How enterprises are investing in tech to succeed with gen AI

Enterprises are planning significant investments in technologies that enhance data fabrics to enable generative AI success, and are looking to a hybrid approach that incorporates generative AI with traditional AI to scale its impact, according to research by Qlik. 

The “Generative AI Benchmark Report” surveyed 200 C-Level executives, VPs, and Directors from Global 2000 firms across multiple industries. The survey explores how leaders are leveraging the gen AI tools they purchased, lessons learned, and where they are focusing to maximise their gen AI investments. 

“Generative AI’s potential has ignited a wave of investment and interest both in discreet generative AI tools, and in technologies that help organisations manage risk, embrace complexity and scale generative AI and traditional AI for impact,” said James Fisher, Chief Strategy Officer at Qlik.

How organisations are creating value from generative AI 

Even with the market focus on generative AI, respondents noted they clearly see traditional AI still bringing ongoing value in areas like predictive analytics. Where they expect generative AI to help is in extending the power of AI beyond data scientists or engineers, opening up AI capabilities to a larger population. Leaders expect this approach will help them scale the ability to unlock deeper insights and find new, creative ways to solve problems much faster.

This vision of what’s possible with generative AI has driven an incredible level of investment. 79% of respondents have either purchased generative AI tools or invested in generative AI projects, and 31% say they plan to spend over US$10m on generative AI initiatives in the coming year. However, those investments run the risk of being siloed, since 44% of these organisations noted they lack a clear generative AI strategy.

Surrounding generative AI with the right strategy and support

When asked how they intend to approach generative AI, 68% said they plan to leverage public or open-source models refined with proprietary data, and 45% are considering building models from scratch with proprietary data.

Expertise in these areas is crucial to avoiding the widely reported data security, governance, bias and hallucination issues that can occur with generative AI. Respondents understand they need help, with 60% stating they plan to rely partially or fully on third-party expertise to close this gap.

Many organisations are also looking to data fabrics as a core part of their strategy to mitigate these issues. Respondents acknowledged their data fabrics either need upgrades or aren’t ready when it comes to generative AI. In fact, only 20% believe their data fabric is very/extremely well equipped to meet their needs for generative AI. 

Given this, it’s no surprise that 73% expect to increase spend on technologies that support data fabrics. Part of that spend will need to focus on managing data volumes, since almost three quarters of respondents said they expect generative AI to increase the amount of data moved or managed on current analytics. The majority of respondents also noted that data quality, ML/AI tools, data governance, data integration and BI/Analytics all are important or very important areas to delivering a data fabric that enables generative AI success. Investments in these areas will help organisations remove some of the most common barriers to implementation per respondents, including regulation, data security and resources.

“Our Generative AI Benchmark report clearly shows leading organisations understand that these tools must be supported by a trusted data foundation,” said Fisher. “That data foundation fuels the insights and advanced use cases where the power of generative AI and traditional AI together come to life.”

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