What do they mean by "AI-powered" solutions?

Bombarded by “AI-powered” solutions? This article explains the biggest distinction between the two types of AI solutions in the market, allowing business leaders to understand their underlying logic and make informed decisions in their AI adoption.

The Key Difference in “AI-Powered” Solutions

The distinction lies in how the solution is developed. There are pre-built solutions that leverage existing AI engines such as ChatGPT, DALL-E, Claude, Copilot, and Gemini to produce prompt results, and there are original solutions where vendors build their own AI engines from scratch.

Pre-built AI tools offer a shorter development cycle, lower costs, and reduced risk, as they rely on proven AI engines that already deliver reliable outcomes (eg., a chatbot). Vendors of these solutions focus on client experience and addressing specific use cases. Think of it as a processor chip used in both laptops and smartphones—solution providers don’t need to develop the chip themselves. However, as the solution scales, vendors face increasing fees from AI engine owners like OpenAI, Microsoft, and Alphabet. This leads to significantly higher costs being absorbed by their clients after the first or second year.

On the other hand, building a custom AI engine from zero involves a costly and lengthy research and development phase. This phase requires vast amounts of data for training, extensive testing, and ongoing model optimization. The benefit, however, is that the vendor owns the AI models, allowing for patents and commercialization with lower scalability costs. In this case, the analogy would be developing a unique processor, which can be offered directly as a solution, ensuring more manageable long-term costs for the clients.

Both approaches have their merits, as long as the solution delivers value to your business.

As a business leader, knowing whether a solution is pre-built or original means you can evaluate your choices based on long-term business objectives.

Making Informed Decisions Based on Business Goals

To make the best choice, start by evaluating your business needs and determining how deeply the solution will be integrated into daily operations. Consider factors like how many employees will use it, how often, and whether the solution will expand to other departments, increasing usage volume over time.

If only a small number of users engage with the solution infrequently (e.g., once a month), the differences between the two approaches described above may not be significant.

However, if usage is expected to grow rapidly year over year, relying on a pre-built solution may become unsustainable. In such cases, developing this solution in-house might be the optimal choice, given its shorter development cycle and lower risk.

Currently, most AI solutions in the market are pre-built, addressing needs like HR process automation (e.g., CV screening, interview scheduling) or improving marketing operations (e.g., automated video editing and content generation). Despite the variety of use cases, these software often rely on the same underlying AI engines.

Another consideration is the potential of your own data. Industries like healthcare, which have pioneered AI-driven innovations, show how in-house AI models based on internal business data can lead to unique products and services. For example, Medtronic's blood sugar monitor is a successful in-house AI solution turned into an innovative product.

Conclusion

Understanding AI principles empowers business leaders to make more informed decisions in crafting an AI strategy. Over the next 5-10 years, AI will profoundly impact how businesses operate. As such, it’s essential to make thoughtful, strategic choices about adopting AI that align with both short-term needs and long-term business objectives.

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