While nearly all data leaders surveyed are building generative AI applications, most don't believe their data estate is actually prepared to support them, according to the State of Reliable AI report from Monte Carlo Data.
Source: Monte Carlo Data
Findings include:
■ 100% of data professionals feel pressure from their leadership to implement a GenAI strategy and/or build GenAI products.
■ 91% of data leaders (VP or above) have built or are currently building a GenAI product.
■ 82% of respondents rated the potential usefulness of GenAI at least an 8 on a scale of 1-10, but 90% believe their leaders do not have realistic expectations for its technical feasibility or ability to drive business value.
■ 84% of respondents indicate that it is the data team's responsibility to implement a GenAI strategy, versus 12% whose organizations have built dedicated GenAI teams.
While AI is widely expected to be among the most transformative technologies of the last decade, these findings suggest a troubling disconnect between data teams and business stakeholders.
Data leaders clearly feel the pressure and responsibility to participate in the GenAI revolution, but some may be forging ahead in spite of more primordial priorities — and in some cases, against their better judgment.
The State of Reliable AI Infrastructure
Even before the advent of GenAI, organizations were dealing with an exponentially greater volume of data than in decades past. Since adopting GenAI programs, 91% of data leaders report that both applications and the number of critical data sources has increased even further — deepening the complexity and scale of their data estates in the process.
"Data is the lifeblood of all AI — without secure, compliant, and reliable data, enterprise AI initiatives will fail before they get off the ground. Data quality is a critical but often overlooked component of ensuring ethical and accurate models, and the fact that 68% of data leaders surveyed did not feel completely confident that their data reflects the unsung importance of this puzzle piece," said Lior Solomon, VP of Data, Drata. "The most advanced AI projects will prioritize data reliability at each stage of the model development life cycle, from ingestion in the database to fine-tuning or RAG."
What's more, the survey revealed that data teams are using a myriad of approaches to tackle GenAI, suggesting that not only is the volume and complexity of data increasing, but that there's no one-size-fits-most method for getting these AI models customer-ready.
How data teams are approaching AI:
■ 49% building their own LLM
■ 49% using model-as-a-service providers like OpenAI or Anthropic
■ 48% implementing a retrieval-augmented generation (RAG) architecture
■ 48% fine-tuning models-as-a-service or their own LLMs
As the complexity of the AI's architecture — and the data that powers it — continues to expand, one perennial problem expands with it: data quality issues.
The Key Question: Is Your Data GenAI Ready?
Data quality has always been a challenge for data teams. However, survey results reveal that the introduction of GenAI has exacerbated both the scope and severity of this problem.
Our findings suggest that while the data estate has evolved rapidly over the last few years to accommodate AI and other novel use cases, data quality management has not. In fact, many respondents still rely on tedious and unscalable data quality methods, such as testing and monitoring, with more than half (54%) of data professionals surveyed depending exclusively on manual testing.
This lack of automated, resolution-focused solutions is reflected in the data, with two-thirds of respondents experiencing a data incident in the past 6 months that cost their organization $100,000 or more. This is a shocking figure when you consider that 70% of data leaders surveyed reported that it takes longer than 4 hours to find a data incident. What's worse, previous surveys commissioned by Monte Carlo reveal that data teams face, on average, 67 data incidents per month.
"In 2024, data leaders are tasked with not only shepherding their companies' GenAI initiatives from experimentation to production, but also ensuring that the data itself is AI-ready, in other words, secure, compliant, and most of all, trusted," said Barr Moses, Co-Founder and CEO of Monte Carlo. "As validated by our survey, organizations will fail without treating data trust with the diligence it deserves. Prioritizing automatic, resolution-focused data quality approaches like data observability will empower data teams to achieve enterprise-grade AI at scale."
Methodology: The Wakefield Research survey — which polled 200 data leaders and professionals — was commissioned by Monte Carlo in April 2024.