DeepSeek’s Ascent Means B2B Firms Need to Consider AI ROI

AI, b2b, investments

The pace of progress across the artificial intelligence (AI) landscape in the past year has been enormous.

Large, general-purpose models like OpenAI’s GPT series, Google’s Gemini and Anthropic’s Claude have dominated the headlines and enterprise use cases alike. These powerhouses, designed to handle a vast array of tasks across multiple domains, have set a high bar for what AI can do.

That all changed last week when the Chinese competitor DeepSeek shocked the world with its capabilities — and its comparatively low cost. For example, OpenAI charges API customers $60 to output a million o1 tokens, while DeepSeek only charges $2.19, making it nearly 27 times cheaper.

Meta Chief AI Scientist Yann LeCun said in a post on LinkedIn that the impact of DeepSeek showed that “open source models are surpassing proprietary ones.”

For many industries, the priority isn’t necessarily having the “best” AI in terms of sheer power, but rather one that is reliable, cost-effective and tailored to specific operational needs.

At the same time, businesses using open-source models like DeepSeek must ensure proprietary or confidential data isn’t exposed in fine-tuning or usage; and depending on the industry (finance, healthcare, etc.), AI deployment must adhere to data protection laws like GDPR, CCPA or HIPAA.

Still, smaller, more specialized models could gain traction in B2B applications, even if they don’t directly challenge OpenAI, Google or Anthropic. For most business users, having the absolute best model is less important than having one that’s reliable and good enough. Not every driver needs a Ferrari.

Read more: AI Agent Systems Are Here — Will They Transform B2B?

The Growing Demand for Specialized AI Solutions

Industries such as financial services, logistics and healthcare are increasingly recognizing the value of AI solutions that are domain-specific rather than the one-size-fits-all approaches offered by larger models. These industries deal with complex, highly specialized data and operational needs that require a level of customization and precision that general-purpose AI may struggle to provide. As AI continues to be integrated into these sectors, businesses are looking for solutions that can be directly applied to their unique challenges, without the need for massive computational power or extensive training datasets.

For instance, in the financial services sector, firms are leveraging AI for a range of use cases, from fraud detection to customer service automation. However, these applications often require AI models that are specifically trained on financial data, customer behavior patterns and regulatory frameworks. A general-purpose model may not be able to deliver the same level of accuracy or reliability when it comes to niche financial tasks, even though it could handle more generic queries.

Similarly, in healthcare, AI is being used to assist with diagnosis, drug discovery and patient care. These applications demand models that are specialized in medical terminology, disease patterns and clinical workflows. An AI solution built for a broad range of industries may lack the deep understanding of medical specifics required for optimal results. In these cases, companies like DeepSeek — whose models are trained on specialized healthcare datasets — are gaining traction as they offer more accurate and contextually relevant insights.

Read more: AI’s Growing Role Across B2B Payments Will Be Impossible to Ignore in 2025

The Benefits of Specialized AI for B2B

For many B2B companies, the shift toward specialized AI solutions offers several advantages. One of the primary benefits is cost-effectiveness. General-purpose models like GPT-4 require massive computational resources to run effectively, which can make them expensive to deploy, particularly for small to mid-sized businesses. Specialized models, on the other hand, can often be more efficient in terms of both computational power and cost, as they are trained on smaller, domain-specific datasets and are optimized for specific tasks.

Moreover, specialized models tend to be more reliable for industry-specific applications. The reliability of an AI model is crucial in B2B contexts where decisions based on AI predictions can have significant financial or operational consequences. A model that is trained to understand the nuances of a particular industry is more likely to provide accurate and actionable insights, which can drive better business outcomes. In sectors like logistics, where predictive models are used to optimize supply chains and reduce inefficiencies, reliability and precision are key.

Customization is another critical factor. Many industries are looking for AI solutions that can be tailored to their specific needs. A model that is built with the ability to adapt to a company’s internal processes, data structures and goals offers far more value than one that operates as a “black box” with limited flexibility. For example, in the logistics sector, companies may want AI systems that can integrate seamlessly with their existing tracking and inventory management systems. In healthcare, AI solutions need to be customized to work with electronic health records (EHR) systems, medical imaging tools and other healthcare technologies. Specialized AI providers are better positioned to offer these kinds of tailored solutions.