Trust Is The Real Metric For AI Success

For the past few years, AI has been treated like the next great race. The winners, we are told, will be the ones who move fastest, experiment the most, and automate anything that can be turned into code.

Yet beneath the rush, another reality is taking shape. Many enterprise AI deployments are failing to deliver measurable value. Error rates remain stubbornly high. Hallucinations, bias, and privacy issues are no longer theoretical. They are showing up in headlines, court cases, and broken customer relationships.

At the center of this story is a simple truth: AI will not succeed without trust.

On The Bliss Business Podcast, we sat down with Dominique Shelton Leipzig, Founder and CEO of Global Data Innovation and one of the world’s leading experts on AI governance, data ethics, and privacy law. Dominique has advised hundreds of companies on responsible innovation and now works directly with CEOs and boards on how to align AI with strategy, governance, and culture. Our conversation on “Building Trust With Responsible AI” explored what it really takes to turn AI from a risk into a competitive advantage.

Trust, Not Speed, Will Decide The Future Of AI

In survey after survey, CEOs overwhelmingly agree that AI will transform their businesses. At the same time, only a fraction of organizations have a clear framework for responsible implementation. That gap between ambition and accountability is where most of the trouble begins.

Dominique described a pattern she sees across industries. Pilot projects are launched like science experiments. New tools are plugged in without a clear use case, measurable outcome, or connection to the company’s purpose. Governance is treated as a brake pedal instead of part of the steering system.

The result is predictable. AI projects that looked exciting in a slide deck either stall out or create problems elsewhere in the organization. Trust erodes, not only with customers and regulators, but also with employees and investors who were promised transformation and instead see confusion.

In Dominique’s view, the real question is no longer “How can we move faster with AI?” It is “How can we build AI systems that people can rely on when it matters most?”

When Innovation Outruns Accountability

AI does not fail in the abstract. It fails in specific, human ways.

Dominique shared examples of systems that misidentified paying customers as criminals, denied vital benefits to vulnerable people, or classified children as violent risks because of how loudly they spoke in a particular region. None of these outcomes were intentional. They emerged when powerful tools were deployed without sufficient guardrails, testing, or human oversight.

These incidents are not only ethical failures. They are strategic failures. They damage brand equity, invite regulatory scrutiny, and erode internal confidence in AI as a whole.

The deeper issue is structural. In many organizations:

• IT sits in one silo, working with vendors and models.

• Legal and compliance sit in another, focused on risk after the fact.

• Security and operations each guard their own domains.

• CEOs and boards are often briefed in technical jargon that obscures where the real vulnerabilities lie.

AI amplifies whatever is already true about how a company operates. If silos, unclear accountability, and weak communication exist, AI will intensify those weaknesses. If values and standards are not already embedded in daily decisions, they will not magically appear inside a model.

The Hidden Cost Of Ignoring Governance

Dominique has spent much of her career helping companies recover after major data and AI incidents. The pattern is familiar:

• The original intent was positive.

• The technology worked as designed.

• The governance around it did not.

The financial impact can be staggering, from regulatory penalties and lawsuits to stock price drops and long term reputational damage. But there is another cost that is often overlooked.

Every highly visible failure sets back the broader adoption of AI inside the organization. Teams become wary. Boards become skeptical. Leaders pull back on innovation because they cannot trust the systems they have put in place.

The irony is that many of these outcomes could have been avoided with the same kind of quality control mindset that already exists in other parts of the business. Dominique’s argument is straightforward: responsible AI is not a philosophical debate. It is an extension of basic quality assurance and risk management into a new technical domain.

A Practical Framework For Trust

To make responsible AI tangible, Dominique and her team developed a simple framework that synthesizes best practices from regulations and case studies across more than one hundred countries. She calls it the TRUST framework.

Each letter represents a pillar that must be present if AI is going to deliver real value without undermining trust.

T: Triage The Right Use Cases
Before deploying AI, leaders must ask basic questions.

• Why are we doing this?

• Does this use case align with our mission and strategic priorities?

• Can we define a clear financial, operational, or strategic benefit?

• Are there legal or ethical obligations we need to respect from the start?

Too many AI initiatives begin without this triage. They feel exciting but lack a measurable purpose. Dominique’s advice is to treat new AI projects like any other critical investment. If they do not map directly to strategy, they should not proceed.

R: Right Data To Train And Inform
Most organizations cannot control the entire internet, but they can control their own data.

Dominique emphasizes that the accuracy and fairness of AI outputs depend heavily on the quality of the data used in the specific enterprise application. That means:

• Knowing where your training data comes from.

• Ensuring it is accurate, relevant, and up to date.

• Avoiding data that encodes bias or violates privacy commitments.

Using “raw” models without aligning them to trustworthy internal data is an open invitation to error.

U: Uninterrupted Testing, Monitoring, And Auditing
Perhaps the most overlooked pillar is continuous testing.

AI systems do not stand still. They drift as new data flows in and conditions change. Without sensors and alerts, that drift can go unnoticed until harm is done.

Dominique compares this to having sensors on every window of a house. The normal state is “closed.” When a window opens unexpectedly, you receive an alert and can act. AI needs the same kind of always-on monitoring, with human-defined standards of what “accurate” and “acceptable” look like.

Those standards should not come from a generic vendor template. They should be drawn from the expertise of the people who used to perform the task manually and know what good judgment looks like.

S: Supervising Humans Ready To Intervene
When an alert triggers, people must be ready and empowered to act.

Hallucinations and errors will always exist to some degree. The goal is not perfection. It is rapid detection and correction. That requires:

• Clear ownership for AI oversight.

• Defined escalation paths when issues are detected.

• Teams who understand both the technology and the business context.

Without supervising humans, monitoring becomes theater. It generates data but not decisions.

T: Technical Documentation And Traceability
Finally, none of this works without documentation.

To diagnose and correct issues, organizations need:

• Logs of how the model was trained and updated.

• Records of what data was used when.

• Results from ongoing tests and audits.

Without that trail, leaders are left guessing when something goes wrong. With it, they can understand when drift began, what caused it, and how to fix it.

Taken together, these five pillars are not an academic framework. They are a practical checklist for any CEO or board that wants AI to be a source of value rather than volatility.

Why Empathy Belongs In AI Decisions

Throughout our conversation, empathy surfaced as more than a talking point. It is a leadership requirement.

Responsible AI asks leaders to imagine what it feels like to be on the receiving end of an automated decision that is wrong, unfair, or opaque. A denied benefit. A misclassification as a risk. A recommendation that undermines care instead of supporting it.

When leaders put themselves in the position of customers, patients, citizens, or employees, the bar for “good enough” changes. AI stops being a toy or a trend and becomes part of the social contract between a company and the people who trust it.

Empathy also has an internal dimension. Many AI failures begin with people who were under pressure, understaffed, or unaware of the risks. Creating psychologically safe spaces to raise concerns, challenge assumptions, and slow down when needed is just as important as any technical safeguard.

Love, Courage, And The Role Of Leaders

One of the most striking parts of Dominique’s story is her motivation. After decades spent helping companies navigate the aftermath of major data breaches, she built her current firm out of something very simple: love.

Love for the customers whose lives are shaped by invisible systems.
Love for the employees who want their work to matter.
Love for the investors who are betting on technology to move society forward, not backward.

In her view, love in AI leadership looks like:

• Taking time to understand the tools instead of delegating them entirely.

• Asking better questions about risk, purpose, and impact.

• Bringing siloed teams together around a shared mission.

• Choosing long term trust over short term convenience.

It is easy to be afraid of AI or to romanticize it. Dominique offers a more grounded invitation. This is not an unsolvable problem. We already know how to build quality systems. We already know how to create governance. The work now is to bring that discipline to AI before small cracks become systemic failures.

Key Takeaways

• Trust Is A Strategic Asset, Not A Side Effect
  AI will not deliver value without trust from customers, employees, investors, and regulators. Governance is a growth enabler, not a brake.

• AI Amplifies Existing Culture And Systems
  Silos, poor communication, and vague values will show up in AI behavior. Fixing culture and collaboration is part of responsible AI.

• Governance Can Be Simple And Practical
  Frameworks like TRUST translate complex regulations and case studies into five clear pillars that leaders can act on today.

• Empathy Must Guide Data Driven Decisions
  Putting humans at the center changes how leaders define accuracy, fairness, and acceptable risk.

• Love And Courage Belong In AI Leadership
  Leading with love means caring enough to design systems that protect people, honor values, and create durable value over time.

Final Thoughts

The future of AI will not be decided only by algorithmic breakthroughs or processing power. It will be decided by whether organizations can pair innovation with responsibility, speed with discernment, and data with humanity.

Dominique Shelton Leipzig’s work is a reminder that responsible AI is not about slowing progress. It is about ensuring that progress serves people. When trust becomes the real metric, AI can move from a source of anxiety to a catalyst for better outcomes across business and society.

Check out our full conversation with Dominique Shelton Leipzig on The Bliss Business Podcast.