Jesse Weltevreden: What 62,000+ AI Tools Reveal About the Future of eCommerce

Ahead of his session, the summit team interviewed Jesse about the trends shaping AI in 2026, the difference between experimenting with AI and building real competitive advantage, and why a clear, data-driven view of the global AI landscape is becoming increasingly important.

We are happy to share that interview here as well.

Jesse, you will be speaking at Balkan eCommerce Summit in Sofia on April 28–29. What key insights can attendees expect from your session “The Global Development of AI and Its Implications for E-commerce”?

At the Balkan eCommerce Summit in Sofia, I will draw on our global database of more than 61,000 AI tools and companies to provide a data driven perspective on how the AI landscape is developing and what this means for eCommerce.

I will share insights into where the main AI hubs are located, how concentrated the market is, and which companies capture most of the traffic and investment. In addition, I will highlight which AI tools are most widely used for eCommerce related use cases such as product content creation, search and recommendation, pricing, customer service, fraud detection, and logistics optimization.

Beyond the landscape itself, I will focus on the implications for eCommerce companies. How can AI be integrated into existing business models? Where does it create measurable value, and where is caution needed? The goal is to move beyond general claims and provide concrete insights that help companies make informed strategic choices.

As CEO of RankmyAI, tracking over 60,000 AI tools globally, what major trends are shaping the AI ecosystem in 2026?

If I look at the data from RankmyAI and at what I see in the market, a few developments stand out in 2026.

First, the rise of agentic AI. And by that I mean systems that can autonomously execute multi step tasks, not just generate text or images. We see a growing number of tools positioning themselves as agents that can plan, decide, and act across software environments. At the same time, there are still significant challenges around reliability, privacy, security, and accountability. The technology is progressing fast, but governance and safeguards need to keep pace.

Second, I do not see the AI wave slowing down anytime soon. Adoption is still in an early or intermediate phase for many companies. A large share of organisations are experimenting, but full integration into core processes is far from complete. That suggests continued growth rather than a sudden correction.

Third, the barriers to entry are extremely low. With access to APIs of large language, image, or video models, plus a payment provider and a simple web interface, it is possible to launch an AI startup very quickly. This explains why the number of tools keeps increasing, especially wrappers that add limited differentiation. As a result, we see both rapid growth in the total number of tools and strong market concentration in terms of traffic and usage.

At the same time, AI is increasingly embedded into existing software. Many established SaaS and commerce platforms integrate AI directly into their products, from CRM and marketing automation to search, pricing, and ERP systems. Instead of using separate AI tools, companies access AI functionality within the software they already rely on. In that sense, AI is becoming a standard layer within software rather than a separate category. It also becomes less visible. More and more applications people use on a daily basis are AI powered, but users often do not explicitly recognise it as such.

Another important point is that, despite concentration at the top, there are many specialised players emerging that create real economic value in specific niches. Not every successful AI company needs to be a global platform. Focused, high quality solutions can build strong positions in well defined markets.

Another trend, still relatively small but growing, is increasing transparency in parts of the ecosystem. A number of AI startups are becoming more explicit about the infrastructure they rely on, such as which cloud providers they use and which underlying AI models power their solutions. This is an important development, because AI applications are rarely fully standalone. They depend on model providers, cloud services, and other subprocessors.

At the same time, our data show that particularly in the generative AI domain, many companies are not transparent at all about their origins, technical architecture, or third party dependencies. It is often unclear whether a solution builds on US or Chinese foundation models, where data is processed, and how responsibilities are divided across the value chain.

This links directly to the broader European debate about strategic autonomy and reducing dependency on a small number of large technology providers. As awareness grows, we see increasing demand for European alternatives, both at the infrastructure and application level. That search for sovereignty can stimulate innovation within Europe and create new business opportunities for companies that position themselves as transparent, compliant, and locally anchored.

Finally, there is an overemphasis on generative AI, both in media coverage and in boardrooms. Generative applications are highly visible, but traditional forms of AI such as predictive modelling, computer vision, and natural language processing continue to deliver substantial value, including in e-commerce. In many cases, these applications have clearer return on investment and are more mature from an operational perspective.

Many eCommerce companies are experimenting with AI, but few are transforming their business models. Where do you see the biggest gap between AI adoption and AI-driven competitive advantage?

The biggest gap in my view is between experimentation and structural integration.

Many eCommerce companies are testing AI tools for content generation, chatbots, or marketing automation. The supply side is growing rapidly, with solutions ranging from marketing automation and recommender systems to supply chain optimisation and customer service. However, adoption is often fragmented and tactical. It improves specific processes but does not fundamentally reshape the business model or create a defensible competitive position.

There is also a structural dependency issue. Especially in the SME segment, companies typically lack the in-house expertise to develop their own AI systems. They rely on third party vendors, many of which are still in an early startup phase. This raises questions about continuity, long term support, and strategic control. If a critical AI provider disappears or changes its model, that directly affects the retailer.

In addition, I see an imbalance in focus. Generative AI receives most attention because it is visible and easy to experiment with. Yet in many e-commerce contexts, traditional AI such as predictive modelling for demand forecasting, dynamic pricing, fraud detection, or churn prediction may create more sustainable value. Competitive advantage is often built on optimisation and better decision making, not only on automated content.

Compliance and governance form another major gap. In the European context, the EU AI Act will introduce clear requirements, particularly for systems that may be classified as high risk. In e-commerce, certain recommender systems or marketing automation solutions could, depending on how they operate, raise compliance questions. For example, if algorithms systematically differentiate between customers based on demographic variables or behavioural profiling in ways that may lead to unfair treatment, this can become legally and ethically sensitive.

At the moment, I do not see many e-commerce companies that treat compliance with the EU AI Act, let alone broader responsible AI principles, as a strategic priority. That is a blind spot. Competitive advantage will not only come from using AI, but from embedding it in a way that is robust, transparent, and aligned with regulation.

From your academic research in Digital Commerce, what long-term structural changes will AI bring to online retail over the next 3–5 years?

I have been researching retail and e-commerce for more than 25 years, and I did my PhD among the early researchers looking at what online retail would mean for physical retail. What I love about this sector is that it never stands still. New technologies, changing consumer expectations, new international players, and new regulation, including sustainability requirements, constantly force companies to adapt. There is never a dull moment.

That said, I am a researcher focused on evidence and observable developments, not a trend watcher or futurist. My scope is mainly what is happening now, what we can already see in the data, and what companies should do in response. And with AI moving so fast, predicting the next three to five years comes with a lot of uncertainty.

Still, I would like to mention a few structural shifts that are already becoming visible.

First, competition is likely to increase rather than decrease. Large technology platforms are moving closer to the transaction itself. For example, tools like ChatGPT are starting to offer commerce flows such as direct ordering of goods and services. That could change where consumers begin their shopping journeys, and who controls the interface.

Second, discovery and marketing may fundamentally shift. If consumers move from traditional search to conversational interfaces, the question becomes: how do retailers stay visible and relevant? What happens to SEO, paid search, and marketplace advertising when the “front door” of eCommerce changes?

Third, we will see more automation in decision making on both sides of the market. On the consumer side, agentic AI could eventually compare offers, terms, delivery options, and service levels at very high speed, and even complete purchases on a shopper’s behalf. On the retailer side, AI will increasingly automate pricing, assortment decisions, customer service, and parts of supply chain planning. The companies that win will be those that combine automation with strong data foundations and clear governance.

So, the long-term change is not only efficiency. AI is likely to reshape competition, customer acquisition, and the role of platforms in online retail. That brings new risks, but also new opportunities for companies that adapt early and build capabilities in a focused way.

How should eCommerce leaders think about balancing AI-driven automation with human creativity and strategic control?
eCommerce leaders should see AI primarily as a tool to support and strengthen human decision making, not as a replacement for strategic leadership.

AI driven automation is highly effective in data intensive and repetitive domains. In eCommerce this includes pricing, demand forecasting, fraud detection, recommendations, content testing, customer service, as well as supply chain optimisation and inventory management. In these areas, automation can significantly improve efficiency, reduce errors, optimise stock levels, and increase speed and consistency across operations.

But automation does not replace strategic thinking. Decisions about brand positioning, value proposition, partnerships, long term investments, and ethical boundaries remain human responsibilities. AI can support these decisions with better analysis, yet it should not define the direction of the company. There are three important principles here.

First, focus on value. Leaders should prioritise AI applications with clear and measurable impact on revenue, margins, operational efficiency, or customer experience, rather than adopting tools out of fear of missing out.

Second, maintain human oversight. Especially in systems that directly affect customers or suppliers, such as dynamic pricing, personalised offers, automated replenishment, or supplier selection, companies need clear accountability, monitoring, and the ability to intervene.

Third, consider the organisational consequences. There are already signals that some eCommerce companies are hiring fewer young professionals for entry level roles because certain tasks are automated. In the short term this may reduce costs, but in the long term it can create a skills gap. If fewer people gain hands on experience at the start of their careers, who will become the future managers, strategists, and AI supervisors? Balancing automation with talent development is therefore not only an ethical issue, but a strategic one.

There is also a more fundamental question. What happens if eCommerce companies rely too heavily on AI? If product descriptions, marketing campaigns, visuals, and even strategic documents are largely generated by AI, and that AI generated content is then used to train future models, we may create a feedback loop. The risk is convergence toward the average. Content becomes more optimised, but potentially less original and less distinctive. Over time, this could reduce creativity and differentiation.

If AI is used to enhance human capability, quality can increase. If it replaces human insight, critical thinking, and domain knowledge too extensively, we may see a decline in originality, depth, and ultimately in the quality of output. For eCommerce leaders, the challenge is therefore not only technological, but also cultural and philosophical: how to use AI in a way that strengthens, rather than weakens, human creativity and strategic control.

In essence, AI should handle scale, optimisation, and operational complexity. Humans should set direction, values, and control mechanisms. The eCommerce companies that manage this balance consciously will be better positioned for sustainable growth.

Why is it important for business leaders in the Balkans and CEE region to understand the global AI landscape right now?
It is important because AI is not a local development. It is a global technological shift with local consequences.

We live in a period of extremely fast technological change. Investments in AI are enormous, expectations are high, and the amount of information, and misinformation, is overwhelming. Almost every company presents itself as AI driven, and almost everyone positions themselves as an AI expert. In that environment, it becomes difficult for business leaders to distinguish between real capability and marketing narrative.

For companies in the Balkans and the broader CEE region, understanding the global AI landscape is essential for two reasons. First, the most influential platforms, models, and infrastructure providers operate at global scale. Their decisions directly affect local markets, competition, and cost structures. Second, opportunities also emerge globally. Niche players from smaller markets can compete internationally if they position themselves well.

One of the challenges is the speed of change. The most popular tools of today can be replaced by newcomers tomorrow. For almost any use case you can think of, there is already an AI solution available somewhere in the world. The question is not whether a solution exists, but which one is relevant, sustainable, and strategically sound.

That is precisely why we developed RankmyAI. By systematically tracking more than 61,000 AI tools and companies worldwide and analysing objective indicators such as traffic and investment data, we aim to provide structure in a market full of hype. Our goal is to help leaders make decisions based on data rather than assumptions.

For business leaders in the Balkans and CEE, understanding what is happening globally is not optional. It is necessary to assess risks, identify opportunities, and position their companies in a rapidly evolving competitive landscape.