Want AI that works the first time instead of the tenth? We sit down with Andreas Schachl, co-founder of Responsible Annotation Services, to unpack the quiet truth behind reliable models: ethical, high-quality training data produced by people who take clarity and precision seriously. Andreas shares how a single internship sparked a company built around neurodivergent talent, turning data labeling from a churn task into a strategic advantage.

We walk through why annotation isn’t going anywhere, even with foundation models and smarter tools. When you’re training on private, business-owned data across text, images, audio, video, and LiDAR, you need a human in the loop and documentation you can defend. Andreas explains how his team co-authors rigorous annotation handbooks with clients, translating fuzzy goals into exact rules, edge cases, and review procedures. The payoff is real: higher consistency, fewer iterations, and a clear compliance trail for regulators and auditors.

Bias mitigation becomes a practice, not a promise. A neurodivergent lens exposes hidden assumptions and pushes for instructions that are unambiguous and testable. We explore practical systems—daily stand-ups, structured chat, and even “coffee calls” with agendas—that help people do their best focused work. We also confront the ethics of the global annotation supply chain and outline a different path: EU contracts, fair wages, social worker support, and leadership that values diligence over hype. From 2D images to complex 3D point clouds, we show how modern tooling plus human judgment builds AI you can trust.

If you care about responsible AI, data quality, and making models perform sooner with less guesswork, this conversation is your blueprint. Subscribe, share with a colleague wrestling with training data, and leave a review with your biggest annotation challenge—we’ll tackle it in a future episode.

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What does it take to make inclusion real for 420,000 people across 55 countries? We sit down with Karine Vasselin, Group Head of Inclusive Futures at Capgemini, to unpack a pragmatic playbook that turns diversity into business value and culture into daily practice. Karine shares how a simple shift in language—“inclusive futures for all”—opened the door for everyone to see themselves in the work, from parents and caregivers to neurodivergent colleagues and people with disabilities.

Across the conversation, we dig into the tools and choices that matter most. Inclusion Circles give managers semi-guided, scenario-based conversations that build psychological safety and shared norms without adding corporate fluff. Employee networks—Women@Capgemini, OutFront, Capability, and NeuroAbility—move beyond awareness to shape policies like safer travel guidance and inclusive benefits that recognize all families. We also examine hard-won lessons from neurodiversity pilots: why early enthusiasm ran into real-world friction, how smaller cohorts and expert partners like Auticon and Ambitious about Autism changed outcomes, and what it takes to scale responsibly.

AI runs as a hopeful throughline. For many neurodivergent and disabled employees, generative AI behaves like assistive tech—organizing ideas, clarifying communication, summarizing meetings, and removing friction through captions and text-to-speech. But tools alone can’t fix culture. We talk hiring pipelines, role design, advancement, and the manager skills needed to spot bias and coach diverse teams. Karine also offers career advice for future inclusion leaders: build credibility through business and talent experience, and learn to influence without authority.

If you care about practical inclusion, leadership training that sticks, and using AI to expand access rather than entrench bias, this conversation delivers a clear blueprint you can adapt tomorrow. Subscribe, share with a colleague who leads teams, and leave a review with one policy you’d change to make work truly work for everyone.

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What if accessibility wasn’t a checkpoint but a capability baked into every release? We sit down with Shlomit Shteyer, a technical program leader at Salesforce, to explore how large organizations make accessibility real, measurable, and scalable without slowing product velocity. Her journey from shipping features at Twitter to building accessibility programs offers a candid look at turning strategy into operations and aligning teams around customer impact.

We unpack the practical models that work at scale: start with a centralized core to set standards, then grow embedded expertise through a Champion Program that upskills engineers, designers, and PMs. Shlomit explains why this blend beats false either-or choices and how it creates durable habits across design, development, testing, and release. Executive commitment proves decisive. At Salesforce, accessibility targets sit in the annual planning framework, right alongside feature delivery and security, so teams have time, tools, and a clear definition of success.

AI enters the story as a helpful colleague, not a shortcut. Think agentic assistance that flags issues early, suggests accessible patterns, and speeds remediation while leaving accountability with humans. We also look at a shifting market reality: customers now demand accessibility at contract time, moving organizations from reactive bug-fixing to proactive, compliant design. Collaboration across companies is a surprising superpower too, with leaders openly sharing training methods, metrics, and automation approaches to raise the bar industry-wide.

From global, inclusive training formats to positioning accessibility within the broader trust layer—security, availability, sustainability—this conversation offers a roadmap for leaders who want impact, not slogans. Shlomit’s advice is grounded and human: cultivate curiosity, connect your strengths to work that matters, and build systems that make good choices the default. If you’re scaling accessibility or looking for a place to start, this episode will give you frameworks, language, and momentum.

Enjoyed the conversation? Follow the show, share with a colleague, and leave a quick review to help more people find it.

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