Minister Solomon Highlights AI Tool CHARTWatch at St. Michael's Hospital

The most ready-to-buy people come from LLMs. Those customers have already done extensive research, carefully weighing their options with the help of AI. So if they call you, they've made their decision and are ready to do business.

AI agents are about to surge across the enterprise 🤖  The big question is how organizations keep those systems trusted, responsible, and aligned as they scale.  At HumanX, Blake Brannon broke down why AI agents themselves will become a key part of governing AI at scale, especially as human review processes struggle to keep up.  Watch it here: https://lnkd.in/eyGNm9Pc

The productivity gains from AI are real. So is the risk. As work becomes faster, cheaper and easier to produce, the premium shifts away from output and towards something much harder to automate: judgment. In his latest opinion piece, Alkemi Collective CEO Bradly Howland explores why organisations may be asking the wrong question about AI. Swipe through for the key ideas. Read the full piece on The Media Online & The Media magazine: https://lnkd.in/dJnv_tWD

"Artificial intelligence has not simply made us more efficient. It has changed what efficiency means… and in doing so, it has created a new and under-appreciated risk: that we begin to mistake fluency for insight," writes Bradly Howland in this piece. I agree 100% - we have to protect time for thinking!

🚨 NEW: The 2026 Government AI Landscape Assessment is here. We’re back again this year with an updated and expanded Government AI Landscape Assessment. Across all 50 states and D.C., the 2026 assessment maps where governments are on their AI journey and what it means for the people they serve. We’re not just looking at whether states are ready for AI, we’re tracking how they’re progressing from readiness to implementation, on the way to real impact. How is your state using AI tools? Explore the full assessment here: https://bit.ly/4oeK4Jz #GovAI #CivicTech #AILandscapeAssessment

The most interesting AI story right now might not be in Silicon Valley. It might be in the State of Maryland. Yes, I said Maryland. Yes, I am biased. No, I am not apologizing. Code for America’s 2026 Government AI Landscape Assessment ranks Maryland as advanced in readiness and established in piloting, implementation, and impact. That is not just “we formed a task force and made an AI Governance PDF.” This is what responsible AI actually looks like in government. No AI is a magic wand. More AI is an infrastructure like plumbing. And plumbing is what keeps the house from flooding. Read the report. Maryland is one to watch. Based on the report is your state Early, Developing, Established, or Advanced? (Great work Stephen Rockwell. Exciting to see the Advanced category go from 3 to 11 states in one year.)

I'm grateful to the Code for America team for trusting me to lead the research and analysis on this important work. This new analysis expands on last year's AI readiness rubric to include the lifecycle of AI development, deployment, and measuring impact with the ongoing learning necessary for how quickly AI evolves. The TLDR is that states are making a lot of progress with AI readiness. Most are experimenting and piloting. Mirroring the private sector, a fewer number of state governments have pushed AI projects into production and fewer still have articulated the impact of AI projects. #AI #AIforGovernment #Civtech

Code for America just dropped its second annual Government AI Landscape Assessment, and the through-line should put every state workforce, Medicaid, and SNAP agency on notice. Nearly every state has piloted AI. Almost none can tell you whether it worked. Only seven states are classified as "established" on measuring public value: Maryland, New Jersey, North Carolina, Pennsylvania, Texas, Utah, and Vermont. They are pulling away from the pack not because they have better tools, but because they paired executive leadership with cross-agency governance, sandbox environments, and actual evaluation systems. Reading this through a work requirements lens, a few things stood out: The 2026 rubric reorganizes maturity around four stages: readiness, piloting, implementation, impact. Most states are stuck between piloting and implementation. Operationalizing AI inside a Medicaid eligibility workflow is a different animal than spinning up a chatbot in an innovation lab. The report zooms in on AI for benefits access, which is the right frame. With HR-1 work requirements coming and states racing to stand up compliance infrastructure by 2027, the gap between states with governance, data infrastructure, and outcome measurement in place and those without is going to be the difference between people keeping coverage and losing it because the system could not verify them in time. The states leading here are treating AI as institutional infrastructure, not a procurement event. That is the bar. We need more states moving towards institutional readiness now, while there is still runway before work requirements hit.

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the mo... #Techcrunch #breakingnews #news #othur https://lnkd.in/gAdz-4Xe

56% of CSCOs say integrating AI with legacy systems is the biggest roadblock to scaling AI, while 50% cite limited internal AI talent. The takeaway? Simply bolting AI onto outdated workflows won’t deliver real value. Dive into three action areas for CSCOs to build AI-native foundations in our latest press release: https://gtnr.it/4t5seK8