As long as there have been busy IT teams, there have been teams using automation to save hours of work, cut down on repetition, and prevent the headaches of fixing human-introduced errors. AI has now amped up the possibilities of IT automation so teams can choose platforms to automate patching, provisioning, ticketing, and more, and explore AI agents to craft entirely automated workflows.

IT cost reduction strategies vary from the everyday details to more major decisions. IT teams running services and supporting business growth can get more proactive, more automated, and more budget-savvy with help from AI and other forward-looking technologies.

What will 2026 bring for AI users, AI experts, and the AI-curious? Look out for common AI trends in 2026: more and better ways to work with AI, lots more use of agents and multi-agent systems, big efficiency gains, and of course, robots. AI technology will continue moving from the broad to the specific, so now is the time to think about how you can get started, if you haven’t already.

The healthcare industry lags behind others in AI adoption at scale, so there’s plenty of room for innovation. But AI has already made an impact in the areas of patient diagnoses, drug discovery research, predictive equipment maintenance, reduced call wait times, and more. See how healthcare companies have already made strides with AI and started to tackle the problems of workforce shortages, operational inefficiencies, and fragmented IT systems.

AI at work can bring relief to workers and take on tasks like workflow automation, data analysis and reporting, and IT department ticketing.

Multimodal AI is emerging as part of the fast-moving world of AI technology, bringing together multiple types and modalities of data to build a model that’s context-aware. Multimodal AI uses sophisticated neural networks, transformer models, and specialized frameworks to pull in text, image, video, and other data to generate rich outputs.

An accurate, high-performing AI model is a valuable business asset, but it requires good data, fine-tuning, updating, and an understanding of how it’s performing over time. Learn more about what an AI model is, how algorithms and LLMs work, what types of models are available, and how to build, train, and fine-tune an AI model that can improve business outcomes.

Multi-agent systems bring together the abilities of generative AI agents to solve real problems. These multi-agent systems work exponentially faster than what a single AI agent can do, bringing together their speed and deep learning into a unified environment and assuring communication and collaboration. Agents can each tackle part of a bigger problem or workflow to automate IT ticketing, ensure smooth transportation in trucking or shipping, and conduct operations for massive sensor networks.

Network monitoring keeps users up and running and often prevents outages, security breaches, and more business-affecting issues. With complexity and data volumes growing massively, AI network monitoring can save time and resources. AI’s ability to learn and iterate quickly can prevent threats, improve performance, and send out alerts with superhuman speed.

AI agents and AI assistants are two popular artificial intelligence tools in use today, with use cases already as varied as human imagination. AI assistants can perform prompt-based tasks, while AI agents can proactively solve problems without human intervention. Both require tuning, planning, and setup, but reward users with increased productivity and saved time.

Per-device pricing is common for help desk and RMM software, but that doesn’t mean it’s a strategic option for growing businesses. Pay-per-technician pricing models offer growth without budget limits or a tax on expanding users and devices.

Modern cloud-based help desk platforms can move fast, scale massively, and save IT teams hours of time with smart automation. But they aren’t all created equal. See which features to explore to make sure the cloud help desk tool you choose can truly automate IT support with AI, streamline both employees’ and IT teams’ work, and stay under budget even as operations scale.