Antonio Grasso

Expertise is no longer only about knowing the right answer. In AI-driven work, it also depends on asking better questions, reading context, and adapting judgment when conditions change. Microblog by @antgrasso

Business internet is becoming a resilience layer. When connectivity is essential to serving customers and adapting to change, the quality of the decision shows up in how easily the business keeps operating.

A connected factory without control is just a faster way to make mistakes. When machines share live data, every signal can affect production, so automation needs purpose, reliable data, and clear human responsibility. Microblog by @antgrasso about #SmartManufacturing #IoT The point is simple: connecting machines does not automatically make a factory smarter. A connection is useful when it helps production work better, improves maintenance, or makes the process safer. Data is where many problems start. If sensor data is wrong, late, or poorly protected, automated systems can act with confidence on bad information. In a factory, that can affect quality and safety very quickly. Security also needs to move closer to the shop floor. Remote access, controllers, and industrial networks are no longer background infrastructure. They are part of the production system. This is why IT and OT cannot work as separate worlds. Connected production needs shared rules and visible responsibility, so people can understand who owns the data, who controls the process, and how automated decisions are checked.

For years, conversations ended with “now go fill out the form.” Magic Canvas explores a different idea: letting the conversation complete the task while it is happening. That could change how we think about digital interaction. @tavus partner.

AI adoption stands on more than the technology people can see. Tools may look like progress, but weak foundations can make the whole structure fragile. Buying AI is the visible part, while making it work is the part that many organizations underestimate. Microblog @antgrasso A company can invest in platforms and consulting, but the investment remains weak if data is unreliable, workflows are disconnected, or governance is unclear. When information quality is poor, AI creates confusion instead of useful support. When people lack the skills to question outputs and apply them well, adoption becomes dependency. That is why AI adoption must be built below the surface, where reliable data and responsible governance give people the confidence to guide the technology.

AI can spread decisions across systems, but accountability still needs clear owners. Traceable inputs, explainable outputs, and human oversight help teams act with control as automation scales. Microblog @antgrasso #AIGovernance #ResponsibleAI #AI

@Design_Sheikh1 Thank you.

@antgrasso Great 👍

Spotlight’s new report shows why credibility matters even more as B2B influence evolves with AI. Technology can support research and content development. Trust still comes from human experience and professional responsibility. @SpotlightAR Report > bit.ly/4wzcD81

The edge-to-cloud continuum is the connected path where some decisions occur close to the device, while others move to the cloud for broader analysis. Data that needs a fast response can stay near the source, while the cloud supports coordination and scale. Microblog @antgrasso Think of a sensor in a factory. If it detects a machine problem, waiting for every signal to travel to the cloud may be too slow. Processing data locally can trigger a faster response, reduce network traffic, and keep operations running even when connectivity is limited. The same logic applies to a smart vehicle, a hospital device, or a retail store. Some signals need immediate local action because timing matters. Other data can move to the cloud, where it can be compared across locations, stored for longer periods, and used to improve future decisions. This balance helps organizations build systems that react quickly when needed and still remain connected at scale. Edge-to-cloud works best when companies design systems around which data needs immediate local action and which data should move to the cloud for wider coordination.

@tavus The most interesting part is that the interface appears only when the conversation needs it. That makes the interaction feel more natural and keeps the user focused on the task.

Introducing Tavus Magic Canvas. Now a PAL can bring anything to life inside a shared canvas at just the right time, a chart dynamically drawn from the numbers being discussed, a question answered on the spot, a calendar with times to pick, a field for the detail that must be exact. Turn it on for any PAL with a click.

Phishing works when pressure beats attention, so the best defense is to pause, question urgency, verify requests, and report suspicious messages before one deceptive click becomes an organizational incident. Microblog by @antgrasso

AI will be useful only if the systems around it are designed to serve people.

AI is becoming an energy infrastructure question. Data centers, computing demand and power systems are increasingly connected. At AIxEnergy, co-located with Gastech 2026, this convergence is the focus. Register > bit.ly/4yg2UFn @GastechEvent Partner #Gastech #Energy Artificial intelligence is often discussed as if it were mainly a digital issue. In my view, the more human question is different: what kind of physical infrastructure are we building around AI, and who will benefit from it? AI growth needs energy systems that can support data centers without putting reliability and affordability under unnecessary pressure for businesses and communities. AIxEnergy, co-located with Gastech 2026, brings these two worlds into the same conversation: those building computing infrastructure and those responsible for the energy capacity behind it. If AI is going to scale responsibly, the energy side cannot remain in the background.