Qlik's Generative AI Benchmark Report: Enterprise Success Strategies

Qlik's recent research reveals a trend among enterprises to make substantial investments in technologies enhancing data fabrics, aiming to bolster generative AI success. These organizations are adopting a hybrid approach, integrating generative AI with traditional AI methods, to amplify its impact throughout their operations.

Conducted in August 2023 by Enterprise Technology Research (ETR) on behalf of Qlik, the "Generative AI Benchmark Report" surveyed 200 C-Level executives, VPs, and Directors from Global 2000 firms across diverse industries. This survey delves into how leaders are utilizing the generative AI tools they've acquired, learning from their experiences, and pinpointing key areas of focus to maximize their generative 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 organizations manage risk, embrace complexity, and scale generative AI and traditional AI for impact,” said James Fisher, Chief Strategy Officer at Qlik. “Our Generative AI Benchmark report clearly shows leading organizations understand that these tools must be supported by a trusted data foundation. That data foundation fuels the insights and advanced use cases where the power of generative AI and traditional AI together come to life.”

The report revealed that although the initial excitement about generative AI's capabilities persists, leaders recognize the importance of implementing appropriate data strategies and technologies to unlock its full transformative potential. While many organizations are embracing generative AI to stay competitive and improve efficiencies, they seek guidance on initial steps and rapid progress while balancing concerns about risks and governance.

Creating Value from Generative AI

Despite the current emphasis on generative AI, survey respondents highlighted the ongoing value of traditional AI, particularly in areas like predictive analytics. Generative AI is anticipated to excel in democratizing AI capabilities, making them accessible to a broader audience beyond data scientists and engineers. Leaders believe this approach will enable them to scale their capacity to uncover profound insights and discover innovative problem-solving approaches swiftly and efficiently.

The potential offered by generative AI has spurred significant investment, with 79% of respondents having acquired generative AI tools or funded related projects. Additionally, 31% intend to allocate more than $10 million to generative AI initiatives in the upcoming year. Despite these investments, there is a concern about potential isolation, as 44% of these organizations acknowledged the absence of a clear generative AI strategy.

Surrounding Generative AI with the Right Strategy and Support

Regarding their approach to generative AI, 68% stated they intend to utilize public or open-source models enhanced with proprietary data, while 45% are contemplating constructing models from the ground up using proprietary data.

Proficiency in these domains is vital to sidestepping the well-known challenges associated with generative AI, such as data security, governance, bias, and hallucination issues. Recognizing the complexity, 60% of respondents indicated their intention to seek support, either partially or entirely, from third-party experts to bridge this knowledge gap.

Many organizations are recognizing the significance of data fabrics in addressing these challenges. Respondents admitted that their existing data fabrics either require upgrades or are not adequately prepared for generative AI. Merely 20% believe their data fabric is extremely well-equipped to meet their generative AI needs.

In light of this, it's understandable that 73% of respondents anticipate increased expenditure on technologies supporting data fabrics. A significant portion of this investment will be directed towards managing data volumes since nearly three-quarters of respondents anticipate a surge in data movement or management in their current analytics systems due to generative AI. Respondents emphasized the importance of data quality, ML/AI tools, data governance, data integration, and BI/Analytics as crucial areas for delivering a data fabric that facilitates generative AI success. Investments in these domains are expected to help organizations overcome common implementation barriers, including regulations, data security, and resource constraints.

The Path to Generative AI Success – It’s All About the Data

Although each organization's AI strategy may vary, one universal truth prevails: optimal AI results hinge on high-quality data. Given the vast volume of data requiring curation, quality assurance, security, and governance to facilitate AI and build effective generative AI models, a contemporary data fabric is indispensable. Once the data foundation is established, the platform should provide comprehensive, AI-enabled functionalities, enabling users of all proficiency levels to gain profound insights through automation and support. Qlik empowers customers to harness AI through three vital approaches:

A trusted data foundation for AI – Qlik’s data integration and quality solutions leverage AI to automate data delivery and transformation, reducing complexity, mitigating risk, and enabling data fabrics.
AI-enhanced and predictive analytics – Qlik has a long track record of delivering AI-enhanced and predictive analytics capabilities. Qlik’s OpenAI connectors extend the power of generative AI to Qlik analytics, bringing even more powerful chat capabilities to a rich user experience.
AI for advanced use cases – Qlik AutoML helps organizations scale data science investments while enabling technically inclined staff to customize AI solutions for new use cases.