The buzz around AI is undeniable. It seems that everywhere you look, companies are integrating AI into their products to stand out in a crowded market. It’s become the “hero” solution that promises to revolutionize user experiences, optimize operations, and drive unparalleled innovation. But here’s a question every product leader should ask: Do you really need AI, or can you be a bigger hero by optimizing your product in simpler, more cost-effective ways?
AI has its place, but not every problem requires it. In fact, in many cases, companies are so enamored with the idea of adding AI that they overlook more immediate and impactful opportunities for improvement. Here’s why traditional methods, better engineering, and a focus on foundational principles could be the better way to success in some circumstances—without the eye-watering costs of deep learning models, endless GPUs, or an army of data scientists.
AI models, regardless of how sophisticated, rely on one thing above all else: data. Garbage in, garbage out. If your data quality is poor, even the most advanced AI systems will deliver suboptimal results. Many companies mistakenly believe that AI can “fix” bad data or that the sophistication of the model can compensate for poor input quality. This is simply not true.
Before considering AI, ask yourself: Is our data accurate, complete, and relevant? You might be better served by investing in processes and tools that clean and organize your data. High-quality data can unlock insights through traditional analytics and basic machine learning models, potentially offering more value than an AI solution ever could.
Investing in data governance, building better pipelines, and creating strong data structures should be your first priority. Once your data is trustworthy, you’ll find that even traditional methods like basic statistics or SQL queries can provide critical insights without needing advanced AI techniques. In short, focusing on data quality often solves problems that businesses try to throw AI at.
Machine learning (ML) has been around for a long time, and the tools available today are powerful enough to solve many problems without the need for cutting-edge AI techniques. Algorithms like XGBoost, Random Forests, and Support Vector Machines (SVMs) have stood the test of time, delivering reliable results with relatively low computational overhead.
In fact, many businesses overestimate their need for deep learning or neural networks when traditional ML libraries are more than capable of handling their predictive needs. These algorithms can handle structured data beautifully and can be fine-tuned with limited resources—often producing results that are just as good, if not better, than what AI can deliver.
The beauty of these traditional ML methods is that they are lightweight and easy to implement. You don’t need to build complex architectures or acquire expensive hardware. Instead, you can run these models on typical CPUs and get excellent performance. Plus, they don’t require massive amounts of data to perform well, making them a better fit for many real-world business problems.
So before jumping to AI, it’s worth evaluating whether an off-the-shelf solution like XGBoost can meet your needs at a fraction of the cost.
In the race to implement AI, many companies forget that the architecture of their product might be the real bottleneck to better performance. Instead of bolting on an AI solution, you might benefit far more from investing in more sophisticated product architecture.
Consider this: Is your product scalable? Is your infrastructure built to handle the kind of data processing you’ll need for AI? Cloud optimization, containerization, and even API improvements might be far more valuable investments. Optimizing your backend systems and optimizing your codebase can lead to faster product performance and better user experiences—without ever touching AI.
This approach can also set you up for future success. By improving the underlying architecture, you’ll create a flexible foundation that can support AI when the time is right, but you’ll be getting immediate benefits in the meantime. Not to mention, architectural improvements tend to reduce long-term costs, as a well-built system will require less maintenance and fewer costly infrastructure upgrades down the road.
While AI is often hailed as the solution to complex problems, great software engineers are sometimes the real heroes. A team of talented developers can optimize your product far beyond what AI can offer in many cases. They can fine-tune your algorithms, refactor code, and streamline processes, all while keeping your costs low and your product lean.
Many problems that businesses want AI to solve—such as automation, data handling, and process optimization—can often be tackled by an experienced engineering team. They can build custom solutions that are tailored to your product’s specific needs without the overhead of AI. These solutions are often faster, more reliable, and easier to maintain than AI-based systems.
Moreover, a skilled engineer can assess when AI is genuinely necessary and when it’s overkill. Having the right people on your team can prevent you from making costly mistakes and ensure that you’re using the best tools for the job—whether or not they involve AI.
AI can be a powerful tool, but it often comes with a significant investment. Implementing advanced AI solutions can lead to substantial costs, including fees from platforms like OpenAI, data science team salaries, and infrastructure expenses such as GPUs. With careful planning and a clear understanding of AI's potential, these costs can translate into valuable, long-term returns.
Before making a choice, it’s essential to assess whether AI is the best solution for your specific challenges. In some cases, optimizing business logic, refining algorithms, or improving user experience (UX) might deliver faster and more cost-effective results. Even when AI holds promise, balancing its potential impact with the associated costs will help ensure a well-justified and rewarding investment.
Are you not sure whether to start with your AI journey? Feel free to get in touch with us at NineTwoThree!
