🟠What Caught My Attention This Week
Curated insights on what I saw this week as it relates to Intelligent Products.
A wave of AI and robotics developments from robot soccer to on-device intelligence, and breakthroughs in business and customer intelligence point to a future where AI not only informs decisions but acts in the physical world.
Product Thinking and Strategy
AWS is consolidating its BI and generative‑AI tools into the new Q Business Suite, combining QuickSight, Q Business, and Q Apps to unify internal workflows.
Oracle and Qlik were both named Leaders in Gartner’s 2025 Magic Quadrant for Analytics & BI Platforms.
A — Artificial Intelligence
Salesforce CEO Marc Benioff revealed that internal AI agents now complete 30–50% of tasks. Their in-house Agent Force has a 93% success rate in customer interactions.
A Gartner–Euromonitor study reports strong enterprise AI adoption but shows that only 40% of users fully trust generative AI outputs.
B — Business Intelligence
The AWS Q Business Suite is designed to make it easier to generate BI reports, even from platforms like Salesforce, directly inside a unified AI dashboard.
InData Labs projects mobile BI will grow at a CAGR of 22% through 2030, making BI more real-time and accessible.
C — Customer Intelligence
At Cannes Lions 2025, CMOs from Mercedes-Benz, Citi, and elf. emphasized how AI is reshaping content creation, workflow automation, and customer insight extraction despite unclear ROI.
Marketers are integrating chatbots and dynamic AI tools to segment and respond to customers faster, but many are highlighting the need to disclose AI usage clearly.
Physical Intelligence
Humanoid robots played a 3-on-3 soccer match in Beijing using reinforcement learning, vision systems, and physical coordination to complete games without human intervention.
Google’s Gemini Robotics On-Device is now powering robots to operate offline critical for latency-sensitive use cases.
World Economic Forum emphasized that “physical AI” should focus on understanding the world, not just automating it.
Data
Synthetic data is enabling safer, faster experimentation
Gartner predicts 60% of data used in AI/analytics in 2025 will be synthetic. Tools like MOSTLY AI, K2View, and DataGen are helping simulate realistic data for testing without privacy constraints.
Use case: A fintech product uses synthetic customer data to stress-test fraud detection AI without exposing real user info.
Why it matters: Synthetic data dramatically speeds up model development while maintaining privacy and compliance.
AI is reshaping database and pipeline automation
Autonomous data systems are emerging that self-optimize, self-heal, and self-document reducing manual intervention .
Use case: A pipeline automatically retries, rebalances, or reroutes jobs when latency or errors appear, without human intervention.
Why it matters: Reduces operational toil, boosts data reliability, and frees engineers to focus on product features not firefighting.
Why it matters to Intelligent Products broadly
Intelligent products are evolving from passive insight generators to real-world action agents.
AI + BI + CI are converging into unified workflows and decision layers.
Transparency, explainability, and control remain the linchpin for user trust.
Edge and offline AI, especially for robotics and smart devices, unlocks new use cases in low-connectivity or real-time physical environments.
My Point of View: What I learned
AI that act is becoming more valuable than AI that only answers, physical intelligence is now core to product strategy.
User trust is fragile. As AI becomes invisible in workflows, clear boundaries and disclosures must exist.
Tool convergence is accelerating what used to be five platforms is becoming one integrated intelligent stack.
Intelligent products now require orchestration across data, analytics, personalization, and physical capabilities not just great UI.
Sources
Other Reads
This image below caught my attention on future of jobs, see more on Generative AI.
Thanks for the weekly recap!