Scaling AI Requires Scaling Trust – What 70+ Practitioners Told Us, and What It Means for the Future of Enterprise AI

When we published “Scaling AI Requires Scaling Trust,” we expected pushback and debate. What we didn’t expect was how much convergence there would be across people who usually sit in very different corners of the AI world: data and AI leaders, cybersecurity and infra architects, strategists, change practitioners and ethicists.

Across more than 70 expert voices, from founders and architects to national-lab scientists and enterprise transformation leaders, a single theme surfaced again and again:

AI is not failing because models are weak. It’s failing because organizations cannot see, interrogate, or trust how AI makes decisions. The blocker is not technology – it’s opacity.

Even commenters who challenged the statistics or the BCG 10-20-70 framing largely agreed on the underlying pattern: most AI spend is not turning into sustained, explainable business impact. One practitioner reduced the whole debate to a simpler test: “Does it make sense?” If people can’t answer that about an AI-driven decision, adoption stalls, no matter how sophisticated the model is.

What follows is a synthesis of some of the core ideas raised in the comments and reposts – and what these ideas collectively suggest about where enterprise AI needs to go next.

1. AI Fails in the Dark: Ambiguity, Loss of Agency, and Organizational Resistance 

Many commenters described a familiar pattern. AI initiatives stall not because people dislike technology, but because they distrust opaque decision-making. The fear isn’t AI itself. It’s not knowing why AI produces the outputs that affect people’s job, workflows, and accountability.

Several commenters made the same point in different language: people are not ready to outsource consequential decisions to a mysterious process. When AI arrives as a black box, employees experience it as something being done to them, rather than with them. In that context:

• Resistance is often rational self-protection.

• “Change fatigue” is a symptom of unclear decision logic.

• Adoption becomes an ongoing negotiation with uncertainty.

As one commenter put it, people don’t resist technology. They resist the ambiguity wrapped around it.

2. The Real Bottleneck is Organizational Cognition, Not Data or Models

Several leaders reframed the problem not as “change management” but as something deeper. Enterprises are not cognitively equipped to adopt AI when its inner logic is unreadable, non-traceable, and non-interrogable.

It’s not enough for technical teams to understand the model.

• Executives need to know what the system optimizes for, and where it might fail.

• Risk and compliance teams need to see how decisions relate to policies and controls.

• Frontline teams need a way to ask, “Why this recommendation instead of that one?” in their own language.

One AI architect in the thread called this a “70% epistemology [problem] – how a company reasons about a system smarter than its org chart.” Companies are wired for predictable, rule-based processes, but modern AI is probabilistic and constantly evolving. The gap makes it harder to understand and govern. Without a way to surface machine reasoning in human-legible terms (policies, workflows, incentives, constraints), even well-understood systems behave like black boxes for everyone outside the AI team.

3. From “Adoption” to “Transplant”

One repost offered a striking analogy: enterprise AI is less like adopting a tool and more “like an organ transplant.”

Consumer technologies (like general-purpose chat interfaces) are easy to absorb. They slip into existing habits with minimal coordination. Impactful enterprise AI is different: it’s introduced into living systems of roles, incentives, controls, and legacy infrastructure. If it’s not clearly defined, well placed, and continuously monitored, the organization will reject it.

In that framing, two conditions determine success:

1. Clarity of problem and purpose: precisely what is the system meant to improve, and how will we measure it?

2. Clarity of process and people: where does AI sit in the decision loop, and who is accountable for its outputs and exceptions?

Trust is earned when people can see how it works, understand the impact, and know who is accountable. With trust, the organization is far more likely to accept the change rather than wall it off as a threat.

4. Opacity Creates a Fog That Kills ROI – in Models and in Workflows

Multiple practitioners stressed ambiguity, not accuracy, as the hidden cost center. Without transparent reasoning, even accurate AI feels risky.

That ambiguity shows up as:

• Hesitation to roll out beyond pilots because leaders don’t know how to explain logic to regulators, auditors, or customers.

• Quiet workarounds where teams revert to spreadsheets and email because those tools, however manual, are at least understandable.

5. Leaders in Ethics and Governance Point to Accountability Gaps

Commenters working in ethics, risk, and governance pointed to a simple reality: AI must become auditable in the same way finance, security, or supply chains are. Without it, scaling AI becomes either a permanent pilot or a compliance nightmare.

In the original essay, we emphasized auditability. The discussion in the comments added an important nuance: governance is socio-technical. Humans define the rules and responsibilities; AI systems can help apply, monitor, and enforce those rules, but only if their reasoning is transparent and their behavior can be inspected and corrected.

To scale AI responsibly, organizations need:

• Clear corporate values and policies that define acceptable decisions and trade-offs.

• Decision pathways where human accountability remains explicit, even when AI is in the loop, and where AI agents can surface policy conflicts, inconsistencies, and edge cases instead of silently routing around them.

Trust now becomes the outcome of aligning AI reasoning with the same disciplines we expect in finance, security, and supply chains: well-defined controls, observable behavior, and documented responsibility.

A glass-box, neuro-symbolic AI framework – with a swarm of reasoning agents, an orchestrator, and humans in the loop – doesn’t replace governance. It strengthens it, by turning policies and constraints into something the machine can follow, explain, and improve on, rather than treat as an afterthought.

6. A Few Voices Challenged the Premise and Revealed Another Insight

Not everyone agreed with the framing. Some commenters questioned the premise behind the statistics or the way the problem was framed. Others argued that AI isn’t truly a black box, or that the real issue lies in brittle middleware and non-AI infrastructure.

The critique is useful because it surfaced a deeper problem: there is no shared definition of trust, transparency, or explainability in enterprise AI. The lack of shared language is itself part of the failure loop.

When stakeholders use the same words to mean different things, even well-intentioned initiatives fragment. Part of scaling trust is agreeing on what we are actually promising: not magic, not perfection, but systems whose behavior is visible, contestable, auditable, and aligned with human intent.

In Summary: The Industry Wants Glass-Box AI, Even If Not Everyone Calls It That

Across 70+ comments and reposts, five universal demands emerged, regardless of terminology or background:

• Transparency: decisions and workflows that are no longer opaque.

• Reasoning explainability: inspectable logic paths rather than inscrutable outputs.

• Governability and auditability: decisions that can be tested, challenged and defended.

• Causal traceability across both decisions and execution: the ability to follow “what caused what” across states and systems with clear evidence.

• Interrogable decision pathways with clear human agency: people can question, override, and redirect AI, and know who owns the outcome.

These are precisely the design pillars of Latent-Sense Technologies.

Glass-box AI is not an enhancement to AI. It is the missing infrastructure that turns intelligence into something organizations can understand, govern, and build around. It is what allows AI to become safe, trustworthy, compliant, and economically more valuable.

And ultimately, ready for real enterprise adoption.