Beyond the black box: Why transparency defines the next era of enterprise AI

At the Gartner IT Symposium in Barcelona, one question surfaced again and again across CIO conversations: How do we trust AI when we can’t see how it works?

To make that tension visible, Frends brought a simple but provocative activation to the event: Black Box Jenga. A tall, simple structure built from light wooden blocks stood in the booth. A black box placed on top, just two holes on opposite sides facing each other. Hands reached inside, attempting to move pieces while keeping the tower from collapsing.

Every piece and uncertain movement represented the undocumented logic, hidden decisions, opaque security assumptions, third-party dependencies no one can inspect.

Each pull made the tower less stable. Each unknown made the system harder to trust.

And that was the point. Many enterprises are unknowingly building their integration and AI strategies on similar structures: powerful, promising, but ultimately opaque. The more they rely on black-box components, the higher the risk of collapse.

The Gartner Symposium underscored this reality. AI is advancing at extraordinary speed. Adoption is accelerating. But organizations are struggling to audit, explain and govern the systems they are increasingly dependent on. The Symposium theme of “human readiness” sharply connects to this: leaders cannot be ready for AI if they cannot see inside the systems they deploy.

The cost of opacity in enterprise AI

Black-box AI creates friction across the entire organization. When logic is hidden, IT teams can't validate outputs. Security leaders can't verify compliance. Developers can't understand model behavior. And business owners don’t know whether the automation they’re approving meets internal and regulatory standards.

Professor Fosca Gianotti, a pioneer in explainable and responsible AI at Scuola Normale Superiore (SNS) in Pisa, Italy, described the issue clearly in an interview with "The Flow", a CIO magazine created and distributed by Frends at the Gartner event:

“Transparency is good business. In predictive AI, explainability should be part of design, from data curation to model training. But with agentic AI, we need new safeguards. Only domain-specific, well-governed systems will be acceptable in the long run.”

Her perspective resonated strongly during the Symposium, where conversations around the EU AI Act, sovereignty, and compliance dominated the floor. Leaders want to innovate, but they’re increasingly aware of the responsibility that comes with AI that influences financial decisions, operational workflows and customer interactions.

Transparency is becoming both a competitive advantage and a regulatory requirement.

What transparency actually means in practice

A transparent AI ecosystem allows enterprises to track how decisions are made, which data is processed, and under what conditions the system takes specific actions. It means there is no “magic” step between input and output.

From the perspective of CIOs and architects we spoke with in Barcelona, transparency has several layers:

1. Visibility into decision paths

Every AI action — whether a classification, extraction, prediction or summary — should be traceable. Leaders want to know what context was provided, which logic was applied, and why a specific output was chosen.

2. Full access to implementation logic and documentation

When integrations or automations are built on proprietary or closed components, debugging becomes guesswork. Transparent platforms expose workflow logic, configuration, operational history and integration details by default.

3. Security guardrails that are inspectable and testable

Without visible guardrails, organizations cannot certify whether their AI meets internal risk thresholds or external requirements. They need audit logs, identity-based access, and clear data handling boundaries that can be reviewed at any time.

4. Predictable, governable behavior across environments

Transparency also means avoiding hidden dependencies — those invisible building blocks that make systems fragile. Enterprise AI must behave consistently across on-prem, hybrid and cloud environments with the same guardrails intact.

These expectations are reshaping what CIOs and technical leaders demand from vendors. The appetite for AI remains high, but uncontrolled, undocumented systems no longer fit the enterprise operating model.

What Gartner Symposium revealed about the transparency gap

The Symposium made something obvious: many organizations are trying to move faster on adoption while governance lags behind. AI models are embedded into processes, but the integration logic around them is often undocumented. Automations operate at scale but lack explainability. Some vendors offer high performance but little control over data flow.

As a result:

• Risk teams lack visibility into how decisions are generated.

• Architects cannot map or correct faulty logic.

• CIOs struggle to prove compliance to regulators.

• Businesses operate with uncertainty rather than confidence.

This disconnect explains why conversations around sovereignty, auditability and data control dominated the event. AI may be powerful, but without transparency, it becomes an operational liability — one block pulled from a Jenga tower already under strain.

Why Frends is engineered for transparent, safe AI automation

Frends was built for enterprises that cannot compromise on transparency.

Every AI capability in the platform, just like anything else with Frends iPaaS, is wrapped in integration logic that teams can inspect, test and verify. Nothing is hidden.

How Frends ensures transparency across the stack

AI actions are fully traceable
Every AI call — its prompt, context, inputs, outputs and downstream actions — is logged and visible. Teams always know what the model did and why.

Integration logic is readable, documented and versioned
The workflow design environment exposes every step, decision, branch and condition. This eliminates the guesswork typical of black-box automation.

Data boundaries are enforced at every layer
Models do not directly access business systems. Frends isolates them, provides only the minimum context required and masks sensitive data before it reaches the model.

Identity-level tracking for AI
Enterprises can see which model accessed which dataset, when and for what purpose. This is essential for auditability and compliance under regulations like the EU AI Act.

Guardrails are built into the platform
Misuse detection, prompt-hacking prevention, throttling controls and environment isolation ensure predictable, safe operation, without limiting innovation.

Transparency as an operational philosophy

Frends does not treat transparency as a feature. It is embedded into how the platform is designed. Enterprises can inspect every automation they deploy, every decision an AI model makes, and every data pathway in the process.

For organizations seeking to deploy AI at scale with confidence, this level of visibility is mandatory.

Stability and reliability by design

At the Gartner event, the Black Box Jenga tower was unstable by design. Enterprise AI shouldn’t be.

As companies move into an era where AI is woven into every workflow, transparency becomes the foundation that keeps systems resilient. Leaders want AI that is powerful, but also explainable. Flexible, but governed. Fast, yet safe.

This balance is achievable. But only when the architecture supporting AI is built with clarity, not mystery.

Frends provides the transparency, guardrails and control that enterprises need to build AI automation that lasts.