The noise around generative AI is impossible to ignore, but it can be hard to decipher what’s real and what’s a buzzy LinkedIn headline. In this article, we separate fact from fiction by dispelling the five most common myths we’ve heard about AI.
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And while there’s a lot of really exciting ways your organization can take advantage of AI today, we’ve seen a lot of misinformation floating around about the impact AI will have on the retail industry. So, we decided to separate fact from fiction by dispelling the five most common myths we’ve heard about the most talked about technology of 2025, AI.
It’s tempting to view AI as a magic wand that can instantly resolve any business challenge we point it to. In reality, the power of AI lies in its capability to be used to address specific pain points identified by an organization as part of a larger strategy.
For instance, in the fashion industry, it can be nearly impossible to not only predict upcoming trends but then also react quickly enough to take full advantage of the market shift. A well-trained machine learning algorithm can ingest billions of data points from social media, user reviews, and market data in a matter of minutes and generate intelligent recommendations for patterns, styles, and colors. Some AI art generators could even assist in creating mock-ups of these products, or visualizing how they'll look on different body sizes and shapes.
But who’s training the algorithm with the right target audience to ensure the AI generator isn’t creating a pink, sparkly button down shirt that’s popular in the Gen-Z market for their professional men’s shirt line? AI can help generate SEO-friendly and channel-specific product descriptions, but who’s identifying which channels and markets content needs to be curated for? Who’s communicating production and launch timelines to marketing and support teams, and ensuring everyone has the information and assets they need for a cohesive launch?
AI is not a one-size-fits-all solution; at least for the foreseeable future, it’s not quite as easy as some people may think to just “ChatGPT it”. Which leads us nicely to our second myth.
With Microsoft laying off more than 6,000 people in favor of automation and Duolingo coming forward as a "AI-first company" that will replace contract workers with machine learning algorithms, it's not hard to see why people are panicking about AI taking their job.
But the truth is that AI needs to be viewed as a collaborator, not a competitor. While it’s true that AI has the potential to automate certain repetitive and routine tasks, its primary impact is on augmenting human capabilities and transforming the nature of work instead of entirely replacing the work itself. AI can help to optimize supply chains and reduce time-to-market, but it can’t (and shouldn't) entirely replace the need for skilled engineers to design and create innovative products.
In actuality, the growth of AI is expected to create over 97 million jobs by the end of 2025; these roles will be more focused on data analysis and AI development and implementation, but will still require the empathy, creativity, and critical thinking of human beings that AI can’t quite replicate (yet). The adoption of this new technology should be seen as a reallocation of responsibilities that can actually lead to a more dynamic and productive workforce.
One of the most common excuses we hear about avoiding AI implementation is that businesses often believe that this process requires substantial upfront costs without immediate results. The truth is, while AI projects can be resource-intensive initially, cloud-based AI platforms and pre-built solutions have made it more accessible and cost-effective for many businesses.
Cloud-based platforms eliminate the need for massive hardware investments and provide access to powerful computing resources on a pay-as-you-go basis, meaning a reduced up-front cost and an accelerated results timeline. Similarly, pre-built AI solutions are often tailored to specific industry needs, coming with predefined models and algorithms that can be customized to suit a business’s requirements, reducing the time it may take to implement.
AI systems are powerful tools, capable of processing massive amounts of data to make complex decisions. However, they are not infallible; just like any technology, especially new technology, AI systems have limitations and can often encounter scenarios they were not explicitly designed to handle. This is particularly relevant in industries where safety regulations, quality standards, and user satisfaction are critical factors.
While AI systems can process massive datasets, recognize patterns, and automate routine tasks with impressive speed, they are ultimately built, trained, and maintained by humans. Every algorithm reflects human choices: from the data selected to train it, to the objectives it's designed to achieve, and the constraints it's programmed to respect. Without careful guidance, AI systems can reproduce biases, make errors, or fail to understand context
Human oversight is not only necessary for ethical and legal accountability, but it's also essential for ensuring AI’s relevance and reliability. Generative AI might be used to write product descriptions or forecast demand, but it still requires marketing teams or merchandisers to validate tone, accuracy, and cultural appropriateness. In customer service, chatbots need escalation paths to human agents for handling nuance and empathy. Even in advanced use cases like autonomous vehicles or medical diagnosis, human-in-the-loop systems are critical for intervening when AI encounters edge cases or ambiguous scenarios. Rather than replacing humans, AI augments them, making oversight and collaboration foundational, not optional, to success.
At least for the foreseeable future, AI will not be entirely autonomous; it will have to be routinely inspected, maintained, and trained by us lowly humans to maintain accuracy, safety, and ethicality.
Deciding to implement an AI solution is a great first step, but it’s important to remember that AI software is not static but should grow and evolve alongside the business it serves. Customer expectations and demand are growing rapidly, and we’ve all seen how unpredictable the market can be over the past few years. Adaptability is the name of the game - as your business expands into new markets, introduces new products, or tests new channels, you’re going to want an AI solution that remains a valuable asset rather than a stagnant technology that becomes obsolete.
Regular evaluations of the AI solutions performance, as well as updates to the underlying algorithms and models, are critical to ensuring that the solution continues to deliver the same value and stays aligned with your business goals.
In a world buzzing with AI hype, it's easy to get swept up in sensational narratives, whether it's the fear of machines taking over human jobs or the fantasy of pushing a button and solving all your business problems overnight. But as we've seen, the truth about AI is far more nuanced. AI is a powerful tool that, when implemented thoughtfully, can transform how retailers operate, innovate, and engage with customers. However, it’s not a silver bullet. It requires strategy, oversight, iteration, and, most importantly, people to unlock its full potential.
As you begin (or continue) your AI journey, remember that success doesn’t come from adopting AI for AI’s sake. It comes from clearly identifying your goals, choosing the right tools, and ensuring your teams are prepared to work alongside these technologies, not be replaced by them. With a grounded, realistic approach and a willingness to evolve, AI can be one of your most valuable allies in creating better shopping experiences, smarter operations, and more agile organizations.
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