As more organizations experiment with GenAI, the landscape of emerging AI models is only becoming more and more vast. The large variety of models available means that organizations that have overcome the first question of whether they should be using AI in the first place are now posed with an even more daunting question: which model should they be using?
With the overwhelming number of options available in the market and new challenger models constantly being developed and rolled out, many businesses are unsure which direction to follow and which model to adopt to best support the development of its applications. As we look to the future and expect more models and versions to be introduced, organizations should adopt a flexible approach when selecting AI models — shifting focus from finding the best suited single vendor to adopting a balanced, future-proof approach with LLM Mesh.
The Risks Posed by the Reliance of a Single Vendor
Relying solely on a single model is risky. For example, let’s say a company centers its commercial healthcare applications around a single AI model without integrating other models. The risk is that the single model relied on can sometimes provide inaccurate results and recommendations, leading not only to potential financial issues but also causing a decrease in trust in the company from the wider market. How do we know this to be true? Because this happened to IBM, which centered its healthcare applications around Watson's AI model. Since the model sometimes provided inaccurate information, this resulted in the erosion of trust, alongside a large negative impact on reputation. The company’s healthcare arm has since struggled to recover.
Despite the prominence of tools such as Open AI’s ChatGPT, concerns over its governance have raised questions and doubts among investors and those involved in integrating new technologies. As with the case of IBM, there is an operational risk when companies jump on one wave and tie themselves to a single AI model. To mitigate this risk, avoiding a single vendor lock-in is crucial in navigating the fast-paced landscape of AI and the ability to reduce concerns about security, ethics and stability. This is why companies are encouraged to shift their perspective from a single vendor lock-in to now jump on all of the different AI waves — using the LLM Mesh.
LLM Mesh: Jump on All Waves
With the LLM Mesh, businesses can ride the wave of AI models while preparing for future changes. By removing the complexities of backend connections and API requirements, the LLM Mesh makes it simple to transition or “wave hop” from one model to another quickly.
The benefit of “wave hopping” is that it allows businesses to develop enterprise applications using today’s top AI models while still having the choice of switching to other models — whether that is jumping to more suitable models now or keeping options open for emerging models to appear in the market.
As businesses make informed decisions about the costs of running LLMs, which can be quite expensive, they must also choose the right model for the performance needs of an application. Keeping options open to consider these needs such as costs, performance, and security allows companies to benefit in a fast-moving landscape.
The Imperative to Jump Now
Why take the jump now? Nearly 90% of executives rank GenAI as a top tech priority. Waiting for the perfect wave is a strategy for competitive disadvantage. While companies look to the future of AI technology, it is important not to wait to jump on the AI wave if they want to avoid getting left behind. To capitalize on the momentum, companies should fully immerse themselves in utilizing AI. As of 2024, there are over 125 commercial LLM models available, with a rapid 120% increase in models released from 2022 to 2023. The landscape is growing, and new emerging models are being introduced in the market — there is no better time than now for companies to hop on the wave.
The bottom line is that companies that want to ride the GenAI wave without suffering the downsides of vendor lock-in really have one option: adopting an LLM Mesh approach. Not only does this approach offer the flexibility to choose which model aligns best with an organization's priorities, but it will also help to future-proof AI applications and projects to ensure a business can always take advantage of the latest AI models. If an organization surfs the AI wave in a smarter, more agile way, then it stands a much better chance of getting ahead of the competition and coming out well from the tide of AI innovation.
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