The LEAP 2026 conference and exhibition kicked off in the Saudi capital, Riyadh, running through September 3. This premier tech event brings together leading companies and prominent industry leaders to discuss the latest developments and innovations. During our coverage of the event, we had the opportunity to interview Federico Pienovi, CEO for the MENA and APAC regions at Globant. He shared key updates regarding the company\u2019s announcements at LEAP and highlighted the current shift among businesses from AI assistants to AI agents.<\/p>\n
The organizations that scale start with a defined outcome, and the ones stuck in pilots start with the technology. That is the real dividing line. A company caught at the pilot stage usually has the platforms, the licenses, and a handful of promising experiments, but no operating model for how AI creates value. It has been measuring usage instead of results, and usage proves nothing. The companies that break through name the business metric they want to move first, whether that is conversion, resolution time, cost to serve, or time to market, and then design the AI around hitting it.<\/p>\n
The second thing that separates them is governance. A pilot can live in a safe internal sandbox, but core operations cannot. To put AI into production, you need policy-based guardrails, approval flows, full visibility into what an agent did and why, and outputs validated against quality and compliance rules. This is the layer that lets a business trust AI enough to run real work through it. The concern every enterprise raises first is how do I trust this, and that trust is earned through governance, not enthusiasm.<\/p>\n
The third difference is how they treat their people. Pilots fail to scale when adoption is handled as a rollout or a training session rather than a genuine change in how work gets done. Real value appears only when AI reshapes how people make decisions and collaborate, which means human expertise moves into an orchestration role rather than being automated away. Anyone can prompt an AI tool. Shipping the result reliably to production takes a defined outcome, real governance, and human accountability.<\/p>\n
The most meaningful impact today is in the shift from AI that suggests to AI that acts. A copilot drafts something and waits for a person to take the next step. An agent can carry a whole process through, qualifying a lead, running a compliance check, coordinating a booking, and handing off to another agent, with a human expert supervising the outcome rather than every click. That is where the economics genuinely change, because you stop paying for hours and start paying for the result.<\/p>\n
We see it landing hardest in the sectors being built new and at scale. In tourism, the clearest example is the Agentic Tourism Corridor we are unveiling at LEAP, where the sovereign AI agents of Red Sea Global and AlUla communicate directly to plan a guest’s journey across both destinations while each keeps control of its own data. That is agents doing real coordination that no traditional booking system could. In sport, through Sportian, a stadium becomes an operating system connecting fan identity, data, content, and venue operations. In financial services, agents are moving past customer-service automation into handling risk, compliance, and fraud through agentic workflows, always supervised by humans in the loop.<\/p>\n
The proof that this works at scale is our partnership with FIFA, where we are building a year-round experience for billions of fans, and the early pilots showed a 20% efficiency gain while quality held or improved. The lesson across all of these is consistent. The impact is largest when an agent runs an actual end-to-end process reliably in the real world, because that is what moves a business result rather than simply making a single task a little faster.<\/p>\n
The balance comes from a simple principle: agents execute, and experts supervise. Automation should carry the work, but a human has to stay accountable for the outcome. The moment an organization automates its own judgment out of the business, it loses the ability to answer for what its systems decide, and in critical decisions that is not a risk worth taking. In practice, that balance is built, not assumed. It rests on policy-based guardrails that keep agents inside defined boundaries, approval flows for the decisions that warrant a human check, and full transparency into what an agent did and why. Every output is validated against quality and compliance rules before it counts. That traceability is also the security foundation, because a decision you can see and explain is a decision you can audit, correct, and defend. When an agent’s reasoning is visible, a hidden error or a commercial bias has nowhere to hide.<\/p>\n
There is also a data dimension to governance that leaders underestimate. As agents work, they build institutional knowledge, and that intelligence has to stay owned by the organization rather than rented from a platform. In our FIFA work, all the knowledge generated is secured in a proprietary token vault that FIFA fully owns. The right balance, then, is automation for speed and scale, governance and security for trust, and human oversight for accountability, with the organization always keeping ownership of the intelligence its systems create. Get those working together and AI can be trusted with critical decisions. Leave one out and it cannot.<\/p>\n
What makes the Kingdom different is how little distance there is between a decision and a live system. In most markets, national ambition lives in a strategy document and takes years to reach something real. In Saudi Arabia, ambition and execution move almost together. A giga-project decides where it wants to go, and the technology starts getting built into the foundation while the place itself is still being built. Our three years here have become one of the fastest scale-ups in Globant’s 23-year history, and that pace is a direct reflection of the market itself.<\/p>\n
The ambition is also structural rather than incremental. Because so much of the infrastructure is being built new, take for example Qiddiya, Red Sea Global, AlUla, and New Murabba, AI can be architected into the ground floor instead of retrofitted onto legacy systems that were never meant for it. Most of the world spends its energy untangling the old. Here you get to start clean, which lets the Kingdom leapfrog rather than catch up.<\/p>\n
The pace is the third distinction, and it is genuinely faster than anywhere else we operate. The region compresses what would be multi-year programs elsewhere into months, and that forces us to deploy our most advanced delivery models here from the very start. That is why we made Riyadh our regional headquarters and a Center of Excellence for AI, creativity, and digital solutions, rather than a satellite office. The Kingdom has effectively become a proving ground where we build things first that we later take to the rest of the world.<\/p>\n
The biggest shift will be from AI that assists to AI that executes, and it will happen faster than most organizations expect. Today a lot of AI still sits in a copilot role, suggesting things to a person who then does the work. Within a few years, the center of gravity moves to agents that carry whole processes through, with people supervising outcomes rather than steps. The analysts see it the same way, expecting most enterprises to move away from assistive copilots toward outcome-driven, execution-based workflows within a couple of years. In MENA, where the infrastructure is newer and the pace is quicker, that transition is likely to arrive sooner than in more mature markets.<\/p>\n
The second shift is that sectors will stop behaving like silos and start operating as connected ecosystems. Across the region, tourism, sport, finance, and entertainment are being transformed at the same time by the same national ambitions, which means they can be designed to share intelligence rather than each building its own island. The Agentic Tourism Corridor, where sovereign destination agents coordinate while each keeps control of its data, is an early, live glimpse of that connected future.<\/p>\n
What leaders should prepare for now is the change in their own operating model, not just their technology stack. That means naming the outcomes they want AI to move, building the governance that lets AI run in production safely, and keeping human expertise in an orchestration role.<\/p>\n","protected":false},"excerpt":{"rendered":"
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