|---|
Implementation Costs | High (extensive system changes) | Medium (step-by-step introduction) | 20-35% |
Time Requirement | 100% (reference value) | 60-70% (through automation) | 30-40% |
Staff Requirement | High (many specialists needed) | Moderate (combination of AI and fewer specialists) | 15-30% |
Training Effort | High (complex system training) | Medium (more intuitive interfaces) | 10-25% |
Error Costs | High (manual processes) | Low (automated validation) | 40-60% |
ROI Period | 24-36 months | 12-18 months | 50% faster |
Maintenance Costs | High (many individual systems) | Medium (consolidated platforms) | 20-40% |
Data based on an analysis by PwC and our own experiences
Transparency Note: The savings potentials mentioned here represent average values from various studies and practical experiences. Actual savings may vary depending on the individual situation. For a well-founded assessment of your specific savings potential, we recommend an individual analysis.
The Four Steps to Successful AI Integration
How can companies concretely use AI as a catalyst for their digitalization? The following 4-step plan offers a practical guide:
Step 1: Identification of "Digital Pain Points"
Start with an analysis of your current digitalization obstacles:
Where do manual processes slow down workflows?
Which systems do not communicate efficiently with each other?
Where do employees spend time on routine data transfer?
These areas are ideal fields for initial AI applications. According to the AI Monitor 2021, 69% of German companies have recognized that AI represents the most important future technology – identifying pain points is the first step to benefiting from this technology.
Step 2: Low-Threshold AI Implementation
Start with easily implementable AI solutions that do not require extensive system changes:
AI-Supported Document Analysis
Text Generation Tools for Standard Communication
It is important to choose cloud-based solutions that can be implemented without large IT investments and are GDPR-compliant.
Step 3: Parallel Development of Employees and Technology
AI implementation and employee development should go hand in hand:
Train employees in basic AI applications
Identify "AI champions" who can act as multipliers
Promote a culture of AI-supported work
According to a study by the Fraunhofer Institute, this parallel approach leads to a 40% higher acceptance rate of AI technologies.
Step 4: Scaling and Strategic Integration
Based on initial successes, you can expand your AI strategy:
Extend successful AI applications to other departments
Integrate AI insights into your digitalization strategy
Invest in more comprehensive AI platforms that cover multiple use cases
"4-Step Plan for Successful AI Integration"
Common Objections and Their Rebuttal
Despite the obvious advantages, there are often reservations against the early use of AI. Here are the most common objections and corresponding counterarguments:
Objection | Rebuttal | Fact-Based Justification |
|---|---|---|
"We do not have enough data for AI" | Modern AI systems can also work with limited data | Generative AI models can be supplemented with general knowledge and even help improve your data collection |
"Our employees are not ready for AI" | The user-friendliness of modern AI applications does not require prior knowledge | Platforms like meinGPT offer intuitive interfaces and integrated training modules |
"AI projects are too expensive and complex" | There are numerous affordable entry solutions | Cloud-based "AI as a Service" solutions significantly reduce both costs and complexity |
"AI results are not traceable" | There are increasingly tools for explainable AI | Explainable AI applications build trust and make decisions transparent |
"We should first modernize our IT infrastructure" | AI can actually help with modernization | AI identifies weaknesses and prioritizes modernization measures |
Conclusion: AI as a Strategic Competitive Advantage
Companies that wait to "complete" their digitalization before employing AI risk missing out on crucial competitive advantages. AI is not the cherry on top of a fully digitized organization, but a powerful catalyst that can accelerate the digitalization process itself.
The successful integration of AI into the digitalization strategy requires a pragmatic approach:
Identify concrete "digital pain points"
Implement low-threshold AI solutions
Develop employees and technology in parallel
Strategically scale successful approaches
Companies that follow this path will find that AI not only accelerates digitalization but also creates immediate efficiency gains and competitive advantages – long before the last process is digitized.
As the study by VDI/VDE Innovation + Technik GmbH emphasizes: "In addition to the technological possibilities offered by Artificial Intelligence in many areas of life and business, legal, regulatory, and ethical aspects must also be included from the outset in the development of innovative applications." This underscores that AI is not an isolated technology project but an integral part of digital transformation.
Do you want to learn how AI can accelerate your digitalization efforts? With an AI platform like meinGPT, you can take the first step – without extensive prior knowledge or system changes.
"Free AI Demo from meinGPT"
Book a free demo now and discover how AI can elevate your digitalization to the next level.
Transparency Note: This article was written by SelectCode, the company behind the AI platform meinGPT. The information presented is based on current studies as well as practical experiences from collaboration with numerous clients. To ensure a balanced perspective, independent sources and studies have also been cited. We recommend consulting various sources and considering the specific requirements of your company when making strategic decisions.
References
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All sources were last checked on May 12, 2025. Please note that the contents of the linked websites may have changed since our last access. For the most current information, we recommend consulting the original sources directly.
