Artificial Intelligence (AI) I didn’t just make it to the big leagues. that teeth big league. Through 2025, AI will be embedded in every workflow, leveraged across IT operations, and used to build content across every corner of the web. In essence, large-scale language models (LLMs) play a central role, and companies are investing heavily in LLMs to further increase autonomous benefits.
Most users are more wary of AI’s capabilities and limitations. Organizations continue to grapple with the challenge of managing (Shadow AI and vibe coding These are just some of the more volatile trends that have emerged in recent years. Massive deployments can pose data breach threats and software supply chain issues, but struggle to derive meaningful benefits.
But therein lies the biggest challenge for autonomous, generalist AI. If not properly managed or configured, its pervasive nature can complicate governance by increasing the likelihood of overdoing it, making critical errors, and subsequently defending it.
AI presents a significant opportunity to advance the way we do business, but what if it’s time to consider a new direction? What if the safest and most effective path for AI is to go smaller, not bigger?
Large models such as general-purpose LLM are non-specialist. They generalize. They connect disparate data points to provide answers, and to do so they examine vast datasets. Broader knowledge is useful in many areas such as research and content creation, but it also provides more room for error. Hallucinations regarding these tools are common; often puzzling. These errors may be trivial in everyday life, but when integrated into broader business workflows, they can create nightmare scenarios.
Flawed AI can have consequences beyond inaccuracy. a recent research We found that 80% of enterprises have discovered AI agents performing fraudulent activities such as accessing unauthorized systems and resources or compromising IT systems.
Additionally, large-scale AI models are resource-intensive (and therefore costly). They require significant computing power, integration layers, and data pipelines to work. These dependencies are inefficient and can obscure visibility into what data is being accessed, shared, and exposed. As new threats and AI-powered exploits emerge, these blind spots can evolve into more adversarial attack vectors. In other words, the more capabilities you give to all-access AI, the more risk your organization inadvertently takes on.
Specific models for specific challenges
The surest way to make AI safer and more efficient is to make it smaller. Task-specific AI models operate within tightly defined boundaries and perform one function very well instead of trying to handle everything at once. This focus makes security easier to protect and manage. This means that access rights are restricted, data exposure is reduced, and behavior is more predictable as a result.
These smaller models can be more easily audited, managed, and isolated in line with Zero Trust security principles. It also speeds deployment in a controlled environment, allowing IT teams to easily maintain oversight while reaping the productivity benefits of automation.
In regulatory fields such as healthcare, finance, and government, visibility and containment are invaluable. Instead of giving the “keys to the kingdom” to an omniscient model, smaller AI systems act as expert assistants. It can provide accurate, auditable insights while keeping humans informed and, more importantly, in control.
Balancing efficiency and security
Security and efficiency should not be opposing forces. Smaller AI models can better deliver both of these values. Larger models require ongoing tuning and require extensive integration effort, whereas smaller models avoid this cost and risk.
By focusing on a single task, you get more consistent results without the risk of unpredictable logical leaps. Its simplicity is an advantage, with fewer assumptions, fewer privileges, and smaller margins of error. The end result is less headaches for the IT teams responsible for managing it.
Organizations can also chain together smaller models to automate workflows without creating single points of failure. If something misfires, the impact will be reduced. This modularity gives IT teams the freedom to expand AI capabilities thoughtfully and intelligently without exposing the organization to unnecessary risk or incurring additional costs.
2026 will be about small AI
In 2026, AI adoption will be defined by accuracy. And organizations will choose smaller, more targeted AI use cases to drive growth. Organizations need systems that are as transparent as possible, and smaller models naturally fit this demand. Furthermore, AI should be used as a means to enhance human productivity and decision-making, not to replace it.
As organizations continue to move towards more targeted AI deployments and smaller purpose-built use cases, they will see more effective results overall. In the long run, small wins will lead to bigger leaps and benefits from more intentional use of AI. Not the other way around.
Joel Carusone is Senior Vice President of Data and AI at NinjaOne and an expert in secure unified endpoint management.
