Trust is becoming scarce in society and has fallen to an extreme low in the U.S. According to a September 2024 Gallup report, only 16% of Americans have "a great deal" or "quite a lot" of confidence in major companies. The current level of public confidence in business is the same as it was at a previous low during the bottom of the 2008-09 recession, and only up slightly from the all-time low of 14% amid the fallout from the COVID-19 pandemic.
The issue is even more complicated with artificial intelligence as consumer trust in AI has declined from 50% to 35% in the past year, despite 76% of consumers indicating they trust technology overall.
This is the backdrop against which businesses today must gauge their data practices. While trust has long been an ephemeral concept, the current environment has created an imperative for structuring tangible frameworks and methodologies around trust and transparent data practices. Doing so will improve an organization's competitive positioning and establish a strong foundation for effective, responsible, trustworthy implementation and use of data.
Reduced compliance and enforcement may further erode public trust in business. Some bad actors may take advantage of this, possibly resulting in increased incidents. Companies may need to point to their compliance with internal policies and standards and their internal enforcement mechanisms if consumers do not believe regulators are meaningfully enforcing compliance.
In an uncertain environment, embracing and weaving trust principles and standards into business processes may provide a cornerstone upon which businesses and consumers can build strong relationships. Organizations that reorient around trust in a meaningful way may have greater flexibility in crafting controls if they no longer need to adhere to overly prescriptive legal requirements that may not always have made sense, or meaningfully reduced the risk to consumers. By crafting and adhering to trust principles and standards, organizations may be more competitive, have stronger brands and be better positioned to capture market share after bad actors are exposed.
Professionals must prioritize several actionable focus areas as they review and refine transparent data practices and AI governance to rebuild and maintain stakeholder trust.
Across the data life cycle from collection and storage to data usage and sharing policies, organizations need to invest in a range of refreshed processes and tools that respond to the demand from consumers, partners and global regulatory pressures for clear, accessible information about data handling. Data tracking and transparency tools are needed to document the flow of data and provide verified insight into how it is protected, which parties have access to it, and for what use.
Additionally, organizations should establish accessible privacy interfaces for consumers and conduct regular audits to prove compliance and mitigate potential stakeholder mistrust.
As AI continues integrating into business operations, products and services, trustworthiness will hinge on deploying AI responsibly and ethically, especially in automated decision-making processes. Therefore, ethical AI deployment should be leveraged as a strategic differentiator. Such standards have become essential, particularly in fostering trust with stakeholders concerned about bias, transparency and fairness in AI-driven decisions.
Emerging AI transparency frameworks, such as those set by the EU AI Act, are set to reshape how companies develop and deploy AI technologies. Companies should align with the frameworks that govern the jurisdictions and industries in which they operate. In addition to meeting regulatory guidelines, overall best practices should focus on AI explainability, stakeholder training and bias detection. Clear documentation of AI processes, outcomes and periodic ethical audits will also ensure AI aligns with legal standards and consumer expectations.
Trust metrics are set to become integral to corporate performance evaluations and success benchmarking, with companies increasingly quantifying trust across data privacy, AI use, compliance and ethical practices. Stakeholders are demanding verifiable measures of trustworthiness and companies are starting to understand that strong trust metrics correlate with higher revenue and better resilience in crises. Quantified trust metrics, such as data handling transparency scores, AI ethics scores and security readiness levels, will serve as tangible indicators organizations can leverage to reinforce credibility with customers, partners, regulators and investors.
Indeed, trust will increasingly be treated as an asset and measured and reported as a quantifiable indicator of business health and market position. To stay competitive, companies must adopt trust frameworks that allow them to measure and track trust indicators. Regular reporting on trust metrics, informed by established trust frameworks, will help set reasonable benchmarks and maintain confidence.
In the face of persistent attention to data privacy issues and the parallel focus on limiting potential harm from AI, organizations must understand the business value of committing to transparent data practices. Organizations will be increasingly scrutinized through the lens of trustworthiness, especially in the wake of a data breach incident or regulatory violation. Additionally, recent consumer polls found people are more likely to embrace AI when institutions manage it well than when AI is deployed without proper governance. Ultimately, organizations that demonstrate responsible AI usage, uphold accountability and invest in trust will gain a competitive advantage, avoid fines and reduce reputational risk.
Michael Spadea, CIPP/US, is a senior managing director and U.S. practice lead for information governance, privacy and security for FTI Technology.
