Wednesday, November 29, 2023

Five Major AI Governance Practices Coming in 2024

By Anushree Bhattacharya - October 27, 2023 6 Mins Read

AI transparently

Global governments are setting up stringent AI governance laws. They will need to ensure that by 2024, AI is used ethically and the tools are compliant and trustworthy.

The widespread adoption of AI tools has created many questions about AI biases, ethics, and challenges. The ongoing attempt to enforce compliance and regulations in AI adoption should change all that by 2024.

By then, enterprise leaders using AI will have effective governance, development, deployment, and overall management policies and processes.

They still look for best practices to control the processes to meet internal and external regulations. This is where AI governance comes into the picture.

This article focuses on AI governance best practices, which will be helpful for enterprises heading to 2024.

What is AI Governance?

IBM defines AI governance as,

“AI governance directs, manages, and monitors AI-driven activities in an organization. Governance includes tracing and documentation of data, models, and audits. The documentation includes the techniques that trained each model and the metrics gained from the testing phases. Such documentation helps gain transparency into AI model behaviors and data used to boost business functionalities.”

Organizations using AI provide legal information about its usage and transparency of models. This is mandatory information provided to regulatory bodies to ensure that organizations use AI legally.

How AI Governance Works?

LinkedIn’s AI Governance Market 2023 Global Landscape: Regional Insights & Forecasts Up to 2031 forecasts that:

AI Governance Market 2023

But let us first understand why AI needs governance and compliance when other tools don’t.

AI models work based on input data. Here is the point where the ML algorithm ‘teaches’ AI to function. AI can deliver its activities only when the tool learns the data from existing language models, some of which are large models.

In fact, the larger the base learning model, the more functionalities the AI tools can deliver.

The ability to make decisions and respond that the AI tool gets from a predetermined data set of responses and decisions. These weaknesses move into the AI delivery if this data set is biased or inaccurate. This constitutes bias.

Lately, many biases have come to light with the increased adoption of AI.

In addition to biases, using AI for purposes that are detrimental to society or deliver false propaganda or information- is considered unethical. A good example is Deepfakes, which can make anyone believe it is a real person doing human things. This can be extremely dangerous to companies, even the world.

Governance will control this kind of wrong delivery or incorrect use of the technology.

Once the governance model is developed, it is important to understand first how AI governance works.

AI models need validation before assessing risks and benefits to the business. Once the tools are functional, it is monitored continuously for security, processing, quality, and friction.

Enterprises provide regulators and auditors access to the tool’s documentation. This includes details of the tools’ behavior and predictions.

The data from the analysis provides complete visibility into its working. It shows how the AI model works, the processes and training received, and what more can be done.

Proper AI governance helps enterprises gain a lot of abilities, such as:

  • It provides facts about the overall AI lifecycle with visibility and automated documentation
  • Perform data analysis to improve functionalities and efficiency to learn more patterns.
  • Build the overall AI framework at scale with a centralized approach and keep control of all accesses across the business.
  • Provide a comprehensive view of all AI activities and functions across business operations.

Why do Enterprises need it?

While entering 2024, enterprises will use AI for almost all platforms.

According to IBM’s findings in the AI Roadmap, it will integrate trust guardrails throughout the AI governance and models at the organizational level. This will boost automation, improve the quality of AI regulatory compliance, and maintain customer trust.

It also mentions that in 2022, 65% of enterprises and their CIOs have already modernized their governance policies. This exercise aimed to get more value from AI – including ML algorithms and data privacy practices.

With this, CIOs gained benefits of:

  • Compliance: They were able to make AI-solutions better with better decision-making power under relevant industry and legal regulations.
  • Efficiency: CIOs improved business efficiencies by optimizing tools and speeding up business operations.
  • Trust: AI governance also helped protect customer data, resulting in trust by using their data wisely and appropriately.

Best Practices for AI Governance

Here are the best practices to establish a robust AI governance structure.

1. Deploy AI Security Framework

Enterprises should deploy AI governance with strong security measures to ensure compliance. It includes reporting AI ethics, data regulations, regular audits, and modifying AI systems.

These parameters ensure awareness of AI functions and practices to streamline processes and promote responsible AI functions throughout the organization.

2. Governance and Security of Data

Over the near future, enterprises will have to focus more on security deployments to keep data safe. This will be one of the significant best practices for governing AI systems to make the most out of the outcomes.

Enterprises should adhere to the latest data security and privacy laws to build strong standards. These standards need to have AI-specific data governance and security rules. This will further help organizations to mitigate data-related risks, threats, or exploitation.

This practice will safeguard sensitive consumer information, foster trust, and ensure responsible governance of AI across businesses.

3. Integrate Automation

Automation in AI governance is one of the crucial practices to meet the latest regulations. Manual processes of governing AI are quite a big challenge.

Data validation, monitoring, and auditing become complex and expensive if done manually.

With automation, AI governance’s documentation and validation processes are much more efficient while meeting the latest standards. So, this practice of governing AI will be important for enterprises. CIOs should invest in automating the processes governing AI systems at scale.

4. Transparency of AI systems

The rise of responsible systems for automated decision-making processes has introduced regulatory measures that allow transparency of AI systems.

Several methods execute transparency in the tools and systems. Proxy modeling processes, such as decision trees, help understand complex models.

Thus, by prioritizing transparency, enterprises can bring accuracy to systems by developing responsible and trustworthy AI systems.

Also Read: Applications of Big Data in Enterprises

5. Involvement of Stakeholders

For a strong governance setup, enterprises will require major involvement of management and other stakeholders such as partners, clients, CISOs, regulators, and technology leaders.

These responsible entities and their collaborative approaches will ensure that the AI governance framework includes expertise and promotes accountability across AI systems. This will be an important practice for enterprises while heading in 2024.


As enterprises navigate the AI-powered future, a strong focus on responsible governance is key to shaping a successful digital business.

However, the right investment is core for leaders at this time to deploy AI and build strong governance across the business.

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Anushree Bhattacharya

Anushree Bhattacharya is a Senior Editor with Ondot Media, where she covers stories on B2B business strategy, thought leadership, and corporate technology culture. She is a quality-oriented professional writer with eight years of experience. She has been curating content for the B2B industry, and her writing style is inclined toward how businesses want to perceive information about emerging digital transformations and technology developments. Anushree blends the best information on trending digital transformations, technology-driven stories, and SEO-optimized content. Anushree is proficient in technology journalism and curates information-driven stories about enterprise tech for EnterpriseTalk publication.

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