Operations | Monitoring | ITSM | DevOps | Cloud

Your employees aren't trying to create compliance incidents by using AI tools. They're trying to finish their work faster. According to a 2026 BCG survey, 74% of frontline employees now use generative AI every day or a few times a week.

Apple Declarative Device Management (DDM) is now the standard for device management across Apple platforms. As part of this transition, Apple is retiring legacy software update controls in iOS 27, iPadOS 27, macOS Golden Gate, watchOS 27, visionOS 27, tvOS 27 and later releases. This is just one of several platform changes arriving this fall that could impact how your team manages and secures Apple devices. The good news?

Organizations today invest heavily in discovery tools, expecting that increased visibility will solve their IT challenges. Yet when an incident hits, a patch fails or an audit looms, many teams must pause to reconcile spreadsheets, validate inventories and confirm ownership before they can act. Visibility alone doesn’t close the gap between insight and execution.

Why do 40% of alerts received by security teams today go completely uninvestigated? It’s not due to a lack of concern but instead caused by shortening attack windows and compounded by overwhelming tech sprawl. Today’s security teams are operating in a threat landscape defined by escalating attacks, tighter budgets and mounting alert fatigue. Organizations process an average of 960 security alerts per day, and large enterprises handle more than 3,000 daily alerts across roughly 30 tools.

Every enterprise has bought AI, but many are still waiting for their investment to pay off. Ivanti’s 2026 AI Maturity Report found that only 2% of organizations say they currently have no AI use at all. As the majority of organizations move beyond the AI experimentation stage, the real competitive differentiator is if that AI is providing continuous, business value at scale.

Ask three teams what assets exist in your environment, and you’ll get three different answers. Most organizations don’t lack tools. They lack agreement on what actually exists in their environment. Asset, endpoint and cloud data exist — but it’s fragmented, stale and trusted differently by teams across every department and function.

Infrastructure and Operations (I&O) teams have long operated under a familiar paradox: the faster the business scales, the more pressure I&O absorbs. Every new application deployment, every endpoint added, and every cloud workload spun up generates more complexity, more risk and more tickets. The traditional responses to this pressure — more headcount, more tooling, more scripts, more APIs — have delivered incremental relief at best.

Security teams are drowning in vulnerabilities. We’re talking tens of thousands of findings per quarter. Hundreds of thousands at larger organizations. Today's IT environments have no boundaries and span across every OS platform. Managing and securing that estate in a linear fashion is no longer viable, and neither is a vulnerability remediation process that treats every fix as a simple, low-impact task.

The IT service management (ITSM) industry stands at a real inflection point. For decades, service desks have operated on a fundamentally reactive model — employees face problems, submit tickets and wait for human analysts to diagnose, triage and resolve their issues. Automation improved throughput within that model, but it never challenged the model itself.

Data will always be unclean. It's just a matter of degree. I internalized that on day one of my master's program in data science, when a professor warned us that roughly 80% of our time would go to preprocessing and cleaning, not building models. Years later, as Principal Product Manager for AI, ML and Analytics at Ivanti, I've found the guidance holds up remarkably well in practice.