Why Policy is the Key to Building Trustworthy AI
Most enterprise AI projects struggle to move from concept to reality. Despite months of development and significant investment, many remain stuck due to a "last mile problem". There’s a gap between AI that works in demos and AI that businesses trust to handle real operations.
This trust gap is more than a minor hurdle. It’s a fundamental issue preventing companies from seeing any benefit from AI. While AI can achieve high accuracy in controlled settings, business leaders hesitate to deploy these systems in the wild. The result is that many AI implementations remain in limited use cases, like human-in-the-loop scenarios, rather than achieving full automation. This limits their economic impact.
The root cause isn’t better models or algorithms. The real root cause is the lack of clear, unambiguous definitions of what success looks like for a particular business.
This article explains why traditional AI development often fails to earn trust, how our approach of “Policy” offers a solution, and how businesses can use this approach to confidently deploy AI systems that deliver real-world results.
Why AI Implementations Fail in Enterprises
85% of businesses experiment with AI, but only 20% successfully scale AI systems in production, according to Accenture. The issue isn’t technical. The issue is trust.
AI outputs can be unpredictable. Unlike traditional software that follows fixed rules, AI systems can vary their results even for similar inputs. This unpredictability makes business leaders reluctant to rely on AI for critical processes.
Human-in-the-loop solutions seem like a safer alternative but often compound the problem. Leaders must now trust that humans will catch AI errors while still relying on the AI to add enough value to justify the expense of developing it. In practice, humans often over-rely on AI suggestions, leading to reduced overall accuracy.
Even when AI demonstrates impressive capabilities during testing, it often fails to transition to real-world applications.
The Underlying Cause: Inconsistent Data Labelling
One of the biggest challenges in enterprise AI is inconsistent data labelling. While tasks like detecting objects in images have clear, objective answers, most business applications involve complex judgment calls influenced by context and priorities.
Take the example of rating an essay for spelling consistency. One teacher might penalise every error, while another might overlook minor mistakes if the overall communication is effective. Both perspectives are valid, but they create incompatible training data for AI models.
Outsourcing data labelling often makes this problem worse. Labeling done by non-experts can result in errors that teach AI systems to make systematic mistakes. For instance, a legal document might be labeled as “low risk” by someone who misses key details, or “high risk” by someone who doesn’t understand the regulatory context.
Traditional quality assurance methods don’t work in this scenario. You can’t debug inconsistent AI behaviour by examining code because the problem lies in the foundational assumptions about what constitutes correct behaviour.
Many organisations realise this too late, after months of building models that fail to deliver reliable results because the training data contains conflicting standards.
Introducing Policy: The Foundation of Trustworthy AI
Our concept of Policy represents a shift in AI development. Rather than trying to make AI think like humans, Policy translates human expertise into systematic, explicit rules that eliminate ambiguity (so that AI can understand).
At its core, Policy removes uncertainty by defining clear, measurable criteria for success. For instance, rather than asking AI to “rate this essay for spelling consistency,” Policy provides explicit instructions: “Award full marks only if there are zero spelling errors according to the Oxford English Dictionary.”
This approach might seem obvious, but it fundamentally changes how AI systems are built. Most business processes rely on implicit knowledge, experience, and subjective judgment. Policy forces these frameworks into the open, making them explicit and reproducible.
The benefits of Policy extend across the entire AI lifecycle:
Better Training Data: Explicit policies create consistent labelling standards, ensuring that training data aligns with the desired outcomes.
Meaningful Evaluation: Clear criteria allow teams to evaluate whether AI systems are genuinely improving.
Explainability: Decisions based on explicit policies are easier to audit and justify.
Trust: When domain experts help define the rules, they gain confidence in the AI system’s behaviour.
The Artanis Methodology: Bringing Policy into Practice
The Artanis Methodology provides a structured approach for integrating Policy into AI development. It focuses on collaboration between domain experts and AI engineers to create systems that businesses can trust.
Immersion in Business Processes
AI engineers begin by observing domain experts in their day-to-day work. This reveals the implicit decision-making frameworks and quality standards that experts use.
Iterative Policy Development
Experts create initial Policy statements, test them on real data, and refine them based on edge cases and exceptions. This iterative process ensures that policies become robust and reliable.
Policy-Driven Data Labelling
Policies guide the labelling of training data, ensuring consistency and reducing errors.
Embedded Collaboration
Engineers work alongside business teams throughout the process, ensuring that the AI system aligns with real-world needs and workflows.
Outcome-Oriented Evaluation
Success is measured by business impact, such as cost savings or efficiency gains, rather than purely technical metrics like accuracy.
Building a Practical AI Strategy
To implement trustworthy AI, businesses need a clear roadmap. Here’s how to get started:
Evaluate Opportunities: Identify processes that involve tasks suitable for AI, can be measured objectively, and have clear business outcomes.
Engage Domain Experts: Choose experts who deeply understand the process and can articulate their decision-making frameworks.
Start Small: Begin with pilot projects that are low-risk but high-impact to build confidence and gain insights.
Plan for Iteration: Expect that policies will need refinement as you work with real data.
Conclusion
The trust gap is the biggest barrier to enterprise AI adoption, but it’s not an insurmountable one. By prioritising clear, systematic policies, businesses can move beyond the limitations of human-in-the-loop systems and deploy AI that delivers measurable value.
Trustworthy AI requires collaboration, iteration, and alignment with business goals. By embracing the Policy-driven approach, enterprises can unlock AI’s full potential and confidently embrace automation.
