MINDS

MINDS Champions

Meet the leaders building Meaningful, Intelligent, Novel, and Deployable Solutions to transform businesses and society. 

MINDS are pioneers of innovation technology transformations

These organizations are leading the way in applying frontier technologies to reshape business models, unlock new value, and deliver tangible impact across industries.

49

Selected Transformations

30+

Countries

16

Sectors

6

Impact Areas

Case Studies

Learn more about each project from the MINDS Champions.

Cohort: 3Members: Mercedes Bidart, Ana Lucía Escobar Velásquez, Juan Constain, Eduardo Carrasquilla, Michael MorenoProblem: Over 80% of informal workers in Latin America - corner shops, street vendors, and delivery drivers - have no access to formal credit because traditional scoring relies on past borrowing history. Without a record, you get rejected. The alternative is loan sharks run by criminal organizations, charging up to 800% interest. Approach: A multimodal AI scoring engine that reads financial SMS notifications (which capture real-time income and spending patterns), combined with business videos, product photos, and social media activity; treating all of these as credit signals. The system extracts over 535 variables and uses visual embeddings to assess business viability from images alone, adding 25% predictive power. Result: The platform has processed more than 300,000 individual scores and supported around 49,000 loans. It reports an 80.1% renewal rate, a reduction in clients’ use of informal “gota-a-gota” lenders from 38.6% to 28.5%, and 13.11% of clients reporting at least 15% income growth.
Colombia | Financial services
AI-Powered Alternative Credit Scoring Platform
Quipu
Cohort: 3Members: (Netsafe) Brent Carey, Sean Lyons, (Standard Security) Michael Aiello, Paul WilliamsProblem: Email scams are not only impacting our economy, with losses reported up to NZ$ 3 billion, but also our near and dear, with heavy burden falling on digitally inexperienced users and financially vulnerable households. As scam prevention remains a proactive system, scammers continue to operate at almost no cost or friction.Approach: changing scam reporting from a passive intake process to an active adversary-disruption workflow, through a platform that creates personalised AI personas to autonomously engage scammers in long conversations, wasting their time and disrupting operations at scale. Result: The platform mobilized over 40,000 citizens who forwarded scam emails for AI engagement, consuming more than 7,500 hours of scammer time across 297,490 emails and 9,510 unique scammer accounts.
New Zealand, USA | Technology, IT and Communications (ICT)
AI-Driven Adversarial Scam Disruption Platform
Netsafe, StandardSecurity
Cohort: 3Members: (The Territorial Delegation of Educational Development, Vocational Training, University, Research and Innovation in Granada) María José Martín Gómez (Executive Sponsor) Rocío Linares Morera (The Mind, Brain and Behavior Research Center (CIMCYC) of the University of Granada (UGR)) Francisca Serrano (Executive Sponsor) Sara Mata Francisca Serrano (Executive Sponsor), Macarena de los Santos-Roig (ATENXIA) Manuel Hernandez (Executive Sponsor) Emilio JimenezProblem: Around 1 in 6 children experience attention and literacy-related difficulties, yet current intervention approaches for children and adolescents still rely heavily on standardized, clinician-led models and manual professional supervision.Approach: An AI-driven gamified platform that continuously adapts exercises and intervention pathways in real time, while keeping professionals in the loop.Result: Literacy-related cognitive outcomes improved by 25–35%, while the time required for monitoring and manual intervention adjustment decreased by up to 50% over a 16-week intervention period.
Spain | Education
AI-Driven Personalized Cognitive Intervention Platform
ATENXIA, CIMCYC, The Territorial Delegation of Educational Development, Vocational Training, University, Research and Innovation in Grana
Cohort: 3Members: Nikolaos Mitsakos, Patrick Bangert, Yuxing Ben, James Cassanelli, Ali Karimi, Vivianne Mahecha MartinezIssue: Before investing millions of dollars in drilling, companies like Occidental desire to better understand geologic reservoirs and where oil & gas are most likely to be found. Today, this process relies on geologists and engineers interpreting huge volumes of geological and seismic data, which takes time and can be difficult to scale. Small interpretation differences can impact drilling decisions, reserve estimates and capital allocation.Approach: The company developed a suite of AI tools that helps geologists and engineers create a shared first-pass interpretation of subsurface conditions. The system analyzes well logs, rock chemistry, sensor measurements, and decades of historical interpretations to identify matching rock layers, predict rock types before drilling, group areas with similar geological behavior, and estimate the presence of oil, gas and water underground. The tools are integrated directly into the software experts already use, allowing teams to review and refine the AI-generated results rather than replacing human decision-making.Result: Geological interpretation work that previously took months now takes days, with less drilling downtime, fewer drilling-tool changes, and reduced horizontal drilling time.
USA | Oil & Gas
AI Subsurface Characterization Investigation Intelligence suite (ASCII)
Occidental
Cohort: 3Members: Lu Bo, Ken Wong, Michael Yao, Simon Liu, Lin XinhuaProblem: As AI, enterprise and HPC workloads continue to grow rapidly, data centers are facing increasing pressure from rising energy consumption, constrained power availability and the need to scale computing capacity while reducing carbon emissions.Approach: Building an AI-driven orchestration system that continuously optimizes enterprise, AI and HPC workloads across cloud and data center infrastructure, transforming energy into a schedulable resource for next-generation computing.Powered by Lenovo xCloud AIOps, the solution introduces a unified full-stack decision engine spanning workload scheduling, compute resources, cloud infrastructure, cooling and power systems, enabling real-time, closed-loop optimization across the entire data center stack.  Result: Deployed and validated at the Shanghai Jiao Tong University data center, the solution achieved up to 11% reduction in energy consumption and up to 310 tonnes less CO₂ emissions annually, while enabling greater compute output and infrastructure efficiency under the same power budget.
People's Republic of China, USA | Education, Technology, IT and Communications (ICT)
Integrated AI Orchestration for Holistic Data Center Energy Efficiency
Lenovo, Shanghai Jiao Tong University
Cohort: 3Members: Xu Tao, Wang Qi, Li Jingxiang, Cai Dan, Yan Jiaxi, Wang YanProblem: In solar cell manufacturing, millions of ultra-thin silicon wafers continuously move through highly automated production lines operating 24/7. While wafer-level traceability systems can record more than 1,800 process parameters for each individual cell, the data is typically distributed across multiple independent systems. When anomalies occur, engineers must manually correlate data across different platforms. Additionally, conventional traceability systems remain largely passive. As a result, corrective actions are delayed, yield performance declines, and manufacturing costs increase.Approach: Based on a wafer-level full traceability data platform and AI-driven algorithms, the company developed a dual-link closed-loop collaborative system framework, enabling precise root cause identification down to abnormal process steps on specific equipment and continuous monitoring of real-time process parameter fluctuations and abnormalities detection through dynamic predictive models.Result: Reduced root cause investigation from three days to three minutes, significantly improving problem response and corrective action efficiency, while increasing per-capita production capacity by 7%. The system also achieved a 0.5% improvement in overall solar cell yield and a 0.1% increase in cell conversion efficiency. 
People's Republic of China | Energy
AI-Driven Anomaly Traceability System for Solar Cell Manufacturing
Tongwei
Cohort: 3Members: Sheng Yang, Jia Tang，Tao Chen ，Quanqi Chen, Zhen Dai,Xueqing SongProblem: Critical power transmission routes can become overloaded when renewable generation, extreme weather, or grid topology changes shift electricity flows. Previously, operators relied heavily on manual checks, on-site experience, and repeated calculations within short decision windows, which made responses slower, more conservative, and harder to scale.Approach: The system continuously reads minute-by-minute grid data, forecasts, and network conditions. A reinforcement learning agent recommends safe adjustments to generators, storage assets, or controllable loads. Each recommendation is checked against operating constraints, tested through fast simulation, and reviewed by human operators before execution.Result: At the Guangxi control center, severe overloads were reduced by approximately 90%. Response speed improved by roughly threefold, with strategies generated in seconds, while also delivering cost savings.
People's Republic of China | Energy
Proactive Control AI Agent for New - Type Power System
China Southern Power Grid
Cohort: 3Members: (Valterra Platinum) Steven Bolleurs, Mark Freed, (Dunia Innovation) Ahmed Ismail, Rishi Kumar, Vineeth NairProblem: Industrial catalyst and materials R&D remains slow, fragmented, and heavily dependent on manual experimentation, with limited comparable evidence across materials, conditions, and performance metrics. This is especially critical in emission control and industrial systems, where material performance directly affects cost, uptime, and large-scale value creation, with development cycles and cost exposure reaching up to USD 300M.Approach: a closed-loop autonomous platform that combines autonomous robotics, AI experiment selection, simulations, and real-time learning to accelerate discovery, delivering experimental capacity in one day that is equivalent to the weekly output of 15 scientists. Result: Increased industrial experimentation throughput up to by up to 5,500%, approximately 56x, reduced R&D timelines from months to weeks, and lowered operating costs from up to USD 2,000 to less than USD 300 per experiment. 
Germany, United Kingdom, South Africa | Chemicals, Materials
Autonomous Catalysts Discovery and Acceleration Platform
Dunia Innovations, Valterra Platinum

MINDS Champions

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