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Early enterprise adopters of generative AI have made it clear that a robust data strategy is the cornerstone of any successful AI initiative. To truly unlock AI’s potential as a value multiplier and catalyst for reimagined customer experiences, an easy-to-use and trusted data platform is indispensable.
Our recent report “The Radical ROI of Gen AI” proves gen AI is a profit engine, with more than nine in 10 surveyed early adopters saying that their gen AI investment is in the black. Survey respondents who quantified the ROI of their gen AI initiatives saw an average 41% ROI. Unlocking gen AI’s full potential hinges on a robust, unified data strategy. Eighty-eight percent of early adopters affirm that they need data strategies and tools spanning all generative AI use cases, meaning enterprises need a modern data platform that’s effortless to build and deploy, reliable by design and seamlessly connected across teams, tools and clouds.
Ninety-six percent of early adopters say they’re training, tuning or augmenting their commercial and open source LLMs, and 80% are fine-tuning models with their proprietary data. These essential steps introduce some real challenges. We’re talking about potential headaches around data quality, the amount of data your systems can handle, the risk of bias getting amplified and those privacy concerns — the ones about proprietary business information or customer personal data possibly leaking out — in the outputs.
That’s why an organization’s approach to generative AI should be built on a strong data platform — to minimize those risks, to reduce surprise costs and to make it easier to adopt the right tools, scale and replicate AI successes, and make sure all of an organization’s data is securely and appropriately leveraged.
It’s easy to get lost in the sheer scale of it — 71% of organizations found that effective model augmentation requires multi-terabytes of data, or several million documents. But the breadth of data isn’t their only issue: Early adopters cite data quality (45%) and quantity (38%) most often among various issues. So your data hygiene and how you manage data becomes a mandatory focus of AI.
To pile onto the challenge, the vast majority of any company’s data is unstructured — think PDFs, videos and images. So to capitalize on AI’s potential, you need a platform that supports structured and unstructured data without compromising accuracy, quality and governance. Only 11% of the early adopters say that more than half their unstructured data is ready to be used in LLM training and tuning. Even these early adopters, the ones who report great overall success, have hit some snags with their data platforms. At the data platform level, we found:
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55% of organizations are hampered by time-consuming data management tasks such as labeling.
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52% struggle with data quality — including issues of error, bias, irrelevance and timeliness.
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51% say data preparation is too hard.
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50% cite issues with data sensitivity.
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42% say they lack the needed range or diversity of data.
All of these challenges are most effectively handled in a unified data platform. Bringing your AI technology to a data foundation that is easy, trusted and connected reduces the challenges that can delay a project or lead to unexpected costs.
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