Business systems tend to outlive the conditions that created them. A payment rail, a distributor model, an ERP stack, or a retail network may have been rational when first implemented, but over time they harden into operating assumptions. What begins as infrastructure eventually becomes constraint. This is often the hidden cost of scale. The larger and more successful a system becomes, the more difficult it is to redesign when a new technological cycle arrives.

That tension sat underneath much of our recent conversation with Fadi Abi-Nader, who today leads one of the largest AI-enabled sales transformation initiatives inside Mars. While the discussion centered on the Middle East, the underlying question was broader: do regions that build later hold structural advantages when the next infrastructure cycle begins?

Europe and the United States built much of their commercial architecture in earlier eras. Distributor networks, formal retail systems, layered compliance environments, legacy ERP systems and deeply embedded operating models created enormous scale, but they also created inertia. AI now has to be layered onto those systems, negotiated through them, and justified against them.

Parts of the Middle East sit in a different position. Incomplete infrastructure often creates obvious disadvantages, but it can also create optionality. Fadi made the point clearly: the Middle East has the opportunity to leapfrog, but it has not done so yet. That distinction is important. Opportunity and execution are rarely the same thing.

The interesting question is what allows some regions to convert late infrastructure into an advantage, while others simply inherit a different set of inefficiencies.

Fadi used mobile adoption as a useful analogy. Large parts of Africa, India and the Middle East never built the same fixed-line infrastructure that Europe and North America did. That absence created limitations at the time, but it also reduced the cost of transition when mobile became the dominant layer. Entire populations moved directly into mobile banking, mobile payments and app-based commerce without having to dismantle older systems first. In Kenya, M-Pesa became one of the clearest examples of this. India followed with UPI, which processed more than 170 billion transactions in 2024 alone, becoming one of the most efficient payment systems in the world.

The same logic may now be reappearing with AI.

Fadi’s argument was not that the Middle East is ahead. In fact, he was explicit that it is not. The region has infrastructure gaps, fragmented supply chains, inconsistent address systems and incomplete data layers. Egypt alone has hundreds of thousands of small retail points, many without formal physical addresses. That would normally be described as a weakness.

But infrastructure gaps change character when the technology itself is designed to work through fragmentation. AI does not require perfect systems to be useful. In many cases it becomes more useful precisely where coordination is weakest. Dynamic route planning, field-sales optimization, predictive stocking, local demand forecasting. These are not futuristic ideas. They are practical responses to operational inefficiency.

That changes the economics of adoption. Mature markets often implement technology to optimize. Emerging markets may implement it to solve structural problems. The urgency is different. And urgency often determines speed more than sophistication.

This is where the conversation became more precise. Prakash ( Currently GM VTEX EMEA- APAC) introduced a useful challenge: across the Gulf, nearly 80% of companies claim some level of AI adoption, yet only around 10% can point to real value creation and fewer still can tie it directly to earnings. That gap changes how we should think about leapfrogging.

Adoption alone says very little. Most companies today have pilots. Many have use cases. Few have operating systems that materially change how the business performs. Fadi made the distinction clearly. Anything below meaningful scale remains a pilot. In large organizations, scale is the threshold where technology stops being interesting and starts becoming economically visible.

That creates an important inversion. Regions often assume that newer infrastructure automatically produces faster outcomes. In practice, the opposite can happen. Newer systems create optionality, but optionality often creates distraction. When organizations begin with technology instead of value pools, they start solving visible problems rather than expensive ones.

Fadi’s example was excellent. A forecasting tool may save three or four people time, but if the largest cost sits in a field-sales operation of ten thousand people, the economic logic points elsewhere. This is where many AI programs lose discipline. They optimize what is easy to automate instead of what materially changes the P&L.

That is often the dividing line between experimentation and transformation. The question is no longer whether the technology works. The question is whether leadership is willing to organize around where value actually sits.

This is where the discussion moved away from infrastructure and into management itself. Fadi’s answer to what slows AI adoption inside large organizations was direct: people, culture and leadership. It is an old answer, which may be precisely why it remains the right one.

Large organizations tend to treat new technologies as additions rather than replacements. They create pilot teams, isolated use cases, and side budgets, while the core operating model remains intact. That creates the appearance of movement without forcing operational change. In that environment, AI becomes another layer of experimentation rather than a new way of working.

There is a historical pattern here. The industrial revolution did not fail because machines were ineffective. It slowed because institutions, labor systems and incentives had to reorganize around them. The same pattern repeated with ERP systems in the 1990s. SAP did not become transformative because companies liked software. It became transformative because once implemented, the software was no longer optional. The operating model changed with it.

Fadi made the comparison explicitly. AI today is still mostly optional. That may be the central reason scale remains elusive. As long as teams can choose whether to integrate it, the technology remains peripheral to the business. Once it becomes embedded into pricing, forecasting, route planning, promotions and customer interaction, the conversation changes. At that point, AI stops being innovation and becomes process.

That is why the real bottleneck may sit higher than most companies admit. Not in the data layer. Not in the tooling. But in leadership’s willingness to impose a new operating discipline before the external market forces it on them..

What makes technological shifts difficult is that companies rarely move because they want to. More often, they move because the environment around them changes first. The pressure usually arrives from outside before it is fully understood inside.

That dynamic is already visible. Fadi pointed to something important: the largest retailers in the world are beginning to build their own agentic systems. Walmart, Tesco, Carrefour and others are moving toward environments where their systems increasingly talk to suppliers’ systems. This matters because it changes the pace of adoption. AI no longer sits only inside a company’s internal workflows. It begins to shape how companies interact with the wider ecosystem.

This is how infrastructure shifts tend to happen. Standards emerge at the edge first. Then they move inward.

The history of EDI offers a useful comparison. Large retailers imposed digital ordering standards on suppliers long before most suppliers would have chosen to invest in them voluntarily. Participation in the market eventually required compliance. The same happened with Amazon marketplace logistics, payment rails, and digital advertising ecosystems. Once the dominant infrastructure changes, optionality shrinks.

That may be where AI becomes unavoidable. Not because executives suddenly become more convinced, but because their customers, partners and competitors begin operating differently. The strategic risk then changes. It is no longer about missing a trend. It becomes a question of relevance.

And this is where late infrastructure becomes more interesting again. Regions still building parts of their commercial systems may find themselves integrating these new standards earlier in their formation, while older systems continue negotiating with what already exists.

The Middle East’s position is unusual because it combines two conditions that rarely exist together: incomplete commercial infrastructure and concentrated capital. Most emerging markets tend to have one or the other. They either have urgency without resources, or capital without structural gaps large enough to justify redesign. Parts of the Gulf sit in a different place.

Saudi Arabia’s Public Investment Fund has committed tens of billions toward AI infrastructure, while the UAE continues to build sovereign AI capabilities, local compute environments and regulatory frameworks designed around long-term digital independence. These are not small experiments. They are infrastructure decisions at state level.

That changes the nature of adoption.

In most mature markets, companies absorb new technology into pre-existing systems. In parts of the Middle East, governments are helping shape the systems themselves. Fadi touched on this when he spoke about the level of state support behind digitization and AI adoption across the region. That creates a different pace of alignment between capital, policy and infrastructure.

The comparison with China becomes useful here. China’s industrial acceleration over the last two decades was never only about technology. It was about coordination. Capital, infrastructure and execution moved in the same direction. BYD’s expansion into Europe is one visible example of what happens when that coordination compounds over time.

The Middle East is earlier in that cycle.

The question is whether it can convert capital and optionality into operational systems before larger incumbents reorganize themselves. That is where the advantage either becomes real or disappears.

What makes this moment worth paying attention to is that technological cycles rarely reward the same players in the same way. Railroads reorganized trade. Electricity reorganized manufacturing. The internet reorganized information. Each cycle changed who moved faster and who carried more inertia.

AI may be entering that category.

For mature markets, the challenge is integration. The systems are already there, the data exists, and the scale is established. The difficulty is reorganizing around a new operating logic without disrupting the old one.

For regions still building parts of their infrastructure, the challenge is different. The opportunity is larger, but so is the execution burden. There is less to dismantle, but also less already working. That creates a narrower margin for wasted capital.

Fadi’s point stayed with me because it stripped away much of the noise around AI. In the end, this is still about business. Selling more. Lowering cost. Improving execution. The technology matters, but only if it changes those outcomes.

The Middle East may still be early in that process. So may many other regions now building with newer systems, newer capital and fewer legacy constraints.

What will determine the outcome is not how much AI they buy.

It will be how quickly they turn infrastructure into operating advantage.

That is where leapfrogging stops being a theory and starts becoming measurable.

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