Agentic AI in CX: Preparing for Agentic Commerce
Personal AI agents are starting to shop, reorder and file complaints for customers. See what that means for CX volume, security and discoverability.
Personal AI agents are starting to shop, reorder and file complaints for customers. See what that means for CX volume, security and discoverability.
Six speakers at an O’Reilly AI Superstream laid out layered controls, runtime steering and agent mandates for securing agentic AI and agentic commerce.
See where small businesses should start with AI, which tasks to automate, how to test safely before roll-out, and which metrics show real business impact.
Automation leaders keep deterministic automation as the backbone, use agentic AI only where rules fall short, and agree governance before agents ship.
Agentic commerce infrastructure comes down to four questions: discoverability to AI agents, agent authentication, liability for mistakes, and fulfillment.
AI strategy should start with business goals, not tools. How leaders delegate to AI, avoid AI slop, build a community of multipliers and stay accountable.
A model developer’s temporary slowdown reveals why CIOs need AI governance, vendor flexibility, and risk-scaled testing built into every roadmap.
Explore why enterprise AI adoption succeeds only after leaders build AI readiness, tackle shadow AI risk, and invest in workforce upskilling.
AI governance controls auditors expect: discovery, classification, authentication, login enforcement and audit evidence proving an AI policy is enforced.
Agentic AI vendor risk grows when model access can vanish overnight. How leaders plan multi-model routing, sovereign AI, delegation, and safe rollouts.
Evaluate SaaS vendors on AI governance: separate core from peripheral software, check AI risk standards and data ownership, and budget for metered usage.
AI in learning and development needs more than tool access: hands-on upskilling, persona-based AI literacy, champions, and lightweight governance.
Agentic AI pilots create value when leaders redesign workflows first, govern risk as autonomy scales, address workforce fear and choose build vs buy well.
SAP agentic AI adoption starts with a KPI-led pilot, pre-built Joule capabilities, trust controls for master data, and transparent workforce change.
Prevent AI workslop by requiring refinement before escalation, training by role, capping token costs, and keeping people accountable for what AI produces.
Apple’s Gemini-for-Siri move spotlights build vs buy. Guidance on AI leadership, total cost of ownership, governance, and workforce needs.
tokenmaxxing rewards AI usage, not outcomes. Why token leaderboards backfire, how metacognition protects judgment, and what controls curb vibe coding debt.
An AI agent control plane shows which agents exist, what they access and how they perform. Govern agent sprawl with lifecycle management and guardrails.
Andreas Welsch offers practical guidance on AI governance guardrails, upskilling, and moving beyond pilot purgatory.
Agentic AI for the workforce is a people and process change. Start with strategy and KPIs, measure business impact, train teams, protect talent pipelines.
AI speeds up drafting, not responsibility for AI output. How leaders curb workslop, protect the talent bench, and govern agentic AI as digital employees.
An agentic AI operating model sets approved tools, data access, oversight and accountability, so AI-first mandates do not create shadow AI or AI workslop.
Measure AI adoption by outcomes, not prompts or logins. What leaders must fix in the prove-it phase: clarity, upskilling, workflows, and accountability.
An executive digital twin can save leaders time and improve proposals, but it brings drift, trust and knowledge-ownership risks the C-suite must govern.
AI skepticism at work persists even as more employees use AI. Why uneven utilization matters, and how training and visibility help leaders manage adoption.
Build AI governance that accelerates adoption without chaos. Key lessons on policy, training, measurement, and vendor selection from an executive panel.
Nvidia AI tokens fund agent usage, but most AI projects have failed since 2018. See the governance and workflow changes that decide whether agents pay off.
Agentic AI for process excellence needs clear roles, rules, ownership and escalation thresholds, or agents speed up tasks while adding rework.
AI and entry-level jobs: why cutting junior roles weakens succession, how entry-level work shifts toward synthesis, and how leaders avoid never-skilling.
Agentic AI in procurement: start with the business problem, fund data readiness, match autonomy to risk and upskill teams. See which workflows are ready.