By Orbifold AI Research Team
The rapid rise of Large Language Models (LLMs) has driven businesses to integrate AI into their workflows, decision-making, and customer interactions. However, enterprise AI presents unique challenges - models need to be accurate, efficient, cost-effective, and domain-specific.
While scaling models like OpenAI, Claude, LLaMA and DeepSeek have improved general AI capabilities, enterprises require AI that understands their specific industry, processes, and multimodal data. Instead of chasing bigger models and larger datasets, the key to enterprise AI success is better data - optimized and business-relevant.
Therefore, leveraging advanced data distillation techniques is essential to refine raw, complex multimodal enterprise data into high-quality, AI-ready datasets. This approach enables businesses to deploy AI models that learn more effectively, optimize costs, and drive meaningful real-world outcomes.
Off-the-shelf foundation models are trained on internet-scale data, making them impressive for general use. However, when applied to enterprise AI, they fall short in key areas:
To solve these challenges, AI needs to be enterprise-first, meaning it must be optimized for business use cases, multimodal processing, and cost-efficient deployment.
A more effective approach involves refining enterprise-specific data to enhance model accuracy, efficiency, and adaptability. By leveraging advanced data curation and augmentation techniques, enterprise AI models can be trained on high-quality, domain-relevant datasets, leading to improved performance and cost efficiency.
Enterprise AI does not necessarily benefit from larger datasets, but rather from more relevant data. Key data optimization techniques include:
By refining dataset composition, enterprises can reduce costs for building AI applications while enhancing model accuracy and contextual relevance.
Enterprise data is inherently multimodal, spanning text, structured databases, images, audio, and video. Effective AI models must be capable of processing and integrating these diverse data formats. Multimodal processing enables AI to:
By enabling AI to interpret a variety of enterprise data sources, multimodal integration enhances the model’s ability to generate more accurate, relevant, and actionable insights.
Building AI models on large-scale datasets is computationally expensive and often inefficient. To improve cost-effectiveness, enterprise AI models can benefit from:
These methods lower computational costs while improving the model’s ability to adapt to evolving enterprise data.
Data security and privacy are critical considerations for enterprises operating in finance, healthcare, and legal sectors, where regulatory compliance is essential. Privacy-preserving AI techniques include:
These approaches allow enterprises to leverage AI-driven insights while maintaining compliance with data protection regulations.
Building enterprise AI requires a data-centric approach rather than relying solely on larger models or increased computational power. By implementing data optimization, multimodal processing, efficient training, and privacy-preserving techniques, enterprises can develop AI systems that are more accurate, cost-effective, and aligned with industry-specific requirements. This shift toward domain-relevant AI applications will be critical for organizations aiming to maximize the impact of artificial intelligence in their operations.