Based on estimates by the Rand Corporation, over 80% of artificial intelligence (AI) projects fail. This figure is double the rate of failure for information technology (IT) initiatives that do not involve AI. Hence, it is vital to understand how AI can be exploited to produce concrete results. A Deloitte article revealed that projects are not successful because leaders pour 93% of budgets into model selection and engineering ignoring the basic tasks of keeping production systems clean and alive. Several factors are invaluable in determining whether an AI project succeeds or not such as data engineering and readiness, continuous monitoring, governance, and adoption infrastructure.
Data Engineering and Readiness
AI infrastructure needs massive and secure data pipelines. One way to achieve this is to use virtual private network (VPN) systems. VPNs handle traffic, encrypt information, and analyze behavioral traffic in real time. Anomalous payloads are also detected and blocked. In addition, internet protocol (IP) masking and standard encryption are performed. However, cybercrime is a thriving industry that keeps on attacking resources and systems. Targeting a VPN is no different because it is an oversight that can easily be exploited. In fact, it can be compared to an unlocked front door where attackers can get in legitimately. VPN access is the start of unwanted intrusion enabling cyber criminals to locate valuable assets, deploy ransomware, steal domain administrator credentials, create backdoors, and install remote monitoring tools. Thus, it is vital to secure VPNs by enforcing multi-factor authentication (MFA), auditing privileged accounts, centralizing logs, and disabling legacy and unused accounts.
Incorporating AI also strengthens VPNs turning them into dynamic and smart defense platforms. However, data quality matters in AI models because they are entirely dependent on them. Unfortunately, data preparation is often a neglected operational bottleneck. According to Gartner research, 38% of infrastructure & operations (I&O) leaders attribute poor data quality as a direct cause of project failure. The survey also emphasized that the return on investment (ROI) does not hinge on the sophistication of the model but by how well the technology is integrated, managed, and revised to meet operational requirements. Therefore, it is critical to constantly collect, clean, validate, and refresh data. Data pipeline work must be carried out regularly.
Monitoring and Compliance
Real-world conditions change all the time making an AI model irrelevant if it is not adapted to user behavior or market shifts. Automated retraining pipelines are imperative because once a model is deployed and conditions change, it is essential that it continues to be reliable, accurate, and performant. Model degradation can occur due to data drift where the distribution of the input features changes over time. Furthermore, concept drift might occur, that is the relationship between input features and target labels shift over time. To illustrate, an e-commerce flags transactions from a new IP address under a certain amount. Fraudsters study this rule and submit transactions above the limit from local residential proxies. Performance tracking is a vital tool as well monitoring actual predictions against immediate feedback when available. As deviation scores are generated, automated alerts must be triggered to remediate the problem such as automatically retraining pipelines using recent data or use a fallback model as applicable.
Furthermore, governance systems including access controls and audit logs are rarely featured in a pilot demo. However, once the system interacts with real customers, regulated data or a compliance review, they are an intrinsic part of the system. In this case, leadership is vital because management gaps are concrete blockers. In addition, even if a system works technically but cannot pass a compliance review, then it is essentially a failed system. Moreover, it is imperative that organizational execution be improved because while 88% of companies now use AI in at least one core function, change management must be put on equal footing with the technology. In parallel, talent gaps can become obstacles to success. To address this issue, a model can be designed and built to keep it alive with people who understand the system and stay around long enough to maintain it.
Creating and developing an AI model might be the easiest part of the process. Sustained value for any organization requires clean data, monitoring, and governance. Taken all together, it is the engine that powers the system for success.
