In this episode we spoke with Damien Wong, Senior Vice President for Asia Pacific at Tricentis, about how Agentic AI is redefining software quality assurance (QA) for enterprises navigating digital transformation. Damien shared his journey through 30 years in enterprise technology and explained how Tricentis is pioneering a future where autonomous testing drives both speed and reliability in software delivery.

We explored why QA is fast becoming a board-level priority, how AI is removing the human bottlenecks in testing, and why quality is now existential in sectors like finance, healthcare, and government.

Key Insights:

• From manual to agentic: QA has evolved from manual testing to script-based automation, to codeless model-based testing, and now to agentic test automation. The equivalent of moving from driving a car to a fully autonomous vehicle.

• AI-accelerated quality: Tricentis' agentic automation enables systems to autonomously create, execute, and adapt tests, dramatically accelerating release cycles and reducing risk from untested code.

• Quality as a board concern: In Tricentis' Quality Transformation Report, two-thirds of organizations admitted to shipping untested code, making QA failures a C-suite and reputational risk rather than an IT issue.

• Compliance through transparency: With acquisitions like SeaLights, Tricentis helps enterprises prove that every code change has been tested—critical for regulated sectors and global compliance.

• Bring-your-own-AI flexibility: Tricentis' support for the Model Context Protocol (MCP) allows companies to plug in their own private AI models, ensuring data security and regulatory control while leveraging AI capabilities.

• Empowering, not replacing, engineers: AI-driven testing shifts QA teams from repetitive test creation to strategic oversight, while tools like NeoLoad with natural language prompts make performance testing accessible to non-technical users.

• Industry impact: From Zespri's self-healing ERP testing to preventing outages like Haribo's global gummy bear disruption, intelligent QA ensures business continuity and customer trust.

• Future outlook: With 90% of code expected to be AI-generated within a year, the ability to test at AI speed will define competitive advantage in enterprise software delivery.

IoT ONE case study database: https://www.iotone.com/case-studies
The Industrial IoT Spotlight podcast is produced by Asia Growth Partners (AGP): https://asiagrowthpartners.com

In this episode, we spoke with Sean Hehir, CEO, and Jonathan Tapson, Chief Development Officer, of BrainChip about neuromorphic computing for edge AI. We covered why event-based processing and sparsity let devices skip 99% of useless sensor data, why joules per inference is a more honest metric than TOPS, how PPA (power, performance, area) guides on-device design, and what it will take to run a compact billion-parameter LLM entirely on device.

We also discussed practical use cases like seizure-prediction eyewear, drones for beach safety, and efficiency upgrades in vehicles, plus BrainChip's adoption path via MetaTF and its IP-licensing business model.

Key insights:

• Neuromorphic efficiency. Event-based compute minimizes data transfer and optimizes for joules per inference, enabling low-power, real-time applications in medical, defense, industrial IoT, and automotive.

• LLMs at the edge. Compact silicon and state-based designs are pushing billion-parameter models onto devices, achieving useful performance at much lower power.

• Adoption is designed to be straightforward. Models built in standard frameworks can be mapped to BrainChip's Akida platform using MetaTF, with PPA guiding silicon optimization and early evaluation possible through simulation and dev kits.

• Compelling use cases. Examples include seizure-prediction smart glasses aiming for all-day battery life in a tiny form factor and drones scanning beaches for distressed swimmers. Most current engagements are pure on-edge, with hybrid edge-plus-cloud possible when appropriate.

IoT ONE database: https://www.iotone.com/case-studies

The Industrial IoT Spotlight podcast is produced by Asia Growth Partners (AGP): https://asiagrowthpartners.com/

In this episode we spoke with Mike Rohrmoser, VP of Product Management for OEM Solutions at Digi, a global provider of mission-critical IoT connectivity products and services. We explored how manufacturers are addressing labor shortages with IoT and automation, the trade-offs between retrofitting existing factories and building new ones, the evolving sensor and connectivity landscape, and practical steps to scale IoT pilots into production.

Key insights:

• Retrofitting existing plants is often the smarter move. Brownfield upgrades can cost 40–60% less than new builds and achieve faster returns when paired with business-focused use cases and retrofit connectivity.

• Sensors and networks must be judged as a whole system. Industrial buyers weigh accuracy, deployment simplicity, and lifetime cost over unit price, with wireless IO-Link and LTE Cat 1 gaining traction and 5G RedCap on the horizon.

• Edge AI is real, but focused. Today it is most effective in computer vision for quality inspection and counting, while new designs anticipate broader workloads as adoption matures.

• GenAI augments people, not machines. Its strengths are in analysis, documentation, and device management, while safety-critical real-time control remains firmly in the domain of conventional automation.

• Scaling pilots requires proving value early. Many initiatives stall when they start with technology instead of problems; success depends on production-ready components, operator trust, and leadership alignment.

IoT ONE database: https://www.iotone.com/case-studies
The Industrial IoT Spotlight podcast is produced by Asia Growth Partners (AGP): https://asiagrowthpartners.com/