Many consider the dawn of AI as marked by ChatGPT’s rapid success, however AI’s evolution did not happen overnight, and the opportunities it offers will not vanish tomorrow.
The AI hype has led many firms to succumb to “AI FOMO” and rush into adoption without a clear strategy. AI FOMO has caused many businesses to make impulsive, short-term decisions which lack the strategic foresight on how best to leverage AI for sustained success. With research revealing over 80% of AI projects fail – now is the time for businesses to avoid being swept up by the AI hype. Instead, businesses should ground their approach in a thorough understanding of this transformative technology.
But where is AI FOMO causing businesses to go wrong, and how should businesses be approaching AI implementation to ensure success?
MD and Co-Founder of Brainpool AI.
Avoid the AI cookie cutter approach at all costs
In a rush to capitalize on the AI hype, almost half of businesses leverage off-the-shelf AI solutions. These pre-built AI solutions can be used without requiring businesses to develop their own technology and are designed to be easily integrated into a business. They are often the favored choice for many because they offer quick deployment and lower up-front costs.
However, despite their efficient exterior – these solutions are not as beneficial as they appear. By leveraging off-the-shelf solutions, businesses will open themselves up to the perils of vendor lock-in.
Firms will see their flexibility greatly limited, unable to switch providers to suit their own business needs and contexts and required to upload all of their data into the infrastructure of their chosen provider. This is not only a time consuming but expensive process especially when firms are looking to scale their AI applications. As they have to re-upload their data into that infrastructure incurring significant additional costs through LLM providers commercial-based token model.
Another issue is that one-size-fits-all AI solutions rarely fit anyone well. Businesses should not underestimate the importance of molding each AI solution around their data and business requirements. A cookie cutter approach to AI implementation will consistently fail to see and understand the nuance of an individual business and its specific requirements. Thereby, producing AI applications that don’t deliver desired accuracy rates, eroding trust in the technology and leading employees to abandon tools that were supposed to enhance productivity.
Perhaps most concerningly, when utilizing off-the shelf AI tools and applications firms completely surrender their own IP. Whilst there has been considerable concerns raised about the likes of OpenAI training their models on users’ data, less has been made of the long term implications of IP loss. Businesses rushing into AI implementations may not be concerned with this in the short term, but the long term negative impacts are substantial. The rapid rate of AI development means that realizing long term success from AI, is not a tick box activity and will require constant development. Firms that don’t own their own IP will experience significant barriers when looking to remain competitive in an increasingly AI driven business landscape. At the end of the day, it is crucial to remember that this is your data, it is your context, it therefore should be your IP.
Embracing an agnostic approach to AI
To avoid the perils of AI FOMO, businesses have to embrace an agnostic approach to AI. Agnostic AI is not just about avoiding tokens – it is a curated methodology that allows businesses to pick the optimal approach to achieve their desired outcome. Ironically, this method yields lower compute requirements, higher accuracy and provides businesses with a solution that can evolve alongside technological advancements.
Those that take a step back and have a long term view of AI implementation will see the clear benefits of avoiding jumping straight into utilizing off the shelf models. Instead, building an agnostic AI model will allow businesses to tap into the most cost-effective and optimal LLM for each use case. This will also allow businesses to tailor each use case to a specific domain to improve the effectiveness of the model.
Utilizing an agnostic AI infrastructure allows business to remain agile and versatile, enabling firms to fine-tune different LLMs to solve unique problems. Rather than relying on a single model to address all challenges, businesses can leverage multiple LLMs to provide tailored solutions and select the most cost-effective and efficient models for each specific problem.
An agnostic approach will also allow businesses to be agile in the face of the ever-changing AI landscape, keeping up with changing market dynamics and regulatory requirements. This approach provides businesses with the freedom and flexibility to switch or update tools as regulations and rival firms evolve to ensure they maintain their competitive edge and are consistently compliant.
The allure of AI can be powerful, but businesses must resist the urge to leap without looking. Succumbing to AI FOMO often leads to missteps, inefficiencies, and missed opportunities. By avoiding cookie-cutter solutions, and adopting an agnostic approach to AI, businesses can position themselves for long-term success in the AI era.
AI is not a race to be won overnight and to realize its true transformative potential requires strategy, adaptability, and a clear focus on the future.
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