Throughout my earlier career days of the various tech booms I observed something wild yet consistent, entrepreneurs would often find themselves cornered by investors with a simple yet daunting question:
The implication was clear: if Google, the behemoth of search and online services of the day, decided to pivot into your market, your startup’s innovative idea could be crushed in an instant. For a tech-generation, this question acted as a litmus test, pushing founders to clearly articulate their unique value proposition and demonstrate how they could compete against giants (while doing lots of deep breathing).
Fast forward to today, and while Google still wields serious influence, there’s a new contender in town: OpenAI. OpenAI has quickly become largely synonymous with AI innovation. Now, the question has evolved: “Can’t OpenAI just do that?” - we clearly get this a lot given the fact that SkillBuilder.io builds Agentic AI that sells products and services. This use case, specialized sales agents, is a great example of what looks easy on the surface and takes more than its fair share of “What if _________ decides to do that?”
While it’s true that OpenAI’s capabilities are remarkable, there are equally present limitations that suggest specialized AI still has a huge future just like literally every specialized version of nearly any product or service that has ever been created.
Here’s why OpenAI, despite its super powers, cannot and should not be expected to handle every niche or local requirement, particularly in rapidly changing fields like sales.
The Breadth and Depth Dilemma
OpenAI’s strength lies in its breadth. Its models are trained on vast datasets scraped from the internet, enabling them to perform an astonishing array of tasks, from drafting emails to generating code. However, the very breadth that gives OpenAI its power also limits its depth. The data it uses is generalized, often several months or even years out of date, and lacks the specialization that certain industries demand or local customer segments near mandate.
Example 1: Local and Niche Content
Consider the challenge of integrating local news or hyper-localized information into an AI’s understanding. While OpenAI’s models can access and regurgitate general information, they often miss the nuances that come from local, real-time updates. For instance, a local bakery’s special menu for the day or the latest promotions at a neighborhood boutique are unlikely to be captured by OpenAI’s training data. These details are too specific, too dynamic, and often not represented on a global scale where OpenAI can readily access them. We saw this first hand with clients like https://www.harvie.farm where the simple question of “Do you deliver to my zip code?” changes on a weekly or monthly basis as they rapidly expand.
Example 2: Industry-Specific Knowledge
OpenAI may excel in handling general knowledge and widely available data, but it struggles with industry-specific content that is not well-documented or is proprietary. For instance, consider the pharmaceutical industry, where knowledge of the latest drug trials, proprietary formulations, or nuanced regulatory changes is paramount. These pieces of information are often locked behind paywalls, buried in industry-specific databases, or disseminated through private communications and journals that OpenAI’s training data cannot access. The risk in giving a great sounding but poorly informed answer, while popular these days, is scary and very real.
Example 3: Sales and Real-Time Competitive Intelligence
Sales information changes rapidly. Product prices, availability, promotions, and competitive positioning are in constant flux. OpenAI, which cannot directly interact with real-time databases or proprietary sales data, will inherently lag behind. Sales professionals rely on up-to-the-minute information, often drawn from internal systems, direct customer interactions, and rapidly updated competitive analysis. OpenAI, trained on static snapshots of the web, simply can’t keep pace with this dynamic environment.
For example, a sales agent using AI to pitch a new product would need to know the latest competitive offers, pricing models, and unique selling propositions that may have changed overnight. OpenAI, with its data lag and reliance on public sources, would miss out on this nuanced and rapidly shifting information. In contrast, a specialized AI system, integrated with real-time data feeds and industry-specific insights, could provide this level of detail, making it an invaluable tool for sales teams. To be clear, this is not an either or but rather a both where OpenAI + specialized AI can work together exactly like generalists work with specialists inside of a business.
Why Specialized AI Will Thrive
The limitations of OpenAI’s generalized approach highlight a significant opportunity for specialized AI. Companies that leverage OpenAI’s foundational models but build on them with proprietary, real-time data, and niche-specific training can offer a depth of service that OpenAI cannot.
Speed and Relevance
Specialized AI systems can be designed to update in real time, pulling directly from proprietary databases, CRM systems, or specific industry feeds. This capability ensures that the AI remains relevant, accurate, and valuable. OpenAI’s models, on the other hand, are updated periodically and lack the ability to integrate real-time data feeds seamlessly.
Customization and Integration
OpenAI provides a strong foundation, but customization is key. A specialized AI can be fine-tuned to understand the specific vocabulary, regulatory requirements, and cultural nuances of a particular industry or locale. This customization is not just about language but also about understanding the context in which specific terms are used. In highly specialized fields like finance, healthcare, or law, this nuanced understanding can be the difference between an AI that’s helpful and one that’s hazardous.
Data Sensitivity and Security
Many industries deal with highly sensitive data. Financial firms, healthcare providers, and other industries governed by strict data privacy laws cannot afford the risks associated with generalized AI models trained on publicly available data. Specialized AI solutions can be developed with robust data security measures in place, ensuring that sensitive information is handled in compliance with industry regulations. OpenAI’s generalized model, while secure, may not meet the specific compliance requirements of every industry, leading to potential vulnerabilities.
Support your LOCAL (and specialized) AI
The rise of OpenAI has brought tremendous value to all of us, but it has also made one thing clear: one-size-fits-all AI has its limitations. The opportunity lies in creating specialized AI systems that can integrate OpenAI’s foundational capabilities with the nuanced, real-time, and industry-specific data that general models cannot provide.
The fear that OpenAI will dominate every sector of AI is unfounded. The future of AI lies in specialization, where customized solutions can coexist with general-purpose models, each serving distinct roles.
Entrepreneurs and investors should see this as a call to action: the time to invest in specialized AI is now. The opportunities are vast, the risks minimal, and the potential for innovation limitless.
Just as Google didn’t do everything, OpenAI won’t either. The path forward is not about competing with giants in their own arena but finding the growing opportunities where specialized AI can shine.
