Building Competitive Advantage with Responsible AI Ecosystems

Forward-thinking boards recognize responsible artificial intelligence as a powerful lever for value creation, market expansion, and building stakeholder trust in an AI-driven economy.

Many boards may view responsible artificial intelligence as a compliance requirement to meet emerging regulations and avoid penalties. Yet, this perspective overlooks its true potential: When approached strategically, responsible AI becomes a powerful lever for creating value, building trust, and unlocking new markets.

In a competitive business landscape, confidence means trust in technology and clarity in governance. Companies and boards that build transparent, trustworthy AI ecosystems will not only meet regulatory requirements but also earn the confidence of customers, investors, and partners who increasingly demand verifiable reliability. 

From Cost Center to Value Driver

AI adoption is already reshaping industries, and the risks are tangible. The 2025 Infosys Responsible Enterprise AI in the Agentic Era study found that 95 percent of C-suite executives and directors experienced at least one AI-related negative incident (e.g., biased outcomes, misinformation, security vulnerabilities) in the last two years, nearly 40 percent of which were considered severe. At the same time, 78 percent of survey respondents viewed responsible AI as a driver of business growth.

This duality shows why boards must reframe their perspective. Responsible AI is not about limiting downside; it is about unlocking upside. The organizations that build credibility through assurance and transparency will position themselves to win contracts, secure better financing, and strengthen brand equity.

Transparent Ecosystems as a Differentiator

AI is not a self-contained capability. It is an ecosystem comprising cloud platforms, foundation models, open-source libraries, annotation contractors, and external data exchanges. Every layer of this ecosystem can either erode or enhance trust.

Boards that encourage management to map dependencies, ensure data provenance, and hold vendors accountable create ecosystems that signal reliability to stakeholders. To achieve this, boards should ask targeted questions such as: What processes verify data integrity? How are third-party risks assessed? What contingency plans exist for critical dependencies? These discussions help strengthen transparency, resilience, and trust in AI-driven initiatives.

Transparent AI ecosystems make the firms that deploy them the partner of choice in highly regulated sectors where trust is nonnegotiable. Infosys's research reinforces this: Organizations that lead in responsible AI practices achieve 39 percent lower financial losses and 18 percent lower severity of AI-related incidents. These outcomes demonstrate that building trust through transparency is not just a compliance measure; it is a strategic advantage that directly translates into measurable business.

Proof, Not Declarations

Stakeholders no longer accept ethical declarations as a guarantee of reliability; they want proof. This is where assurance mechanisms become central to value creation. Independent audits, continuous monitoring of bias and drift, and third-party certifications move governance from aspiration to demonstrable performance.

Boards play a pivotal role by ensuring these mechanisms are in place and effective. This means asking management for evidence of audits, clarity on data verification processes, vendor compliance measures, and contingency plans for critical dependencies. Through these actions, boards help create a transparent and resilient AI governance framework.

McKinsey & Co.’s 2025 Global AI Trust Maturity Survey shows that most organizations are still at a moderate level of maturity, with an average score of 2 out of 4. This means that responsible AI practices, including defined key risk indicators and data quality guidelines, are being integrated into these organizations, but that these efforts are often partial and operational rather than strategic.

Most organizations at this stage lack enterprise-wide governance frameworks, formal accountability structures, and robust assurance mechanisms. They may have policies and tools in place but struggle to scale them consistently across business units and vendor ecosystems. This gap between policy intent and execution capability limits their ability to manage systemic risks and demonstrate trustworthiness to regulators and stakeholders.

Boards that advance the AI trust maturity of the organizations they serve by requiring verifiable assurance mechanisms will elevate their companies above competitors who can only offer promises. Proof of trustworthiness is a differentiator in competitive bids, investor evaluations, and public reputation.

The Investor Perspective

Boards are already responding to this shift. The 2025 NACD Public Company Board Practices and Oversight Survey found that 62 percent of boards now dedicate agenda time specifically to AI, but few have integrated it fully into committee oversight or management reporting dashboards. Investors are watching this gap closely.

Increasingly, they use AI governance maturity as an indicator of a company’s resilience and long-term value.  For instance, Allianz Global Investors emphasizes that responsible AI practices aligned with environmental, social, and ethical principles are essential to mitigate systemic risks and sustain growth.

This underscores a growing trend: Firms that demonstrate responsible AI ecosystems by publishing governance reports, securing third-party certifications, and conducting independent audits of algorithms and data pipelines gain preferential access to capital and lower financing costs. This is because investors see strong governance as a risk mitigator.

In health care and finance, assurance is becoming a prerequisite for procurement, with standards such as the Coalition for Health AI’s blueprint setting expectations.

For boards, this is a matter of creating future revenue streams that weaker oversight will leave inaccessible. Boards play a key role by requiring responsible AI disclosures, validating audit results, and ensuring vendor accountability frameworks are enforced. By asking management for evidence of compliance and risk mitigation, boards signal trustworthiness to investors and procurement partners.

Building Trust as Enterprise Value

The future will not be defined solely by the companies that build the most powerful AI systems. It also will be defined by which businesses earn the most trust. Boards that treat responsible AI as a compliance exercise will forever play catch-up. Those that embrace it as a value driver—embedding transparency into vendor chains, assurance into monitoring, and trust into every ecosystem touchpoint—will scale with confidence and secure enduring advantage. Responsible AI is not the cost of doing business; it is the foundation of growth in the AI-driven economy.

The views expressed in this article are the author's own and do not represent the perspective of NACD.

Infosys is a NACD partner, providing directors with critical and timely information, and perspectives. Infosys is a financial supporter of the NACD.