Martina Dinardo
Account Executive
GetFocus
Netherlands
Martina Dinardo is an Account Executive at GetFocus, technology intelligence company based in Rotterdam. She builds and interprets patent-based technology analyses for CTOs, R&D Leaders and Strategy Teams facing the question of which technologies to back before the market settles on a winner. Her work applies GetFocus's improvement rate method, developed with MIT, which uses global patent data to measure objectively how fast a technology is improving and which competing approach is likely to become the standard.
Participates in
TECHNICAL PROGRAMME | Energy Technologies
The Energy Transition: The Role of Digitalisation, AI, and Cybersecurity
Forum 23 | Hall 10 - PRINCIPAL BUYER Technical Programme 4
14
October
14:30
15:45
UTC+3
As the world accelerates toward a net-zero future, the question is no longer if we’ll transition to sustainable energy, but how fast and which technologies will lead the way. This keynote explores the innovations transforming energy generation and storage. Built on GetFocus’s unique forecasting approach, this session delivers a data-driven view of what’s coming next and where the real opportunities lie.
The energy sector is undergoing a profound transformation, where the pace of innovation often outstrips decision-making cycles. In this environment, AI-enabled forecasting is rapidly becoming a strategic necessity for R&D and innovation leaders tasked with navigating complex supply chains, decarbonisation demands, and geopolitical uncertainty. This paper presents a unique approach developed by GetFocus, a Rotterdam-based AI company, in collaboration with MIT, to quantify and forecast technological change across energy-relevant domains.
Our proprietary model leverages global patent databases and natural language processing to compute Technology Improvement Rates (TIR). A predictive metric that reveals how fast technologies are advancing before they disrupt markets. With the U.S. Department of Defense as our launch customer, and now adopted by leading firms such as 3M, Hess, BASF, and Caterpillar, this framework is used to guide R&D strategy, reshape supply chain design, and accelerate sustainable product innovation.
We share case examples from chemical process innovation (e.g., Direct Lithium Extraction), materials R&D (e.g., composites in EV applications), and consumer product sustainability (e.g., plastics-free packaging) that illustrate how predictive technology intelligence can inform investment and resource allocation decisions with a forward-looking lens. The model’s predictive power has helped reduce costly misalignments between R&D priorities and market reality, while also surfacing early opportunities in emerging domains like green hydrogen catalysts, solid-state batteries, and carbon-to-value platforms.
This session will explore the broader implications of embedding AI into strategic energy decisions from identifying innovation white spaces to building early-mover advantage in volatile, high-tech segments of the energy economy. We also outline how cybersecurity, data governance, and system transparency are integrated into our platform to ensure trustworthy, audit-ready decision support for energy and industrial leaders.
The energy sector is undergoing a profound transformation, where the pace of innovation often outstrips decision-making cycles. In this environment, AI-enabled forecasting is rapidly becoming a strategic necessity for R&D and innovation leaders tasked with navigating complex supply chains, decarbonisation demands, and geopolitical uncertainty. This paper presents a unique approach developed by GetFocus, a Rotterdam-based AI company, in collaboration with MIT, to quantify and forecast technological change across energy-relevant domains.
Our proprietary model leverages global patent databases and natural language processing to compute Technology Improvement Rates (TIR). A predictive metric that reveals how fast technologies are advancing before they disrupt markets. With the U.S. Department of Defense as our launch customer, and now adopted by leading firms such as 3M, Hess, BASF, and Caterpillar, this framework is used to guide R&D strategy, reshape supply chain design, and accelerate sustainable product innovation.
We share case examples from chemical process innovation (e.g., Direct Lithium Extraction), materials R&D (e.g., composites in EV applications), and consumer product sustainability (e.g., plastics-free packaging) that illustrate how predictive technology intelligence can inform investment and resource allocation decisions with a forward-looking lens. The model’s predictive power has helped reduce costly misalignments between R&D priorities and market reality, while also surfacing early opportunities in emerging domains like green hydrogen catalysts, solid-state batteries, and carbon-to-value platforms.
This session will explore the broader implications of embedding AI into strategic energy decisions from identifying innovation white spaces to building early-mover advantage in volatile, high-tech segments of the energy economy. We also outline how cybersecurity, data governance, and system transparency are integrated into our platform to ensure trustworthy, audit-ready decision support for energy and industrial leaders.
