Discover how generative AI is reshaping enterprise strategies. Explore key findings from Qlik’s Generative AI Benchmark Report and learn how organizations are leveraging data and technology to maximize AI’s transformative potential.
A recent study conducted by Qlik® reveals that enterprises are planning substantial investments in technologies designed to enhance data fabrics and facilitate the success of generative AI. These businesses are adopting a hybrid approach, combining generative AI with traditional AI to amplify its impact throughout their organizations.
The “Generative AI Benchmark Report,” which was carried out in August 2023 by Enterprise Technology Research (ETR) on behalf of Qlik, involved a survey of 200 C-Level executives, VPs, and Directors from Global 2000 firms across various industries. The survey aimed to explore how leaders are making use of generative AI tools they have acquired, the lessons they’ve learned, and their strategies for maximizing their generative AI investments.
James Fisher, Chief Strategy Officer at Qlik, emphasized the burgeoning interest and investment in generative AI. He pointed out that leading organizations recognize the importance of underpinning these tools with a reliable data foundation, as this foundation fuels insights and advanced applications where generative AI and traditional AI can work in harmony.
The report underscores that while the initial excitement surrounding generative AI persists, business leaders acknowledge the necessity of aligning these tools with appropriate data strategies and technologies to fully unlock their transformative potential. As companies embrace generative AI to address competitive pressures and enhance efficiency, they are also seeking guidance on how to initiate and progress rapidly while keeping a keen eye on risk and governance concerns.
Despite the focus on generative AI in the market, respondents highlight that traditional AI continues to deliver ongoing value, particularly in predictive analytics. Generative AI is seen as a means to broaden the accessibility of AI capabilities beyond data scientists and engineers, enabling a broader audience to harness the power of AI for deeper insights and faster problem-solving.
This vision of the potential of generative AI has led to substantial investments. The report shows that 79% of respondents have either acquired generative AI tools or invested in generative AI projects, with 31% planning to allocate over $10 million to generative AI initiatives in the upcoming year. However, these investments carry the risk of being isolated, as 44% of organizations lack a clear generative AI strategy.
To address generative AI, 68% of respondents plan to leverage public or open-source models refined with proprietary data, and 45% are considering building models from the ground up using proprietary data. Expertise in these areas is critical to mitigating the well-documented issues related to data security, governance, bias, and hallucinations associated with generative AI. Therefore, 60% of respondents express their intention to rely on third-party expertise partially or entirely to bridge this gap.
Many organizations are incorporating data fabrics into their strategies to address these challenges. Respondents admit that their data fabrics either need upgrades or are unprepared for generative AI. Only 20% believe their data fabric is very well equipped to meet their generative AI requirements. Consequently, 73% anticipate increased spending on technologies that support data fabrics. This includes investments in data volume management, as nearly three-quarters of respondents expect generative AI to increase the amount of data handled for current analytics. Data quality, ML/AI tools, data governance, data integration, and BI/Analytics are all deemed important or very important in creating a data fabric conducive to generative AI success.
The road to success with generative AI revolves around data. While every organization’s AI strategy may vary, one constant remains: optimal AI outcomes start with high-quality data. The volume of data that requires curation, quality assurance, security, and governance to support AI and create effective generative AI models underscores the necessity of a modern data fabric. Once this data is in place, the platform should offer end-to-end, AI-powered capabilities that empower all users, regardless of their expertise, to access potent insights through automation and assistance.
Qlik empowers customers in three key ways:
- A trusted data foundation for AI: Qlik’s data integration and quality solutions harness AI to streamline data delivery and transformation, reducing complexity, mitigating risk, and facilitating data fabrics.
- AI-enhanced and predictive analytics: Qlik has a proven track record of delivering AI-enhanced and predictive analytics capabilities. Qlik’s OpenAI connectors extend generative AI’s power to Qlik analytics, enhancing the user experience with more powerful chat capabilities.
- AI for advanced use cases: Qlik AutoML™ helps organizations scale their data science investments while allowing technically inclined staff to tailor AI solutions for new applications.
The “Generative AI Benchmark Report” underscores the growing prominence of generative AI in the enterprise landscape. As organizations continue to invest in this transformative technology, it is evident that they recognize the need for a solid data foundation, effective strategies, and a focus on mitigating potential risks. The path to generative AI success begins with robust data management, and Qlik stands as a partner in this journey, offering a range of solutions to empower organizations to leverage AI’s full potential. With the momentum building behind generative AI and a growing commitment to the responsible and efficient application of these technologies, the future holds exciting possibilities for businesses looking to harness the power of AI to drive innovation and growth.
To learn more about how Qlik is supporting organizations in achieving success with AI, visit Qlik Staige™.