AI Frontier Network

📢 New Episode of Humans & AI Show — featuring Jimmy Mesta 🤙 , Co-Founder & CTO of RAD Security! What happens when AI becomes both the attacker and the defender in cybersecurity? In this episode, Jimmy shares: 🔹 Why security teams must fight AI with AI as threats become faster, smarter, and more automated 🔹 How AI is already generating vulnerabilities and attack patterns faster than humans can keep up  🔹 The rise of “bot vs. bot” defense — where autonomous systems detect, correlate, and respond to attacks in real time  🔹 How AI reduces alert fatigue, speeds up triage, and acts as a co-pilot for overwhelmed security teams  🔹 Why foundational practices — like clean data, workflows, and context — are essential before layering AI on top  🔹 The reality: AI won’t eliminate threats, but it will redefine how we defend against them 💡 “AI isn’t optional in cybersecurity anymore — it’s the only way to keep up with AI-powered threats.” — Jimmy Mesta If you want to understand the new cybersecurity battlefield — where machines defend against machines — this conversation is for you. 🎙️ Listen now: https://lnkd.in/eeDa6ksm Host: Flor Laorga | Humans & AI Show powered by AI Frontier Network #AI #Cybersecurity #AIvsAI #HumansAndAI #Security #Automation #FutureOfWork #ResponsibleAI

Agentic AI should do more than automate. It should accelerate revenue. tvScientific's CEO and Co-founder, Jason Fairchild, was featured in AI Frontier Network for his piece, “Making Agentic Advertising Accountable to Revenue,” where he unpacks why revenue-linked intelligence is the real test of AI maturity in advertising. Explore the entire piece: https://hubs.la/Q0484y1V0

💡 Patterns Emerging Across Enterprise AI Conversations with CEOs and AI leaders point to a transition from building AI systems to operating them at scale. These insights draw from recent interviews published via AI Time Journal and AI Frontier Network. Several patterns stand out: ✦ AI is collapsing layers into orchestration Across customer experience and engagement platforms, systems are moving toward real-time orchestration rather than stacked tooling. → Vasili Triant (ujet.cx) — Why AI Is Replacing CRM Layers, Not Enterprise Systems Read the article: aitj.nl/triant-crm → Ethan Gustav (Infobip) — Building Always-On Fan Engagement Through Conversational AI Read the article: aitj.nl/gustav-infobip ✦ Governance is becoming operational infrastructure From education to compliance, governance is embedded directly into system design. → Jim Wetekamp (Riskonnect, Inc.) — Connected Risk Intelligence for the AI Enterprise Read the article: aitj.nl/wetekamp-risk-ai → France Q. Hoang (BoodleBox) — Building Governable AI Systems for Universities Read the article: aitj.nl/hoang-governance ✦ The real failure point is operational, not technical Most AI initiatives stall at the boundary between models and real workflows. → Jeff Fettes (Laivly) — Why Most CX AI Pilots Fail at Scale Read the article: aitj.nl/fettes-cx-ai → Casey Hite (Aeroflow Health) — Engineering Predictable Access in AI-Driven Healthcare Operations Read the article: aitj.nl/hite-healthcare-ai → Niraj Ranjan Rout (Hiver) — Embedding Human-Centered AI Across the Customer Service Lifecycle Read the article: https://lnkd.in/dN2aChzd ✦ Infrastructure discipline is the constraint Scaling AI requires compute, orchestration, and system reliability discipline. → Nithin Mohan (Hewlett Packard Enterprise) — Why AI Breakthroughs Depend on Supercomputing Discipline Read the article: aitj.nl/mohan-infra-ai → Ravi Teja Alchuri (Assured Telematics) — Engineering Trustworthy AI for Production-Scale Fleet Systems Read the article: https://lnkd.in/dabvHneQ ✦ AI must prove economic value Systems are increasingly evaluated against revenue, cost, or access outcomes. → Jason Fairchild (tvScientific) — Making Agentic Advertising Accountable to Revenue Read the article: https://lnkd.in/dFgf4-4D → Glen Tullman (Transcarent) — Consumer-Directed Care and the Rise of AI-Powered WayFinding in Healthcare Read the article: https://lnkd.in/dUmGDE6w The shift is from AI as capability to AI as operating discipline. #EnterpriseAI #AIInfrastructure #AIOperations

Jeff Fettes — Why Most CX AI Pilots Fail at Scale - An article by Martin Russo Read the article: https://lnkd.in/dM2xJTap

Manual tendering is still a major bottleneck in construction: fragmented data, high error cost, slow cycles. And this isn’t unique to construction. Similar pain shows up in any industry with vendor selection, tendering, and procurement-heavy workflows. I shared my take on where AI fits best (practically, with oversight) in the article for AI Frontier Network https://lnkd.in/d-hvzxBi

Three executive interviews across healthcare, enterprise sales, and AI infrastructure examine where AI meets operational constraints. 🔹 Casey Hite (CEO, Aeroflow Health) explains how predictable access is engineered inside AI-driven healthcare operations. → https://lnkd.in/dZX3gJCa The constraint is operational reliability. AI creates value when it reduces friction in eligibility, intake, and patient flow under real regulatory pressure. 🔹 Joey Gilkey (CEO & Founder, TitanX) on rebuilding outbound around phone intent and predictive precision. → https://lnkd.in/djfcqYDb When outbound is instrumented around real buyer signals, AI shifts from volume automation to revenue-aligned targeting. Precision beats activity. 🔹 Nithin Mohan (AI & Supercomputing Leader at Hewlett Packard Enterprise) on why AI breakthroughs depend on supercomputing discipline. → https://lnkd.in/dW5A2QZB Model progress follows infrastructure rigor. Without HPC discipline, scaling AI systems becomes economically and operationally fragile. Common thread: AI advantage emerges from signal precision, workflow reliability, and infrastructure discipline, not model novelty. #EnterpriseAI #ProductionAI #AIOperations #AIInfrastructure #AIinHealthcare #RevenueOperations #HPC #AIGovernance

Two candid CEO interviews and a six-expert insight panel reveal how production AI converts narrative into measurable outcomes. 🔹 Jason Fairchild (CEO & Co-Founder, tvScientific) on why agentic advertising must be accountable to revenue, not workflow optics. When AI cannot show impact on CAC, LTV, or incrementality, it is automation dressed as innovation. 👉 Read the article → https://lnkd.in/dwrX-2xv 🔹 Niraj Ranjan Rout (Co-Founder & CEO, Hiver) explains the shift from AI-first messaging to AI-effective operations in customer service. The frontier is lowering cognitive load, protecting human judgment, and embedding AI exactly where workflows break. 👉 Read the article → https://lnkd.in/diAbPpEJ 🔹 Why AI Pilots Fail to Become Real Impact brings field insight on the pilot-to-production gap with Laxmi Vanam, Vivek Pandit, Ram Kumar N., Anshul Garg, Dr Anuradha Rao, and Junaith Haja. Most pilots stall because ownership, economics, governance, and workflow integration are missing from day one. 👉 Read the article → https://lnkd.in/dbsRrUKj Common thread. Revenue accountability, human-centered workflows, and disciplined governance separate experiments from real impact. #EnterpriseAI #ProductionAI #AgenticAI #AIOperations #CustomerServiceAI #AdTech #MLOps #AIGovernance

AI pilots rarely fail on the model. They stall in the organization that is supposed to adopt, govern, and scale them. Across AI Frontier Network, practitioners consistently report the same pattern. Strong demos get internal attention, then momentum fades when ownership, workflow integration, and economic accountability come into focus. The real gap is not technical maturity. It is operational readiness. In this Insight Panel, engineers, data leaders, and product operators map what actually blocks AI from reaching production impact: • Why pilots and production optimize for different realities • Where decision ownership quietly breaks scaling • How workflow friction leads teams to ignore AI • What production discipline most teams underestimate • Why ROI must be tied directly to P&L This is a field view from teams working on real systems. 👉 Read the full Insight Panel: https://lnkd.in/dbsRrUKj Where do AI initiatives tend to stall inside your organization? #ArtificialIntelligence #EnterpriseAI #MLOps #AIGovernance #DigitalTransformation

AI systems are no longer experimental. They are making decisions that affect customers, patients, finances, and infrastructure every day. The real question is no longer can AI act autonomously. It is: who owns the consequences when it does? We asked engineers, healthcare leaders, product executives, and AI ethicists where humans must stay in the loop and where autonomy is justified. The result is a practical operating doctrine for modern AI systems: • When autonomy is safe • Where accountability must remain human • What breaks when automation arrives too early • Why tiered autonomy is becoming the dominant design pattern • How organizations can structure real oversight This is not a debate about AI replacing people. It is about designing systems where responsibility is explicit. 👉 Read the full roundup: https://lnkd.in/dX7uEMrU Curious how others are drawing this boundary inside their organizations. Where do you refuse to automate? #ArtificialIntelligence #EnterpriseAI #Leadership #AIGovernance #DigitalTransformation

📢 New Episode of Humans & AI Show — featuring Michael True, Co-Founder & CEO of Prescient AI! What happens when a pandemic pivot transforms a music touring app into an AI-powered platform redefining how advertising impact is measured? In this episode, Mike shares: 🔹 How Prescient AI evolved from a music-focused startup into a data-driven AdTech company 🔹 Why traditional marketing attribution models miss hidden cross-channel patterns — and how machine learning uncovers them 🔹 How AI helps brands understand the true impact of campaigns across Amazon, TikTok, retail, and more 🔹 Why keeping humans in the loop ensures AI systems reflect real-world behaviors and ethical considerations 🔹 His perspective on autonomous agents reshaping the future of advertising 🔹 Practical startup lessons on pivoting, staying customer-focused, and balancing analytics with authenticity 💡 “AI can reveal what’s really driving growth — but humans ensure those insights are meaningful and responsible.” — Mike True If you’re curious about the future of marketing measurement, AI-driven attribution, and how autonomous systems may reshape advertising, this episode is for you. 🎙️ Listen now: https://lnkd.in/e_4Zs-mz Host: Flor Laorga | Humans & AI Show powered by AI Frontier Network #AI #AdTech #MarketingAnalytics #HumansAndAI #StartupJourney #MachineLearning #FutureOfWork #Innovation