Building Trust in AI: A Framework for Responsible Innovation

Artificial intelligence (AI) is no longer a futuristic concept—it’s a disruptive, efficiency- and productivity-generating tool that is reshaping how businesses operate. As AI becomes increasingly embedded in our daily workflows, building and maintaining trust throughout the organization has become critical to ensuring its successful adoption.

It has been my experience that fostering trust in AI hinges on several key principles: transparency in how AI systems are set-up, data protection to safeguard sensitive information, and ethical practices to guide responsible innovation. Together, these elements help ensure that AI not only delivers on its transformative potential but also supports sustainable growth.

Why Trust is Foundational to AI Adoption

Trust is the bedrock of any new technology’s adoption, and AI is no exception. In many ways, establishing trust in AI mirrors the early days of cloud adoption—a process that required transparency, education, and customer references and success examples to help address user concerns. Just as organizations initially hesitated to migrate critical operations to the cloud without clear assurances of security and control, building trust in AI requires addressing similar concerns through openness and reliable safeguards.

Today, AI tools are rapidly transforming collaborative work management. Yet fragmented AI solutions and decentralized IT operations are also posing some significant challenges. Without centralized governance, it becomes harder to manage data security, eliminate bias, and maintain consistent ethical standards across platforms.

Beyond organizational trust, the broader perception of AI by the general public also plays an essential role. Many users are hesitant to fully embrace AI due to fears of data misuse, job displacement, or opaque decision-making processes. Addressing these concerns proactively through clearly communicated practices can help mitigate apprehension and build confidence in all that AI can offer.

Transparency in AI: A Foundation for Trust

Transparency ensures that users understand how AI models make decisions and is the first critical element to building trust in AI. But for transparency to be effective, it must be paired with explainability, which helps clarify the logic behind those decisions and why a particular outcome was reached.

At Smartsheet, we prioritize transparency by establishing and communicating clear internal guardrails that protect data ownership across vendors. For example, our AI features are designed to enhance end-user productivity without leveraging customer data to train our models. This commitment reduces the risk of bias while adhering to ethical standards, ensuring that Smartsheet’s AI capabilities remain trustworthy, secure, and unbiased.

Transparency also involves providing users with clear insights into the limitations of AI tools. By openly communicating where AI excels and where human oversight is necessary, organizations can foster a sense of collaboration rather than competition between AI and human decision-making. This balanced approach helps users feel more confident and engaged with AI-driven solutions.

Data Protection: Proactively Securing AI Workflows

Data security is another essential pillar of trust in AI. Robust security measures—such as centralized governance, role-based access controls, and legal hold policies—are critical to safeguarding sensitive information.

At Smartsheet, we address common data security concerns by embedding security at every stage of AI workflow development. For example, our centralized governance ensures that users can confidently manage and secure their data, while our role-based access controls help restrict data access to only those who need it. Practical measures like these enable organizations to adopt AI without compromising data security. 

Additionally, educating users about data protection protocols enhances their confidence in AI systems. Providing clear documentation, regular training, and accessible resources can demystify data security processes and empower teams to leverage AI tools in their workflows.

Responsible Experimentation: Balancing Innovation and Compliance

AI innovation and compliance must go hand-in-hand. As regulations like the EU AI Act become more prevalent, organizations will face increasingly complex rules for responsible AI use. To navigate this landscape, organizations need clear, practical steps to align their AI efforts with compliance standards and ethical considerations.

Smartsheet helps businesses strike the right balance by providing tools for creating internal AI policies and testing environments. This allows teams to experiment responsibly while staying aligned with any changes to regulatory requirements. By integrating compliance into the innovation process, organizations can maintain a competitive edge without sacrificing trust.

Responsible experimentation also means continuous monitoring and refining – this includes regularly evaluating the performance and ethical implications of AI systems to help spot potential risks early and address them. This proactive approach not only avoids compliance challenges but also reinforces a commitment to ethical AI development.

Actionable Strategies to Build Trust in AI

To build trust and successfully integrate AI, organizations can adopt the following practical and proven strategies:

• Foster transparency: Use internal guardrails and AI principles to eliminate bias and ensure data ownership across platforms. Microsoft’s AI principles, for example, include transparency initiatives like publishing model limitations and providing open documentation for tools like Azure AI. This allows users to understand how decisions are made and what constraints exist. 
• Conduct iterative rollouts: Introduce AI features in stages, incorporating user feedback to refine and improve functionality. Google’s Bard AI and OpenAI’s ChatGPT were introduced and rolled out in phases with detailed disclaimers. Both organizations actively sought user feedback to refine functionality and enhance performance.
• Establish centralized governance: Maintain oversight of AI-driven workflows to address data security concerns proactively. Proctor & Gamble, the #2 publicly owned consumer goods company, adopted centralized AI governance to enforce consistent data security standards across their AI-driven supply chain operations.
• Build cross-functional teams: Collaboration between IT, legal, and business teams plays a pivotal role in ensuring AI efforts meet both user expectations and compliance standards. Boeing has been actively integrating artificial intelligence into its operations to ensure its AI-driven systems in aviation adhere to strict compliance standards, and enhance efficiency and safety.

Trust as a Competitive Advantage

In an era where AI is reshaping how we work, trust is more than just a guiding principle—it’s a competitive advantage. As AI continues to evolve, the companies that succeed will be those that prioritize trust as an integral part of their innovation journey. With the right framework, organizations can ensure that AI becomes not just a tool for efficiency, but a driver of meaningful progress.

Smartsheet is committed to delivering secure, transparent, and collaborative AI-driven tools, helping organizations navigate the complexities of AI adoption. By putting trust at the center of our AI strategy, we enable our customers to unlock the full potential of AI—confidently and responsibly. For more information, visit smartsheet.com/ai.