Artificial intelligence (AI) has become a burning hot topic. The emerging capabilities, especially around generative AI, are undeniably exciting and bring myriad possibilities. However, as AI becomes ubiquitous, from boardroom strategies to tech blog headlines, it’s crucial to look beyond the hype and understand the nuanced reality of AI technologies.
AI is not a blanket term
One of the most common misconceptions is treating AI as a one-size-fits-all solution. In reality, AI encompasses a wide range of technologies, each with its own strengths and limitations. Take, for example, Optical Character Recognition (OCR), a technology often labelled as AI. OCR can be remarkably effective in digitising typed documents or recognising characters – in our industry, for example, reading a vehicle identification number (VIN).
However, this example illustrates a critical point: AI’s effectiveness is highly context-dependent. The medium on which data is written, the environment where AI is deployed, and the specific task at hand all play crucial roles in determining an AI system’s success.
Consider an OCR system that excels at reading text and can pick up vehicle identification numbers. But what if those numbers are handwritten on a windscreen? Or what if the vehicle isn’t even present? These scenarios highlight the importance of understanding AI’s limitations and the need for more sophisticated solutions.
True AI vs automation
It’s important to distinguish between automation and true AI. While both fall under the broader umbrella of AI in popular discourse, they represent different levels of sophistication. Automation typically involves simple, rule-based processes, while true AI refers to adaptive, learning systems.
To illustrate this difference, let’s continue our example around VINs. A simple OCR technology can scan and read VINs (or numbers that look like VINs). But what is really needed is full and intelligent validation. This verification should ensure:
- The VIN is displayed in the correct font
- It’s located on a vehicle
- The vehicle is where it should be
This level of validation requires a more sophisticated AI system that can understand context, detect anomalies, and make intelligent decisions based on multiple data points.
Real-world applications and misconceptions
While recent developments like ChatGPT have brought AI into the spotlight, it’s important to recognise that AI has been quietly evolving and proving its worth in various industries for over a decade. The flashy, general-purpose AI that makes headlines is just the tip of the iceberg.
In many industries, specialised AI systems have been working behind the scenes, delivering tangible benefits without the hype. For instance, in the automotive industry, AI systems are changing how inventory is managed and validated. These systems can accurately identify and catalogue vehicles, verify their locations, and even detect discrepancies in reported inventory – tasks that would be time-consuming and error-prone if done manually.
At CheckVentory, we’ve developed a suite of integrated tools designed specifically for floorplan funders and asset validation. Our validation checks go beyond simple OCR or location tracking. Together they answer four questions about every photo:
- Where: the photo was taken where the vehicle is supposed to be, or at one of the dealer’s documented sites
- Which vehicle: the VIN read from the photo, and the right type of asset for the record
- Is it genuine: taken live rather than uploaded, not a photo of a screen, not edited or AI-generated, and not reused
- When: a genuine time stamp on every photo
Anything that fails a check is flagged to the funder’s team with the reason, so people spend their time on the photos that need a second look.
Conclusion: the path forward
As we continue to integrate AI into various aspects of business and daily life, it’s essential to move beyond oversimplification and overhype. Understanding the specific use cases, strengths, and limitations of different AI technologies is key to using them well.
At CheckVentory, we recognise that the real value of AI lies not in buzzwords but in its practical applications and measurable outcomes. We advocate for an informed approach to AI adoption – one that acknowledges both the transformative potential of AI and the importance of context-specific implementation.
As business leaders and decision-makers, it’s our responsibility to look beyond the AI hype cycle. By understanding the nuances of AI technologies and their real-world applications, we can make informed decisions that drive genuine innovation and value creation in our respective industries.
The future of AI in business isn’t about adopting the latest trend; it should focus on implementing intelligent, tailored solutions that address specific challenges and drive tangible results.
Learn more about how we use technology to support floorplan funders and request your personal demo.