Do you remember how early 2023 was rocked by stories of sensitive Samsung code and boardroom transcripts leaking into ChatGPT’s dataset, or when a physician’s choice to input patient names and diagnoses into the same AI tool for an insurance letter raised urgent HIPAA alarms?
Together, these high-profile incidents underscore the urgent necessity of embedding strong data stewardship and responsible AI measures at every stage of the AI lifecycle. Organizations that integrate AI governance, AI ethics, transparency, and compliance into each stage of their AI lifecycle, not only protect themselves from costly breaches and regulatory fallout but also cultivate the trust and agility needed to innovate at scale. The research conducted by Accenture found that:
Companies that prioritize responsibility consistently outperform, converting ethical rigour into measurable revenue growth. The survey involving C-level executives in over 1000 companies revealed that participants find that responsible AI practices lead to an increase of 18% in AI revenue on average.
The same study also revealed that while most executives agree that responsible AI is essential, the majority admit their organizations haven’t yet reached their benchmarks. It’s hardly surprising embedding rigorous enterprise AI governance and ethical standards is much more difficult in practice than it is in principle.
This is why 42% of the companies surveyed have already allocated more than 10% of their total AI budget to governance and compliance.
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We believe this trend will continue, leading companies to make responsible AI investments a standard part of their AI implementation strategy.
Next, we will outline three reasons why responsible AI drives profitability in AI projects.
1. Responsible AI Enables Proactive Management of AI Risks
In March 2025, 78 percent of respondents in McKinsey’s Global Survey said their organizations use AI in at least one business function. High growth in AI adoption, has a two-fold impact concerning risk management in companies:
- The emergence of new threats, including reliability failures (e.g. output errors, hallucinations, model crashes), algorithmic bias, and opaque, “black-box” models.
- Amplification of existing vulnerabilities, from privacy and data-governance challenges to cybersecurity breaches, copyright and IP infringement, and the unlawful disclosure of proprietary information and trade secrets.
According to the Accenture survey, the company executives ranked these risks as the top three:
1. Privacy and data governance-related risks (a concern for 51% of executives)
2. Security (47%)
3. Reliability (45%).
That 51 percent of CEOs naming privacy and data governance as their top AI risk reflects several converging pressures. First, the regulatory landscape has become highly fragmented and stringent, making it difficult for multinational organizations to comply with privacy laws. As of March 1, 2025, EU regulators have imposed 2,245 fines under the GDPR, totaling roughly €5.65 billion, underscoring the hefty penalties now levied for privacy violations. This demonstrates that companies must ensure their AI governance and data-privacy compliance are in sync.
On the other hand, security and reliability can be connected to a steep trend in the rise of AI-driven incidents (bias, deepfakes, hallucinations, IP infringement, etc.), increasing by 32.3% in 2023, according to the AI Incident Database. The C-level executives in the survey took the view that a single major, AI-related incident would, on average, erase 24% of the value of their firm’s market capitalization.
Together, these statistics underscore the urgent need for robust AI governance and risk mitigation strategies to address the accelerating and multifaceted threats posed by emerging and amplified vulnerabilities.
2. Responsible AI Enables Better Use of AI Products
Many recent studies clearly show that organizations with mature AI governance frameworks enjoy higher staff adoption rates and increased revenue growth from their AI products.
- In WRITER’s 2025 Enterprise AI Adoption report, organizations with a comprehensive generative AI strategy (a core part of the AI governance) reported 80 % “very successful” adoption and implementation, compared with only 37 % at companies without such a strategy.
- In McKinsey’s May 2025 Global AI Trust Maturity Survey, companies scoring higher on the responsible AI maturity report indicated that responsible practices unlock greater value from AI tools. Some of the benefits these companies reaped were:
42 % improved efficiency and cost reductions
34 % increased consumer trust
29 % enhanced brand reputation
22 % fewer AI-related incidents.
In summary, these findings demonstrate that investing in robust AI governance drives stronger user adoption and delivers measurable business value, boosting efficiency, trust, and reputation.
Whisperly’s policy templates included in AI Governance simplify the creation of comprehensive AI policies by providing pre-defined frameworks that ensure consistency, compliance, and best practices across your organization.
3. Responsible AI Protects Businesses from High Fines for Non-Compliance
The European Union Artificial Intelligence Act has started the regulation trend across the globe with now over 40 countries are working to regulate AI. By combining an extraterritorial scope with a clear, risk-based framework, the EU AI Act sets a new standard that other jurisdictions are likely to emulate in crafting responsible AI regulations.
This is a brief EU AI Act summary:
a. Comprehensive Extraterritorial Regulation
The EU AI Act is a regulation with uniform legal force across all Member States, applying to any AI system placed on the EU market or used within its borders, regardless of where it’s developed, mirroring the GDPR’s “extraterritorial” reach.
b. Risk-Based Classification
AI systems are divided into four tiers: prohibited, high, limited, and minimal risk, with the strictest requirements (conformity assessments, documentation, transparency, human oversight, cybersecurity) reserved for high-risk systems, and transparency obligations for limited-risk applications.
If you wish to determine the risk of your AI systems, go to our free AI Act Compliance Checker.
c. Phased Implementation Timeline
- 1 Aug 2024: Act entered into force
- 2 Feb 2025: Bans on unacceptable AI and general rules become enforceable
- 2 Aug 2025: Governance measures, notifications, confidentiality rules, GPAI obligations and most penalties start to apply
- 2 Aug 2026: Full enforcement of general compliance measures
- 2 Aug 2027: Mandatory conformity assessments and registration for high-risk systems and existing GPAI models.
d. Tiered Penalties for Non-Compliance
A closer look at the penalty provisions is available here, and below is a summary:
- Up to €35 M or 7 % of global turnover for prohibited practices (e.g., manipulative social scoring)
- Up to €15 M or 3 % of turnover for breaches of general and high-risk requirements
- Up to €7.5 M or 1.5 % of turnover for false or misleading information to authorities
- SMEs pay the lower amount; EU bodies face reduced caps; GPAI providers face fines up to €15 M or 3 % of turnover.
This poses a major financial liability. Waiting until your AI solutions are largely built before addressing compliance could force extensive system overhauls effectively doubling the investment in the development.
Moreover, AI-specific rules aren’t the only hurdle businesses must clear. As novel risks from generative AI emerge, governments are scrambling to enact or revise existing legislation. Because these laws are being rolled out at both national and subnational levels, compliance has grown even more complex. For instance, China, Singapore, Brazil, and Saudi Arabia have all proposed or passed updated IP and copyright statutes; South Korea introduced an AI liability law in 2023 (predating similar EU plans).
Whisperly AI Governance and Privacy Management modules automate tedious compliance tasks — from conducting risk assessments and generating required technical documentation to managing audit trails, conformity-assessment workflows, incident reporting, and registration data.
4. Responsible AI Reduces the Risk of Third-Party AI Products
As external partners introduce new risks, businesses must look past their own responsible AI policies. They need to perform comprehensive third-party assessments and must ensure that every participant in the supply chain formally accepts and fulfills all legal and regulatory obligations. In high-risk AI deployments, companies will be held fully responsible by both customers and regulators for how they oversee these use cases. Even with potentially severe repercussions, only 43% of companies surveyed report conducting third-party assessments
Organizations need to act now to verify that any third-party AI solutions adhere to their internal AI standards and are continuously monitored to ensure ongoing compliance and risk management.
Whisperly AI Governance and Privacy Management modules automate third-party vendor assessments, empowering organizations to ensure that external AI solutions conform to their internal standards and are continuously monitored for compliance and risk management.
How to Maximize Your Return on Investment from AI?
(Spoiler Alter: Responsible AI by Design)
Pioneers in responsible AI are organizations that place ethical and compliant AI at the very heart of their strategy, proactively establishing cross-functional governance and continuously enhancing their capabilities. Analogous to the GDPR’s “privacy by design,” they embrace a “responsible by design” mindset that embeds safeguards into every stage of the AI lifecycle, ensuring they stay ahead of technological advances and shifting regulations.
By investing in both organizational and operational maturity, through ongoing horizon-scanning, future-proof planning, and regular updates to principles, policies, and standards, they enable real-time decision-making, accelerate safe adoption, and confidently scale AI solutions.
Whisperly’s AI Governance suite equips these trailblazers with automated workflows, policy templates, vendor assessments, and continuous monitoring, giving them the tools to maintain their competitive edge and lead the way in responsible AI.
