Generative AI has caught the attention of various industries because it spurs the development of discrete tools, each tailored for specific applications, from natural language generation to image synthesis and even music composition.
This diversification of generative AI tools gives organizations the chance to make the most of the technology's creative capabilities to streamline their processes, cut costs and deliver innovative products and services.
Because generative AI creates a vision of “what’s possible,” 79% of organization leaders have procured generative AI tools or allocated funds to generative AI undertakings, with an additional 31% expressing intentions to allocate more than $10 million toward generative AI endeavors within the next year, according to the “Generative AI Benchmark Report,” executed in August 2023 by Enterprise Technology Research on behalf of Qlik.
Still, there exists a challenge in these investments, as 44% have acknowledged the absence of a well-defined strategy for the integration and coherence of generative AI within their organizational frameworks, potentially leading to isolated and underutilized resources.
Leaders understand they need to surround these tools with the right data strategies and technologies to fully realize their potential, which is why they are looking for guidance on where to start and how to move forward quickly while keeping an eye on risk and governance issues.
In addressing generative AI, survey respondents revealed a variety of strategies:
- 68% plan to harness public or open-source models, refining them with their proprietary data
- 45% are considering constructing models from the ground up using their own proprietary data
Expertise in these aspects is deemed crucial to avoid well-documented issues such as data security, governance, bias and hallucinations associated with generative AI. To bridge this knowledge gap, almost two-thirds intend to rely on third-party expertise.
Organizations also plan to turn to data fabrics as a key part of their strategy. However, their data fabrics may require upgrades as they are currently ill-equipped for generative AI, with only 20% believing their data fabric is well-prepared for generative AI.
As a result, many leaders anticipate increasing their investments in technologies that support data fabrics. These investments will focus on managing data volumes, as nearly three-quarters of respondents expect generative AI to amplify the amount of data being moved or handled in their existing analytics systems.
The respondents also highlighted the importance of data quality, machine learning/artificial intelligence tools, data governance, data integration and business intelligence/analytics in achieving a data fabric conducive to generative AI success. These investments are poised to address common implementation barriers, including regulatory compliance, data security, and resource constraints.
Qlik highlights the importance of establishing a robust data foundation as the fundamental prerequisite for optimal AI performance.
“Our Generative AI Benchmark report clearly shows leading organizations understand that these tools must be supported by a trusted data foundation,” said James Fisher, Chief Strategy Officer at Qlik. “That data foundation fuels the insights and advanced use cases where the power of generative AI and traditional AI together come to life.”
The handling and curation of extensive data, along with ensuring data quality, security, and governance, is essential for building effective generative AI models. Qlik offers a modern data fabric that harnesses AI to automate data delivery and transformation, simplifying the process and reducing complexities and risks.
Qlik's approach aims to empower a wide range of users, regardless of their AI expertise. In its platform, Qlik offers three core AI capabilities:
- A Trusted Data Foundation for AI: Qlik's data integration and quality solutions leverage AI to streamline data processes, making data fabrics more accessible and secure.
- AI-Enhanced and Predictive Analytics: Qlik has a strong track record of delivering AI-enhanced and predictive analytics, now further enhanced by OpenAI connectors to provide powerful chat capabilities for an enriched user experience.
- AI for Advanced Use Cases: Qlik AutoML empowers organizations to scale their data science efforts and customize AI solutions for new use cases, bridging the gap between technical experts and non-technical staff.
The Qlik platform provides end-to-end AI-enabled capabilities designed to assist users in extracting valuable insights, with a strong emphasis on automation and guidance. This ensures that both technical and non-technical staff can benefit from the power of AI.
Be part of the discussion about the latest trends and developments in the Generative AI space at Generative AI Expo, taking place February 13-15, 2024, in Fort Lauderdale, Florida. Generative AI Expo discusses the evolution of GenAI and feature conversations focused on the potential for GenAI across industries and how the technology is already being used to create new opportunities for businesses to improve operations, enhance customer experiences, and create new growth opportunities.
Edited by
Alex Passett
