Be cautious of solutions that appear easy to implement.

In the current AI hype, the potential effort involved in a future system change is often overlooked. Cloud platforms, low-code/no-code solutions, and AI tools promise efficiency and agility. In practice, however, they increasingly lead companies into expensive dependencies. Imagine using a technical system or service that only works properly with products from the same vendor. If you want to switch to another provider, this involves significant effort, high costs, or technical issues — or it may not be possible at all. This is known as vendor lock-in, i.e. dependency on a single provider. A well-known example is Apple: devices and software work seamlessly together, but poorly with third-party products. Switching from Apple devices to Android or Windows requires time-consuming data migration, alternative cloud storage solutions, and additional precautions. In a private context, this may still be manageable. In mid-sized companies, however, the effort — both financial and organizational — quickly becomes substantial. That is why it is essential to take an end-to-end view before deciding on a specific vendor.

Why vendor lock-in is more than just a cloud problem

The major hyperscalers such as AWS, Azure, and GCP benefit from increasingly deep integration of their cloud services. Switching providers is often hardly possible without high costs, complex data migrations, downtime, and retraining employees on new systems. But vendor lock-in is no longer limited to the cloud alone.

• Low-code / no-code platforms: Once introduced, entire workflows depend on the vendor’s ecosystem. Proprietary data formats, custom API standards, and limited export options make exiting or switching expensive and risky. In addition, it is often unclear how data is processed and stored behind individual connectors.
• AI startups: Many promise fast automation and efficiency gains, but are often black boxes without proper documentation or clean integration into existing landscapes. Switching providers after implementation? In most cases, very costly.
• Pricing models: What starts with attractive pilot pricing often becomes significantly more expensive after the lock-in phase. Alternatives? Rarely viable, because migration costs become too high for decision-makers.

AI between hype and risk

Enormous amounts of capital are currently being invested in the AI market. Investors are, of course, a key driver of innovation and economic growth. Without venture capital or private equity, many technological breakthroughs and jobs would not exist. However, it must be clear that investment decisions in AI or cloud startups are not made out of altruism. For companies, this creates two key risks that must be considered:Risk 1: The startup fails, the technology is discontinued, and your process design collapses. There is no support, no data access, and no roadmap — plus the migration effort already incurred. Risk 2: The startup becomes a market leader and raises prices significantly. What was once a good price-performance ratio disappears, as investors seek to recoup their investment with profit. Customers are locked in. Processes can no longer be migrated easily because the software is deeply embedded in operations. Migration becomes prohibitively expensive and no longer proportionate to the value created. In addition, many startups process data on non-European systems, potentially conflicting with GDPR requirements.

AI projects fail without an end-to-end perspective

AI promises major efficiency gains. However, an MIT study has shown that 95% of enterprise AI pilots currently fail to deliver measurable ROI. The reasons include:

• Wrong expectations: AI is “glued onto” inefficient processes, amplifying existing weaknesses.
• Exponential output from poor input: Without structured data, AI becomes a data garbage machine, producing exponentially poor results.
• Lack of an end-to-end approach: Downstream processes spiral out of control when source data is processed incorrectly — inefficient processes scale into chaos, not success.

The result: AI initiatives can cause more harm than benefit. In the overall picture, processes become less efficient and systems risk collapse. Experiments are still important to understand system limits — but they must be tested in pilots first, to avoid ending transformation initiatives with lower productivity than before.

Checklist: How to avoid vendor lock-in

Before introducing new cloud services or AI tools, ensure you embed exit strategies from the start and retain control over your processes:

• Rely on open interfaces and well-documented APIs.
• Avoid proprietary data formats and ensure export capabilities exist.
• Prefer platforms with clear data sovereignty — ideally EU-hosted or even self-hosted AI solutions
• Review pricing, licensing, and business models carefully, especially for pilots and AI startups.
• No AI without solid process analysis and a true end-to-end view. Automate only where processes are mature, lean, and future-proof. Bordmittel® is happy to support you here.

Planning process improvement and automation with Bordmittel®

Those who adopt solutions today often pay the price tomorrow — especially when a switch becomes necessary, vendors change the rules, or geopolitical conditions restrict certain providers. Successful digitalization requires maximum flexibility and full control over company data and underlying processes. Vendor lock-in is not inevitable. With the right expertise, you can maintain high innovation speed without losing control — and use AI and cloud technologies as true enablers rather than dependencies. Bordmittel® supports you from neutral process analysis to independent design of your digital infrastructure — including exit strategies, data management, and end-to-end process design. This preserves your digital sovereignty and prevents short-term technology hype from becoming an expensive burden tomorrow. Bordmittel® designs digital strategies before the first API key is generated — neutral, vendor-independent, and focused on end-to-end impact.