Keys to fostering AI Governance that creates Business Value

As new startups and cutting-edge companies join AI-driven businesses, a new context emerges in which organizations need to leverage the advantages of this technology, not only to differentiate themselves but also to survive in the market.

With this scenario comes the need to talk about AI governance, which requires a solid orchestration in all areas to leverage the benefits of potential synergies and mitigate risks. We analyze what AI Governance is, its challenges, the paths it opens, and the best practices to adopt it in your business model.

What is AI Governance

AI Governance encompasses the policies, procedures, and ethical considerations necessary to oversee the development, implementation and maintenance of artificial intelligence systems.

Effective AI governance includes oversight mechanisms that address risks such as bias, privacy violations and misuse of AI, while fostering innovation and building trust. To achieve this ethical approach, the involvement of all stakeholders, such as developers, users, policy makers, ethicists, etc., is needed. This is the only way to ensure that AI-related systems are developed and used in accordance with societal values.

AI is a product of code created by people, making it susceptible to human bias and error, which can result in collective harm or discrimination. A governance approach addresses the inherent failures arising from the human side of AI creation and maintenance, which helps mitigate these potential risks.

This can include robust policies, regulations, and data governance to help ensure that ML algorithms are monitored, evaluated, and updated to avoid erroneous or harmful decisions, which will ensure that datasets are properly trained and maintained.

Why is AI governance important?

AI governance is essential in achieving a state of compliance, trust, and efficiency in the development and application of AI technologies. With its increasing integration into different operations, its potential negative impact has become more visible.

Without proper oversight, AI can cause social and ethical harm, which makes the importance of governance in managing the risks associated with advanced artificial intelligence more obvious. If we have guidelines and frameworks in place, technological innovation can be balanced with safety, thus ensuring that AI systems are not harmful to society.

Another crucial point is transparency in decision-making and the ability to explain things, which can ensure that AI systems are used responsibly and build trust. It is very important to understand how AI systems “make decisions” to hold them accountable for their decisions and ensure that they make them fairly and ethically.

In addition, governance not only ensures compliance with rules but also helps to maintain ethical standards over time. AI models can deviate and generate changes in the quality and reliability of results, so trends in governance aim to ensure the social accountability of AI, protecting against financial, legal, and reputational damage, while promoting the responsible growth of the technology.

Components of AI Governance

To manage the rapid advances in technology, AI governance has become a key pillar, especially with the emergence of GenAI. The latter is transforming how industries operate, from improving creative processes in design and content creation to automating tasks in software development.

Responsible AI governance principles are critical to protect businesses and their customers. These include:

• Fairness: organizations must understand the social implications of AI, as well as anticipate and address its impact on all stakeholders.
• Bias control: it is crucial to thoroughly examine training data to avoid incorporating biases in algorithms. This will help decision-making processes to be fair and unbiased.
• Transparency: there must be clarity on how algorithms operate and make decisions, so organizations must be prepared to explain the logic and reasoning behind AI-driven outcomes.
• Responsibility: companies must proactively set and meet high standards to manage the significant changes that AI can generate, maintaining responsibility for the impacts of this technology.
• Accountability: roles and responsibilities must be defined, as well as human oversight mechanisms to hold people accountable for AI outcomes.

Global Regulatory Frameworks

Several jurisdictions have already implemented approaches to regulate artificial intelligence technologies across the global landscape. Understanding these regulations goes a long way in helping organizations develop effective compliance strategies and mitigate legal risks.

Some examples include the following:

European Union’s Artificial Intelligence Law

This law has been one of the major legislative milestones in the global AI regulatory landscape.

This comprehensive framework adopts a risk-based approach and classifies AI systems according to their potential impact on society and individuals. It aims to ensure that AI systems placed on the European market are safe, respect fundamental rights, and adhere to EU values.

To this end, it introduces strict rules for high-risk AI applications, such as mandatory risk assessments, human oversight, and transparency requirements.

United States

Another example is the executive order issued by the U.S. Government at the end of 2023, whose strategy provides a framework for establishing new standards to manage the inherent risks of technology:

• AI safety and security: obliges the developers of these systems to share security test results and critical information with the government. Requires the development of standards, tools, and tests to help ensure that AI systems are secure and reliable.
• Privacy protection: prioritizes the development and use of privacy-preserving techniques and strengthens privacy-preserving research and technologies.
• Fairness and civil rights: it prevents AI from exacerbating discrimination and bias in various sectors, such as guiding those involved, addressing algorithmic discrimination, and ensuring fairness.
• Consumer, patient, and student protection: helps promote responsible AI in key sectors such as healthcare and education.
• Worker support: develops principles to mitigate the harmful effects of AI on jobs and workplaces.
• Promoting innovation and competition: fosters research, as well as a fair and competitive AI ecosystem.
• International leadership: expands international collaboration in AI and promotes the development and implementation of vital AI standards with international partners.
• Use of AI within government: helps ensure the responsible use of AI by public administrations, providing guidance for its use, improving procurement, and accelerating the hiring of AI professionals.

OECD Principles on AI

The Organization for Economic Cooperation and Development’s AI Principles, adopted in late 2019 and updated in May 2024, provide a set of guidelines that have been widely adopted and referenced in numerous countries.

These principles emphasize the responsible development of reliable AI systems, focusing on aspects such as values that revolve around the human being.

Initiatives in China, Australia, and Japan

China took important steps in AI regulation by launching, in 2021, the Algorithmic Recommendation Management Provisions and Ethical Standards for Next-Generation AI.

These address issues such as algorithm transparency, data protection, and the ethical use of AI technologies.

For their part, countries such as Australia and Japan have opted for a more flexible approach. The former is committed to leveraging existing regulatory structures to oversee AI; while the latter relies on common guidelines and allows the private sector to manage the use of technology.

DPDPA in India

The Indian Digital Personal Data Protection Act, 2023 (DPDPA) applies to all organizations processing the personal data of individuals in India.

In the context of AI, it focuses on high-risk AI applications and represents a move towards more structured governance of AI technologies.

AI Governance Tools

AI automation capabilities can significantly improve efficiency, decision-making, and innovation, but also pose challenges related to accountability, transparency, and ethical considerations.

Effective governance structures are multidisciplinary and involve stakeholders from diverse fields, such as technological, legal, ethical, or business. Therefore, AI governance best practices involve an approach that goes beyond regulatory compliance and encompasses a robust system for monitoring and managing AI applications.

Some of the most common proactive compliance strategies include:

• Conduct periodic regulatory assessments: create a compliance roadmap that pivots according to current regulatory requirements.
• Implement risk management frameworks: develop a comprehensive risk assessment process for systems that classify AI applications according to their potential impact and apply appropriate security and control measures.
• Ensure transparency and explainability: document AI development processes, data sources, and decision-making algorithms.
• Prioritize data governance: establish rigorous data management practices that address data quality, privacy, and security issues, as well as ensure compliance with data protection regulations such as GDPR.
• Encourage ethical AI development: integrate ethical considerations into the AI development lifecycle and conduct periodic reviews,
• Establish accountability mechanisms: define clear roles and responsibilities for governance within the organization, implementing audit trails and reporting mechanisms for follow-up.
• Invest in training: it is very important to provide continuing education to employees involved in AI development and implementation to ensure that they understand regulatory requirements and ethical considerations.

To this end, many companies are already following roadmaps that include best practices that help establish a robust framework to ensure that AI systems are compliant and aligned with ethical standards and organizational goals:

1. Visual dashboards that show the health and status of AI systems clearly and quickly.
2. Health scoring metrics that simplify monitoring.
3. Automated monitoring that ensures models are operating correctly and ethically.
4. Performance alerts that enable timely interventions.
5. Customized metrics that help ensure AI results contribute to business objectives.
6. Audit trails that facilitate reviews of AI system decisions and behaviors.
7. Support for open-source tools that can provide flexibility.

A Pathway to AI Governance: AI Data Governance

According to the AI & Information Management Report conducted by AvePoint, 92% of companies believe that AI will improve their business. In fact, 65% already use ChatGPT for some of their processes and 47% use Microsoft 365 Copilot.

However, in the age of AI, the need for new data governance standards is at an all-time high. The main concerns range from the increasing volume of data that organizations handle on a daily basis, to the increased use of AI tools (especially generative AI) or the need to have data updated and correctly categorized.

This is one of the main challenges faced by companies, as the potential of AI is linked to the quality of the data with which the models are trained. In addition, organizations also have to face new risks when adopting this technology, such as the exposure of their data or possible attacks from malicious parties.

Therefore, having a robust governance framework in place is key when it comes to using artificial intelligence correctly. Some of the best practices for doing so are:

Ensuring data quality

This is a vital step when introducing AI into an organization, as poor data quality can lead to poor AI performance, which can produce inaccurate or dangerous results.

Therefore, companies must ensure that their data repositories are clean and up-to-date so that AI can be trained on the most reliable and relevant data available. To do this, the following steps can be taken:

1. Detect and analyze the data environment: this is the first step in understanding the types of data we have and where it is stored in digital workspaces. This will give us an idea of which ones are actively used and how many are redundant or obsolete. This will make it easier for us to clean up our workspace and ensure that we only keep the useful and accurate ones.
2. Remove ROT (Return On Time) data: after understanding how much ROT data we have, it is time to remove it. Keeping them in our workspace makes it possible to compromise the results of AI usage, which creates greater risks of exposing sensitive, but unused data. In addition, these consume valuable storage space and reduce data quality.
3. Centralize data: Fragmented data repositories can also contribute to inaccurate AI results. Having centralized data on a single cloud platform makes it easy to access, integrate, and analyze data from different sources and formats.

Improve Data Security

Data security is one of the pillars of business today. With AI it has become an even more critical need and has become a major concern for companies.

AI is providing great benefits given its capabilities to improve access to data, but it also comes with risks. Therefore, some of the best practices when it comes to improving security are:

1. Determine risks based on current approach: Potential risks include inactive guests, orphaned users, users with excessive permissions, etc. Through analytics tools, you can better understand these risks, which will help you take action on these potential vulnerabilities.
2. Refine permission and access controls: Creating permissions and controls is a very important step in protecting sensitive or confidential internal data for both AI and employees.
3. Establish usage policies: many companies do not have accepted usage policies in place, leaving them vulnerable to AI misuse. While not foolproof, they help ensure that employees understand where and how they can use corporate data with AI, making users more aware of appropriate use.

Establish a Data Governance Framework

Organizing the workspace is essential for maintaining data security, but it is not the only thing. Appropriate strategies must also be implemented to maintain it. This is where the data governance framework comes in, which helps to further protect sensitive and personal data from unauthorized access, use, or disclosure.

The keys to achieving this are:

1. Establish clear guidelines for data management: One of the main challenges is to ensure that different types of data are stored and accessed according to their sensitivity and relevance. Organizations must ensure a consistent application of controls to ensure that new confidential or sensitive files are not compromised. A good way is to establish data management guidelines that define the purpose of each space, making it easy to follow the rules needed to keep important data safe.
2. Periodically review permissions: this helps to control who has access to what data, how they use it, and to see if data policies are being followed. It also helps to check for any unauthorized or inappropriate access, as well as examine the activities and purpose of each workspace, with the goal of updating permissions for those that have changed or removing inactive ones to avoid exposure risks.
3. Automate policy monitoring: this helps ensure that nothing slips through the cracks, ensuring compliance with the governance framework without manual intervention and allowing enterprise administrators to be notified of any deviations from the configuration or non-compliance with the framework.

Implementing Data Lifecycle Management

To keep data repositories organized and secure, it is essential to implement effective data lifecycle management. This is an ongoing process that requires attention and diligence to ensure that files and data do not accumulate.

Without proper management, companies face a proliferation of data, which can introduce new risks to the organization. To avoid these problems, it is recommended that:

• Implement data classification: data can be classified based on its confidentiality, compliance requirements, or business needs, which helps manage data more effectively, as well as prioritize data protection and governance based on its sensitivity. Automating this classification helps to easily manage data as more of it is brought into the organization by the continued use of AI.
• Create data retention and archiving policies: Creating these policies helps to curb significant growth in file volumes in the organization by deleting data that is no longer needed or relevant, as well as ensuring that data is securely deleted. Policies should also be created to determine how long they should be retained, when and where they should be archived, etc.
• Refresh the workspace: In addition to periodic review of data classification, retention, and archiving, ongoing assessment of permission controls is very important for effective lifecycle management. This ensures that access to the workspace remains for those who should be authorized to do so.

AI Governance Framework

As mentioned above, having an AI and data governance framework in place will be critical to achieving the expected results and accessing new business opportunities.

Creating an AI strategy requires continuous alignment between long-term strategic goals and day-to-day business needs. In addition, every decision must be evaluated through the lens of potential AI risks and address implications related to AI ethics in every development and implementation.

Organizations must be aware of the need to achieve a human-centered and human-driven AI model, based on an accountability framework that guides teams and structures the relationship model between AI stakeholders. It is therefore crucial that companies and governments build an AI culture that fosters transparency of AI activity, taking care of critical aspects such as the explainability of AI, as well as being prepared to communicate what is behind automated decision-making.

This culture transformation will change as AI governance engages the organization in a culture of experimentation that seeks to continuously innovate and elevate analytics capabilities. Furthermore, to achieve the goal of scaling AI with agility and robustness, governance must define and integrate the necessary processes and infrastructure across AI lifecycle operations. This is made visible in MLOPs practices and tools that strengthen the transparency, traceability, oversight, and auditability capabilities of the systems.

At Plain Concepts we are specialists in unlocking the potential of technology and providing solutions to our clients’ challenges by applying the latest techniques available. Whether you are not familiar with AI or generative AI, you don’t know how to apply it or you already know what you want, we can help you accelerate your way through artificial intelligence with the best experts.

We’ll analyze where your data is at, explore the use cases that best align with your goals, create a customized plan, create the patterns, processes, and teams you need, and implement an AI solution that is secure, modern, and meets all compliance and governance standards:

1. We train your technical and business teams.
2. We help you identify the use cases with the greatest impact and best ROI.
3. We guide you in the generation of the strategy to launch these use cases effectively.
4. We define the infrastructure, security, and governance of services, models, and solutions.
5. We develop a strategic roadmap with all activities, POCs, and AI projects.
6. We accompany and advise you throughout the process until the final deployment, consumption, and maintenance.

Together we will establish a solid foundation to bring out the full potential of AI in your organization, enabling new business solutions with language generation capabilities and you will adopt a high-value AI framework at high speed and scalability.

We join your team and work together, establishing a long-term relationship of trust to explore and understand the business value of AI, the technical architecture, and use cases that can be realized today. We conduct workshops to identify the business scenarios that drive the greatest benefit. Finally, we move on to building and testing the value of this new technology for the business. If you want to take your business to the next level, don’t wait any longer and start today. Contact us!