3
min read
When AI ends up being the only business tool
ChatGPT's new Chat with Apps turns the language model into the central interface to every system, which collapses the value of manual reporting work and makes private, RAG-based language models the actual prerequisite for any company that wants to stay in control of its data and business model.
Chat with Apps lets ChatGPT talk directly to your systems, which means clients will soon stop ordering reports and start asking the AI themselves. The value of manual data processing collapses overnight, and the only sustainable competitive advantage left is owning a private language model that actually knows how your business works.
Chat with Apps is a before-and-after moment for the enterprise
How ChatGPT's new "Chat with Apps" feature changes everything for knowledge-based companies, and why the demand for private language models is exploding.
The launch of Chat with Apps marks a before-and-after moment in enterprise digitalization. OpenAI now lets language models connect directly to external applications and data sources. This means users no longer need to switch between systems, files, and menus. You simply ask ChatGPT to retrieve a report, update a document, or generate an analysis, and it does it for you.
The implications are profound. Where organizations once needed consultants to build integrations between systems, a language model can now connect directly to cloud solutions with a single click. For accounting firms, law offices, and consulting companies, this means clients will increasingly expect to retrieve information themselves. Instead of ordering reports and analyses, they'll ask the AI directly.
This doesn't just change workflows. It changes business models.
Before Chat with Apps | After Chat with Apps | |
|---|---|---|
How clients get information | Order reports from professionals | Ask the AI directly |
Who builds the integrations | Consultants, on custom projects | The language model, with one click |
What the firm gets paid for | Execution and data processing | Insight, judgment, and trusted context |
Time to an answer | Days or weeks | Seconds |
An accounting firm that once billed for reporting and data collection will now find that a client can ask ChatGPT to "show all invoices over 50,000 from last quarter" and get the answer in seconds. Much of the value in manual data processing disappears. The competitive advantage shifts from execution to insight.
What will matter going forward is building and owning private language models that understand how the business actually works, with proprietary sources, internal guidelines, and verified data.
Here lies the challenge: while ChatGPT and similar systems are powerful in the consumer market, they cannot be used directly on all enterprise data. The information is confidential, complex, and often distributed across legacy servers, local files, and custom systems. To unlock real value, a dedicated layer must be built between the model and the data: a private, secure information foundation that can be indexed and queried across sources.
The need for private language models
Once a language interface gains access to your systems, the question is no longer if you use AI, but how. What previously required custom development will now be available to everyone, provided the right data access is in place. This makes data governance, access control, and indexing mission-critical topics.
Private language models, built using Retrieval-Augmented Generation (RAG), make it possible to combine proprietary data sources with the model's linguistic understanding. Instead of guessing based on probable words, the model retrieves answers from actual documents, systems, and databases. This lets organizations maintain security, traceability, and control, while users enjoy the same simple experience they know from ChatGPT.
For companies with many employees and diverse systems, the key question becomes: how do you bring everything together in one solution? How do you give employees a unified gateway to information without risking data leakage or loss of control?
The new architecture
The next phase of digitalization is not about buying more systems. It's about connecting them. As ChatGPT begins to communicate directly with apps, it becomes crucial that organizations have an internal architecture capable of managing:
Capability | What it ensures |
|---|---|
Access control | Sensitive information is shared only with the right people |
Indexing | Historical documents, emails, and reports can actually be retrieved in real time |
Integration with private language models | All content is used securely and remains traceable |
This requires a new kind of IT strategy. The winners won't be those who buy the most AI tools. They'll be those who build the right infrastructure to use them.
Europe's dilemma
Europe now faces a dilemma. American players are leading the development, but they don't necessarily meet European standards for security, data storage, and compliance. If AI is to be used in industries handling sensitive information, such as healthcare, finance, law, and the public sector, we have to adopt local, private solutions.
This is where Norway and Europe can take a position. We need language models with local data processing. It's not just a question of technology. It's about sovereignty and trust.
With Chat with Apps, AI becomes the work tool among systems. Users will expect to communicate directly with their systems and get answers. The real differentiation going forward will lie in who can make this happen securely, efficiently, and on their own data.
Private language models are not an addition to ChatGPT. They are the prerequisite for companies to truly use the technology.
And that's where the real competition begins.
Are you in need of a private language model and a secure, compliant AI solution? Reach out to us and we'll help you.
