The rise of AI is reshaping business technology at an unprecedented pace. From IT to HR, finance to customer service, few departments remain untouched by the wave of automation and intelligence sweeping companies and industries today. However, with this surge in interest comes a growing challenge: distinguishing between truly transformative AI tools and those merely dressed up in buzzwords.
For CIOs and business leaders, the mandate has shifted from exploration to execution. Deploying the wrong AI solution doesn’t just stall progress; it burns time, budget, and internal credibility. The challenge now is clear: cut through the noise, ensure enterprise-grade security, and back only the AI that drives measurable impact.
CEO and co-founder of Atera.
Perception problems around AI
At a surface level, many AI solutions look the same: slick interfaces, automated responses, bold claims. But there is a distinct difference between basic AI bots and true agentic AI. Some AI products automate tasks only within rigid boundaries, while agentic AI is designed to think, act, and adapt with no intervention required.
The confusion often stems from how AI is marketed. Some platforms tout predictive insights but rely on limited or shallow data, resulting in misleading outputs. Others claim “full autonomy,” yet still depend heavily on human input. Most are wrappers for outdated automation, only a few are truly built to drive action, solve real problems, and evolve with your environment.
Similarly, many products only scratch the surface by simply passing user prompts to large language models (LLMs) through an API – what you might call a very thin layer of AI. They look impressive at first, but lack any meaningful depth.
This creates a perception problem. AI is either seen as a cure-all or dismissed as hype. In reality, the value lies between. Real improvements in productivity and efficiency come from using the right tools, not just any tools.
The shift from automation to autonomy
Although hype still surrounds AI, we’re also seeing real progress as it evolves from basic automation to true autonomy. In IT specifically, autonomous AI is starting to take on entire workflows from start to finish, including resolving low-level support tickets without any intervention from IT personnel, even though end users may still interact with the AI.
The depth of these solutions is critical. When AI systems layer orchestration, coordinate multiple processes, or use specialized agents for different tasks, they become much more than a simple interface to a language model. And when they can take informed action on real business systems, drawing on an organization’s unique data and historical context rather than merely offering recommendations, that’s when you see what can truly be considered a deep AI product.
The effect on an organization is threefold. For end users, it delivers a zero-time SLA experience: instant support, self-service resolution, and frictionless access to help anytime. This shift dramatically improves the digital employee experience (DEX), which is now a key driver of productivity and satisfaction in mature IT environments. For IT teams, it frees up hours each week, reduces backlog, and improves response times. For the organization, it cuts costs without compromising quality and enables scalable IT support without additional hiring. However, with this power comes responsibility. IT leaders must ensure these systems operate within clear guardrails, especially when interacting with sensitive data, employee devices, or live environments.
A central concept here is closed-loop AI. These systems are designed to ensure that inputs remain within the organization’s control. Unlike open models that may use your data to enhance results elsewhere, closed-loop systems are built with enterprise-grade governance in mind. This approach gives IT leaders greater confidence to adopt AI without compromising security or compliance.
Three warning signs of hype
To effectively evaluate AI tools, it’s important to look past the branding and focus on the core mechanics. Here are three common red flags:
Lack of specificity: If a product claims to “revolutionize business” but cannot point to a specific workflow or use case it improves, that is a concern.
No explainability: If you can’t trace how a decision was made, or what data was used to make it, that’s a sign of a black-box system. Trustworthy AI should be auditable and understandable, especially in high-stakes enterprise settings.
No real learning or depth: If the AI lacks any meaningful learning mechanism or only relies on a small, shallow set of data points, it’s unlikely to improve over time. True AI products get smarter by processing large, relevant datasets, whether through training robust models or continuously absorbing business context. Without this depth, you’re often looking at a thin layer that may impress in a demo but quickly fall short in the real world.
As more tools claim to offer autonomy, it’s more important than ever to understand what to look for in a reliable AI solution and what to avoid.
What to look for instead
Instead of getting distracted by flashy demos or inflated claims, decision-makers should evaluate AI tools based on three key pillars:
Relevance and integration: Is it trained on data that reflects your business context, and can it be customized to fit your company’s workflows, policies, and operational guidelines? Just as important, will it integrate with your existing tech stack or require major reengineering? AI works best when it adapts to how your organization already operates, not the other way around.
Transparency: Can you understand and control how it works?
Impact: Does it save time, reduce errors, or improve outcomes in measurable ways? Does it actually do the work? Are there any stats or data points that can show proven impact?
Ultimately, the strongest AI solutions build layers of capability, from orchestration to specialized agents to learning engines that can take real action, creating something far more valuable than tools that simply pass prompts to a language model. They don’t just mimic intelligence; they deliver tangible value by empowering teams to focus on strategic work, improving efficiency, and generating a clearly demonstrable return on investment.
The future Is functional, not flashy
The future of AI in enterprise technology will not be defined by the tools with the boldest announcements or the most dramatic demos. Instead, it will be shaped by smart, adaptable systems that take ownership of tasks from start to finish and operate independently within clearly defined parameters. These tools quietly improve everyday operations and deliver consistent results with minimal oversight.
AI on its own is no longer enough. To truly deliver value, it needs to be connected to real-time systems, historical data, and the operational context where it’s deployed. That’s what unlocks its full potential. When AI is paired with an on-the-ground agent and backed by rich historical insights, it can go beyond recommendations and solve problems autonomously. It’s the combination of real-time visibility, institutional memory, and intelligent execution that makes for a truly transformative solution.
For IT leaders, the goal is not to chase hype, but to make informed decisions by asking tough questions, demanding clarity from vendors, and staying focused on business outcomes.
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