In the current phase of AI adoption, many technology leaders remain focused on algorithmic novelty, often searching for a competitive edge through model optimization. However, recent industry trends and empirical evidence suggest that the true differentiator in large-scale AI systems is not the model itself, but the underlying data.
This principle is increasingly echoed across leading AI research. As Jack Morris argues in a recent post, “There are no new ideas in AI, only new datasets.” At Forte Group, we see this confirmed daily in our client work. The organizations that succeed with AI are not necessarily those who fine-tune the best models, but those who engineer systems capable of ingesting, organizing, and governing diverse and high-volume data at scale.
One of the most overlooked constraints in enterprise AI is what Morris calls the “upper bound” on performance: when a model has extracted all useful signal from a dataset, further improvements plateau. Research by OpenAI and DeepMind has repeatedly confirmed this: performance gains from new model architectures tend to diminish when data volume and diversity are held constant.
For example, the performance gap between GPT-3 and GPT-4 is marginal in several downstream tasks, despite the latter's architectural complexity. The largest improvements came not from novel training techniques, but from increased dataset size and improved modality coverage.
In practical terms, this means that enterprises investing in model iteration without investing in data pipelines and architecture are likely to experience declining returns.
Most current LLMs and vision models rely on well-established datasets: Common Crawl, ImageNet, and similar. These are largely saturated.
The next wave of innovation will come from underexploited data modalities:
Enterprises with the ability to harness these modalities will unlock disproportionate value—not by building new models, but by structuring pipelines to access and learn from data others cannot.
To capitalize on these opportunities, organizations must first invest in scalable, secure, and composable data platforms. At Forte Group, we advise clients to prioritize four capabilities:
When these elements are in place, organizations gain not just AI capabilities, but adaptive intelligence—the ability to continually learn from the environment, customers, operations, and products.
From a financial perspective, data engineering and platform modernization offer a superior return on investment compared to one-off model development:
The conclusion is clear: if you are still approaching AI as a model-centric endeavor, you are operating at a disadvantage. Data—not code—is the substrate from which intelligence emerges. Therefore, engineering access to new and diverse data types is not an infrastructure task—it is a strategic imperative.
As AI expands into domains like embedded systems, real-time analytics, and agent-based decision-making, the organizations that thrive will be those with the foresight to invest in data architecture now.
If your team is exploring how to modernize your data stack or evaluate readiness for AI adoption, Forte Group can help. Our approach combines pragmatic delivery with enterprise-grade governance—ensuring you do not just pilot AI, but integrate it sustainably into your business.