Solving the AI Black Box Problem

One question keeps cropping up in my conversations with risk and compliance officers: How should they risk-assess agentic AI use cases to understand the controls that need to be associated with these tech assets?

According to Deloitte, 74% of leaders believe nearly half of their business processes will be redesigned for AI agents within the next four years. Just 39% consider themselves prepared for agentic AI from a risk, security, and governance perspective.

Without clear governance frameworks to ensure explainability, autonomous AI agents can introduce regulatory, financial, and reputational risks. Safely implementing agentic AI means opening the AI "black box" and making reasoning transparent from day 1.

What is the AI black box problem?

While AI inputs and outputs might be visible, internal decision-making paths and parameters are typically buried, sometimes in vendor code, creating a black box around AI.

Many organisations are focused on rapid agentic AI experimentation, running low-cost pilots to prove value quickly. More than half (59%) of enterprises now use agentic AI, according to the ServiceNow Enterprise AI Maturity Index 2026, with another 30% piloting the technology.

Securing long-term buy-in requires leaders to prove agentic AI capabilities to boards, regulators, and auditors. Audit teams are asking fundamental questions:

• How was the AI agent built: by a third party or in-house?
• What rules dictate its outputs, and how were those defined and tested to confirm accuracy?
• Which data sources does it use to execute tasks, and in particular, do they include any personally identifiable information (PII) or protected health information (PHI) from our internal systems?

Answering these questions can be challenging, and this challenge is widespread. More than three-quarters (76%) of software vendors have embedded AI capabilities into their products, according to Simon-Kucher.

Obscuring AI reasoning isn't necessarily malicious. Many vendors view model logic as proprietary intellectual property that requires protection against theft or misuse. However, the lack of visibility is a dealbreaker for business leaders. You can't safely invest in AI to power enterprise decision-making if you can't explain its actions.

The benefits of explainable AI

Inspecting the underlying logic behind automated decisions makes it easier to detect hidden biases, systemic errors, and unfair outcomes. These checks need to align with the organisation's policies and regulatory obligations. Adding these insights back into feedback loops can help optimise models and ensure accurate AI outputs aligned with organisational values and corporate and commercial objectives.

Proof of decision-making can support compliance with evolving legislation, such as the EU AI Act. The act requires high-risk AI systems to be designed for transparency and human oversight, and gives people affected by certain decisions based on high-risk AI systems the right to request "clear and meaningful" explanations of the AI system's role in those decisions.

Beyond regulatory compliance, explainability aligns organisations with global best practices, such as the NIST AI Risk Management Framework. This signals strong governance to risk-conscious stakeholders.

Workforce AI adoption hinges on trust. Employees are more comfortable relying on AI outputs when they can see how AI systems reason. In fact, employees override AI most frequently when a system fails to explain its reasoning, regardless of whether they think the output is correct, according to SAS.

As one business leader recently shared with me, agentic AI implementation comes down to balancing risk with innovation. The ultimate goal is to identify where agentic AI can be scaled safely.

5 ways to take AI out of the black box

Follow these five best practices to move explainable AI from ambition to reality.

1. Build on a clean data foundation

AI agents may not be able to easily explain reasoning if the underlying data is inconsistent or fragmented. Establishing a high-quality data layer before deploying agentic AI can help ensure reliable outputs based on transparent decisions. ServiceNow Workflow Data Fabric helps organisations unify systems in real time without duplicating data.

2. Map the underlying workflows

Business leaders often tell me they struggle to understand how AI agents interact with data and systems without clearly mapped processes. Collaborating across teams to chart end-to-end workflows can help identify every system, data point, and decision gate an agent uses to execute tasks.

3. Align with regulations

Embedding regulatory criteria, such as requirements under the EU AI Act, directly into vendor audits can help mitigate against compliance risks. Mapping AI capabilities to applicable risk tiers helps promote adherence to appropriate explainability standards.

4. Provide context for AI operators

Displaying real-time reasoning summaries alongside AI actions within the user interface can equip employees with immediate context. This approach can help standardise explainability in daily operations and boost trust among users. ServiceNow Super Intelligence Control Tower enables continuous monitoring for enterprise AI models.

5. Keep humans in the loop

Setting confidence thresholds to route low-certainty outputs to human experts before execution can help maintain control. A hybrid oversight model creates a clear audit trail of accountability behind every automated action.

Scale agentic AI without losing control

Opaque AI models that conceal their reasoning can create unacceptable audit risks and stall enterprise AI adoption. Implementing agentic AI doesn't mean choosing between innovation and control.

When you mandate proof of decision-making, transparency becomes your accelerator. Clear rationale helps satisfy regulators, protect enterprise assets, and give your workforce confidence in autonomous execution.

The ServiceNow control tower helps organisations sense, decide, act, and secure their AI use across the business. Organisations can govern AI across your cloud and vendor environment on the platform that already runs the business. By taking AI out of the black box, you can implement agentic technology without sacrificing transparency.

Find out how ServiceNow can help you put AI to work with transparency.