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Kalman Tiboldi - CTO & Founder at GemOne-TVH - Simplify or Die: Why Complexity Is the Real IT Challenge
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Belgium Oct 23, 2025 Invitation Only Physical english
How to lead responsibly when technology can deceive better than a human? AI’s ability to shape our perceptions and create new realities is becoming increasingly sophisticated. While it opens significant opportunities, it also carries risks. What happens when AI goes beyond data and algorithms to influence - or even deceive - our senses? How does this change not only our understanding of reality but also the foundations of trust, creativity, and truth in business? AI isn’t just transforming business, it is altering how we experience and interpret everything around us. From AI-generated deepfakes to personalised experiences designed to influence behaviour, the line between truth and illusion is increasingly blurred. This event combines a roundtable discussion among peers with a hands-on sensory workshop, where you will experience illusions through sight, sound, taste, smell, and touch. Through these experiments, we will confront the ethical dilemmas that arise when AI has the power to fabricate realities and influence decisions.
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Germany Oct 23, 2025 Country Members Physical german
Längst ist klar: Die KI-Transformation im Unternehmen beginnt mit der richtigen Dateninfrastruktur. Auf dem Weg zu einer “Enterprise AI-Engine”, in der Unternehmen KI für die eigenen Daten effizient nutzbar machen, müssen bewährte Fundamentals jedoch nicht gleich über Bord geworfen werden. Der CIO im Zentrum eines strategischen Transformationsprozesses sollte vor allem die richtige Balance zwischen Bestand und Innovation finden. Die zentrale Frage lautet daher: Wie lassen sich modernste KI-Technologien in hybriden Umgebungen nutzen, wenn viele Daten noch in gewachsener Infrastruktur liegen? Genau diese Fragestellung – und die nachfolgenden Diskussionspunkte – greifen wir am 23. Oktober im Rahmen eines exklusiven CIDO Roundtables auf.
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Germany Oct 23, 2025 Country Members Physical german
KI-Initiativen stehen heute unter doppeltem Druck: schnell Wirkung entfalten und dabei verantwortungsvoll skalieren. Der AI Act bringt neue Anforderungen, aber auch die Chance, Governance neu zu denken, Use Cases sicher zu operationalisieren und Vertrauen aufzubauen.
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UK Oct 23, 2025 Invitation Only Physical english
At this CIONET executive roundtable – brought to you in association with UiPath – we’ll explore this question and more. Bringing together senior IT professionals from across multiple vertical markets, we will focus on the benefits of agentic AI as applied to end-to-end processes. And we’ll seek to understand how organisations might overcome automation barriers to drive greater value creation.
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Spain Oct 23, 2025 Invitation Only Physical spanish
En este debate estratégico, acompañados por Vanessa Escribá, Group CIO de Mapfre, y en el exclusivo restaurante Gaytán pondremos el foco en el papel esencial de la red como base para garantizar el control, la confianza y la resiliencia de nuestras compañías.
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Poland Oct 28, 2025 All Members Physical Polish
OCTOBER CYBER FEST
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Workday Accelerates Generative AI & ML Product Development Using Amazon SageMaker
Learn how Workday fuels engineering productivity by using Amazon SageMaker.
with customers’ data residency requirements
of inference requests
engineering workflows
improvement for ML inference
Overview
Workday Inc. (Workday), a leading provider of solutions that help organizations manage their people and money, is highly focused on putting its engineering effort toward developing products that have built-in artificial intelligence (AI) capabilities. To help free its engineers from infrastructure maintenance, Workday adopted Amazon SageMaker, a fully managed service that helps its teams build, train, and deploy machine learning (ML) models for any use case. By using AWS services, Workday’s engineering teams can rapidly iterate and deploy complex models, including large language models (LLMs), to production.
Opportunity | Using AWS Regions to Meet Data Residency Requirements for Workday’s Global Customers
Workday offers software solutions that help its customers make accurate decisions and drive performance across human resources planning, financial planning, supply chain management, and other areas of their operations. For years, Workday has been investing in AI to help its customers make the most of their operational data with AI/ML-driven insights. “We consider ML a core backend technology for Workday,” says Shane Luke, head of Workday AI. “Our goal is to make AI-based solutions that provide our customers with real value.”
Because the company serves a global customer base, Workday needs to run its ML inference in alignment with its customers’ data residency requirements. “We have customers who are very sensitive,” says Luke. “We came to the realization that we needed a federated, distributed system that could run in many regions.” While building out a backend for its ML, the company wanted to avoid investing in its own regional private clouds.
Workday’s teams found that they can run their workloads in the AWS Region of their choice, which has supported the company’s business growth. “Our global expansion has been done on AWS,” says Luke. “It really has been a key point for us. We can deliver regionality to customers based in Europe, the Middle East, and Asia. For us, that’s been a major win.”
“Using AWS, we’ve gone from scaling to a thousand inference requests to tens of millions that are coming in daily,” says Luke. “It’s been very rewarding to see.” Further, the company has been able to scale with virtually no downtime.
Using AWS, we’ve gone from scaling to a thousand inference requests to tens of millions that are coming in daily. It’s been very rewarding to see.”
Shane Luke
Head of Workday AI
Solution | Improving Inference Latency by Five Times Using Amazon SageMaker
For its generative AI use cases, Workday uses Amazon SageMaker to simplify searching, evaluating, customizing, and deploying LLMs. “Workday has been an early adopter of LLMs, and we are actively building new generative AI capabilities that will help our customers increase productivity, grow, retain talent, streamline business processes, and drive better decision-making,” says Eddie Raffaele, vice president of Workday AI. “Workday can quickly tap into the power of generative AI and realize its value by bringing the best solutions to customers safely and responsibly.”
To support collaboration across its global teams, Workday provides its engineers access to Amazon SageMaker Studio, a web-based, integrated development environment for ML. Workday’s engineers can then compare and evaluate new foundation models by using Amazon SageMaker Jumpstart, an ML hub with foundation models, built-in algorithms, and prebuilt ML solutions. “For tasks such as creating job descriptions, which must be high quality, we use the model evaluation capability in Amazon SageMaker and select the best foundation model that reflects our company’s priorities and metrics in a responsible way,” says Luke.
Workday’s engineering team has also adopted Amazon SageMaker Ground Truth Plus, which applies human feedback across the ML lifecycle to create and evaluate high-quality models. The team has used this solution across eight labeling use cases, including named entity recognition, entity linking, sentiment and theme analysis, and more. “There’s a lot of labeling and annotating that is needed to manage our LLM outputs and receive high-quality data within our guaranteed SLAs,” says Luke. “Amazon SageMaker Ground Truth Plus has become an intrinsic part of our LLMs.”
Next, its engineers can fine-tune their LLMs with high-quality data by using Amazon SageMaker Notebook Instances to prepare and process the data to train their LLM models. Workday’s engineers then deploy their models for inference to achieve optimal performance and costs while reducing operational burden. For example, Workday used Amazon SageMaker to pilot a closed-book ML application that could analyze job descriptions, invoices, and contracts. During this pilot, Workday saw its ML inference latency improve by a factor of five.
Workday also uses LLMs to power friendly, personalized reminders that help its customers stay on track with their project and organizational goals. “There are more than 13,000 tasks available through Workday,” says Luke. “We’ve built and trained an ML model for a tenant that delivers the three top task recommendations based on the user’s activity.” With these tools at their fingertips, Workday’s customers can maximize their operational efficiency and prioritize projects with data-driven insights.
Outcome | Experimenting with Generative AI Using Amazon Bedrock
Workday received early access to Amazon Bedrock, a service that provides the simplest way to build and scale generative AI applications with foundation models. Workday uses Amazon Bedrock to facilitate product prototyping and test multibillion-parameter ML models. “We’re able to rapidly experiment and identify which AI capabilities we should invest in and put in front of our customers,” says Luke.
The Workday team is also working toward immediate deployment of new features for its customers instead of rolling out features one region at a time. “We’re pleased with the flexibility that AWS has given us,” says Luke. “We can deliver value to our customers and scale horizontally.”
More than 10,000 organizations worldwide rely on Workday to manage their most valuable assets—people and money. Workday provides customers with efficient financial and human resources solutions that help facilitate decision-making and performance.
Kalman Tiboldi - CTO & Founder at GemOne-TVH - Simplify or Die: Why Complexity Is the Real IT Challenge
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