Google is embedding Gemini AI across the Android app lifecycle and boosting enterprise app adoption by addressing key concerns.
The centrepiece of Google’s announcement is the Alpha release of the ML Kit GenAI Prompt API. This API allows developers to go beyond pre-built functions like summarisation and build custom AI features that run using the on-device Gemini Nano model.
For organisations managing sensitive information, this is a key development. By processing data locally, the Prompt API enables generative AI features where user data “never leaves their device”.
This on-device approach is a distinct advantage for applications in regulated industries like finance, healthcare, and legal services, where data sovereignty and GDPR compliance are non-negotiable. It also enables low-latency capabilities for field service, logistics, and manufacturing apps that must function reliably in areas with poor or non-existent connectivity.
Google highlighted an illustrative enterprise use case from parcel delivery service Kakao. By implementing the Prompt API to automate the extraction of delivery details from unstructured text messages, Kakao transformed a “slow, manual process” into a simple request. The business impact was immediate and quantifiable: Google’s report notes this single feature “reduced order completion time by 24 percent and boosted new user conversion by an incredible 45 percent”.
Beyond logistics, the Prompt API opens a range of enterprise Android app possibilities, such as intelligent document scanning to categorise receipt data, real-time sentiment analysis of customer feedback without a cloud round-trip, or secure information extraction from emails to populate an agent’s calendar.
However, for capabilities requiring more power than an on-device model can provide, Google is also strengthening its cloud-based AI offerings via the Firebase SDK.
RedBus, for example, used Gemini Flash via Firebase AI Logic to enhance its user review system. Users can now leave voice reviews in their native languages, which Gemini then processes into “longer, richer and more reliable user reviews”. This hybrid ecosystem allows tech leaders to make case-by-case decisions on whether to prioritise the privacy of on-device AI or the raw power of cloud AI.
To support the creation of these new AI experiences, Google is infusing “agentic experiences” into Android Studio. The new Agent Mode allows developers to describe complex goals in natural language, after which the AI agent “plans and executes changes on multiple files across your project”.
This effort directly targets developer efficiency, a key metric for any CIO. Google cited Pocket FM as a reference, which reported “an impressive development time savings of 50 percent”. Furthermore, Google is developing a new Android benchmark for LLMs. The goal is to provide a “north star of high quality Android development” to help model makers improve their AI assistants, giving enterprise development teams a wider choice of effective AI coding partners.
This focus on efficiency also extends to business management within the Google Play Console. New Gemini-powered features aim to help product owners “spend less time interpreting data and more time acting on key insights”.
One such feature is AI-powered localisation, where integrating Gemini directly into the Play Console now provides high-quality translations for app strings “at no cost”. This lowers the financial and logistical barriers for enterprises looking to scale their applications globally. Another new tool provides automated chart summaries; this feature on the ‘Statistics’ page automatically generates natural-language descriptions of key trends and events affecting app performance metrics.
While these tools promise welcome gains, enterprises must approach implementation with operational realism. The new Prompt API is still in Alpha. Importantly, it “performs best on the Pixel 10 device series”, which runs the latest Gemini Nano. This creates a potential fragmentation challenge for businesses managing a diverse fleet of corporate devices or supporting a ‘Bring Your Own Device’ (BYOD) policy. Prototyping can begin using the related Gemma 3n model, but full-scale deployment will require careful hardware and software lifecycle management.
For high-privacy or offline needs, teams should begin prototyping with the ML Kit Prompt API immediately to identify high-value use cases in compliance-heavy or field-service-oriented parts of the business. The potential for a secure, low-latency, offline-capable AI is a powerful competitive differentiator.
Google is providing the AI tools to build smarter, more secure, and more efficient Android enterprise apps. The challenge now lies in deploying them effectively to create business value.
See also: Can an open-source framework solve AI agent complexity?
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