Major technology companies are embedding artificial intelligence (AI) into their core products and services, creating new revenue opportunities and enhancing user experiences.
Integrating AI across various sectors marks a shift in how companies monetize technology and engage with users. From search engines and productivity tools to freelance marketplaces, AI is becoming a key differentiator and revenue driver. This trend is reshaping business models and consumer interactions while also raising questions about the future of work and the implications of AI deployment.
“Companies are increasingly focusing their AI spending on developing lightweight and compressed models, which are crucial for efficient deployment in resource-constrained environments,” Jiahao Sun, CEO at Flock.io, told PYMNTS.
Google has been integrating AI capabilities into its search engine through initiatives like Search Generative Experience (SGE). Google CEO Sundar Pichai has emphasized, “AI is the most profound technology we are working on today.”
Not to be outdone, Microsoft has launched Copilot, an AI assistant embedded across its Office suite, charging enterprise users $30 per person per month. Microsoft CEO Satya Nadella noted, “Copilot is already improving productivity for more than 40% of the Fortune 100 who participated in our early access program.”
Amazon leverages AI for personalized product recommendations and search results. In a shareholder letter, Amazon CEO Andy Jassy stated, “We’re investing heavily in large language models and generative AI across our businesses.”
“Generative AI will significantly impact how people discover topics on the internet, such as asking ChatGPT for recommendations rather than browsing through search rankings and reviews,” Brad Null, head of AI at Reputation, told PYMNTS. He added, “AI will consume all of the information out there about a business to make such recommendations, so brands will need to stay on top of those advancements, especially when it comes to their search strategy.”
Industry experts are expressing optimism about AI’s potential in the commerce sector while warning of hurdles in its implementation. Their insights paint a picture of an industry on the cusp of major change, grappling with both excitement and caution.
Sun highlighted AI’s capabilities. “Advancements in AI, particularly in large language models [LLMs] and machine learning [ML], are poised to revolutionize the commerce sector by automating a wide range of processes,” he stated. This automation, Sun suggested, could streamline operations across the board, from inventory management to customer service.
AI could enhance customer insights. Null said, “For years, we have had tools that mine customer feedback data to surface insights about brands. With new advancements in AI, these tools are getting increasingly more powerful, helping us more quickly aggregate feedback, discover emerging themes, and surface more actionable insights.” This improved ability to understand and respond to customer needs could give businesses a competitive edge.
However, both experts quickly pointed out that the road to AI integration is fraught with challenges. Sun highlighted the financial barriers, stating, “There are high API fees associated with using centralized AI services, which can quickly escalate as usage scales.”
He added, “Companies must frequently upgrade their hardware, particularly GPUs, to keep up with the latest AI developments and model requirements.” These costs could prove prohibitive for smaller businesses or those operating on tight margins.
Data management remains a critical issue, even as AI capabilities advance. “The biggest challenge today is the same as what it was five years ago, getting the most useful, actionable data and positioning it so that you can maximize value from that data,” Null said. This sentiment underscores the importance of not just having data but also having it in a format that AI can effectively use.
He elaborated on this point, saying, “If you don’t already have the data you need, and have it formatted in a way that it is easy to leverage — meaning, if you haven’t already applied AI and ML to your data — then you probably have a lot of work to do to gather and position this data before using it to find consumer insights.” This suggests that many businesses face a preparatory phase before leveraging AI’s capabilities.
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