Why responsible AI will need to be your new USP

Although AI has been the No. 1 trend since at least 2023, organizational AI governance is just slowly catching up.

But with rising AI incidents and new AI regulations, like the EU AI Act, it’s clear that an AI system that is not governed appropriately has the potential to cause substantial financial and reputational damage to a company.

In this article, we explore the benefits of AI governance, its key components, and best practices for minimal overhead and maximal trust. Additionally, we cover two cases of how to start building an AI governance, demonstrating how a dedicated AI governance tool can be the game changer.

Summary

Efficient AI governance mitigates financial and reputational risks through fostering responsible and safe AI.
Key components of AI governance include clear structures and processes to control and enable AI use. An AI management system, a risk management system, and an AI registry are part of that.
Start governing AI in your organization by implementing a suitable framework, collecting your AI use cases, and mapping the right requirements and controls to the AI use case.
The EU AI Act poses additional compliance risks due to hefty fines. AI governance tools can help in creating compliance, e.g. by mapping of the respective requirements to your AI use cases and automated processes.
When selecting a dedicated AI governance tool, look beyond traditional GRC functionalities, but for curated AI governance frameworks, integrated AI use case management, and the connection to development processes or even MLOps tools. This is part of our mission at trail. </aside>

A lack of effective AI governance can lead to severe AI incidents that can create physical or financial harm, reputational damage, or negative social impact. For instance, Zillow, a real estate company, suffered a $304 million loss and had to lay off 25% of its staff due to incorrectly predicted home prices, causing the company to overpay for purchases, relying on suggestions by AI models.

Similarly, Air Canada was facing financial losses stemming from a court ruling triggered by a chatbot's wrong responses regarding the company's bereavement rate policy.

These incidents highlight the importance of having AI governance measures in place, not just for ethical reasons, but to protect your business from avoidable harm. The demand for effective governance structures, safety nets, and responsible AI has been increasing drastically in the past months.

Companies, especially large enterprises, can only scale AI use cases when ensuring trustworthiness, safety, and compliance throughout the AI lifecycle.

However, AI governance is often perceived as slowing down performance and decision-making, as additional structures, policies, and processes can create manual overhead. But when implemented in an efficient and actionable manner, AI governance structures accelerate innovation, create business intelligence, and reduce the risk of costly issues later on. Put differently: You can move fast without breaking things.

By now, there are already dedicated AI governance tools out there that can help you to automate compliance processes, for instance, the writing of documentation or finding the evidence needed to fulfil your requirements.

But they also help you to bring structure to your AI governance program and curate best practices for you. As AI continues to reshape businesses, robust and automated governance practices will be the cornerstone of responsible AI adoption at scale.

At trail, we developed a process with actionable steps and specific documents you can request here to jump-start your strategy.

But what exactly is AI governance? AI governance is a structured approach to ensure AI systems work as intended, comply with the rules, and are non-harmful. It’s much more than classical compliance, though, as it tries to put the principles of responsible AI, such as fairness or transparency, into practice.

Some key components include:

Clear management structures, roles, and responsibilities
A systematic approach to development or procurement processes, fulfilling requirements and meeting controls (that includes risk management, quality management, and testing)
AI principles implemented in a company-wide AI policy with C-level support
AI registry as an inventory of all AI use cases and systems
AI impact assessments
Continuous monitoring (and audits) ofan AI system’s performance
Mechanisms to report to all relevant stakeholders

It’s becoming clear that companies’ focus is shifting — from “if” to “how” when it comes to AI governance implementation. Especially with the EU AI Act coming into force in August 2024 and first deadlines having already passed, it transitioned from a pioneering effort to a non-negotiable when scaling AI in the enterprise.

When starting to think about how to set up your AI governance program, it can make sense to start with your legal requirements and to tackle compliance challenges first.

This is because non-compliance can come with hefty fines, as seen in the EU AI Act, for instance, and it could open the door for discussions about responsible AI in your organization. However, it is important not to think of AI governance as merely a compliance program.

As regulatory compliance in itself can become quite complex, automated AI governance structures that cover e.g. EU AI Act provisions and that facilitate the categorization of AI systems will free up the resources needed for important innovation.

The EU AI Act 🇪🇺

The EU AI Act is the first comprehensive regulation on AI, aiming to ensure that AI systems used in the EU are safe, transparent, and aligned with fundamental rights. Non-compliance can result in penalties up to 35 Mio. EUR or 7% of the global turnover, whichever is higher.

Put in simple terms, it categorizes AI systems into four risk levels: unacceptable, high, limited, and minimal risk.

Unacceptable risk systems—like social scoring or real-time biometric surveillance—are banned entirely.

High-risk systems, such as HR candidate analyses, credit scoring, or safety components in critical infrastructure, must meet strict requirements, including risk management, transparency, human oversight, and detailed technical documentation.

Limited-risk systems, or those with a “certain risk” (e.g. chatbots interacting with humans) require basic transparency measures, and minimal-risk systems face no further obligations.

General-purpose AI (GPAI) models, like large language models, are covered separately under the Act, with dedicated obligations depending on whether the model is classified as a foundational GPAI or a high-impact GPAI, focusing on transparency, documentation, and systemic risk mitigation.

The requirements organizations face can differ on an AI use case basis. To identify the respective requirements, one must consider the risk class and role (such as provider, deployer, importer, or distributor) of the use case under the AI Act.

A transparent inventory of all AI use cases in the organization enables the categorization and lays the first step towards compliance. However, even if AI use cases across the organization are not accompanied by extensive requirements, implementing AI governance practices can speed up innovation and position an organization at the forefront of responsible AI practices.

But how can a company implement AI governance effectively?

The answer to this question is dependent on the status quo of the organization’s AI efforts and governance structures.

If no governance processes or even AI use cases have been established, the organization can fully capitalize on a greenfield approach and build up a holistic AI governance from scratch. If there are already compliance structures in place, aligning such structures across departments that use AI is crucial to achieve some sort of standardization.

Additionally, established IT governance practices should be updated to match the dynamic nature of AI use cases. This can result in shifting from static spreadsheets and internal wiki pages to a more efficient and dynamic tooling that enables you to supervise AI governance tasks.

Working with many different organizations on setting up their AI governance in the past years has let us find common structures and best practices that we are covering in our starter program, “the AI Governance Bootcamp,” so you don't have to start from scratch. Find more information here.

First steps for getting started with AI Governance:

Choosing a framework: Don't reinvent the wheel — start with existing frameworks like EU AI Act, ISO/IEC 42001, or NIST AI Risk Management and align them with your organization. The next step is to translate the frameworks’ components into actionable steps that integrate into your workflows. This cumbersome procedure can be fast-tracked by using dedicated software solutions that already embed the frameworks and respective controls, while leaving enough room for you to customize where needed.
Collection of AI use cases: The next step is to collect existing AI use cases and evaluate them according to their impact and risk. If the AI Act is applicable to your organization, it must also be categorized according to the EU AI Act’s risk levels and role specifications. This AI registry also gives you a great overview of the status quo of past, current, and planned AI initiatives, and is usually the starting point for AI governance on the project level.
Using tools to streamline governance: When setting up AI governance or shifting from silos to centralized structures, a holistic AI governance tool can be of great benefit. While a tool in general can be helpful to reduce manual overhead through automation, it facilitates the tracking of requirements, collaboration, and out-of-the-box use of an AI registry and categorization logic. Additionally, tools, such as those by trail, often already cover most of the AI governance frameworks, translate the (often vague) requirements into practical to-dos, and even automate some evidence collection processes.

Best practices for successful AI governance implementation

For AI governance to be as efficient as possible, some best practices can be deduced from pioneers in the field:

First and foremost, AI governance should not be just a structural change, but should always be accompanied by leadership and cultural commitment. This includes both the active support of ethical AI principles, but it can also shape an organization’s vision and brand.

This can be best achieved if the entire company is invested in responsible AI and involved with the required practices. Start by showing C-level support through executive thought leadership and assigning clear roles and responsibilities.

Additionally, having an AI policy is a crucial step to building a binding framework that supports the use of AI. The AI policy outlines which AI systems are permitted or prohibited to use, and which responsibilities every single employee has, e.g. in case of AI incidents or violations.

Another key driver for successful AI governance implementation is the ability to continuously monitor and manage the risks of AI systems. This means having regular risk assessments and implementing a system that can trigger alerts when passing certain risk and performance thresholds, and when incidents occur. Combining this with risk mitigation measures and appropriate controls ensures effective treatment.

Involving different stakeholders in the design and implementation process can greatly help to gather important feedback, but also includes providing ongoing training programs for all stakeholders to ensure AI literacy and promote a culture of transparency and accountability.

Real-world examples

To help you understand where to begin, we’re sharing two real-world client examples tailored to different stages of organizational maturity. One shows how we helped a client build AI governance from the ground up. The other highlights how we scaled AI governance within a highly regulated industry.

1.) The SME: Innovation consulting firm for startups and corporates

Our first client is an innovation consultancy for startups and mostly mid-cap corporates. Their processes need to be dynamic and fast, and should not block their limited human resources, with about 400–500 employees. As they did not have any AI governance structures in place and a small legal team, we’ve helped them to create their governance processes from scratch.

We focused on three main aspects: building up an AI policy, fostering AI literacy to comply with Article 4 of the EU AI Act, and setting up an AI registry with respective governance frameworks and workflows. All of this was conducted via our AI governance tool trail.

Especially the AI training, which was implemented by their HR, was a central part in enabling the workforce to leverage their new AI tools effectively. Building up the AI registry laid the foundation for transparency about all permitted AI use cases in the company, and is now used as the starting point for their EU AI Act compliance process per use case.

2.) The enterprise: IT in finance

Our second client example is a company operating in the financial industry with 5000+ employees. This firm already had lots of existing risk management and IT governance processes that were scattered across the organization,e.g. and several AI initiatives and products.

Together with a consultancy partner who was actively contributing to the change management, we started to aggregate processes to find an efficient solution for AI governance. We started by assessing the status quo with a comprehensive gap analysis of their governance structures and AI development practices.

This allowed us to pinpoint areas in need of adjustment or missing components, e.g., EU AI Act compliance. Stakeholder mapping and management is an essential part when distributing responsibilities of governance and oversight — this requires a lot of time.

In the next step, we built up an AI registry via the trail platform to have a single source of truth on what AI systems and use cases exist. The main challenge was the identification of these, as they were scattered across internal wiki pages and multiple IT asset inventories. Finally, this enabled us to classify all AI use cases and to connect them to the respective governance requirements, starting with the EU AI Act requirements.

In-house developed AI systems, in particular, require thorough documentation, which creates manual overhead for developers and other technical teams.

Therefore, technical documentation is being created using trail’s automated documentation engine, which gathers the needed evidence directly from code via its Python package and creates documentation using LLMs.

This helps to automatically track the relevant technical metrics and directly recycle them into documentation. This decreases the manual overhead drastically and increases the quality of documentation and citation.

As this client already had an established organization-wide risk management process, it was essential to integrate the management of AI-related risks into their existing process and tool. By integrating an extensive AI risk library and respective controls from trail, a dedicated process of risk identification and mitigation for AI could be established and fed into the existing process.

After starting to work with trail, our client was able to speed up processes, increase transparency, embed a clear AI governance structure, and save time with more efficient documentation generation. This positioned them as a pioneer of responsible AI among their clients and allowed for in-time preparation for the EU AI Act.

What to look for in governance tools

Especially the latter case shows how clever solutions are necessary in order to balance innovation and compliance when existing governance structures are not uniform.

As AI governance is evolving, traditional tools like spreadsheets no longer meet the complexity or pace of modern compliance needs. And rather than viewing regulation as a roadblock, teams should embed governance directly into their development workflows to speed up innovation processes by streamlining documentation, experimentation, and stakeholder engagement.

However, not every GRC tool is a good fit for AI governance. Here are some key features every efficient AI governance tool should have:

Curated governance frameworks: A good AI governance tool should offer a library of different governance frameworks, AI-related risks and controls, and automate the guidelines or requirements as much as possible. Such frameworks can either be ethical guidelines or include regulations like the EU AI Act or certifiable standards such as ISO/IEC 42001. Some tasks and requirements cannot be automated, but a tool should give you the guidance to complete and meet them efficiently.
AI use case management: A governance tool that helps you to manage AI use cases and depict your AI use case ideation process, not only supports you in thinking about governance requirements early on. It also allows you to make AI governance the enabler of AI innovation and business intelligence by having a central place to oversee how individual teams make use of AI in your company. From here, each stakeholder can see the information relevant to them and start their workflows. The AI registry is a must-have even for those who are less concerned with governance. Why not make it multipurpose?
Connection to developer environments: An effective tool lets you think about AI governance holistically and across the whole AI life cycle. This means that you need to integrate with the developer environments. By using a developer-friendly governance tool, you can directly extract valuable information from the code and data to automate the collection of evidence and create documentation. Further, it can also show you which tasks must be done during development already to achieve compliance and quality early on.

An example of such a tool, enabling effective AI governance, is the trail ****AI Governance Copilot. It’s designed for teams that want to scale AI responsibly, without slowing down innovation by getting stuck in compliance processes.

It helps you turn governance from a theoretical checkbox into a practical, day-to-day capability that actually fits how your team works. With trail, you can plug governance directly into your stack using reusable templates, flexible frameworks, and smart automation. That means less manual work for compliance officers and developers alike, but more consistency without having to reinvent the wheel every time.

As a result, trail bridges the gap between tech, compliance, and business teams, making it easy to coordinate tasks along the AI lifecycle. It reduces overhead while boosting oversight, quality, and audit-readiness. Because real AI governance doesn’t live in PDFs, wiki pages, and spreadsheets — it lives in the daily doing.

If you want to make responsible AI your competitive advantage, let us help you — we are happy to explore the right solution for you in a conversation. If you want to continue with your AI governance learning journey, you can do so here with our free AI governance framework.