Building the business case for responsible AI: 10 steps to success

Responsible AI is a business imperative for organizations to capitalize on billion-dollar opportunities safely.

In brief

• Responsible AI — the creation and use of AI that centers on human values and objectives, risk management, and governance — is vital for today’s organizations.
• Realization, reputation and regulation are the three pillars that underpin the business case for responsible AI.
• Through 10 key steps, organizations can develop and adopt responsible AI by design to capitalize on financial opportunities.

In a world of unexpected challenges and unimaginable change, artificial intelligence (AI) has the power to act as a catalyst for a new transformative age, pushing the boundaries of computing capability and human ingenuity. As AI assumes an increasingly prominent role in our lives, we must confront fundamental questions about the nature of intelligence, consciousness and existence. How do organizations ensure that AI serves humanity’s aspirations, rather than amplifying the flaws of systems?

How can an organization implement responsible AI?

In practice, responsible AI involves integrating values and principles into the very fabric of system development by converting policies into standards and guardrails that help achieve any AI system’s objectives while still considering the potential risks and consequences. Embedding responsible AI by design involves designing AI systems that are transparent, explainable, robust and fair from the outset rather than addressing these issues after the fact. Much like the research around AI in general, a multidisciplinary approach is required to implement responsible AI, so that the AI system remains trustworthy and effective.

The level of interdependency on a multitude of divisions across the organization, combined with a general lack of understanding of risk proportionality, has led several organizations either to defer responsible AI until clearer laws exist around the extent of AI governance or question the appropriate extent of responsible AI for their own organization. While responsible AI is required for complying with evolving laws and regulations like the EU’s Artificial Intelligence Act (EU AI Act), it achieves so much more than that.

For organizations wondering why responsible AI should be considered a crucial practice despite the cost and efforts involved, three interconnected pillars comprise its business case:

1. Realization
2. Reputation
3. Regulation

Let’s explore each pillar in more detail.

Realization

Realization, in this case, has dual imperatives:

• Recognizing our responsibility as stewards of AI’s development and deployment
• The need to realize tangible value from AI investments

On one hand, we must acknowledge that we stand at the precipice of a new AI boom, with far-reaching implications for economies, societies and individuals. As such, it is our collective responsibility to develop and deploy AI in ways that prioritize human wellbeing and enhance visibility into AI usage. On the other hand, we must also recognize that AI investments require rigorous evaluation and validation to deliver meaningful returns and drive sustainable growth. By acknowledging both these aspects of realization, we can set the stage for a more thoughtful, effective and responsible approach to AI. By prioritizing responsible AI throughout the entire AI lifecycle, organizations have a better grasp on their overall risk tolerance and AI portfolio, understanding which specific use cases for AI systems can have a higher impact, and design suitable solutions more readily adopted by the larger, less-technical groups across the organization.

A study by the US Government Accountability Office (GAO) found that federal agencies that implemented AI-related risk management practices experienced fewer AI-related incidents and reduced associated costs.¹ Conversely, the financial consequences of irresponsible AI can be severe: Stanford University’s 2024 AI Index Report highlighted the growing importance of responsible AI, noting that funding for generative AI increased by nearly eight times from 2022 to reach $25.2 billion in 2023.²

From a value and ROI perspective, responsible AI can drive significant benefits for organizations , such as new revenue streams, improved operational efficiency and enhanced decision-making. According to the 2024 AI Index Report, AI enables workers to complete tasks more quickly and improve the quality of their output, leading to increased productivity and competitiveness.³ Furthermore, responsible AI can also help organizations reduce costs associated with AI development and deployment, such as data preparation, model training and maintenance.

The intersection of responsibility and value is particularly evident in the context of AI governance. By establishing robust governance frameworks, organizations can develop and deploy AI systems in ways that align with business objectives, risk management strategies and ethical principles. According to a 2023 Gartner study, organizations that establish effective AI governance frameworks are more likely to achieve successful AI deployments, with 75% reporting improved AI outcomes.⁴

Reputation

Any organization leveraging AI is doing so to positively impact its reputation in the market. In this context, the reputation pillar refers to the actions required for cultivating trust and transparency in AI development and deployment. This requires verifying that stakeholders can hold developers, providers and deployers accountable for their actions, and that AI systems are designed responsibly. By prioritizing transparency and accountability, organizations can build trust in AI among users, developers and regulators alike, ultimately leading to more widespread adoption and more effective AI governance.

The misuse of AI can severely harm a company’s reputation. When AI systems are biased,  lack transparency or perpetuate misinformation, they can lead to unfair outcomes, damage customer trust and ultimately tarnish a brand’s image. For instance, a Reuters report found that AI-powered hiring tools can discriminate against certain groups, leading to reputational damage for companies that use them.⁵ Recognizing these risks, regulators have introduced laws to address this issue. For example, New York’s Local Law 144 prohibits employers and employment agencies from using an automated employment decision tool (AEDT) in New York City, unless they confirm that a bias audit has been conducted and they give prior notice to candidates. This law was put into effect on January 1, 2023, and is enforceable as of July 5, 2023.⁶

Deepfakes and other AI-generated content can also inflict significant reputational harm. These sophisticated forgeries can be used to create fake videos, audio recordings or social media posts that appear genuine, leading to confusion, misinformation and reputational damage. As reported by the Financial Times, deepfakes have already been used to manipulate public opinion and damage reputations.⁷ Companies must be proactive in mitigating these risks and protecting their brand reputation.

AI systems developed by organizations with a strong responsible AI framework can yield significantly better results, which increases the reliability of the system, thereby improving the reputation of not just the AI system but the organization as a whole. According to the same 2023 Gartner report, organizations that prioritize responsible AI are more likely to achieve successful AI deployments and reap the associated benefits.⁸ By demonstrating a commitment to responsible AI practices, organizations can enhance their reputation, differentiate themselves from competitors and ultimately drive long-term success.

Regulation

Finally, regulation is essential for developing and deploying AI in ways that align with human values and promote the public interest. Effective regulation can help prevent AI-related risks and harms, while also fostering a culture of responsibility and ethics among AI developers and deployers.

The global regulatory landscape for AI is rapidly evolving, with governments and regulatory bodies around the world introducing new laws, guidelines and standards to govern the development and deployment of AI. The EU AI Act aims to establish a comprehensive framework for AI regulation, covering issues such as transparency, accountability and fairness.⁹ In the US, the Federal Trade Commission (FTC) has issued guidance on the use of AI in decision-making, emphasizing the importance of transparency, explainability and fairness.¹⁰ In January 2025, the FTC issued new guidelines on the use of AI in advertising, emphasizing the importance of transparency and disclosure.¹¹ Meanwhile, in Asia, countries such as China, Japan and South Korea are also introducing new AI regulations and guidelines. In a global economy, organizations have to contend with competing and often contradicting regulations, especially as they relate to a rapidly evolving field like AI.

While regulation has always been seen as a primary driver for responsible AI and governance, noncompliance with the extraterritorial EU AI Act has significant monetary implications, with fines up to 7% of global turnover or EUR35m. Regulations can impact organizations’ ability to realize value and efficiency out of their AI adoptions, and cases of noncompliance can affect organizations’ reputations negatively.

By adopting a comprehensive responsible AI framework, organizations have the ability to identify, understand and manage the risks related to AI, and the flexibility to increase or decrease the efforts involved in implementing controls to mitigate them.

10 no-regret actions management can take to activate responsible AI

To address these challenges and capitalize on the opportunities presented by AI, organizations must adopt a comprehensive approach to responsible AI that prioritizes realization, reputation and regulation. By doing so, they can unlock the full potential of AI while minimizing its risks and negative consequences, and ultimately drive sustainable growth, innovation and success.