AI implementation myths
Visionary leaders recognize the importance of adopting cutting-edge technologies to enhance their business operations. This is a bold move, especially in the early stages when few businesses have embraced it. Generative AI is currently at such a juncture. Although AI has experienced several setbacks over the years, recent advancements in foundational models like large language models (LLMs) have reignited interest and investment in the field.
Generative AI never fails to impress those who see it in action. At Cere Labs, my company, we have developed a Gen AI framework, and whenever I demonstrate it, the feedback is unanimously positive, with many stating it is "useful." However, despite this enthusiasm, there is hesitation when it comes to integrating this technology into business practices. Addressing certain misconceptions and concerns could help leaders more easily adopt Generative AI to enhance their operations.
With my 8 years of experience assisting businesses in incorporating AI into their operations, I've had numerous conversations with leaders. I'd like to share some of the insights and concerns I've observed during these discussions. I aim to address the most common apprehensions leaders have when considering AI implementation. While Generative AI is just one area of AI, these concerns are equally relevant to it.
Myth
AI is a one-cure-for-all solution that can solve any problem, in any context, across all industries.
Reality
AI is not a magical solution that works instantly. It requires careful, deliberate practice and commitment. Leaders need to develop a strategy by understanding AI’s potential and identifying specific areas and operations where it can be effectively applied. Since no two companies are identical, the processes where AI can be utilized will differ significantly from one organization to another. It’s crucial to pinpoint areas where early successes are possible and, most importantly, to take the first step toward implementation.
AI is just a fad! It’s not for me
Myth
It’s a passing trend and will soon die down. It’s not relevant to me.
Reality
AI is here to stay, just like computers were in the 1980s. Today, almost every organization uses computers, and even our mobile phones are really just small computers. In the same way, AI is already part of our daily lives. We might not always notice it, but we use AI when we navigate with maps, watch recommended videos, or get smart suggestions while writing emails. As a leader, it's important to use AI for your business, even if it means starting with small steps.
AI needs a lot of data
Myth
AI systems need enormous datasets to function properly, making it impractical for organizations that do not have access to large volumes of data.
Reality
Some AI models such as those based on deep learning definitely need a lot of data. These models are trained on large amounts of data and get better with training on large diverse datasets. This is however not true with some other AI models such as machine learning models or foundation models. The new class of AI models such as LLMs are pre-trained and can be easily repurposed and fine tuned using small amounts of relevant data sets. Techniques such as transfer learning, fine tuning can be used to train models for your specific needs using a much smaller dataset.
AI is prohibitively expensive
Myth
Implementing AI solutions is prohibitively expensive and only affordable for large corporations with deep pockets.
Reality
The cost of implementing AI solutions depends on the scope and complexity of the project. It's important to evaluate the feasibility of the solution to ensure a strong return on investment (RoI). Many AI models today, particularly those designed for text-based tasks, can be applied to a wide range of document-related processes. These models are often affordable, with pay-per-use pricing that makes them accessible to small and medium-sized businesses. When calculating RoI, consider not only the cost of AI but also the potential productivity improvements and savings from reducing errors.
AI systems are fully autonomous, there is no need of human expertise
Myth:
Once I have AI systems in place, I don’t need humans
Reality
In my experience, AI systems are most effective when a human is involved to assess the results and make the final decisions. This approach is known as "human-in-the-loop." AI is designed to assist rather than replace human judgment. It excels at handling repetitive tasks and processing large volumes of data. However, for nuanced decisions that involve context or ethical considerations, AI has its limitations. Many AI applications are most successful when combined with human insights, especially in complex, high-stakes, or creative situations that require understanding beyond what data alone can offer.
AI implementation is a one step process
Myth
Implementing AI is a one-time task that, once completed, does not require further effort or adjustment.
Reality
AI is a journey, not a destination. Implementing AI is an ongoing process. As leaders, it's crucial to recognize that you shouldn't expect immediate and perfect results from AI right away. It's not a one-time effort. AI models learn from data, and as the data evolves, needs change and external conditions change, these models need to be retrained and fine-tuned to stay relevant. Failing to maintain and update them can result in outdated models and reduced performance.
AI understands and learns on its own
Myth
AI models have some super human powers and they have the ability to learn and understand
Reality
Machines do not truly understand; they simply learn. Unlike humans, they lack cognitive abilities and cannot engage in self-directed learning. Their learning is based entirely on the data provided by trainers during the training process. When we say they "learn," we mean they identify patterns in the training data through advanced mathematical processes. However, they do not have consciousness or the ability to grasp the context or meaning behind the data. Any learning AI does is driven by human input, including the creation of algorithms, data selection, and ongoing training and fine-tuning. Without continuous human guidance, AI would neither improve nor adapt on its own.
I've addressed some common misunderstandings about implementing AI systems, and I hope you found this information helpful. By clearing up these misconceptions, you can approach AI implementation with a well-informed strategy, which will likely result in significant productivity gains for your business. Remember, understanding AI's true capabilities and limitations is key to unlocking its full potential and ensuring successful integration into your operations.
Comments
Post a Comment