It is becoming increasingly clear that AI has the potential to bring superpowers to people in a variety of roles and change the way people work. Soon, all the software companies build and buy will be AI software, making it more flexible and adaptable than today’s rules-based systems. This reduces the time between opportunity discovery and action. Agents dynamically bridge systems, integrating fragmented business processes into interconnected flows, making integration easier. This transition will create immense value for businesses of all types, increasing speed, productivity, and innovation.
But trust is key to getting there. As perceived risk increases, so do stakeholder expectations, leading to rapid experimentation with new regulations. However, regulatory patterns and attitudes around the world are not yet established. Compliance is essential, but building trust in AI goes far beyond compliance.
Companies that derive the most value from AI are those that build trust with their customers, employees, and stakeholders. Essentially, people need to trust AI enough to take over tasks. Enhanced evaluation, transparency and explainability all contribute, as does flexible governance that puts principles into practice while fostering innovation. Organizations can start with a principles-based approach to determining not just what can be built, but what should be built. These ethical decisions must be rooted in the unique values of each organization and the values of a society that places humans at the center of the AI ecosystem. This approach to building trust is responsible AI (RAI). And when properly implemented, RAI can lead to real ROI.
Our research shows that the majority of large enterprises (72%) are now implementing AI in at least one business process, but only 18% have an RAI board with decision-making authority. It’s just that. For AI governance to work, you need to bring together people who can provide complementary perspectives across departments.
Getting RAI right means implementing guidelines for everyone and operating a formal AI trust policy. This creates a psychologically safe environment where employees feel empowered to boldly innovate. But organizations also need technical guardrails to keep their AI systems running fast and securely. We’ve seen time and time again across industries how the right AI guardrails can accelerate innovation, rather than hinder it.
Trusted data is also key to AI innovation. AI builders should always ask themselves: “How do we create the right metadata to track where our data sets come from, how we collected them, and how we use them?” By deploying data manipulation guides, AI builders can Ensure well-curated and documented data for innovation.
“Today, the majority of large enterprises have implemented AI in at least one business process, but only 18% have a responsible AI council with decision-making authority.”
Alongside strengthening data governance and guardrails, leaders must take on the hard work of building and implementing trusted AI adoption processes. I often advise leaders to take three steps to move quickly.
First, educate the company. Create a clear communication plan about what trust in AI means for the entire organization and why everyone needs to be committed to trust in AI. Define how executives should lead in the age of AI and deploy structured reskilling and upskilling programs. Even the best engineers may be unfamiliar with many aspects of RAI and need to learn new human-centered AI engineering practices.
Second, invest in trust in AI. Allocating appropriate resources requires treating them as assets to be built, rather than compliance costs to be “managed” under regulatory oversight. This means creating a multi-quarter roadmap for increasing RAI maturity that incorporates people, processes, and technology into a well-coordinated action plan.
Third, involve cross-functional teams to deploy a strong governance platform, including a registry of software and data resources that need to be built or purchased, and an end-to-end workflow to ensure proper management. Masu. Additionally, machine learning operations (MLOps) must continuously monitor the performance, quality, and risk of AI tools at the model and product level. These “engine room” technologies are critical to enabling leaders “on the bridge” to make decisions with confidence.
Getting AI trust right is a shared responsibility between organizations deploying AI and platform providers, governments, international organizations, and standards bodies that aim to ensure AI is secure and reliable. In this dynamic environment, academic researchers, open source communities, and developers also have a major role to play in building more trustworthy, transparent, and explainable AI. CEOs and CTOs can do their part by cleaning up their data houses, enabling their teams to innovate safely, and monitoring all AI deployments for signs of bias or misinformation. You can.
Written by Roger Roberts
