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.

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.

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.

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.

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 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.

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.

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!