Insights from the Digital with Purpose Summit Buenos Aires 2026
For years, sustainability and technology ran on parallel tracks. One measured impact while the other improved efficiency. Sustainability teams tracked emissions, water, resources and social outcomes. Technology teams built platforms and automated operations. The only bridge between them was reporting. Technology helped organizations understand their footprint, but it rarely changed it.
That separation is dissolving.
At the Digital with Purpose Summit, hosted by Globant at Globant Tower in Buenos Aires, more than 50 speakers and 150 participants from government, energy, mining, finance, agriculture, academia and multilateral organizations gathered across 15 panels. UN Climate Change, COP30, FAO, WWF, the Inter-American Development Bank, Natura, Barrick, Xylem and BYMA were among the institutions in the room.
What struck me most was not the range of sectors but the tensions: Efficiency versus systemic redesign, Artificial intelligence versus the logic of nature, and the promise of data alongside real concerns about its extraction, governance, ownership and who captures its value.
Beneath those tensions ran one shared recognition. AI is shifting from a tool that reports on impact to one that can produce it. Whether that shift delivers better outcomes is still an open question, and possibly the defining one for sustainability and business over the next decade.
From reporting impact to owning it
Sustainability has long been treated as a measurement problem. Better data, better reporting, regulatory compliance, demonstrable progress. Those things still matter. They are no longer enough. What is needed now is leadership, not compliance.
AI is increasingly used to change outcomes, not just monitor them. It optimizes energy consumption in real time, improves water management, raises agricultural productivity, cuts operational waste and strengthens supply chains. The question is no longer whether a company can report its sustainability performance, but whether it can improve it at scale.
The summit offered examples at every stage of that transition. The ICT sector accounts for roughly 2% of global emissions, but its enabling impact has grown across GeSI’s successive assessments: from 5.5 times its own footprint in SMART2020 (2008), to 7.2 times in SMARTer2020 (2012), and to 9.7 times by 2030. In SMARTer2030 (2015), GeSI estimated an ICT footprint of 1.25 Gt CO₂e in 2030 against 12 Gt of emissions avoided through ICT solutions. This report concludes that ICT-enabled solutions could hold global emissions at 2015 levels while promoting sustainable economic growth.
Natura has digitalized its Integrated Profit & Loss, the accounting model that monetizes environmental, social and human impact alongside financial results, turning what was once an annual exercise into a live management tool Natura Integrated Profit & Loss Accounting Reports – Valuing Impact +2. Greenspark designs data centers around locations with energy surpluses so they do not strain the grid, and pairs that with a community-centered approach to building social trust. Cheaf, a startup, has rescued 15 million kilograms of food. Satellites on Fire processes data from 13 satellites every five minutes across 21 countries and has supported wildfire responses . And for WWF and INTA we built a tool that correlates meat productivity with carbon emissions, aligning incentives for regenerative agriculture.
Measurement is not disappearing. If anything, being accountable for results demands more rigor, not less. What changes is its role. Reporting stops being the destination and becomes the link between commitments, decisions and results.
The rise of outcome-based business models
The same logic is reshaping how organizations think about value. For decades, business models were built on effort. Hours worked, projects delivered, headcount deployed. As AI automates tasks and accelerates execution, effort becomes a weak proxy for value. What matters is the outcome produced. This is the thinking behind Glob.AI’s outcome-based approach, which aligns growth with measurable results rather than activity.
But committing to outcomes raises a harder question. Which outcomes should count? My answer is development impact. Organizations too often split purpose from performance, treating one as a values conversation and the other as a business conversation. The summit suggested the opposite. Tying growth to purpose is one of the most durable and defensible ways to define success.
Across energy, finance, agriculture, mining and urban development, the organizations making real progress shared one trait. They had stopped treating sustainability as a compliance requirement and started treating it as an outcome their technology was accountable for.
Why is the convergence visible in Latin America
Regulators, investors, customers, employees and communities are demanding tangible progress. AI, meanwhile, is unlocking efficiencies that directly hit the bottom line. Purpose and growth are converging, and in Latin America, the convergence is concrete. Water reuse supports energy production in Vaca Muerta. Community connectivity networks operate across the Gran Chaco. AI is processing decades of Antarctic scientific data. Agriculture is raising productivity while easing environmental pressure. The region’s sustainability challenges are becoming technology challenges.
One idea recurred across panels, and that idea was trust. Not trust in technology, but trust among institutions, communities, businesses, and citizens. Several speakers argued that the traditional “social license” no longer suffices because it frames acceptance as a transaction. They proposed reciprocal social trust instead. Whether the topic was mining, connectivity networks, carbon markets, water infrastructure, or data centers, the same principle held. Impact only counts if the people affected consider it legitimate.
The AI paradox
AI has its own footprint. The digital sector’s share of global emissions is still small, but AI-related data centers could consume as much electricity as Japan by 2030, and demand will keep climbing as billions of new users and AI agents come online.
So the question is not whether AI has an impact. It is whether its net impact is positive.
The paradox extends to governance. Speakers highlighted the gap between responsible AI policies and practice. Only a small share of organizations have operationalized responsible AI, while most employees already use AI tools their organizations never approved. As adoption accelerates, transparency, accountability and trust become harder to secure, and more essential.
Not yet, but visibly possible
Two trends surfaced at the summit, and they are really one. Technology is being judged by the outcomes it produces rather than the activity it generates, and sustainability is turning from a reporting obligation into a measurable business objective.
So, is AI getting smarter while our results get better? The honest answer is not yet, at least not at a systemic scale. Global emissions are still rising. Biodiversity is still declining. Most of the hardest sustainability problems remain unsolved.
But the examples in Buenos Aires show what a different model could look like. AI processing decades of scientific data, optimizing critical infrastructure, sharpening decisions, creating new forms of accountability. They do not prove the transition has happened. They show how it could.
The opportunity is not for smarter systems. It uses them to produce outcomes that society recognizes as valuable. And that depends on something technology cannot manufacture: trust.
