Generative AI (genAI) has passed the point of novelty. It’s helping software engineers write code, lawyers draft contracts, and physicians summarize medical notes. It has quickly and quietly woven its way into the tools people use every day.

The speed of genAI adoption has been staggering, but so has the uncertainty it has ushered in. As adoption continues to accelerate, one significant question looms: When will genAI transition from “experimental” to a truly “enterprise-grade” technology that we can trust with our data?

Building Confidence Through Oversight

For all the enthusiasm around AI’s potential, most technology providers today treat genAI as an optional add-on — one paired with disclaimers that distance them from responsibility for its accuracy, reliability, and compliance. That tension is particularly stark in highly regulated industries where organizations handle sensitive financial, health, or personal data. Here, genAI uncertainty often translates into fear and results in its outright rejection despite all it stands to offer.

But the way forward isn’t to sideline genAI, or even restrict its use to niche situations. It’s to put people at the center of this revolutionary technology. Human oversight is the missing link in building trust. Both genAI users and providers alike must take responsibility to guide safe, accurate, and compliant adoption.

GenAI Requires a Human Safety Net

Unlike traditional software, genAI doesn’t always give deterministic answers. Outputs can be brilliant or deeply flawed. In a high-stakes environment, blind trust in genAI is risky. Biases can creep in, hallucinations can be overlooked, and sensitive data can be shared without proper authorization. While it may seem obvious, human oversight is critical for ensuring the proper adoption of genAI tools.

Before businesses move toward enterprise-wide genAI adoption, there are a few foundational guardrails they should put in place:

• Train employees on safe inputs: Staff must understand the risks of feeding sensitive data into genAI tools, particularly when third-party large language models (LLMs) process that data. They also should not submit copyrighted content without explicit permission to do so. Annual AI-specific training should become as standard as compliance modules on privacy, anti-corruption, and data security.
• Review outputs for accuracy and bias: GenAI can accelerate workflows, but the final accountability still rests with people. Human review, also known as a “human-in-the-loop” model, can ensure that outputs meet compliance, accuracy, and ethical standards.
• Build incident response into genAI use: Just as organizations prepare for security incidents, they must plan for genAI misuse or data leaks. This preparation should be complete with clearly defined escalation paths, remediation steps, and root cause analyses.

Moving Beyond Disclaimers

Human oversight doesn’t stop with end users — genAI tool providers, too, must evolve. As genAI becomes integral to business operations, disclaimers alone become insufficient. These providers must take additional steps to establish themselves as trustworthy for large enterprise use:

• Adopt stronger data handling practices: Enterprise-grade commitments should include options for zero data retention, regional data residency, and assurances that customer data won’t be repurposed for model training. 
• Increase transparency: Customers deserve visibility into how inputs and outputs are processed, stored and reviewed. Providers must cite what internal data resources were accessed and referenced in order to deliver the output for genAI features. Generated content should be deleted or anonymized within a reasonably short period of time. Compliance documentation should also be accessible, detailed and auditable. 
• Offer enforceable assurances: If providers review or fine-tune outputs, there’s a reasonable argument for offering contractual warranties around accuracy or compliance. Much like core software-as-a-service (SaaS) offerings, genAI features can no longer remain “use at your own risk.”

This shift is already being nudged forward by regulators. The EU AI Act and the NIST AI Risk Management Framework, for instance, emphasize human oversight as a cornerstone of safe genAI deployment. Providers that align with these emerging standards early will set the benchmark for enterprise trust.

Building a Trust Framework for Third-Party GenAI Providers

For organizations unwilling to (or unable to do so due to regulatory restrictions) trust third-party genAI providers, self-hosted software deploying LLMs offers maximum control and assurance. Data never leaves the organization’s environment, customer data is isolated, and organizations can apply additional security controls (VPC SC + Org policies (GCP) or SCPs (AWS)).

But the trade-offs are significant. At present, running self-hosted LLMs is expensive, resource-intensive, and requires specialized expertise. For most businesses, particularly outside of the Fortune 500, self-hosting isn’t a realistic long-term solution. That means widespread genAI adoption in highly regulated industries will depend on trust in third-party providers, and in the safeguards those providers are willing to build into their genAI tools.

So what does a trustworthy genAI ecosystem look like? For one, it’s built on a partnership model where both sides — users and providers — shoulder responsibility. Users must commit to education, oversight, and incident response; providers need to deliver transparent governance, robust data protection practices, and enforceable commitments; and regulators should set high expectations, like the EU AI Act, that bring global consistency to oversight and accountability. Collectively, these efforts create the cultural, legal, and technical foundation for mainstream enterprise adoption.

The Road Toward More Empowered GenAI Use

The workplace history of transformative technology tells a consistent story. First comes hype, then fear, then steady integration. Email, mobile devices, and cloud all faced compliance roadblocks before they became indispensable business tools. GenAI is no different, save that the speed of its adoption has been exponentially faster.

Companies in all fields, as well as public sector organizations, cannot miss this crucial opportunity to use genAI to drive efficiency, innovation, and competitiveness. If providers continue to hide behind disclaimers or fail to build in robust data protection practices, they’ll put the brakes on trust as demand moves towards a peak. The answer isn’t to reject genAI or rush into blind adoption. It’s to put people firmly at the center of their strategy.

Ultimately, human oversight will determine when genAI becomes not just mainstream, but enterprise-grade.