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4 min read time
On October 6, OpenAI announced an expansion of ChatGPT: developers can now build and distribute interactive apps that live directly inside the ChatGPT interface. The move turns ChatGPT from a conversational assistant into a platform where users can browse, act, and transact without leaving the chat window.
For businesses building AI-enabled products or digital experiences, this new model could become an entirely new distribution channel as well as test of data governance, safety, and reliability.
What OpenAI introduced
Developers can now use OpenAI’s new SDK, built on the Model Context Protocol (MCP), to build third-party integrations or interactive modules that run inside ChatGPT’s environment The apps can display custom interfaces, call APIs, draw from live databases, and remember context across sessions.
At launch, OpenAI is partnering with major consumer brands such as Booking.com, Canva, Spotify, and Zillow. A built-in app directory will follow later this year, along with monetization options that let developers earn revenue from in-chat transactions or premium features.
This turns ChatGPT into something closer to an AI super app. Instead of jumping between websites or mobile apps, users can complete multiple tasks, from booking travel to editing slides to making purchases, within a single conversational hub.
Implications for your AI and data products
Here are some implications for enterprises already investing in GenAI:
New channel for engagement
Your customers and employees may soon expect to access your products or data through ChatGPT as easily as they now use a browser or mobile app. That creates opportunities to deliver real-time, conversational interfaces to existing services.
Integration and orchestration
To perform reliably inside ChatGPT, apps must synchronize with enterprise systems. Real-time data, secure APIs, and model governance will determine whether experiences feel seamless or risky.
Higher experience expectations
When AI is both the interface and the engine, users expect it to be context-aware, accurate, and responsive to live data. That places greater pressure on backend orchestration, not just model performance.
Competitive acceleration
Early adopters that design trustworthy, human-centered in-chat experiences could differentiate quickly. The ability to combine AI intelligence with operational reliability will become a new measure of brand credibility.
Caveats and risks to watch
The arrival of embedded apps in ChatGPT creates new possibilities, but it also introduces new responsibilities. Here are some caveats for enterprises integrating AI into mission-critical workflows.
Data privacy and exposure
When an app inside ChatGPT accesses user or corporate data, sensitive information could inadvertently be shared beyond approved systems. Maintaining clear data boundaries (through minimization, encryption, consent, and retention controls) is essential to protect both compliance and customer trust.
Instruction and prompt leakage
Users can sometimes infer or extract hidden system prompts that govern an AI’s behavior, exposing proprietary logic or business rules. This can be done by accident or deliberately. Regular red-teaming, adversarial testing, and obfuscation strategies help prevent leakage of this intellectual property.
Prompt injection and security attacks
Bad actors can manipulate text inputs to override an app’s intended instructions, reveal confidential information, or trigger unsafe actions. Input validation, layered guardrails, and ongoing monitoring for prompt-based exploits are critical defenses for any AI system that executes real actions.
Regulatory, compliance, and liability risk
Embedded AI falls under growing scrutiny from global and sector-specific regulators. Frameworks such as the EU AI Act and emerging U.S. state AI laws require transparency, auditability, and human oversight. Organizations must document how decisions are made, which data is used, and who is accountable when things go wrong.
Quality, hallucination, and correctness risk
As ChatGPT apps take on real tasks (like booking, recommending, or advising) the cost of a wrong answer rises. A single hallucinated detail can trigger a failed transaction or misinformed decision. Enterprises should implement verification layers, human-in-the-loop review for sensitive cases, and continuous monitoring to preserve accuracy and trust.
Each of these caveats reinforces the same principle: as GenAI moves from experimentation to execution, governance becomes the differentiator between innovation and exposure.
Centific can help you
Building safe, scalable AI experiences requires rigorous governance and responsible design. Centific helps organizations operationalize Responsible AI by combining domain expertise, advanced data security, and continuous oversight.
Our Responsible AI practice helps businesses design, deploy, and monitor AI systems that are transparent, compliant, and resilient. We apply proven frameworks for privacy engineering, prompt-injection defense, and model validation to ensure your AI operates safely across platforms like ChatGPT.
As AI ecosystems evolve from tools to platforms, the challenge is whether you can build responsibly. Centific can help you.
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