In the competitive landscapes of banking, healthcare, government, and aerospace, organizations are acutely aware that their sectors are adopting artificial intelligence at a significantly slower pace than their unregulated counterparts, creating a frustrating paradox. The very industries that stand to gain the most from AI’s potential to enhance operations and sharpen their competitive edge are the ones most constrained in their ability to leverage it, primarily because the compliance and security frameworks they must adhere to erect formidable barriers. This disparity stems from a fundamental mismatch: the architecture of most modern AI platforms was not conceived for the rigorous demands of regulated environments. Recent industry surveys underscore this reality, revealing that while approximately 71% of all organizations have integrated AI into their support operations, the adoption rates diverge sharply by sector. An overwhelming 92% of technology companies have deployed AI for customer support, whereas just over half (58%) of organizations in regulated industries have managed to do the same. This gap is not a matter of reluctance but a direct consequence of how mainstream AI is commonly deployed versus what regulated industries fundamentally require from their infrastructure to implement these technologies safely and compliantly.
1. Security and Compliance as Primary Blockers for AI Adoption
Many vendors in the AI space attempt to address the adoption disparity by highlighting their platform’s comprehensive security measures, but this approach often misses the core of the problem, which is architectural rather than feature-based. The standard deployment model for AI involves a company’s primary platform, such as a help desk, running in a public cloud environment like AWS or Azure. Concurrently, the AI capabilities powering that platform often operate on a completely separate segment of the cloud or even on a different cloud provider’s infrastructure altogether. The critical issue arises from the data flow between these disparate services, which frequently occurs in an unencrypted state. While the cybersecurity posture of major public cloud providers is undeniably robust and sufficient for most organizations, this model presents an insurmountable obstacle for many entities in highly regulated industries or those bound by strict data sovereignty requirements. The movement of unencrypted data across public cloud services is a clear violation of the security and compliance protocols that govern these sectors, making adoption a non-starter regardless of the platform’s other merits.
The challenge, therefore, extends far beyond a simple checklist of security features and delves into the foundational design of the AI ecosystem. For industries where data integrity, privacy, and residency are paramount, the conventional cloud-native architecture creates an inherent conflict. Regulations often mandate that sensitive data remain within a specified security perimeter or geographical boundary, and the lack of encryption during transit between microservices hosted in different cloud regions or providers introduces an unacceptable level of risk. This architectural flaw means that even if a vendor can demonstrate strong endpoint security or data-at-rest encryption, the data-in-transit vulnerability remains a deal-breaker. Compliance officers and IT security teams in regulated fields are trained to identify and mitigate such risks, and the common multi-cloud or distributed-cloud AI deployment model immediately raises red flags. This forces these organizations into a difficult position, where they must either forgo the benefits of cutting-edge AI or seek out the rare solutions built from the ground up with a unified, secure architecture in mind.
2. The Decisive Influence of IT Security Teams on Platform Selection
The divergent adoption patterns between technology companies and regulated industries reflect fundamentally different philosophies toward security and risk management. Technology companies frequently operate with a reactive security posture, prioritizing speed and innovation. They leverage the robust security infrastructure of public cloud providers to deploy cutting-edge technologies rapidly, planning to address any additional security concerns as they emerge. In stark contrast, organizations in regulated industries must adhere to a far more rigorous and proactive approach. For them, strict compliance requirements are not guidelines but absolute prerequisites that must be met before any new system can go live. The potential consequences of failure—including severe regulatory fines, operational shutdowns, and lasting reputational damage—necessitate a deliberate and cautious evaluation process where security and compliance are the primary considerations, not afterthoughts. This fundamental difference in operational mindset directly shapes how each type of organization approaches technology procurement.
This distinction in security posture is most evident in the purchasing process itself, where IT and security teams in regulated industries hold significant sway. Across all sectors, less than half of organizations (43%) rate AI security as a critical purchasing factor, but in regulated fields, that number jumps to 56%. Furthermore, the vast majority of these organizations (78%) involve IT or security teams in the final purchasing decisions, effectively granting them veto power over any platform that fails to meet their stringent standards. When these teams evaluate AI-enabled platforms, they are often confronted with a limited and frustrating set of choices. On one hand, there are modern, cloud-based solutions offering advanced AI features but implicitly requiring unencrypted data to flow between external systems. On the other, there are on-premises solutions that may offer better security control but come with expensive and often limited AI capabilities. Consequently, more than half (53%) of organizations currently in pilot or evaluation phases are focused specifically on defining security and compliance requirements before even committing to a vendor, moving methodically because the cost of a wrong decision is simply too high.
3. Four Core Criteria for Evaluating AI Platforms
For organizations operating within the stringent confines of regulated industries, the evaluation of AI-enabled platforms for support operations must be guided by a specific set of architectural and security criteria. The first critical question to ask is whether the platform has its own dedicated data centers where it runs its AI foundation model services, or if it relies on services hosted in the public cloud. A vendor-owned and operated infrastructure can offer a higher degree of control and security assurance compared to one that pieces together services from various public cloud providers. Following this, it is essential to confirm that the platform offers genuine deployment flexibility. An ideal solution should be capable of running within an organization’s own virtual private cloud (VPC) or on-premises data center, rather than being restricted to the vendor’s multi-tenant public cloud infrastructure. This flexibility is crucial for maintaining a secure perimeter and ensuring that sensitive data never leaves the organization’s direct control, a non-negotiable requirement for many in finance, healthcare, and government sectors.
Beyond infrastructure, the evaluation must extend to the AI models themselves and the governance capabilities surrounding them. A key consideration is whether the platform supports AI model selection, which determines if an organization can bring its own trusted AI provider or model or if it is locked into the vendor’s chosen foundation model. This choice is vital for risk management and for aligning AI capabilities with specific business needs and ethical guidelines. Furthermore, it is imperative to verify that the platform can meet all data sovereignty requirements, ensuring that data will always remain within the relevant geofenced area or security perimeter. Finally, a thorough assessment of the platform’s governance and visibility capabilities is necessary. Regulated organizations must be able to confirm that they can see exactly what is happening with their data at all times and maintain comprehensive, immutable audit trails. These features are not just beneficial; they are essential for proving compliance to auditors and regulatory bodies, making them a cornerstone of any viable AI solution in a regulated context.
An Architectural Shift Paved the Way for Broader Adoption
The significant adoption gap that once existed between technology companies and regulated industries was never a permanent state; rather, it signaled a market imbalance where vendors had overwhelmingly built solutions for one segment while the complex challenges of another remained largely unaddressed. This pattern has historically proven to be temporary in the enterprise software market. The unmet demand from the neglected market eventually grew substantial enough that incumbent vendors were forced to adapt their offerings or risk being displaced by more agile competitors who recognized and catered to these specialized needs. The organizations that successfully rethought their architecture to support secure, compliant AI deployment ultimately established the new standard for how enterprises would deploy AI-enabled tools and platforms moving forward. This shift was accelerated by a clear market signal, as nearly three-quarters (74%) of organizations indicated they would increase their focus on AI security over the subsequent two years. As regulations evolved and AI’s role in business operations deepened, the influence of security and compliance on procurement decisions only intensified, cementing the importance of an architecture built for trust and control.
