In today’s tech landscape, it seems like everyone is making AI technologies. The term “Artificial Intelligence” has become a buzzword many companies are eager to apply to their products and services. However, using AI as a blanket term without substance can be not only misleading but also damaging to the industry’s credibility. In this blog post, we explore the pitfalls of overusing the AI label, why developing genuine AI solutions is so challenging, and how SECO is taking a more pragmatic and valuable approach.
Why Building Real AI Is So Difficult
Developing true AI solutions is no small feat. The following factors highlight the complexity and resource intensity involved:
- Provisioning of GPUs and NPUs: Training AI models requires significant computational power, often relying on G/NPUs (Graphical/Neural Processing Units). These specialized hardware components are not only expensive but also scarce due to the massive hardware requirements of both AI model developing and cryptocoin mining projects: GPUs and NPUs enable the unmatched parallel computing performance that these technologies require. Companies need to invest heavily in infrastructure just to get started.
- Extreme Capital Intensity: AI research and development is costly. Companies need to pour millions, if not billions, of dollars into their projects to stay competitive. This includes not just hardware costs but also the expenses of hiring top-tier talent and maintaining ongoing research, as AI is a field that demands deep technical expertise. Creating meaningful AI applications requires a blend of skills in data science, machine learning, and domain-specific knowledge. This level of expertise is scarce and commands high salaries, adding to the overall expense.
Given these challenges, it’s no surprise that some companies fail to deliver on their AI promises. The market has seen its fair share of hyped-up AI products that fell flat upon delivery.
AI Hype: Lessons from the “Metaverse” Buzzword
The misuse of the AI label is reminiscent of previous hype cycles, such as the “metaverse” craze. Just like with AI, companies rapidly embraced the metaverse concept, promising immersive virtual experiences designed to transform the way we live and work. Yet, despite the buzz, the metaverse has largely failed to deliver on its promises, with many projects being scaled back or abandoned entirely. Meta, formerly known as Facebook, invested heavily in the metaverse only to face lukewarm reception and significant financial losses. The lessons are clear: overhyping technology without a clear, practical path to value creation can lead to market failures and loss of trust.
The allure of AI has led even big companies to over-promise and under-deliver. Earlier this year, a Canadian tribunal ruled that Air Canada must compensate a customer $600 plus tribunal fees after the airline’s chatbot provided incorrect information about bereavement policy rules – Air Canada argued that the chatbot was a separate legal entity “responsible for its own actions,” making the defense—and the relatively small amount in dispute—seem even more absurd. Another example is Amazon’s AI recruiting tool that was designed to automate and streamline the hiring process, but it was soon discovered to be biased against female candidates, leading to significant criticism and the eventual scrapping of the project.
These examples illustrates that, while AI has immense potential, realizing that potential is far from straightforward and requires careful consideration of ethical implications.
SECO’s Practical Approach as an AI-Enabler
At SECO, we recognize the challenges and potential pitfalls of jumping on the AI bandwagon without substance: that’s why we position ourselves as an AI enabler. Our extensive experience in IoT data collection, qualification and consumption through our robust software and hardware solutions provides a solid foundation for practical, real-world AI applications.
SECO’s approach focuses on two main customer segments:
- OEMs (Original Equipment Manufacturers): By leveraging our vertical IoT solutions, OEMs can seamlessly integrate third-party AI solutions into their products. This capability allows them to offer enhanced, AI-driven functionalities—such as predictive maintenance—without the need to develop complex AI algorithms from scratch. Predictive maintenance AI models, for example, can significantly reduce downtimes, leading to cost savings and improved operational efficiency.
- System Integrators and Cloud Service Providers: Our open-source IoT suite, Clea, is designed specifically for developers. It provides a flexible, scalable platform for building AI pipelines using Docker containers and deploying them on the field easily, while it takes care of balancing and scaling AI workloads in a native-Kubernetes environment. Clea’s user-friendly GUI drop-in mechanism simplifies the integration of AI capabilities, empowering developers to focus on creating great AI-driven vertical solutions rather than wrestling with the intricacies of AI deployment.
Our customers can leverage our technology to develop AI applications or easily integrate third-party AI models that are meaningful, effective, and aligned with their business goals. We provide the tools and infrastructure needed to make AI a reality, rather than just a marketing buzzword.
Conclusion
The AI hype is real, but so are the challenges. At SECO, we believe that a practical, value-driven approach is the key to leveraging AI effectively. By positioning ourselves as enablers and continuing investing in our AI-enabling technologies, we help our customers navigate the complexities of AI, turning the promise of AI into a tangible, impactful reality. Whether you are an OEM looking to enhance your product with AI or a system integrator aiming to develop robust IoT+AI solutions, SECO provides the platform and expertise necessary for success.
Ready to explore how AI can transform your business beyond the buzzword? Contact our experts today to discuss how SECO’s solutions can help you navigate the complexities of AI and turn your vision into reality.
