Mohini
Todkari
Senior Technology Advisor (Developer Evangelism), Location Intelligence & Autonomous Systems at HERE Technologies
"From Maps to Machines: Building Safety-Critical AI Infrastructure at Scale"
"From Maps to Machines: Building Safety-Critical AI Infrastructure at Scale"
Tue May 12 - 7:00 PM EDT/New York (See in local time)
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05/12/2026 7:00 PM
05/12/2026 07:20 PM
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"From Maps to Machines: Building Safety-Critical AI Infrastructure at Scale"
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"From Maps to Machines: Building Safety-Critical AI Infrastructure at Scale"
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"From Maps to Machines: Building Safety-Critical AI Infrastructure at Scale"
"From Maps to Machines: Building Safety-Critical AI Infrastructure at Scale"
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Session: From Maps to Machines: Building Safety-Critical AI Infrastructure at Scale
As AI systems move from experimentation to real-world deployment, organizations face a new class of decisions around safety, accountability, and infrastructure readiness. These challenges are especially visible in autonomous and location-driven systems, where AI must operate in real time, under uncertainty, and with direct human impact.In this talk, Mohini Todkari draws on her experience building and scaling safety-critical location and AI platforms used in autonomous mobility and enterprise navigation systems to examine how AI transitions from innovation to infrastructure. Using examples from autonomous and highly automated systems, the discussion highlights why foundational technologies such as mapping, real-time data, and system integration often determine outcomes long before AI models are deployed.
The session explores how leadership teams can assess AI readiness at the infrastructure level, align operating models across engineering and business functions, and make informed decisions as AI becomes embedded in mission-critical systems across industries.
Key Takeaways
- How lessons from autonomous and location-driven systems expose why AI readiness depends on foundational infrastructure, such as real-time data and system integration, across enterprise environments.
- What executive teams can learn from safety-critical AI deployments about managing accountability, governance, and risk in systems with real-world impact.
- Why location intelligence and mapping serve as early indicators of whether AI initiatives can scale from pilots to reliable, enterprise-grade infrastructure.
- How to align engineering, product, and business leaders when AI moves from experimentation into operational, mission-critical systems.
- Strategic frameworks, informed by real-world autonomous and data-driven systems, for making long-term AI investment and operating model decisions.
Bio
Mohini Todkari is a senior technology leader specializing in safety-critical AI, location intelligence, and real-time data infrastructure. She brings more than a decade of experience designing and scaling enterprise platforms at the intersection of mapping, autonomous systems, and digital transformation, working across engineering, product, and executive stakeholders to drive business-critical outcomes.Her work focuses on translating complex, real-time technologies into reliable, scalable infrastructure, with an emphasis on safety, accountability, and long-term system readiness for AI-driven systems.
