AI as an accelerator for digitalisation: why waiting until you are "ready" costs you

Introduction: the myth of the "perfect moment"

"We are not ready for artificial intelligence yet. First we have to finish our digitalisation." You hear this, or something close to it, constantly in mid-sized companies. But the thought rests on a basic misunderstanding: digitalisation is not a linear process↗ with a defined end point. It is a continuous transformation.

More problematic still: assuming AI belongs at the end of that process misses the potential of artificial intelligence as an accelerator of digitalisation. According to a study by the German digital association Bitkom↗, 78 % of German companies plan to increase their AI investment over the next two years — a clear sign that AI is being understood as a catalyst rather than a finishing touch.

"Common misconceptions about adopting AI"

This article shows why you do not need to be "perfectly digitalised" to benefit from AI, and how AI can help you deliver your digitalisation projects faster and more efficiently.

AI as catalyst, not as end point

The myth of completed digitalisation

Many companies wrongly assume they must first digitalise every process, hold structured data and comprehensively train their staff before they can introduce AI. That view overlooks the fact that AI systems can help create those supposed prerequisites in the first place:

• Data preparation: modern AI systems can help organise and categorise unstructured data — without that having to happen manually first.

• Process optimisation: AI can identify inefficient processes and suggest improvements, rather than waiting for those processes to already be optimally digitalised.

• Knowledge transfer: AI tools can capture individual knowledge across the company and make it accessible, without a complex knowledge-management system being implemented up front.

Experience shows that companies integrating AI into their digitalisation strategy early move faster and achieve better results than those waiting for "complete" digitalisation.

AI has already arrived

McKinsey's global survey on AI↗ finds that 55 % of companies already use AI in at least one business function, growing at an average of 25 % a year. What is striking: many of them use AI deliberately to advance their digitalisation, not the other way round.

The PwC study "Künstliche Intelligenz sorgt für Wachstumsschub"↗ forecasts that AI will lift German GDP by 11.3 % by 2030. That economic potential rests in no small part on AI acting as a digitalisation accelerator.

Accelerating digital transformation with AI

AI plays a double role in this transformation:

1. As part of digitalisation: AI applications are themselves an important component of digital solutions.

2. As an accelerator of digitalisation: AI helps optimise and speed up the transformation processes themselves.

"AI as an accelerator of digital transformation"

The Deloitte AI study↗ shows that companies treating AI as an integral part of their digitalisation strategy reach digital maturity on average 40 % faster than companies running separate AI and digitalisation strategies.

Five concrete ways AI accelerates digitalisation

1. Automating manual data transfer

One of the biggest obstacles to digitalisation is media breaks and manual data transfer between systems. AI helps considerably here:

• Document and text recognition: OCR↗ combined with AI can extract information from paper documents automatically and move it into digital systems.

• Intelligent data integration: AI-based tooling can analyse, harmonise and normalise data from different sources into consistent formats.

In practice: a mid-sized machine builder used AI tools to digitalise the daily production reports that previously had to be entered by hand. The 15 hours saved per week were reinvested directly into further digitalisation projects.

2. Accelerating systems integration

Connecting IT systems is often a slow and expensive process. AI can act as an intelligent bridge:

• Intelligent data-mapping tools: AI systems can analyse the data structures of different systems and identify connection points automatically.

• Bridging solutions: rather than waiting for full integration, AI tools can serve as an interim layer, transferring and transforming data between systems that are not fully compatible.

Case: a retailer used AI-supported integration tooling to connect its warehouse management to its ERP system. What was originally planned as a project of several months was delivered in three weeks.

3. Structuring unstructured information

Most companies hold large volumes of unstructured data — emails, documentation, customer notes. AI can help structure it and make it usable:

• Semantic analysis: AI systems can analyse unstructured text for topic, sentiment and calls to action.

• Automatic categorisation: documents can be classified automatically and filed into existing structures.

In practice: an insurer used AI to analyse and categorise hundreds of thousands of customer emails. That produced not only faster response times but valuable insight into customer needs, which fed the next round of digitalisation.

"The five most important AI use cases for digitalisation"

4. Intelligent process analysis and optimisation

Optimise a process before you digitalise it — a basic tenet of process management. AI supports that decisively:

• Process mining with AI support↗: AI systems can reconstruct existing processes from log data and identify where the potential sits.

• Intelligent process modelling: from descriptions and examples, AI can draft and refine process models.

In practice: a mid-sized supplier used AI-supported process mining to speed up order processing by 30 % while identifying the manual steps that deserved digitalising first.

5. Knowledge management and decision support

• Intelligent knowledge bases: AI can make company knowledge accessible and deliver it in context.

• Decision-support systems: AI can supply decision-makers with relevant information and forecasts.

Case: an industrial company implemented an AI-based knowledge base that helped staff during digitalisation by surfacing best practice and solutions to common problems. The implementation speed of new digital tools rose by 45 %.

Traditional versus AI-supported digitalisation

In practice: LAUDA-GPT

A clear example of integrating AI into an ongoing digitalisation comes from LAUDA, a leading manufacturer of temperature-control equipment. The company implemented "LAUDA.GPT", a customised AI platform for more than 600 employees — even though digitalisation in several areas was not yet complete.

"LAUDA-GPT: a successful AI implementation alongside ongoing digitalisation"

The rollout ran in three phases:

1. Setup and staff training (3 months)
2. Key-user activation (100+ users)
3. Global rollout in 5 languages

The results speak for themselves:

• 76 % usage rate across all employees
• €1.62 million saved per year
• 1,400+ working hours saved per month
• 300+ daily prompts for process optimisation

What is particularly notable is that the AI implementation itself helped identify and close digital gaps. LAUDA used what it learned through AI to target and accelerate further digitalisation projects.

Transparency note: the LAUDA example is based on a meinGPT customer. The figures were determined from concrete measurements and customer interviews and are fully documented.

Sector-specific potential

Note: this assessment is based on an analysis of available case studies and sector reports. Actual results vary by company, starting position and implementation approach.

Cost-benefit: conventional versus AI-supported digitalisation

Based on an analysis by PwC↗ and our own experience.

Transparency note: the saving potential shown here represents averages from various studies and practical experience. Actual savings vary with the individual situation.

Four steps to successful AI integration

Step 1: identify the digital pain points

Start with an analysis of what is currently blocking your digitalisation:

• Where do manual processes slow down the work?
• Which systems fail to communicate efficiently with each other?
• Where do staff spend time on routine data transfer?

These are the ideal places for a first AI application.

Step 2: low-threshold implementation

Begin with AI solutions that are easy to implement and require no comprehensive system change:

• Chatbots for internal knowledge bases
• AI-supported document analysis
• Text generation for standard communication

What matters: choose cloud-based solutions that work without major IT investment and are GDPR-compliant↗.

Step 3: develop people and technology in parallel

• Train staff in fundamental AI applications
• Identify "AI champions" who can act as multipliers
• Encourage a culture of AI-supported work

According to the Fraunhofer Institute↗, this parallel approach produces an acceptance rate around 40 % higher.

Step 4: scale and integrate strategically

• Extend successful AI applications to further departments
• Feed what AI reveals back into your digitalisation strategy
• Invest in broader AI platforms covering multiple use cases

"A four-step plan for successful AI integration"

Common objections, and what answers them

Conclusion: AI as a strategic advantage

Companies waiting to "finish" their digitalisation before adopting AI risk giving away a decisive advantage. AI is not the icing on a fully digitalised organisation — it is a powerful catalyst that can accelerate the digitalisation process itself.

Integrating AI successfully calls for a pragmatic approach:

1. Identify concrete digital pain points
2. Implement low-threshold AI solutions
3. Develop people and technology in parallel
4. Scale what works, strategically

Companies that take this route find that AI does not only accelerate digitalisation — it produces immediate efficiency gains and competitive advantage, long before the last process is digitalised.

As the VDI/VDE Innovation + Technik↗ study stresses, legal, regulatory and ethical considerations have to be built into innovative applications from the outset alongside the technological possibilities. Which underlines the point: AI is not an isolated technology project but an integral part of digital transformation.

Want to see how AI could accelerate your digitalisation? With a platform like meinGPT you can take the first step — without extensive prior knowledge or system changes.

"A free meinGPT demo"

Book a free demo and see how AI can take your digitalisation to the next level.

Transparency note: this article was written by SelectCode, the company behind the meinGPT AI platform. The information presented is based on current studies and on practical experience from working with many customers. To keep the perspective balanced, independent sources and studies are cited alongside. For strategic decisions we recommend consulting several sources and weighing your company's specific requirements.

Sources

in order of appearance

1. IT-P (2023). "KI in der digitalen Transformation." https://www.it-p.de/blog/ki-digitale-transformation/↗
2. Bitkom (2024). "Künstliche Intelligenz kommt in der Wirtschaft an." https://www.bitkom.org/Presse/Presseinformation/Kuenstliche-Intelligenz-kommt-in-der-Wirtschaft-an↗
3. VDI/VDE Innovation + Technik GmbH (2023). "Chancen und Risiken Künstlicher Intelligenz." https://vdivde-it.de/de/thema/digitalisierung-und-kuenstliche-intelligenz↗
4. McKinsey & Company (2023). "The State of AI in 2023: Global Survey." https://www.mckinsey.com/capabilities/quantumblack/our-insights/global-survey-the-state-of-ai-in-2023↗
5. PwC (2023). "Künstliche Intelligenz sorgt für Wachstumsschub." https://www.pwc.de/de/digitale-transformation/business-analytics/kuenstliche-intelligenz-sorgt-fuer-wachstumsschub.html↗
6. Deloitte (2024). "KI-Studie: Beschleunigung der KI-Transformation." https://www.deloitte.com/de/de/Industries/technology/research/ki-studie.html↗
7. BigData-Insider (2022). "Künstliche Intelligenz beschleunigt die Digitalisierung." https://www.bigdata-insider.de/kuenstliche-intelligenz-beschleunigt-die-digitalisierung-a-775426/↗
8. meinGPT (2024). "LAUDA case study: their own ChatGPT for 600 employees." https://meingpt.com/en/case-studies/lauda-case-study
9. BigData-Insider (2023). "Die Bedeutung von ChatGPT: Datenschutz und Sicherheit." https://www.bigdata-insider.de/die-bedeutung-von-chatgpt-datenschutz-und-sicherheit↗
10. Fraunhofer Institute (2023). "Vergleich von KI-Plattformen." https://www.iais.fraunhofer.de/ki-plattformen-vergleich↗
11. VDI/VDE Innovation + Technik GmbH (2022). "Erklärbare KI: Anforderungen, Anwendungsfälle und Lösungen." https://vdivde-it.de/de/publikation/erklaerbare-ki-anforderungen-anwendungsfaelle-und-loesungen↗

Sources last checked on 7 August 2026. Content at the linked pages may have changed since; for the most current information we recommend consulting the originals directly.