What If Shops Had to Bid for You?

msamezika

4 hours ago

8 min read

Rethinking the future of shopping, consumer power, and the rise of AI-driven commerce

By Dr. Milad Armani Dehghani

Imagine a future where you no longer spend hours searching for the right product, comparing prices across dozens of websites, or waiting for a discount that may never arrive. Instead, you simply tell an intelligent system what you want, how much you are willing to spend, and when you plan to buy it.

Then something interesting happens. Rather than you chasing the shops, the shops begin competing for you.

This might sound like a radical departure from how shopping works today, but I believe it raises one of the most interesting questions about the future of digital commerce: What if we redesigned shopping around the consumer's intentions rather than the merchant's desire to sell?

For decades, technology has transformed the way we shop. Yet one fundamental relationship has remained largely unchanged. Businesses present their products, invest in attracting our attention, and compete to persuade us to buy. Consumers, meanwhile, are expected to navigate an increasingly complex marketplace, evaluate alternatives, and make decisions, often under pressure.

Artificial intelligence may be about to change that relationship.

The future of shopping is not just about buying faster

Think about how online shopping has evolved over the past twenty years.

The first generation of e-commerce brought products to our screens. Search engines made them easier to discover. Recommendation algorithms learned our preferences, while mobile shopping turned almost every moment into a potential purchasing opportunity.

Today, generative AI is introducing another transformation. Instead of entering keywords into a search bar, we can describe our needs in ordinary language. AI can compare products, explain differences, interpret preferences, and increasingly assist with purchasing decisions.

This is not simply speculation. A McKinsey study published in March 2026 found that 38% of respondents surveyed across France, Germany, and the United Kingdom were already using AI tools to research products or services and inform purchasing decisions.

The business world is preparing for an even more significant change. According to Visa's 2026 research, 53% of surveyed US business decision-makers would be willing to let AI agents negotiate prices or terms with other AI agents on their behalf. These findings point toward what is increasingly described as agentic commerce, where intelligent systems do more than recommend products. They may eventually negotiate offers, coordinate purchasing decisions, and execute transactions within boundaries established by users.

But amid all this innovation, I think we should ask a more fundamental question.

Are we using AI simply to make buying easier, or can we use it to make shopping genuinely better? There is an important difference.

An intelligent shopping assistant that identifies a product and completes a purchase in seconds may be technologically impressive. But speed alone does not mean that the purchase was necessary, affordable, or made at the right time.

The next real innovation in commerce may therefore come not from accelerating transactions, but from helping consumers make better decisions before those transactions occur.

What happens when consumer intention becomes valuable?

Consider someone planning to buy an engagement ring, a new laptop, or a family holiday.

These purchases rarely happen without some preparation. People establish budgets, research options, compare alternatives, and sometimes spend months putting money aside.

Now imagine that these intentions could become the starting point of an entirely different shopping experience.

Instead of searching through hundreds of products, a customer could express a simple goal:

"I'm planning to buy a new electric bicycle in six months. My budget is around £1,500, and I would prefer to buy from a local business."

An intelligent platform could help the customer explore suitable options and potentially invite relevant merchants to present competing offers.

One retailer might propose a lower price. Another could include maintenance, accessories, or a longer warranty. A local shop might offer a personalised fitting service or flexible collection arrangements. Suddenly, the customer is no longer navigating the market alone.

The market is responding to the customer.

This is the principle behind a reverse auction, in which suppliers compete to meet a buyer's requirements rather than consumers simply accepting the prices and offers placed in front of them.

Reverse auctions are not new. They have been used in procurement and business markets for decades. What interests me is how artificial intelligence could make similar ideas more accessible, personalised, and relevant to ordinary consumers. The opportunity is not simply to make merchants offer lower prices. It is to create an environment where businesses compete on the overall value they can deliver.

For consumers, that might mean better choices and greater negotiating power. For merchants, it could mean reaching people who have already expressed an interest in purchasing something relevant. Of course, expressing an intention does not guarantee a sale, and competitive offers will not always produce savings. But the possibility of connecting planned demand with suitable supply presents an interesting alternative to conventional product advertising.

Could this create a new opportunity for local businesses?

One of the aspects I find particularly exciting is the potential for smaller and independent merchants.

Large online retailers have enormous advantages in digital marketing, product visibility, and advertising technology. A small jewellery shop in Glasgow or an independent bicycle retailer in Edinburgh may offer excellent products and personal service, yet struggle to compete for attention online. In a marketplace increasingly influenced by advertising budgets and search rankings, being a good merchant does not always guarantee being discovered.

AI-mediated shopping could potentially change some of these dynamics.

Imagine a local merchant being able to respond directly to a consumer who is actively planning a relevant purchase. Rather than investing heavily in advertising to reach thousands of people who may have little interest in buying, the business could focus on a smaller number of meaningful opportunities.

This could be especially valuable in categories where service, proximity, reliability, and personal relationships matter as much as price.

A local jeweller may not beat an international retailer on price alone. But perhaps it can offer a better overall experience, after-sales support, or a personalised product that a large online marketplace cannot easily provide.

For this vision to work, however, intelligent marketplaces must be designed carefully. Commercial incentives should be transparent, consumers should remain in control of their decisions, and recommendations should not simply favour whichever merchant pays the most. Otherwise, we risk recreating the problems of traditional digital advertising within a more sophisticated technological environment.

From artificial intelligence to consumer intelligence

I believe the most interesting development in AI shopping is not the possibility that machines will eventually purchase products for us. It is the possibility that they will become better at understanding what we are trying to achieve.

Most recommendation systems have traditionally relied on behaviour. They analyse what we click, what we browse, and what we have previously purchased. But our past behaviour does not always represent our future priorities.

Someone browsing expensive watches may not want to buy one tomorrow. Someone searching for university accommodation may be planning a move six months away. Someone interested in travelling might be trying to work out whether the trip is financially realistic.

These distinctions matter.

An intelligent shopping system should ideally understand not only what someone wants, but also the context behind that desire. This introduces a different perspective on personalisation. Instead of trying to predict the next purchase, AI could increasingly support the journey toward a considered purchase. It could help people understand alternatives, assess affordability, compare merchant propositions, and decide whether to purchase now or later.

In this future, saving and shopping need not be treated as two completely separate activities.

They could become parts of the same consumer journey. I find this especially relevant when considering younger consumers, who are growing up in an environment where shopping is increasingly immediate but financial stability can be difficult to achieve.

Making it easier to borrow or complete a purchase is only one side of financial innovation.

Making it easier to plan, save, and purchase responsibly deserves just as much attention.

Looking ahead: A different kind of digital marketplace

By the end of this decade, I expect the relationship between consumers, merchants, and shopping platforms to look increasingly different.

AI assistants may become a more common starting point for product discovery. Retailers may need to communicate not only with human customers but also with intelligent systems acting on their behalf.

Products could be evaluated according to preferences and requirements that are much more specific than traditional search keywords.

Perhaps the most interesting development will be the emergence of marketplaces where demand becomes more visible before a transaction takes place, with consumers choosing whether and how their intentions are shared.

In such a marketplace, competition would not necessarily begin when a shopper enters a store or visits a website. It could begin when someone establishes a meaningful purchasing goal.

This would change how merchants think about customer relationships.

Instead of asking only how to attract a customer today, businesses might begin asking how they can become part of that customer's future plans.

It could also change how we think about loyalty. A merchant relationship might develop before the purchase itself, through relevant advice, helpful offers, and an understanding of what the customer values.

None of this means traditional shopping will disappear. People will still enjoy browsing shops, discovering unexpected products, and buying things spontaneously. Nor will every purchase justify a lengthy planning process.

But for purchases that matter, especially those involving significant financial commitments, there is an opportunity to create a more thoughtful alternative. The greatest challenge will not be making AI capable of negotiating offers or processing transactions. It will be establishing trust, protecting consumer information, ensuring fair competition, and preserving meaningful human choice. These are not secondary considerations. They are essential to whether this new generation of commerce can succeed.

Where LayawayGo fits into this future

Much of my academic and applied research has explored financial technology, digital payments, innovation, and the ways people interact with emerging technologies.

Over time, I became increasingly interested in a question that conventional digital payment solutions do not fully address: why has so much innovation focused on making purchases faster or borrowing easier, while comparatively less attention has been given to making planned purchases more rewarding?

That question eventually contributed to the development of LayawayGo, a Scottish fintech venture built around the concept of Save Now, Buy Later.

Our vision is to help people plan their purchases, work towards savings goals, and connect with relevant merchant opportunities without relying on consumer credit.

At the centre of this work is AI MatchBuy, our developing AI-powered approach to connecting consumer intentions with shopping opportunities.

Rather than beginning with a product catalogue and encouraging someone to purchase immediately, we want to explore a model that starts with what the customer actually hopes to achieve.

This could mean helping someone plan for a product or experience and discover relevant merchants at the right stage of their purchasing journey. It could also mean giving participating businesses an opportunity to compete through tailored offers and incentives that reflect what the customer values.

Our ambition is to bring together intelligent shopping, goal-based saving, and merchant competition in a way that benefits both consumers and businesses.

The development of LayawayGo has been supported by £122,000 in Scottish Proof of Concept funding, and we have filed a UK patent application relating to aspects of our goal-driven financial contract and merchant auction technology.

These are important milestones, but they are not the destination. The real test is whether we can turn this vision into a practical service that consumers find useful and merchants consider commercially worthwhile. We are working towards that objective through continued product development, validation, and preparations for market testing. I do not believe the future of shopping should be defined only by how quickly someone can move from discovering a product to paying for it.

I would rather see a future where technology helps people take greater control of their purchasing decisions, where local businesses can compete more effectively, and where financial innovation encourages planning as much as spending. Perhaps one day, rather than asking consumers to search endlessly for the best offer, we will simply ask them what they are planning.