AI makes software more expensive - and opens the door to a different model

Software vendors raise prices due to AI modules, often based on the same language models. This leads to higher costs and fragmentation. Cross-application AI platforms offer a cheaper, smarter and more future-proof alternative that consolidates functions and centrally secures knowledge.

Anyone who has looked at software vendors' invoices in recent months sees a clear trend: prices are going up. Where you used to pay for a CRM license or HR package for basic functionality, you are now increasingly offered an AI module. Smart, convenient and presented as a must-have. But those extras also come with a price tag. And that's exactly where the real discussion begins.

Why software with AI is getting more expensive

The movement is easy to understand. Vendors want to get in on the AI hype and are rapidly adding smart assistants and generative features to their products. Microsoft introduced Copilot in Office, Salesforce has Einstein GPT, Google is putting Duet AI in its Workspace. In fact, Gartner predicts that more than 80% of companies will be using AI applications or APIs by 2026, up from just 5% in 2023 (Gartner). So AI will no longer be an optional gimmick, but a standard part of any software package.

The problem: AI is not free. Under the hood, all those vendors use the same small group of providers of large language models, such as OpenAI, Anthropic, Microsoft Azure OpenAI or Amazon Bedrock. Each query to such a model costs tokens, and thus money. Vendors have to pay that, and pass it on to their customers. A survey by Boston Consulting Group found that 68% of software vendors charge separate fees for AI functionality (BCG). Microsoft charges an additional $30 per user per month for Copilot - good for a price increase of 60-70% over the standard 365 package (L.E.K. Consulting). Salesforce is doing something similar with Einstein: $50 per user per month on top of the existing license.

This trend is not limited to the big players. According to SaaS management platform Zylo, nearly half of organizations now pay extra for AI tools, even if those features are barely used (Zylo SaaS Management Index 2025). And 66.5% of IT leaders report budget overruns due to AI or consumption-based pricing. So costs add up quickly - and often without immediate commensurate value.

The pitfall of "silo-AI"

This brings us to a more fundamental point: AI built into each application separately is called "silo AI." It sounds appealing - your HR system gets a smart assistant, your CRM can take notes automatically, your service desk answers tickets faster - but in practice this poses some major problems.

The first problem is that costs pile up. Each package charges a premium for its own AI module, even though they often all run on the same underlying model. So you pay ten times margin for effectively the same technology.

The second problem is fragmentation. Each AI assistant lives in its own silo. Your HR assistant knows nothing about sales, your finance assistant can't see into the service desk. While the reality is that workflows are always cross-application. An employee falling ill affects not only HR but also planning, finance and operations. A separate AI in one package can never fully oversee that.

And then there's the third problem: loss of memory. Suppose you used AI in your CRM for years to analyze customer interactions and make smart suggestions. If you ever replace that CRM, you lose that accumulated knowledge. The "brain" of your AI is stuck in the application. So you've paid to get smarter, but as soon as you switch, you start from scratch again.

"Silo-AI" is thus an expensive, fragmented and vulnerable route.

The alternative: cross-application AI platforms

On the other hand, new models are emerging. Application-independent AI platforms such as Langdock and ChatGPT Enterprise position themselves as a central layer between organization and AI. Not tied to one tool, but widely deployable.

The benefits are clear. First, cost advantage: you're not paying ten increments for ten separate modules, but one increment for a platform that can buy and deploy more efficiently. AlixPartners writes that AI-native newcomers often have leaner business models and can therefore be cheaper than traditional software companies selling AI as an add-on (AlixPartners).

In addition, such a platform is workflow-oriented. Business processes run across systems, and a central AI can support just that. German pharmaceutical company Merck, for example, rolled out Langdock to 63,000 employees, not to have an AI assistant in each tool separately, but to deploy AI broadly and give employees a single point of contact (TechCrunch).

Finally, there is memory and continuity. An independent platform builds knowledge and context separate from applications. If you ever switch CRM or HR systems, just take the memory with you. AI thus becomes not just a smart function, but a strategic asset that grows with your organization.

And there is an even more fundamental effect: if AI platforms centrally support workflows and knowledge, some of the logic that is in separate applications today simply becomes redundant. Why have three different dashboards or reporting modules, when a central AI can answer your questions directly across all data? Our work is changing, and with it the support we demand from applications is also changing. What are ten tools today may be reduced tomorrow to one integrated AI layer that consolidates and simplifies functions.

Where do you guarantee the value of AI?

This development is becoming increasingly visible, just watch: software products with AI are becoming more expensive, and the sum can add up sharply. "Silo-AI" offers short-term convenience, but leads to higher costs, fragmentation and loss of knowledge. At the same time, there is a market opening for platforms that separate AI from specific applications and organize it centrally, sustainably and across applications.

So the question is not only: "What AI feature do I buy with it?" but above all: "Where do I secure the value of AI in my organization?" Those who bet on a central platform now are investing in grip, continuity and a cost structure that is future-proof.

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