Generative, agentic, autonomous, adaptive—these terms define today’s AI landscape. However, responsible AI—the ethical and safe deployment of AI that maximizes its benefits while minimizing its harm—must also be a critical part of the conversation. As AI technology increasingly integrates into workforces, systems, and customer experiences, the responsibility to maintain ethical standards no longer rests solely on the shoulders of AI developers. It must be championed by business leaders, who will bear increased responsibility to ensure that the AI they deploy not only performs but does so in alignment with fundamental human values.

Responsible AI is not business altruism; it’s business strategy. As AI increasingly undertakes complex tasks, drives decision-making, and interfaces directly with customers and employees, the value and safety it provides, in addition to its functionality, will determine employee productivity and customer satisfaction. 

Innovation fueled by comprehension and empowerment

Responsible AI includes compliance, privacy, and security and extends to AI systems’ ethical, safe, and fair deployment. While these aspects are more challenging to quantify and enforce, they are critical business imperatives that impact employee experience, brand reputation, and customer outcomes.

At the start of our current AI revolution, Grammarly developed a responsible AI framework to guide ethical deployment. The framework centers around five core pillars: transparency, fairness and safety, user agency, accountability, and privacy and security. In 2025, each of these pillars will remain paramount, but two will undergo the most significant evolution and require increased attention: transparency and user agency. These two pillars will have the largest impact on how people experience AI and will dictate the trust earned or lost in those experiences. 

Transparency: Building trust through comprehension

In its simplest form, transparency means people can recognize AI-generated content, understand AI-driven decisions, and know when they’re interacting with AI. Though “artificial,” AI outputs carry intent from the models that power them. Transparency enables users to grasp that intent and make informed decisions when engaging with outputs.

To date, AI developers have been responsible for transparency, with efforts from the public to hold companies like OpenAI, Google, and Grammarly accountable for the behavior of their tools. However, as large language models (LLMs) and AI applications permeate business systems, products, services, and workflows, accountability is shifting to the companies deploying these tools. In the eyes of users, businesses are responsible for being transparent about the AI they deploy and can incur reputational damage when their AI produces negative impacts. In the coming year, with new and proposed regulations like the EU AI Act and NIST AI Risk Management Framework, we can expect that businesses may also bear more legal responsibility. 

Achieving transparency is challenging. However, users aren’t looking for absolute specificity; they want coherence and comprehension. Regulatory bodies and people expect businesses to understand how their AI tools work, including their risks and consequences, and to communicate these insights in an understandable way. To build transparency into AI practices, business leaders can take these steps:

1. Run an AI model inventory. To effectively inform people about how your AI behaves, start by understanding your AI foundation. Work with your IT team to map the AI models used across your tech stack, whether third-party or in-house, and identify the features they drive and the data they reference.
2. Document capabilities and limitations. Provide comprehensive––and comprehensible––information on your AI’s functionality, risks, and intended or acceptable usage. Take a risk-based approach, starting with the highest impact use cases. This ensures people understand the most important information while helping your security team identify the source of potential issues. 
3. Investigate AI vendors’ business models. If you are deploying third-party LLMs or AI applications, understand the motivations behind your vendors’ practices. For example, Grammarly’s subscription-based model is aligned with the quality of user experience rather than ads, ensuring security and fostering user trust.

By taking these steps, business leaders can become responsible stewards of AI, fostering transparency, building trust, and upholding accountability as they navigate the evolving landscape of advanced AI technologies.

User agency: Improving performance through empowerment

User agency means giving people, including customers and employees, control over their experience with AI. As the ultimate decision-makers, people bring contextual expertise and must understand AI’s capabilities and limitations to apply that expertise effectively. In its current form, rather than replacing human judgment, AI should empower people by enhancing their skills and amplifying their impact. When AI is a tool that enables individual autonomy, it reinforces human strengths and builds trust in its applications.

Prioritizing user agency is both ethical and smart business. Businesses need employees and customers to be empowered AI allies with the skills to guide powerful AI use cases and autonomous agents––not just to protect against malicious activities but also against unproductive ones. Similarly, AI in product and customer experiences will not always be perfect. Earning customers’ trust encourages them to report errors, bugs, and improvements to help enhance your business offerings. 

Supporting user agency requires equipping people to critically assess AI outputs and their fit for particular use cases. It also includes making people aware of the technical settings and controls they can apply to manage how AI interacts and when it does so. Leaders can drive user agency by taking the following steps:

1. Provide user education. To foster informed engagement, offer users guidance on interpreting AI recommendations, understanding its limitations, and determining when human oversight is essential. This education should be available on your website, in employee training, provided by customer-facing teams, and potentially in your product where users interact with AI. 
2. Establish straightforward IT controls and settings. Empower users by giving them control over AI settings, such as preferences for personalized recommendations, data-sharing options, and decision-making thresholds. Transparent settings reinforce autonomy and let users tailor AI to their needs.
3. Build policies that reinforce user autonomy. Ensure that AI applications complement, rather than replace, human expertise by setting guidelines around its use in high-stakes areas. Policies should encourage users to view AI as a tool that supports, not overrides, their expertise.

Implementing these steps can help business leaders ensure that AI respects and enhances human agency. This will foster a collaborative dynamic in which users feel empowered, informed, and in control of their AI experiences.

Looking forward: Responsible AI as a business advantage

As AI advances and becomes further embedded within business operations, the role of business leaders in promoting responsible AI is more crucial than ever. Transparency and user agency are not just ethical imperatives but strategic advantages that position companies to lead in a landscape increasingly defined by AI. By embracing these pillars, business leaders—particularly those in security and IT—can ensure that AI applications align with organizational values and user expectations, creating trusted and effective systems.