Beyond the Buzzword: Busting AI Myths in Personalization

Figuring out how to separate authentic AI capabilities from clever marketing messaging isn’t for the faint of heart. You need to understand what motivates companies to do this and learn how to spot false AI claims. In short, you need to be an AI detective.

Beyond the Buzzword: Busting AI Myths in Personalization

Put on your deerstalker cap because we’re about to investigate:

  • The Culprit: The advent and evolution of “AI Washing”
  • The Motive: Why companies are desperate to slap “AI-powered” on everything
  • The Evidence: Dead giveaways that help you spot false AI claims
  • The Case Files: How to find the right AI solution for industry-specific needs

Think of this as your crash course in AI claims investigation. We’re not here to compile a most-wanted list of every tech company that’s ever stretched the truth about their capabilities. Instead, we want to help you solve the mystery around which AI solution actually fits your business. Because in a world where everyone’s suddenly an “AI expert,” you need to be both skeptic and strategist.

What is “AI Washing”?

It seems like every tech company on the planet wants to jump on the artificial intelligence (AI) bandwagon, even when their tool or platform doesn’t incorporate AI in any meaningful way. When tech companies falsely claim a product or tool is AI-powered — or overstate the role AI plays in the tech — it’s called “AI washing.”

Even well-known brands often stretch the truth about their AI capabilities, misrepresenting themselves to the detriment of their customers — and the law. One of the world’s biggest companies (their name rhymes with “dapple”), for example, is currently facing a class-action lawsuit for allegedly overpromising the AI capabilities of the latest high-tech gadget. Last March, the SEC fined two investment firms hundreds of thousands of dollars in civil penalties for making false claims or misleading statements about their AI and machine learning capabilities.

Many companies simply integrate basic automation tools or off-the-shelf AI components, then market themselves as innovators. It’s a misleading practice that forces those evaluating these solutions to take a closer look, sussing out what’s real and what isn’t in the realm of AI tech.

The Motive: Why is AI Washing so Prevalent?

To understand why AI washing has become so pervasive, it’s important to frame the current market environment from both the buyer and seller perspective. Here’s a breakdown.

The Money Trail

According to a 2024 PwC Survey, investors are demanding companies embrace AI-driven growth. Of the 345 investors surveyed by PwC, 73% indicated they want companies to scale AI solutions quickly, and they have high expectations for immediate results. They want to see productivity gains, revenue growth, and improved profitability — all within 12 months.

This kind of intense pressure creates a “deploy first, verify later” mentality. Companies are racing to position themselves as AI innovators, even when their actual AI capabilities are still in development or, worse, nonexistent.

The Cost of Faking It

AI washing can cause significant reputational damage to companies that get caught doing it, but the heaviest toll falls on businesses who invest substantial resources in technology that can’t possibly live up to the hype. That’s time, money, and trust that can’t be recouped.

There’s a widening “AI gap” between early adopters and cautious organizations that’s contributing to the pressure-cooker environment. As this gap grows, companies desperate to bridge it become prime targets for misleading AI claims, often rushing into partnerships with vendors who promise quick fixes, then can’t deliver.

The Corporate Cover-up

What makes this situation particularly troubling is how it perpetuates itself. When companies see competitors claiming “revolutionary” AI capabilities (whether real or not), they feel compelled to make similar claims to stay relevant. This vicious cycle makes it increasingly difficult for buyers to separate genuine AI innovation from clever marketing.

The Evidence: Spotting False AI Claims

A good detective needs to know the telltale signs of deception. For businesses looking for real AI solutions, understanding common myths and false narratives around AI helps you make more informed decisions. Let’s examine four major red flags that should trigger some healthy skepticism.

The “Replace Your Team” Promise

One of the most misleading claims in AI marketing is that artificial intelligence can fully replace human teams. For retail merchandisers, this is especially concerning, as AI has transformed how products are curated and displayed. While AI automates some tasks, saying it can replace the strategic work merchandisers do is a stretch. In reality, AI helps manage challenges like catalog exposure and boost business KPIs.

AI works best as a support system. According to a 2025 World Economic Forum report, nearly 80% of employers plan to reskill staff to work effectively with AI.

The “Never Wrong” Narrative

AI isn’t infallible, and any claims suggesting otherwise should raise an eyebrow. Deloitte research shows 77% of businesses are concerned about AI hallucinations—cases where AI generates convincing but false information. Rightly so: even top AI models have a 15% hallucination rate

Maximizing AI’s value requires reliable data and human know-how. People are essential for checking and verifying AI outputs. Until an AI can pause and say, “Hmm, that doesn’t sound right—let me double-check,” humans will remain critical to responsible AI oversight.

The “Industry-Specific” Illusion

AI is a broad technology, meaning there’s an AI application that exists out there to help every business across every market sector. If someone says otherwise, your BS sensors should activate. It’s true that certain industries like banking, retail, and professional services are leading the way with AI adoption, but AI technology isn’t an industry-specific capability.

AI can streamline and automate many aspects of your business, but it still takes a holistic approach to reach your goals. It’s more about how an AI solution is applied to your unique business needs than which industry you operate in.

The “Magic Bullet” Misconception

One of the most persistent myths is that AI alone can solve all your business challenges. While 78% of organizations in a 2024 McKinsey survey used AI in at least one function, it’s no magic bullet for retooling every process. Risks like hallucinations, data governance, and transparency remain critical concerns.

A strong AI solution—built on proven, enterprise-ready technology—should support, not replace, your digital transformation strategy. It’s not a standalone fix, but part of a broader, thoughtful approach to driving long-term business value.

The Case Files: A Field Guide to Legitimate AI Solutions

To help you identify the right AI-powered solutions for your organization, we’ve compiled detailed intelligence reports for different sectors, starting with our findings from the retail frontlines.

Ecommerce & Retail

AI technology is a perfect match for retail because it touches every aspect of the digital buying journey. Leading personalization platforms use a combination of data, machine learning, and technology like natural language processing (NLP) and large language models (LLMs) to create exceptional customer experiences while supporting — not replacing — your existing teams. Here’s how that looks for our retail and ecommerce customers:

  • Intelligent product search: Personalized “smart” search results adapt to customer behavior, showing the most relevant products at the top of the search results (based on searcher context and user intent).

  • Enhanced product discovery: Personalized product recommendations appear at important points of the shopping journey, like search results pages, category pages, and shopping cart pages. Elements like social proof, product bundles, and customized layouts further help customers discover the products they are most likely to buy based on their own shopping history and in-session behavior.

  • Customer experience optimization: Shopping journeys are individualized based on user behavior patterns. This means that content automatically adjusts to each visitor, smart pricing and promotions can be automated based on real-time behavior data, inventory availability, and merchandising priorities.

  • Merchandising and inventory optimization: AI analyzes historical sales data, current trends, and real-time shopping behavior to predict demand, optimize inventory levels, and suggest product bundles. This helps merchandising teams make smarter decisions about stock levels, product assortments, and cross-sell opportunities.

Travel & Hospitality

AI technology is particularly valuable in travel and hospitality because it customizes what is typically a very complex buying journey. Combining customer data, behavioral patterns, and real-time interactions helps support hospitality teams in these key areas:

  • Streamlined Booking: AI analyzes search patterns and booking history to show the most relevant travel options, while creating urgency through smart social proof messaging — helping increase conversion rates by up to 3.8% for today’s airlines.

  • Guest Experience Personalization: NLP and LLM technologies, combined with customer behavior data and predictive analytics, can tailor content and messaging across all channels travelers use to book a trip. Machine learning algorithms then analyze this data to tailor room recommendations, add-on services, and promotions to individual customers.

  • Dynamic Pricing & Packages: AI can continuously monitor demand signals, competitor pricing, and inventory to suggest optimal rates and create personalized package offers that maximize revenue — all while matching guest expectations.

Quick Service Restaurants (QSRs)

AI is a perfect fit for QSRs, a sector where customer expectations for personalized experiences have skyrocketed. A 2024 Tillster report notes that 33% of diners have abandoned orders due to lack of personalization, up from 21% in 2023. AI-powered personalization platforms can help QSR brands turn their wealth of customer data into meaningful dining experiences through:

  • Mobile and Digital Ordering: AI analyzes customer behavior patterns and past orders to personalize mobile app interfaces and digital menu boards. It adapts in real-time based on factors like time of day, weather, and local events. For example, the app might suggest hot chocolate on a snowy afternoon or a frozen drink when the temperature heats up. This tailored recommendation strategy has helped QSRs boost digital menu board sales by up to 38%.

  • Smart Menu Personalization: Using a combination of NLP and machine learning, AI creates individualized menu experiences across kiosks, apps, and digital displays. When customers log in, they see personalized recommendations based on their dietary preferences, order history, and current promotions.

  • Location-Based Intelligence: AI monitors customer proximity, order patterns, and local data to trigger perfectly-timed offers. This might mean sending a birthday treat when someone’s near a restaurant or suggesting popular lunch combos to office workers during their break.

Automotive Suppliers

The automotive industry presents a unique challenge. With over 14 million aftermarket parts and 30,000 components per vehicle, finding the right part is like searching for a needle in a haystack. Here’s how intelligent technology can help automotive suppliers make this process precise and personal:

  • Dynamic Repair Bundling: AI features like dynamic bundling make it easier for searchers to complete a specific job by presenting all items needed in one place. For example, when a customer searches for brake pads, the system automatically bundles all components needed for a complete brake job — rotors, calipers, brake fluid, and necessary tools. Bundling products this way helps prevent incomplete orders and reduce return rates.

  • Vehicle-Specific Search: If there’s one thing AI is good at, it’s getting the details right. AI models like natural language processing and natural language understanding combined with machine learning analyze customer inputs about their exact vehicle (make, model, year) and cross-reference this with ACES and PIES fitment data to ensure 100% parts compatibility. So when a customer searches for “oil filter,” she’ll only see filters designed for her specific engine.

  • Real-Time Inventory Intelligence: By analyzing local inventory data, purchase patterns, and customer location, intelligence engines can display real-time parts availability at nearby stores. They can then create urgency through social proof messaging like “5 people are looking at this part” or “Only 3 left in stock at your local store.”

AI washing is a sleight of hand that can cost businesses time, money, and trust. The most reliable AI vendors are transparent about their technology’s capabilities and limitations. They focus on specific, measurable improvements rather than vague promises of revolutionary change.

Orchid AI, the intelligence layer that powers Monetate’s platform, exemplifies this approach. Built on over a decade of innovation, Orchid AI is the result of Monetate’s extensive experience in AI-powered ecommerce. Unlike newer, unproven solutions, Orchid AI has processed hundreds of billions of interactions, continuously learning and evolving to meet the needs of retailers.

Made up of a robust interplay of Machine Learning, Natural Language Processing (NLP), and Large Language Models (LLMs), Orchid AI can analyze customer behavior, predict intent, and optimize experiences in real time.Ready to see what genuine AI can do for your business?

Discover Orchid AI’s capabilities