In the world of enterprise technology, confidence is a volatile indicator. According to a recent survey of 800 IT leaders in the United States and the United Kingdom, the perception of artificial intelligence (AI) maturity dropped from 40% to 23% in just six months. A 17-point decline that, at first glance, could be interpreted as a failure. However, experts agree that this is a sign of market health: organizations are leaving the testing phase behind and facing the real challenges of production. This decline is not a loss of faith in AI, but an adjustment of expectations based on practical experience.
When IT teams deploy AI agents in controlled environments or pilots, the results are often promising. The real challenge arrives when migrating those agents to production systems, where they must interact with real infrastructures, make decisions that affect workflows, and operate continuously without direct human supervision. This is where many companies discover they have not built the proper foundations. The governance required in production is radically different from that of a pilot. Uncomfortable questions emerge: Can we see all agents running in our environment? Do we know what each one can access? If an agent behaved unexpectedly last week, how long would it take to find out? For most, the answers are concerning.
This phenomenon reflects a structural gap between deployment and control. While 84% of organizations plan to expand their AI use in the next two years, confidence only grows among those that have closed that gap. How do they achieve it? They have consolidated their technology environments rather than adding tools for each new problem. They have started treating AI agents as governed identities, not as tolerated shadow processes. And they measure what AI actually produces, not just what it deploys. The result is tangible: companies at the top maturity level are five times more likely to report no barriers to expanding their AI agents.
One of the most critical points revealed by the study is the management of non-human identities. In 83% of organizations, non-human identities (such as AI agents, services, and machine accounts) already outnumber human users. However, only 21% of companies have implemented security practices for these identities. Most lack formal records, assigned owners, defined scopes, or deactivation processes. These 'zombie agents' keep running, accumulating permissions and accessing systems without control, replicating the old service account problem but at machine speed and across all departments. Accountability breaks: when a human acts, there is an implicit chain of responsibility; when an autonomous agent acts, that chain vanishes unless deliberately engineered.
This is where companies like Q2BSTUDIO, specialized in software development and technology, provide concrete solutions. Experience shows that deploying artificial intelligence is not enough; it must be integrated into a solid governance architecture. For example, when developing AI-based applications for clients, Q2BSTUDIO focuses not only on the model logic but also on identity, permissions, and traceability. An AI agent accessing sensitive data must be as controlled as any human employee. This involves building authentication, authorization, and auditing systems specifically for non-human entities, something many companies overlook during the pilot phase.
Furthermore, the cloud plays a fundamental role. AI agents often run in cloud environments like AWS or Azure, where security and identity management become even more complex. Q2BSTUDIO offers cloud services on AWS and Azure that include IAM (Identity and Access Management) configurations for machines and processes, ensuring each agent has only the necessary permissions and that its activity is logged. Without this foundation, AI expansion becomes a source of risk rather than a competitive advantage.
Another key aspect is cybersecurity. The proliferation of autonomous agents multiplies the attack surface. A misconfigured agent can expose critical data or be used as an entry point by attackers. Companies that have integrated cybersecurity services from the design phase —like those provided by Q2BSTUDIO in their projects— manage to maintain control even as agent autonomy grows. Continuous monitoring and automated response are essential to prevent 'zombie agents' from becoming active vulnerabilities.
Also worth attention is the role of business intelligence (BI). AI agents generate and consume massive amounts of data. To measure their real performance —beyond simple deployment metrics— it is necessary to have dashboards and analyses based on tools like Power BI. Q2BSTUDIO implements Business Intelligence solutions that allow organizations to visualize the impact of their AI agents: Are they reducing costs? Improving productivity? Generating new risks? Without this visibility, confidence in AI remains a leap into the void.
The 17-point drop in confidence is not, therefore, bad news. It is a symptom of maturity born from experience. Organizations that have revised their self-assessment downward are the ones doing the hard work: building identity infrastructure, unifying environments where governance must apply, and measuring results instead of counting deployments. They have not lowered their AI ambitions; they have raised their standards for managing it responsibly. In a market where 84% plan to expand their use, those that will succeed are the ones honest enough today to admit what they have not yet built.
From a technical perspective, the lesson is clear: AI in production is not a single-department project but a cross-functional effort involving identity, security, cloud, and data. Companies like Q2BSTUDIO, with experience in custom application development, cloud, cybersecurity, and BI, provide the necessary support so that confidence in AI is not an illusion but a sustainable achievement. The next time you see a survey with a confidence drop, remember that honesty is often the first step toward real progress.
