Top 5 Ways Technology Companies Truly Leverage Artificial Intelligence

The term "artificial intelligence" (AI) is often wielded today as a universal solution capable of addressing almost any problem. The futuristic vision from the 1950s, when the concept of AI first emerged, has essentially become a reality. AI now beats humans in chess, aids in disease detection, engages in conversations, performs calculations, and even programs. Global tech giants have started rolling out their own AI models – Apple introduced its own AI, Google unveiled Bard, and Microsoft launched Bing AI. Companies that neither utilize AI nor claim to do so can undoubtedly be said to be resting on their laurels.

But what does real-world practice look like? What are the specific areas where artificial intelligence can truly be utilized on a daily basis across various sectors? For instance, is it possible to not only automate the product itself but also its "production" through artificial intelligence?

1) The Tool Itself – Product Recommendations

The Zoe.ai tool helps e-shops provide more accurate product recommendations based on user behavior data and individual preferences, enabling even more precise personalization. The term AI in its name is well-earned, as previously mentioned. The models leverage user behavior and purchase data from e-shops, extensively employing artificial intelligence, particularly machine learning.

In essence, the tool observes the behavior of hundreds of thousands of users on a specific e-shop, "learns" trends and typical purchase journeys, analyzes scenarios where the user’s journey ended in a purchase versus where it didn’t, and examines what users interacted with during these journeys. Based on this analysis, it recommends products at the right moments, which the tool determines to have a high likelihood of capturing the user’s interest—and ideally, prompting a purchase.

However, it’s important to note that not all models generating personalized recommendations must rely on artificial intelligence. In some cases, sophisticated rule-based models, while not AI-driven, can be sufficient to achieve effective outcomes.

2) Chat GPT and others

A Cure-All? Hardly. But There Are Situations Where Chat GPT Truly Helps. Chat GPT significantly streamlines our work in the continuous development of the robust platform powering Zoe.ai. Where do we use Chat GPT in practice?

Developers typically rely on Chat GPT for generating code, saving them from reinventing the wheel. It’s particularly useful for routine development tasks and during refactoring—the process of optimizing code structure without altering its functionality. This is something we commonly do, both on the back-end (BE) side and in Data Science.

Speaking of our data team, we also use tools like GitHub Copilot or Perplexity for designing and optimizing models. These tools belong to the same family as Chat GPT—large language models—though with a more technical focus. Perplexity, for example, helps explore new modeling possibilities and avoid dead ends, which even we encounter regularly.

For more advanced research, we use tools like Wiseone, which excels at accurately searching academic articles for specific passages essential for further development. These capabilities are invaluable in pushing our work forward efficiently and effectively.

3) Coding 

Is Everything API-Based Nowadays and Widgets Dead? Not Quite. Most of our current implementations rely on custom Zoe.ai widgets, meticulously designed to be pixel-perfect and seamlessly fit the UX/UI of each specific e-shop. This attention to detail is something we truly pride ourselves on.

Of course, crafting these widgets involves a lot of coding, especially when implementing, for example, seven different recommendation zones on an e-shop, each with unique visuals and flows—not to mention Search components. Starting everything from scratch isn’t feasible, which is why, in addition to tools like Chat GPT or GitHub Copilot, our front-end (FE) developers use tools like Supermaven.

That said, let’s be honest—fine-tuning, especially the small details, is often still manual work. Unfortunately, not everything can be fully automated.

4) Sales 

Manual Search for Potential Collaborators on LinkedIn or Websites? Not in Today’s World. If you aim to gather relevant feedback on your product or attract new potential customers in B2B sales, you could go the traditional route of manually searching for contacts and reaching out to them one by one. However, this painstaking approach is unlikely to deliver the desired success.

The initial stage of the sales process can be significantly simplified. Tools like Apollo.io can identify the right contacts based on detailed filters you set in advance. With these contacts, you can then launch personalized LinkedIn or email acquisition campaigns, even leveraging a virtual avatar representing your company to ensure precise targeting and increase the likelihood of a response—be it positive or negative.

Once the conversation gains momentum, a real person takes over, arranging, for instance, a meeting. This approach can save not just hours but days or even weeks of work in the presales phase, allowing teams to focus on higher-value tasks while still effectively building their pipeline.

5) Document Translation

Remember the Days of Spending Hours Translating Documents or Paying Hefty Fees for a Translator?

Gone are the times when you’d either spend hours translating a document, such as a service agreement, or pay significant fees to a translator, only to have the documents revised six months later.

Today, AI-powered translation tools like DeepL Pro can translate entire .doc or .pdf files in mere seconds. Once translated, these documents can be run through tools offering automatic proofreading, such as Grammarly, as we often do. However, it’s important to keep in mind that highly technical terms or niche expressions may not always be translated accurately.

Blind trust in these tools—or their equivalents—is not advisable. It’s always a good idea to review the translated text to ensure everything is correct. That said, the time savings are tremendous compared to the days of manual, word-by-word translations or costly professional translators.

Conclusion

Beyond Product Recommendations, Code Automation, Sales Optimization, and Document Translation AI also finds applications in many other areas, including big data analysis for gaining deeper business insights, automating customer support through chatbots, and even enhancing cybersecurity by detecting and mitigating threats.

That said, I dare to assert that the use of AI in recommendation systems and process automation will likely remain among the most frequent and effective ways to integrate AI in technology companies. This trend is undoubtedly driven by the increasing demand for personalization and efficiency, which are becoming ever more critical in today’s highly competitive landscape.

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To arrange a demonstration with one of our experts, or to request a free consultation with one of our e-commerce specialists, please contact us. We will demonstrate how Zoe.ai can help you and your e-commerce store become more profitable.