GenAI presents hidden challenges for chief data officers

GenAI has swept across enterprises with transformative potential. From fraud detection to personalized customer engagement, AI has been deployed in use cases once thought impossible to scale. But even though 97% of organizations expect GenAI to be transformative, only 31% have made significant investments in the technology.

Why the disconnect? One key reason: data readiness: 47% of CXOs identify data readiness as the top barrier to applying GenAI effectively. For chief data officers (CDOs), this statistic reflects a stark reality.

While the promise of GenAI feels urgent, the path to making it real is filled with technical and operational challenges that are often hidden beneath the hype.

Let’s explore four of the biggest, and most overlooked, challenges CDOs face with GenAI adoption.

Humans need to curate AI data

There’s a common misconception that feeding GenAI more data will automatically make it smarter. In reality, language models are rapidly exhausting the stock of human-generated public text, estimated at 300 trillion tokens. If current trends hold, this pool may be fully consumed as early as 2026.

This leads to a deeper issue: not all data is created equal. It’s no longer enough to have massive data quantities. What’s needed is precision—task-specific, high-quality, and human-evaluated data tailored for particular use cases. For instance, understanding “how people shop on Amazon” requires curated prompt design and nuanced training data that reflects actual user shopping behaviors together with demographic information, not just generic web content.

Is production-scale inference feasible?

Enterprises eager to deploy GenAI at scale quickly learn a painful truth: production-scale inference is a cost management puzzle. Most GenAI services are offered through subscription-based APIs with strict token limitations (tokens per minute and requests per minute). Your chatbot or agent simply stops scaling beyond the limits due to the subscription limits.

Many LLMs offer a workaround: provisioned throughput units (PTUs), which are dedicated compute allocations designed to support production-scale performance. However, these units introduce significant cost overhead, making large-scale GenAI applications financially impractical for many organizations.

In a live experiment using GPT-4o API subscriptions, Centific tested throughput under various concurrency loads. A single subscription failed beyond 150 concurrent requests. Even two subscriptions capped out at 300, with significant latency. Only with three subscriptions did scale become viable, but at a potentially steep cost.

We further devised subscription rotation policies to achieve production scale LLM inference for several of our cutting-edge LLM-as-a-judge solutions. We’ll discuss our LLM-as-a-judge solutions in a future blog article. Stay tuned.

This raises critical questions for CDOs: how do you scale reliably without overcommitting to costly provisioned throughput units? And how do you guarantee service-level agreements (SLAs) when user demand is unpredictable?

Energy consumption of GenAI models is soaring

Behind every GenAI interaction lies a hidden energy bill. Running GenAI models at inference scale, especially multimodal ones, demands tremendous power. One ChatGPT query consumes approximately 10 times more energy than a Google Search. Training GPT-3 used 1.3 GWh of electricity, equivalent to the yearly power consumption of 120 U.S. homes.

But here’s the bigger surprise: inference now consumes more total energy than training. Especially with image and video generation, energy usage can spike up to 1,000 times more than basic text generation. This not only creates sustainability concerns but also introduces real operational costs for data leaders who must weigh carbon footprints and energy bills into their infrastructure choices.

Talent models are changing

The GenAI boom is reshaping talent models. For CDOs, staffing for AI success now means hiring for roles that didn’t exist five years ago.

Today’s AI teams may include synthetic data specialists, language engineers, prompt engineers, LLM operations engineers, and GenAI UX researchers. Traditional data science alone isn’t enough. These emerging roles demand cross-disciplinary fluency in linguistics, NLP, model deployment, user research, and system evaluation.

For CDOs, this means rethinking not only how they hire but how they train, upskill, and retain employees in a fast-moving talent landscape.

CDOs can respond in four ways

Despite these challenges, the path forward is clear if you know where to look. Here’s how data leaders can navigate GenAI adoption more successfully.

Invest in human-in-the-loop data curation

Investing in human-in-the-loop (HITL) data curation is essential for organizations deploying GenAI in specialized business contexts. Foundation models trained on broad public data often miss the nuance and structure required for enterprise-grade applications. Tasks like financial summarization, legal document review, or retail product categorization depend on context, terminology, and workflows that are specific to the domain.

HITL workflows bring expert judgment into the data pipeline through careful annotation, prompt development, and evaluation of model responses. These inputs allow models to respond accurately to real-world queries that generic pretraining does not prepare them for.

HITL methods also help close the gap between model performance in benchmark tests and actual user needs. A model might pass a general-purpose evaluation but still fail to produce useful results in a regulated or customer-facing environment.

Human feedback, collected through structured ranking, precision scoring, or task-specific success criteria, helps shape model outputs to reflect company policies, brand tone, and compliance requirements.

This feedback loop is especially important when applying reinforcement learning techniques like RLHF, which rely on consistently applied human judgment to improve model behavior.

As organizations move beyond simple use cases to more advanced GenAI deployments, HITL becomes a continuous design-and-testing process. Data teams need to define tasks that challenge reasoning, track context across interactions, and measure output quality over time. Subject matter experts should be involved in evaluating results for subtle issues such as factual drift, ambiguity, or unintended bias.

These evaluations often need to happen at scale, especially when the model supports multilingual output or multi-turn interactions. HITL curation provides the structure and scrutiny needed to build GenAI systems that can operate in high-stakes, dynamic business environments.

Plan your inference architecture for elasticity and cost

Before launching GenAI applications, stress-test throughput under realistic workloads. Consider hybrid approaches, offloading some inference tasks to smaller models or edge devices, to reduce token loads. Structure your subscriptions to match actual usage patterns and budget for bursts.

Prioritize efficient model and hardware configurations

Energy consumption is a business issue as well as an environmental concern. Choose sparse architectures and quantized models where possible. Partner with providers who optimize for energy-efficient inference using TPUs or custom ASICs instead of default GPU stacks.

Build the next-gen AI workforce

Start small but start now. Identify internal candidates with adjacent skills and begin upskilling them into GenAI roles. Partner with universities or bootcamps to source talent. Make emerging roles part of your workforce planning. They’re the future of your data team.

GenAI is here, and it’s powerful. But for CDOs, success depends on looking past the promise and addressing the practical. The challenges are real, but so are the opportunities. With thoughtful planning, technical pragmatism, and the right people in place, GenAI can deliver on its transformative potential without derailing your data strategy.

Centific’s frontier AI data foundry can help CDOs

The frontier AI data foundry can help a CDO tackle myriad challenges with data readiness. For example, a frontier AI data foundry can enable data curation for LLM supervised fine-tuning and transform raw, fragmented data into purpose-built, high-quality datasets that are tailored for specific GenAI use cases.

Unlike traditional data pipelines, a frontier data foundry combines human expertise, custom task design, and continuous evaluation to produce domain-specific training data that reflects real-world complexity and intent.

This helps address the looming shortage of high-value human-generated content and enables more precise, context-aware model performance, which is especially critical as off-the-shelf data reaches saturation. It also comes with a critical advantage: responsible GenAI practices are embedded from the ground up across the data, model, and application lifecycle.

At Centific, we work with CDOs to develop grounded, production-ready AI strategies backed by curated data, sustainable architectures, and expert guidance. If you’re ready to operationalize GenAI, let’s talk.

Learn more about Centific’s frontier AI data foundry platform.

Author’s note: thank you to Harshit Rajgarhia for conducting the experiments with the GPT-4o API subscriptions