AI creates business value when strategy, workflows, and execution work together. Leaders need to connect these layers, redesign how work gets done, and build the skills that turn AI capabilities into measurable outcomes. Source @McKinsey Link mck.co/4hVixKN v @antgrasso

Two patients have the same diagnosis. Would the same treatment be right for both? Clinical decision support helps clinicians examine the differences. Patient information supplies the context. Medical knowledge helps interpret it before a decision is made. Microblog @antgrasso Consider a clinician preparing to prescribe a medication. The patient record contains kidney function results that may affect the appropriate dose. A clinical decision support system can combine that information with medical references and flag a possible dosing concern for review. Now the clinician has something to examine. Are the patient data current? Does the recommendation make sense in this particular care situation? The software can bring relevant evidence to the attention of the clinician while the decision is being considered. The clinician interprets that evidence and decides how to proceed. The purpose is to make medical knowledge available in the context of the patient, without transferring clinical judgment to the software. #ClinicalDecisionSupport #DigitalHealth

Cybersecurity is a business decision. Connect AI governance, identity protection, and cryptographic readiness to clear ownership and measurable risk reduction. Make resilience a leadership priority. Source @Gartner_inc link gtnr.it/4jjawSk via @antgrasso

AI adoption does not automatically build trust. With 30% of Americans worried about its pace of development, leaders need to make AI's value tangible: show measurable results, explain its limits, and make human accountability clear. Source @StatistaCharts via @antgrasso

A KPMG survey finds that 89% report measurable value from AI, but only 12% consistently assess that value against its cost. The figures raise a question: what can organizations conclude about AI economics if costs and benefits are not compared consistently? Microblog @antgrasso In its Global AI Pulse Q3 2026, KPMG reports that 61% review AI costs during approval and 59% monitor them once systems are running. Consider an AI application that processes supplier invoices. The finance team may know how much it costs each month. But how much time does it save once the manual corrections are taken into account? Without that comparison, deciding whether to expand the application is harder. Among organizations reporting established ROI, KPMG finds that 48% consistently compare AI value with cost. Financial control tells an organization what it spends. Connecting cost to results helps it decide whether further investment makes sense. #AIEconomics #AIManagement

AI-powered wildlife alerts on rural roads are a useful application of connected technology, especially with measurable speed reductions during testing. @TMobileBusiness Partner

Data privacy depends partly on where AI runs. Gradium's Phonon 1.0 generates speech on the user's device, keeping input text local and avoiding per-request cloud fees. You can hear the result in this French-speaking game demo.

The next tech advantage won’t come from AI alone. It will come from the ability to turn it into the physical world—through energy, chips, infrastructure, talent, and operating models built to make innovation work at scale. @McKinsey Link buff.ly/WBTnQUb via @antgrasso