Are You Ready For Enterprise 2030?
Executives who invest in the right infrastructure and develop a sound AI strategy today will be well positioned for Enterprise 2030, a not-so-distant future state where the most prepared organizations will turn AI into a competitive advantage. Learn how 1,001 executives view the AI-driven decade and get actionable steps to get your organization ready.
Read the Enterprise 2030 Report
Phase 01
Create A Strong And Scalable Data Foundation
Rather than heading straight to the algorithms, first identify the most critical challenges your organization faces. “Asking ‘How can we solve that problem better, faster and cheaper than we do today?’ is a much better starting point,” said Martin Willcox, vice-president of analytics and architecture at Teradata.
Willcox recommends this approach to ensure that the most promising AI initiatives become useful and scalable prediction tools that deliver results.
“When we focus just on the technology and training the model, we get stuck in the innovation lab,” he said.
Because data feeds the algorithms and models that run AI applications, organizations must also build a strong data foundation. However, only 9% of executives said their organizations are completely data-ready and employ real-time analytics supported by AI-driven automation.
Vedat Akgun, vice-president of data science and AI at Teradata, said most organizations have data stored across different environments—in structured and unstructured formats—so having a way to centrally access all this information becomes critical. “You need a very effective, cloud-native data and analytics platform to easily bring it all together,” he said. “Prioritize the right infrastructure to achieve an AI-powered organization.”
Phase 02
Use Tools Specific To Your Industry Or Use Case
Once organizations get their data in order, they can deploy AI models both internally and externally to start solving problems, many of which are virtually impossible for humans alone to tackle with the same degree of speed and accuracy.
In the banking industry, for example, financial institutions use AI to power always-on fraud detection across customer transactions. Auto manufacturers use the technology to detect safety issues on the assembly line, training AI models to more accurately anticipate malfunctions. In retail, AI is used to forecast consumer product demand, leading to smarter decisions about what merchandise to stock or offload.
Enterprises on the path to becoming AI-ready prefer “flexible, open and connected ecosystems that empower them to leverage their preferred tools and technologies for AI and generative AI,” Akgun said.
Phase 03
Scale And Operationalize Trusted AI Models
Good governance is essential to minimizing the risks of AI, including biases that can unfairly exclude or restrict access to products and services for certain segments of the population.
In the Teradata survey, 70% of executives said company success in 2030 will require striking the right balance between pursuing relentless growth and using AI/ML responsibly.
Organizations can forge greater trust in AI systems with effective AI model operations.
“Good AI models start with good, representative data,” Willcox said, adding that organizations must exercise due diligence to ensure historical data is absent of bias.
Organizations can reduce the risk of bias by incorporating diverse data samples, creating guidelines for how to assess bias in underlying data and documenting the data sources used to train AI models.
“Operationalizing AI, especially emerging generative AI and large language models, has an added complication: ensuring one can trust the analytics and AI to deliver
ethical outcomes and stay compliant with strong
model governance over time. This is a must for putting any AI model into production. We refer to this as Trusted AI,” Akgun added.
Feedback loops and reinforcement learning can help AI algorithms improve after they make mistakes. Implementing risk scores, using fresh data sources and incorporating user feedback all improve algorithm performance over time.
“Transparency is critical for effective AI model management,” Willcox added. “AI should deliver shared value for organizations and their customers—and it should be clear what benefits customers are getting in return from AI-driven applications that use their data. If we're transparent about what we're doing and sharing the value we create with consumers, in general, they're much more accepting.”
Vedat concluded, “People thrive when empowered with better information.”