AI Product Development & Innovation in Indonesia

Our programme covers the March 2026 compliance deadline, data localisation requirements, and progressive sanctions under GR 71/2019's implementing regulation. We help tech teams assess which of their systems qualify as Public Electronic Systems and build compliance plans.

Yes. If your company serves financial sector clients, we cover OJK's Banking AI Governance guidelines (April 2025) including human-in-the-loop requirements, AI Committee structures, and audit trail mandates that your products and services must support.

We focus on applied AI for business outcomes rather than data science theory. While Algoritma and Indonesia AI offer excellent technical training, our programmes build the governance, change management, and strategic capabilities that complement technical skills.

Yes. We help teams identify complementary pathways including the Digital Talent Scholarship (60,000 scholarships in 2025), elevAIte Indonesia (Microsoft-KOMDIGI partnership for 1 million participants), and Kartu Prakerja subsidies for individual team members.

Traditional product analytics teaches you to use tools (Amplitude, Mixpanel) to track events and build dashboards. This AI-focused training teaches you to use AI to predict user behavior, automate insight discovery, and build intelligent product features. You'll learn behavioral segmentation (AI clustering), impact forecasting (AI prediction), and feedback synthesis (AI NLP) — capabilities beyond traditional event tracking.

Minimum: (1) Some form of event tracking (even basic GA4 or Mixpanel free tier), (2) User feedback channels (support tickets, NPS surveys, or reviews). Ideal: Product analytics platform (Amplitude, Heap, Mixpanel) with historical data. During Discovery, we'll assess your current setup and recommend quick-win improvements to maximize training value. Even basic event data enables powerful AI insights.

Yes! Module 4 is designed for non-AI-native products adding intelligent capabilities. You don't need a team of ML engineers. We'll show you how to: (1) Use pre-built AI APIs (OpenAI, Google AI) to add smart features quickly, (2) Build simple recommendation engines using collaborative filtering, (3) Partner with AI vendors for specialized capabilities. Goal: embed AI in your product to improve UX, not become an AI research company.

Critical concern addressed throughout the training. We teach AI feature validation: (1) Use AI analytics to identify high-frequency pain points first, (2) Prototype AI features using Wizard of Oz testing (manual behind the scenes), (3) Launch AI features to small user segments with opt-in/opt-out, (4) Measure adoption and satisfaction before full rollout. AI should solve real user problems, not be AI for AI's sake.

Highly relevant for both. B2B SaaS examples in the training: AI usage insights identifying expansion opportunities, AI onboarding optimization reducing time-to-value, AI feature adoption tracking for CSM prioritization, AI feedback analysis from customer calls and support tickets. Consumer product examples: AI personalization, recommendation engines, smart notifications. We customize content to your business model during Content Customisation.