4 Rules For Unleashing Radical Innovation In The Era Of Generative AI

The following comments from Teradata Chief Product Officer Hillary Ashton have been lightly edited for clarity and length. Generative AI has ushered in new frontiers in creativity, productivity and innovation. Leading organizations now expect to unlock game-changing value by harnessing generative AI to hyper-personalize customer experiences, democratize insights for knowledge workers and vastly accelerate coding and product development.

But to achieve these lofty goals, businesses will need the right tools to manage data and AI initiatives effectively—as well as a new outlook. We recently partnered with Forbes Insights to survey 1,001 executive leaders on their vision for the AI-driven decade ahead. Their answers revealed four rules for unleashing radical innovation and building the foundation for Enterprise 2030 , the AI-powered company of the future.

Our survey found that leaders see major opportunities for their organizations to transform into AI-driven enterprises, including using data analytics like AI and machine learning to drive decision-making in customer service, risk management, supply chain optimization, internal process efficiency and financial management. One use case where Teradata already supports organizations with generative AI is democratizing data to improve insights. Knowledge workers spend approximately 20% of their time searching for and gathering information.

With our new generative AI capability, Teradata ask. ai , employees can instantly sift through mountains of data by asking questions in plain human language without the need to code. This new feature empowers more people to draw insights and make better decisions.

To make AI use cases like this one a reality, however, enterprises will need high-quality data and lots of it. Unfortunately, our survey showed that many enterprises report a data bottleneck and often struggle to extract and deploy usable data. To create a culture that gets away from “elbows and opinions” and instead emphasizes facts, more people within your workforce will need access to enterprise data.

And that data will need clear governance to instill faith in a “single version of the truth. ” What does this mean for you? Smart organizations will create reusable data products, or curated sets of known good data. These data products will empower employees with shared, useful information.

In turn, your organization will better control its data estate, improve data quality and transform existing business intelligence into actionable insights. In the world of data analytics, we like to say, “respect data gravity. ” That means sharing data without moving it, which is the best way to democratize information.

Uprooting data unnecessarily increases resource requirements, application and system interdependencies, and latency. Overall, moving data across different environments is a surefire way to increase cost and complexity. But what are leaders to do, considering our survey finds “integrating data resources across enterprise silos or domains” as a top data challenge? Contemplate using a query fabric , or a unified data integration and management layer.

This option allows organizations to use data from any source without having to move it into a new system. This accelerates data delivery, reduces computing costs, automates data management and enables self-service for data users across the enterprise. All data and AI leaders know it’s a big leap from a successful pilot to widespread business transformation.

After all, the only way to get value from data and AI initiatives is to run them in production and see actual business results. But according to our survey data, most companies can’t easily progress from rapid experimentation to production at scale. Only 11% of surveyed leaders say their current technology is excellent for scaling immediately with business requirements.

To enable this leap, put scalability at the top of your criteria when making technology investments. Look for a powerful analytics engine designed to scale end-to-end AI/ML pipelines with robust governance. In addition, prioritize tech that enables an open and connected ecosystem to avoid the costly and time-consuming custom integrations for the many systems that are required to drive value from analytics at scale.

Leaders in our survey expressed a growing concern that their data could morph into a source of disinformation. In fact, when asked for their most pressing data challenges, ensuring data security, privacy and compliance topped the list. Operationalizing AI, especially generative AI, has an added complication: delivering trusted and ethical outcomes with strong model governance over time.

This is a high but required bar for putting any AI model into production. We call this principle “Trusted AI. ” At Teradata, we approach Trusted AI through a comprehensive framework consisting of people, process and technology.

The goal is to create accountability, transparency and ethical stewardship to positively impact customers and organizations. People must be committed to using AI ethically —from actively avoiding or removing bias to being transparent about how they are using AI to make decisions. In this context, the AI-driven enterprise is actually the “human-driven enterprise,” because no matter how advanced the technology is, if it isn’t focused on improving people’s experiences and quality of life, then it will not perform as needed or be trusted.

Now that you know the new rules of Enterprise 2030, it’s time to take action. Fulfilling the promise of data and AI will take organizational change, the right tools and a willingness to take big swings to get big wins. Don’t venture ahead alone.

Join us in discovering how your organization can reap the rewards of the AI-driven decade. .