Artificial intelligence (AI) has increasingly moved to the core of strategic decision making across industries globally and remains a hot topic for the coming year. AI serves as a basis to optimize processes, personalize the customer experience and drive competitive differentiation and we are still learning about the full potential of Generative AI and Agentic workflows. However, the transformative potential of this technology can remain untapped if businesses and AI practitioners alike overlook the important concept of trust in the end-to-end process starting with the data right through to the actions taken.
Many organizations across various industries believe that deploying a highly accurate AI model, built on clean and organized data, is sufficient to create significant value when it is deployed throughout the business. However, that is not always the case and the issue here is not one of data cleanliness, nor risk of bias, lack of innovation or privacy, rather it is a lack of trust in the output from the AI model itself and this is often a barrier that results in lack of return on investment (ROI).
So, how can businesses build a competitive edge with trusted AI innovation?
Senior Director, AI/ML at Teradata.
The criticality of trusted AI
The key for business decision makers is to build trust within their AI systems and its results. Unless this trust in the output an AI model exists it will not be adopted nor recognized in the organization. Indeed, the majority of businesses (78 percent) believe that they should be doing more to build trust in AI amongst their employees. If they do not, however, there will be no value produced.
It is also worth noting that the importance of trusted AI is increasingly being looked at as a mandatory requirement. In Europe, in particular, the introduction of the EU Artificial Intelligence Act (EU AI Act) on 1 August 2024, which aims to build trust through the first-ever global legal framework on AI. This is based on the AI’s intended use and potential risk of harm, from those that pose high risk all the way to minimal risk.
The aforementioned date marked the start of the transitional phase requiring organizations to meet specified criteria to make sure that they are using AI tools in a trusted, ethical and transparent way. This development further reinforces how essential trusted AI is, not only to remain compliant, but in accelerating value.
How can businesses deliver trusted AI?
Trusted AI cannot be delivered without trusted data which should be curated, integrated and harmonized across an organization. This creates a strong foundation for accuracy, reliability and governance to be built upon. These concepts are not new in the world of data management and best practice exists for many data types and domains.
However, this can be overlooked and gets more complex as we gain the ability to incorporate more complex data types such as free text, voice and images. Creating Enterprise Feature Stores and Vector Stores coupled with a robust ModelOps capability are important steps on the way to trusted AI as they provide an easy way to trace the output of models back to the input features or vectors and from there back to the source data.
Put simply if a regulator asks “Why did you make that decision about a customer” we can trace back to which model made the decision using the ModelOps catalogue, on which data it was made using the Feature Store and how that was derived from the source using the repository of feature engineering transformations.
Finally, there must be a strong focus on governance throughout an open and connected ecosystem. This will not only create flexibility, but also ensure that relevant regulations are adhered to and the business gains the confidence to drive innovation further.
All enterprises should strive for AI and data solutions which are accurate, fast and reliable. Doing this at scale enables businesses to unlock cost-effective growth and create breakthrough transformations which will drive positive impacts for themselves and their customers. The good news for organizations is that there are a number of technologies which are already available on the market for them to be able to leverage with ease. The best-in-class models use AI and machine learning functions and open Application Programming Interface (API) integrations to connect trusted AI and data ecosystems.
Through this, enterprises can significantly improve productivity and see return on investment that much sooner. The ubiquitous use of AI within businesses will only get stronger as more organizations leverage this technology, and it becomes even more deeply embedded into people’s lives. AI is a transformative force within enterprises, and it has the power to reimagine business processes, products and services; but this cannot be done without building foundational trust and accountability. Organizations will only be able to drive value and innovation with trusted AI provided that they are using trusted data to fuel these models. After all, trusted data enables and continues to empower trusted AI.
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