Operations | Monitoring | ITSM | DevOps | Cloud

Speed with Confidence: Managing Delivery Risk in an AI-driven Development World

In the modern development landscape, we are seeing a shift in how work is managed. The rise of AI-assisted development and highly distributed teams means that work is moving faster than ever before. However, this increased velocity often comes with a hidden tax: complexity. We are seeing more parallel work streams, more intricate dependencies, and a constant stream of shifting priorities. In this environment, simply moving fast is not enough to guarantee success.

AI Governance: Closing the Policy Gap feat. Brooke Johnson, Ivanti

AI governance isn't optional — it's the difference between scaling AI confidently and exposing your organization to serious risk. Watch Brooke Johnson, Ivanti's Chief Legal Counsel, SVP HR and Security, break down why AI policy alone isn't enough and what it actually takes to close the governance gap.

How Fragmented Data Breaks AI Strategy feat. Sterling Parker, Ivanti

Your AI is only as good as the data it sits on — and fragmented IT data isn't just inefficient; it's dangerous. Watch Ivanti's Sterling Parker, SVP of Global Solutions and Services at Ivanti, explain why a unified IT platform and a clean system of record are the true foundation of secure, scalable AI.

AI inference vs. training: What they are and how they differ

AI inference and training are terms you'd run into if you have been around software engineering or even just scrolled through the news. Both are integral to delivering the AI-powered experiences we have come to expect from many of the applications we use daily. According to McKinsey, by 2030 inference will overtake training as the dominant workload in AI data centers, making up more than half of all AI compute and roughly 30-40% of total data center demand.

Autonomous IT Is Here. Are You Prepared?

Enterprise IT was built for a more predictable workplace, where support began when an employee reported a problem and IT worked backward from the details they could provide. That model made sense when devices, applications, and ways of working were easier to control. Today, the digital workplace moves too quickly for IT to rely on reported issues alone. By the time a ticket appears, employees may have already lost time, worked around the problem, abandoned the tool, or turned to an unmanaged alternative.

iFrame Expands AI Infrastructure Offering With Hosted Inference Service for Open-Weight Models

Organizations looking to reduce AI operating costs while maintaining performance are increasingly turning to open-weight models. This trend accelerated throughout 2024 as businesses sought alternatives to expensive proprietary systems and greater control over their AI infrastructure.

Why AI Evaluation Is Becoming a Business Priority, Not Just a Technical Task

Artificial intelligence products are evolving at a pace that challenges traditional quality assurance and validation processes. As organizations race to release new AI-powered features, many product teams face the same question: how do they know a system is ready for real-world use? As reported by AI Journal, conversations with product leaders across different sectors reveal a growing focus on AI evaluation as a critical part of product development. Their experiences highlight the challenges of balancing innovation, risk management, customer expectations, and future regulatory requirements.

DNS Spy Now Has an MCP Server. Ask Your AI About Any Domain.

DNS monitoring should be simple. You want to know if something changed. You want to know if a record propagated. You want to know if a phishing site just went live with your brand name in the domain. But in practice it takes work. You log in to a dashboard. You click through menus. You run a check, copy the output, paste it somewhere else. You repeat that process every time someone on the team asks a question. AI assistants like Claude and ChatGPT could help.