The Reality Check on AI Hype

Let’s step away from jargon and get practical about why a typical customer makes a purchase and how AI really fits in (if at all).

Why Do People Actually Buy?

At its core, purchasing decisions are driven by basic human needs and emotions. People buy because:

• They need a product or service: Whether it’s food, clothing, or a tool, customers make purchases to fulfil a need or solve a problem.
• Convenience: If a product or service makes their life easier, they’re more likely to buy it.
• Trust and Familiarity: People prefer brands and products they know or feel comfortable with.
• Emotion: Purchases are often influenced by emotions like happiness, stress relief, or status-seeking.
• Recommendations: Word of mouth and personal endorsements from friends or influencers are strong motivators.

Is AI Adding Value to This Process?

AI tools claim to improve customer experiences, but often the actual value to a typical customer is overstated. Here’s a more realistic look at how AI might—or might not—be helpful:

1. Real Example: Personalised Email Recommendations

Some stores send emails that recommend products based on what you've browsed or bought before. To a busy shopper, this can be useful because it saves time. However, many people find these emails annoying or too pushy. The actual value depends on whether the AI is accurately guessing what a customer wants or if it’s just spamming them with irrelevant suggestions.

Real Impact: These recommendations can sometimes help busy customers find what they need faster, but they can also be intrusive and don’t always reflect what a customer really wants.

2. Voice Assistants for Basic Tasks

Devices like Amazon's Alexa or Google Assistant use AI to let people shop, play music, or set reminders by speaking. While this is convenient for simple tasks like reordering household items, most people still prefer shopping on a website or app where they can see options and compare.

Real Impact: For small, repeat purchases (e.g., “Order toilet paper”), this AI functionality can be handy. For more involved shopping, it doesn't add much value because customers want to see, compare, and choose items themselves.

The Drawbacks and Hype

Many AI tools used by businesses are more about making the business feel modern than actually improving the customer experience:

• Too much automation can hurt the personal touch: Automated customer service bots often frustrate customers who just want to speak to a real person.
• AI that feels forced: Some companies implement AI features that don't actually solve any problems for the customer, like overly complex chatbots that can't answer questions effectively.

The Reality Check

AI in its current state is often hyped up to be more impactful than it is. While it can add value in specific situations (e.g., speeding up small, routine tasks), it isn’t as revolutionary for customer experience as some might claim. A lot of what makes people buy—trust, genuine human connection, and personalised service—still relies heavily on people, not machines.

So, yes, AI can have a place, but it's far from being the be-all and end-all. And when not used thoughtfully, it can actually detract from a customer's experience, making things feel cold and impersonal. The key is to use AI where it genuinely helps people, not just because it sounds impressive.

Cutting Through the AI Hype

When businesses talk about AI improving customer experience, it often sounds like they're doing something revolutionary, but many of these strategies aren't fundamentally different from what was done decades ago with simpler tools:

• Email Recommendations: Yes, they were done in the 90s using basic purchase history and frequency analysis. AI now just automates the process on a larger scale, but the core concept is the same.
• Customer Segmentation: This used to be done with simple database queries. Today, AI models might automate segmentation, but they aren't necessarily better at understanding why customers buy than the people who analysed this data manually before.

Where AI is Overstated

AI is often dressed up with complex language to sound more sophisticated than it is. For example:

• "Predictive Modelling": Often just means looking at past behaviour and guessing what someone might do next—a practice that has been around for decades.
• "Hyper-Personalisation": Is just using more data points to segment customers, something businesses have done for years, just faster with better automation.

The Real Takeaway

AI tools today make some things faster and can process more data at once, but they aren’t magical. Many so-called AI-driven solutions aren't more effective than the good old methods of analysing data, understanding customer needs, and applying human intuition. AI only helps when used in a way that complements human decision-making, not when it replaces it or tries to do something that simple analytics could achieve.

So, yes, much of what is claimed as "AI innovation" could be done with simple statistics and a good understanding of customer behaviour, just as it was in the past. AI can be useful for automating these processes, but it’s often oversold as groundbreaking when, in reality, it’s just a tool in a long line of data-driven methods.