Name: Operationalizing GenAI: From Pilot to Enterprise-Wide Value Creation
Start: 2025-03-06T17:00:00Z
End: 2025-03-06T17:00:29.000Z
Location: BrightTALK
Rating: 4.2

The Enterprise Architecture channel  presents the next generation of Enterprise IT: recognizing the strategic importance of digital transformation and the data center to make companies more nimble and competitive. These presentations will help demanding IT and BizOps professionals achieve flexibility, scalability and performance with reduced risk and complexity.

In today’s evolving IT landscape, selecting the right storage architecture is critical for optimal performance, scalability, data governance, and cost-efficiency. Furthermore, AI workloads have uniquely influenced how we meet these demands from our storage infrastructure. This webinar provides a technical deep dive into three fundamental storage deployment models – on-premises, cloud, and hybrid – examining their architectures and operational trade-offs through the lens of two key concepts: indirection (accessing data through mapping layers that provide flexibility and abstraction) and redirection (rerouting data requests to enable failover, load balancing, and optimized performance). 

We are going to take some of the key stages of AI lifecycle development as sample use-cases (such as data ingestion, preparation, training, inferencing, and retrieval) and compare how each storage model can serve these use-cases across varying access patterns, data volumes, and performance requirements.

Attendees will gain a practical framework for aligning AI workloads with the most suitable storage architecture, balancing cost, scalability, and latency. Whether you are building AI infrastructure from scratch or optimizing existing deployments, this session will help you make informed decisions for AI-ready storage.

Learning Objectives and Key Takeaways:

• Introduction to the 3 different types of storage deployment models – on-prem, cloud, and hybrid
• Trade-offs for each deployment model
• Importance of indirection and redirection
• Understand how AI-specific data types and access patterns (e.g., embeddings, checkpointing) influence storage performance and design
• Evaluate trade-offs in latency, scalability, security, and cost when choosing storage for different stages of the AI pipeline
• Gain a decision-making framework for selecting the right storage model based on workload characteristics and infrastructure goals

AI Meets Storage: Comparing On-Prem, Cloud, and Hybrid Architectures Across the AI Lifecycle

As AI workloads continue to scale, the physical infrastructure supporting them must evolve to deliver the performance, reliability, and efficiency required by modern data-intensive applications. This webinar will focus on the interconnect foundations of AI infrastructure, including storage. There will be an emphasis on the form factors, connectors, cables, and transceivers standards that enable scalable and interoperable systems.

We’ll explore how SNIA’s SFF Technical Work Group is contributing to the development of physical layer standards that support high-performance interconnects and storage devices used in AI environments. Topics will include the importance of transceiver innovation and the need to evolve; the role that form factors like EDSFF play; and how innovative cabling and connector designs support the adoption of high-speed signaling technologies like PCIe 8.0 and beyond.

This session is ideal for architects, engineers, and technologists who want to understand how physical infrastructure standards are enabling the next generation of AI systems.

Key Concepts:
• Transceiver development for out-of-box signaling 
• Connectors and cables for high-speed inbox signaling
• Copper vs. optical and the challenges of both
• SSD form factor innovations and their impact on density and thermal design

Accelerating AI Infrastructure: The Role of 400G and PCIe 8.0 in Next-Gen Interconnects

Storage security changes and adaptations are a fact of life to deal with the ever-changing threat and regulatory landscapes. A black swan event, a new standard, or a regulation often serves as a catalyst for organizations to review their controls and practices. The recent publication of NIST Special Publication 800-88 Rev. 2, Guidelines for Media Sanitization, is drawing attention to storage security, but it is not the only development worth noting. SNIA, Open Compute Project (OCP), Trusted Computing Group (TCG), and IEEE are developing or have developed standards and specifications that could be important going forward. One central theme underlying several of the activities is in storage sanitization (i.e., controlled eradication of data).

This session brings together a unique panel of experts who have served as editors of some of the most important storage sanitization standards. These experts also have broad knowledge of storage security. As a panel, they will provide insight into these new developments as well as observations on what organizations are experiencing. A moderator will engage the panel with thought-provoking questions, but participants are also encouraged to bring their own questions.

Join us to understand: 
• Changes to storage sanitization and what that means to organizations
• Primary drivers for storage security
• Difference between Storage Sanitization and Data Sanitization
• Interplay and interdependencies for storage security standards/specifications

The Storage Security Shake-Up: Adapt Now or Get Left Behind

Magnetic RAM, Resistive RAM, Ferroelectric RAM, and other new memory technologies have been pushed to the forefront by issues with Moore’s Law scaling, but the ancillary benefits they bring to computing will provide significantly more important longer-term benefits.  In this session, analysts Jim Handy and Tom Coughlin will explain how these new nonvolatile technologies will help accelerate and cost-reduce inference through compute-in-memory engines while standard computing will also see lower costs and higher performance by bringing inexpensive persistence closer to, and even within the processor chip itself.  The session will also address ways that new frameworks like CXL will help provide persistence-friendly structures at all levels of the computing hierarchy.

How New Memories Will Accelerate Both Inference and General-Purpose Compute

Smarter data management is the key to unlocking the full potential of next-generation data infrastructure for AI. This webinar explores how the SNIA Cloud Data Management Interface (CDMI™) standard enables government labs, HPC centers, and organizations leveraging the power of AI/ML to reimagine and innovate their infrastructure to meet next-generation data management demands. CDMI is a mature ISO standard (ISO/IEC: 17826:2022) with over 15 years of development and implementation, and is purpose-built for cloud data management. CDMI's extensive capabilities enhance key aspects of AI operations by transforming data management challenges into streamlined, intelligent workflows. 

CDMI 3.0 (in development now) will further extend the standard with:
• Automated resource discovery that accelerates AI workload efficiency
• Portable data movement that enables preservation of metadata fidelity during migrations, including graph relationships and knowledge graph
• MCP-based data management that enhances AI accuracy while improving access speed

Building on a proven, standards-based foundation, CDMI enables true machine-readable data management. CDMI 3.0 will further improve how teams discover resources, orchestrate AI agents, and protect data across complex cloud environments while establishing the infrastructure for automated, self-describing data ecosystems with full auditability that drive future innovation. 

Read the SNIA CDMI 3.0 white paper https://www.snia.org/educational-library/introduction-cloud-data-management-interface-cdmitm-30-2025

Intelligent Data Management: Shaping the Future for AI Workloads

Join us for an insightful webinar and demonstration as we explore the foundational process of training machine learning models, with a special focus on computer vision applications. This session will guide participants through the essential stages of model development—from data preparation to algorithm selection—highlighting how these steps influence performance and accuracy.
Through practical demonstrations and conceptual discussions, attendees will gain a deeper understanding of how vision models are built and validated, and how these techniques can be extended to broader predictive analytics. We’ll also examine common challenges and share strategies to overcome them, emphasizing the value of simplicity, modular design, and thoughtful evaluation.

Key Takeaways:
• Core steps in the model training lifecycle
• Why data quality is critical to success
• Understanding inferencing and metrics for model validation
• Shifting from engineering mindset to modeling intuition
• Avoiding common pitfalls in training workflows
• Embracing parsimony and modularity in design
• Using checkpoints to monitor and refine progress

From Data to Decisions: Understanding How AI Models Learn

Storage networking consumers are fully aware of the classic benefits of Fibre Channel as a dedicated storage networking solution that provides reliable and lossless data transfer as well as high availability and redundancy.  What is less known is that Fibre Channel supports several Upper Layer Protocols (ULPs) including SCSI, FICON, NVMe, Avionics and RDMA. In this webinar, we explore the FC-4 layer of the Fibre Channel architecture that provides the services used by the various ULPs deployed in Fibre Channel products. Join us for an exploration into:

• The history of Fibre Channel’s versatility 
• Fibre Channel architectural layers
• Overview of the current FC-4 transports: FCP, FC-SB, FC-NVMe, FC-RDMA

Attendees of the webinar will become well versed in the scope and capabilities of Fibre Channel and develop a well-rounded understanding of the technology.

Versatility of Fibre Channel: Multi-Protocol Magic

Edge computing is transforming AI deployment by processing data locally rather than in distant cloud data centers. This session explores how distributed intelligence enables both sustainability and capabilities that centralized architectures cannot provide.

Through two contrasting deployments—precision agriculture in resource-constrained environments and real-time manufacturing optimization in advanced facilities—we demonstrate how edge computing addresses fundamentally different challenges using the same architectural principles. Both achieve 50-70% energy reduction with compelling ROI, while solving problems where cloud computing fails due to latency, connectivity, bandwidth, or data sovereignty requirements.

Beyond the case studies, attendees learn decision frameworks for edge versus cloud architecture, strategies for deploying AI in environments with limited infrastructure, and how to build business cases that balance sustainability goals with operational performance. The session provides actionable guidance for organizations seeking to scale AI deployments while reducing their environmental footprint and enabling new capabilities.

Edge AI from Farm to Factory: Building Sustainable Intelligence across the world

On-premises enterprise data centers have been in decline since the creation of co-lo’s and the cloud, but a new age is emerging with the advent of AI. While most AI is served by the mega data centers mushrooming in “data corridors”, some applications require the low latency response times that edge computing was designed to provide. The industry readily builds large numbers of small processing centers at the edge to serve things like 5G cell service. But constructing AI data centers close to every high demand user is financially impractical and subject to local restrictions due to their enormous demands on power and water.

Tune into this session presented by industry thought leader Bob McFarlane to learn how reviving enterprise data centers as edge facilities may be the answer to acquiring virtually instant response times. Despite significantly greater power and cooling demands, they are unlikely to reach the levels that concern local citizens, enabling them to be renovated or built where necessary without pushback. Companies with significant AI demands may need to start planning and budgeting for high capacity, high reliability infrastructures with costs and staffing beyond anything they have previously experienced.

Learning Objectives:
- Understand the key elements of Edge Computing, and how they relate to AI applications.
- Learn the limitations AI services providers confront in constructing edge facilities.
- Learn to identify AI applications that require the fast response times of the “edge”.
- Prepare for the challenges your organization may face in utilizing AI in critical applications.

Will Enterprise Data Centers Become ‘The Edge’ for AI?”

This webinar will explain the process of model inferencing and deployment options. We will discuss how trained models are used to make predictions and the considerations for deploying models. Attendees will understand the challenges and strategies for optimizing model performance, covering:
 
• What is model inferencing  
• Model inferencing process 
• How inferencing deployment differs in Gen AI and visual inspection
         - Inferencing deployment software options 
          - Inferencing hardware considerations (e.g., on the edge, on-prem, etc.)
• Deploying AI models for production best practices and lessons learned 
• Strategies for maintaining model performance post-deployment

AI Model Inferencing and Deployment Options

As breakthrough technologies like AI mature, they inevitably evolve from centralized to decentralized architectures. With global edge computing spending projected to reach $378 billion by 2028, organizations that continue relying solely on centralized AI risk falling behind in both efficiency and performance. The pervasive waste and expense of centralized generative AI models have underscored the urgent need for intelligence at the edge.

While edge computing concepts have existed since the 1990s with content delivery networks, today's businesses face the complex challenge of transforming their architectures to process data closer to its source. This session explores how leading organizations are navigating this transition, reducing latency and costs while competing with decades of established centralized cloud infrastructure.

Join Alan Morrison (Advanced Data Technologies Consultant and Writer) to discover how decentralized AI and edge computing are revolutionizing data processing across industries.

Key Takeaways:

- Understand why centralized AI approaches are becoming unsustainable and how edge computing addresses these limitations
- Learn practical strategies for implementing edge intelligence from organizations already succeeding with this approach
- Discover the economic benefits of processing data at the edge versus transmitting everything to centralized systems
- Gain insights into market growth projections and how to position your infrastructure investments accordingly
- Identify the architectural transformation challenges of edge computing and how to overcome them

Decentralized AI: The Future of Edge Computing

Today’s enterprise workflows don’t live in one place — they move across edge devices, cloud infrastructure, and on-prem systems in milliseconds. Some start at the edge — like sensor data or customer interactions — while others finish there, after running through cloud analytics or AI inference. The result is a constantly shifting architectural boundary where speed, security, and compliance collide.

This session looks at how to design cross-architectural workflows that maintain consistency, security, and data integrity from end to end. We’ll unpack how different architecture types — edge-native, hybrid, and multi-cloud — impact data handling, governance, and latency. And we’ll dive into the new layer of complexity AI introduces: model hallucinations, prompt injections, and evolving regulatory constraints when sensitive customer data moves from edge to cloud.

Join Eugina Jordan, CEO of YOUnifiedAI, as she breaks down how to architect distributed systems that are both intelligent and compliant — not in theory, but in practice.

Key takeaways:

- Understand how modern workflows traverse edge, cloud, and on-prem environments — and where control points break down
- Design architectures that protect customer data and maintain compliance even when processing happens across geographies
- Implement AI workflows that minimize hallucination risks and prevent prompt-injection attacks at the edge
- Balance performance with governance — using architecture, not just policy, to enforce trust
- Build an operational model that lets security, data, and AI teams collaborate on a single distributed workflow

Building Secure AI Workflows Across Edge, Cloud & AI Without Breaking Trust

Information systems vary in their primary roles and objectives. Real-time information systems, in particular, impose strict deadlines for generating control responses, as delays in data generation, processing, or transfer may lead to critical failures. For such systems, ensuring proper transactions depends not only on their logical correctness but also on their ability to operate within the required time constraints—as excessive latency renders them ineffective.

Timely data processing is essential for data intelligence, influencing the methods and tools used for classifying and integrating real-time information. Achieving low-latency solutions requires leveraging local resources instead of relying solely on centralized servers or cloud infrastructure. This shift brings computing closer to the network's physical endpoints, introducing the edge computing paradigm. Expanding the network edge extends computational and data processing capabilities to the point of data generation, reducing cloud dependency while enhancing real-time analytics, security, and efficiency.

Discover the key technologies driving edge expansion, including edge computing, cloud-edge advanced communication protocols, and AI-driven edge networks, and gain expert insights into the future of edge computing.

Discover Technologies Driving the New Network Edge

In today’s data-driven world, ensuring the resilience of your storage systems is critical for long-term success. This vendor-neutral webinar, hosted by the STA, will bring together industry experts to explore proven methodologies for testing the robustness of Serial Attached SCSI (SAS) and other data storage infrastructures. Craig Foster (Teledyne LeCroy) and Paul Coddington (Amphenol) will discuss practical approaches and the latest standards that enable secure, reliable, and high-performance storage systems. Rick Kutcipal (Broadcom) will moderate the Q&A and offer other perspectives. Join us to gain insights on ensuring your storage solutions meet the demands of modern workloads and prevent costly data loss or downtime.

Key Takeaways:

- Understand the key factors impacting the robustness of Serial Attached SCSI (SAS) and other data storage systems.
- Learn about the latest testing methodologies and standards in SAS and storage infrastructures.
- Discover how vendor-neutral best practices can enhance the security and reliability of SAS-based storage.

Reduce Your Risk of Data Loss! "Critical Testing Insights for Developers"

In the midst of rapidly advancing technological progress, the rise of digitization and remote work has become a prevailing trend. As a result, critical infrastructures are increasingly susceptible to new risks, continually expanding the realm of cybersecurity threats and attack methods. 

Consequently, organizations are more inclined to entertain the notion of paying ransoms to threat actors. It is imperative for organizations to remain vigilant against these emerging threats to safeguard their systems. The recent proliferation of artificial intelligence (AI) technology has given rise to AI-powered cyber-attacks, which are experiencing exponential growth. This development has facilitated cybercriminals in creating sophisticated and innovative malware embedded with new zero-day vulnerabilities. 

AI-powered cyber-attacks leverage machine learning to adapt to evolving defenses and devise novel methods to circumvent them, all while remaining undetected. This enables them to establish an illicit, enduring presence within networks for the purpose of extracting highly sensitive data. 

In order to effectively counter advanced cyber-attacks, organizations must adopt a comprehensive approach and incorporate zero trust principles into their architecture. This involves: 
- Treating every device, node, or entity as a potential threat point. 
- Implementing explicit identification, authentication, and authorization measures. 
- Embracing a default deny model as opposed to a default access model. 
- Developing proactive incident response plans. 
- Providing cybersecurity best practices training for employees. 
- Deploying AI-powered cybersecurity solutions. 

We invite you to engage in thoughtful discourse on these topics, cultivating a clear understanding and exploring strategies for integrating zero trust into your scalable enterprise architecture.

Integrating Zero Trust in a Scalable Enterprise Architecture

This webinar will provide a foundational understanding of Artificial Intelligence (AI) and Machine Learning (ML). We will explore the basic concepts, history, and applications of AI/ML, setting the stage for more advanced topics in subsequent webinars. Attendees will gain insights into how AI/ML technologies are transforming industries and everyday life and have the opportunity to see one of the earliest AI experiments: Training a neural network to recognize written letters.
 
Key Concepts:
• Definition and History of AI and ML
• Differences Between AI, ML, and Deep Learning
• Real-World Applications of AI/ML
• Basic Terminology and Concepts

Introduction to AI and Machine Learning

In this webinar, we’ll introduce the PCI Express (PCIe) interface which is an essential component of AI systems due to its ability to provide high bandwidth and low latency over short distances. We’ll introduce the basics, then discuss the evolution, requirements and benefits. We’ll cover features in PCIe Gen6 including bandwidth, AER (Advanced Error Reporting), DPC (Downstream Port Containment), different modes and signaling techniques. We will also cover PCIe switching and its applications for AI. Join us for an insightful discussion on:
- What is PCIe and why it matters? 
- Discovery/Enumeration and Hot-plugging 
- NTB (Non-Transparent Bridging) applications and uses
- Shared credit/received buffer
- Selective NAK vs. Standard NAK for Flits replay
- PCIe Gen6 Enhancements
- PCIe Switch and applications for AI

Everything You Wanted to Know About PCIe But Were Too Proud to Ask

Monitoring the dark web for threats targeting your organization or industry will enhance your ability to understand the real and ever-changing threat landscape to prevent breaches. Tune into this summit from CISO Alex Holden to learn the practical approach of deriving value from threat intelligence and dark web monitoring.

Key Takeaways 
1) Learn advantages of proactive monitoring of the dark web.
2) Learn how adjusting your defenses to the dark web threats can improve your overall threat landscape.
3) Learn how to use threat intelligence to prevent losses.

Getting ROI From Monitoring the Dark Web

CIOs are no longer just technology enablers—they're the architects of enterprise transformation. This session provides an exclusive, actionable roadmap for mastering AI-driven innovation at scale, tailored specifically for large, complex organizations. You'll explore advanced hybrid AI models that combine the scalability of open systems with the precision and security of proprietary tools, solving real-world challenges like scaling AI across business units and securely integrating AI within existing enterprise structures.

What sets this session apart is its focus on enterprise-tested frameworks and exclusive case studies from leading organizations. Learn how hybrid AI strategies are delivering measurable ROI and driving competitive advantage. Dive into bold, innovative approaches for closing the AI talent gap, from leadership-driven AI literacy to embedding AI fluency across cross-functional teams. The session will also tackle cutting-edge governance strategies, addressing risks like shadow AI, compliance hurdles, and bias, while navigating rapidly evolving global regulatory landscapes.

This isn’t just about theory—it’s about real-world execution. The session features insights and frameworks designed specifically for CIOs ready to lead their organizations into the next era of AI innovation. 

Key Takeaways:
- Enterprise-Tested Hybrid AI Frameworks: Go beyond generic approaches with actionable insights and case studies showcasing how leading organizations are building scalable, secure AI systems tailored to enterprise needs.
- Strategic Talent Ecosystems: Learn bold strategies to close the AI talent gap through advanced upskilling, leadership-driven AI literacy, and cross-functional team alignment that embeds AI into every level of your organization.
- Future-Proof Governance: Gain cutting-edge tools to navigate shadow AI risks, ensure compliance, and integrate ethical AI practices into global operations, fostering trust and adaptability in an AI-powered future.

Architecting Enterprise AI Excellence: Leading with Innovation, Talent and Governance

As AI technologies rapidly transform enterprise operations, we're witnessing an unprecedented shift in workforce dynamics. Gen Z and Alpha generations are entering a workplace where AI isn't just a tool but a fundamental business driver, requiring a complex blend of technical prowess, adaptability, and ethical decision-making capabilities. While these digital natives possess an intuitive understanding of technology, the challenge lies in channeling this affinity into meaningful business value and innovation.

The stakes are higher than ever for CIOs and technology leaders. Unlike previous technological transitions, AI's impact on workforce development demands a complete reimagining of talent strategies. Organizations must move beyond traditional technical training to foster a unique combination of AI literacy, human-centric skills, and ethical judgment. This requires a transformative approach to talent development that bridges the gap between native digital fluency and the strategic capabilities needed to drive AI-powered innovation and value creation.

In this session, Giles Lindsay (Chief Information Technology Officer, Lif3away Inc.) provides a practical roadmap for CIOs to build and nurture a future-ready workforce that can thrive in an AI-augmented business landscape.

Key Takeaways
- Transform digital natives into strategic AI leaders, combining technical proficiency with business acumen and ethical decision-making capabilities.
- Create effective learning frameworks that balance AI literacy with distinctly human skills like creativity, emotional intelligence, and critical thinking.
- Implement practical approaches for continuous skill development and adaptation, ensuring your workforce remains relevant and innovative in a rapidly evolving AI landscape.
- Integrate ethical considerations and bias awareness into AI talent development, building teams that can responsibly drive AI adoption while maintaining transparency and fairness.

From AI Users to AI Leaders: Cultivating Next-Gen Digital Talent

Are you prepared for the future of IT support? Emerging technologies and shifting customer expectations are reshaping the industry. 

In this webcast, industry thought leader Jeff Rumburg will explore the trends and predictions that will define IT support in 2025. He’ll discuss AI, automation, remote support, and other innovations that are transforming the landscape. Attendees will learn how to stay ahead of the curve and position their organizations for success in the years to come.

The Future of IT Support: Trends and Predictions for 2025

How many experienced drivers get behind the wheel and commute to a routine destination without thinking about it? Almost on autopilot, driving becomes routine, even if it’s not the most ideal mode of transportation. Legacy data storage mirrors manual driving in this way.

For instance, legacy storage architectures are managed separately (how many cars do you pass on the interstate with a single driver?), are complex to maintain, and miscalculations often create incidents as storage becomes increasingly costly to scale. 

Tune into our webinar as Michael Leworthy, Marketing Strategist at Pure Storage, digs into the current dynamics of legacy storage, why it’s failing, and how autonomous and intelligent data management helps resolve these historical storage issues in the AI era.

You’ll also see how the Pure Storage Platform makes obtaining a seamless, secure, unified data management experience possible—regardless of where your data lives.

Autonomous & Intelligent Data Management for the AI Era

IT teams will continue to face unprecedented challenges such as new remote and distributed work models, increased endpoint complexity, a growing talent gap in IT and automation. On the flip side, there will be plenty of opportunity for IT teams to become a value generator by enabling business transformation while improving customer experiences and driving business growth with technology.

NinjaOne’s Jeff Hunter will dive into the exciting trends for this year and share ways your organizations can improve decision making and align technology to business goals. Join us to learn more about:

• Supporting the complexity of your hybrid workforce and increased endpoints
• Using automation and AI enhancements to your advantage
• Addressing the talent gap with IT automation
• Consolidating your tech stack
• Improving DEX and fostering user-centric IT strategies
• Strengthening security convergence to streamline protection across tools and processes

Navigating the Future of IT Management & Innovation

Recently, we asked 4,500 executives and AI thought leaders how they’re guiding their organizations at this crucial time.

Hear their insights by joining us for a deep-dive conversation with ServiceNow thought leaders at Scenes from an AI Revolution: Learn how enterprise AI pacesetters create value. Our speakers will discuss the findings from our first-ever Enterprise AI Maturity Index survey. You’ll learn:

- How to ensure that AI investments pay off
- Why a clear AI strategy driven by senior leadership is the best barometer of success
- How board members, CEO offices, operations, and legal teams are involved in AI adoption plans

Learn what so many AI leaders are doing to gain an advantage over their competitors—and how you can benefit.

Scenes from an AI Revolution: Learn How Enterprise AI Pacesetters Create Value

Managing IT operations for enterprise organizations with multiple locationsis no small feat. With growing endpoint complexity and the need for global visibility, traditional approaches often leave IT teams stretched thin. In this virtual discussion, join us to explore how NinjaOne’s endpoint management platform revolutionizes IT operations. From automating routine tasks and providing real-time visibility across a distributed workforce. Listen into this discussion to learn how to unlock operational efficiency, boost IT performance and alleviate IT misery all while staying ahead in a competitive landscape.

Key Takeaways:

- Discover how automation streamline endpoint management tasks, improving efficiency and reduce manual effort.
- Learn how global visibility enables IT teams to monitor and manage endpoints across multiple locations seamlessly.
- Explore how exceptional support will empower your IT team to solve challenges faster and with greater confidence.
- Gain actionable insights on transforming IT operations into a proactive and strategic function.

Elevate IT Efficiency: Delivering Seamless Endpoint Management

As digital transformation reshapes industries, CIOs face mounting pressures to drive innovation while upholding the ethical principles that foster trust and responsible progress. In this session, we'll explore strategies to navigate this critical balance.

Join Dr. Abilio Oliveira (Consultant, TOGAF Distinguished Architect) as he shares real-world examples and forward-thinking strategies that empower CIOs to lead with integrity, guiding their organizations through the complex interplay of technological advancement and ethical stewardship.

Topics for discussion include:
- Principles of ethical innovation and how they apply in the digital age.
- Case studies demonstrating the impact of values-driven technological change.
- Foster transformative innovation without compromising trust and accountability.
- Preparing for the evolving ethical challenges posed by emerging technologies.
- Cultivating a culture of responsible leadership in the face of disruptive innovation.

About the Speaker
Dr. Abilio Oliveira is an experienced technology executive with over 30 years of expertise in technical and operational leadership. He specializes in artificial intelligence, digital transformation, and emerging technologies, with a strong ability to deliver complex infrastructure projects globally.

Recognized as a best-selling author and inventor with over 20 patents, Dr. Oliveira has led digital transformation for numerous organizations worldwide. Currently, he serves as a technology consultant, conducting AI research aimed at enhancing quality of life and digital safety. He is a digital influencer and mentor to IT professionals, committed to developing efficient, well-being-focused solutions.

Ethical Innovation: Navigating the Boundaries of Technological Progress

AI is the ultimate accelerant for application development – fueling innovation at breakneck speed and making the impossible look easy. But here’s the catch: fire is a tool until it’s out of control.
In this session, we’re not just talking about AI risks – we’re playing with fire and watching what happens, straight from the terminal. We’ll exploit vulnerabilities, uncover adversarial tricks, and walk through real-world case studies that show just how easy it is to take advantage of AI risks (and how attackers are already doing it).
Expect technical code examples and a behind-the-scenes look at how attackers manipulate AI – from jailbreaking LLMs to creating AI-driven zero-days. More importantly, we’ll cover how to fight fire with fire by strengthening prompts, setting up guardrails, spotting rogue agents, and running AI-specific red team exercises.
Learn how to adapt to ensure AI delivers on its promise of a brighter future – instead of its burning threat of unforeseen risks.

Hacking & Securing AI Systems: Playing with Fire and Controlling the Flare of AI

As AI investments surge across large enterprises everywhere, CIOs are facing a new spin on familiar challenges. The rise of "FOMO AI" (Fear of Missing Out on AI) is driving a renewed focus on issues like business engagement, board influence, team development, and change management. 

Despite these challenges, the wave of technological advancement also presents new career opportunities for CIOs at both the executive management and board levels. CEOs are adding new technology officer positions to their executive committees, and boards are eager for technology representation. 

In this session, Martha Heller, CEO of tech executive search firm Heller, discusses the future of the CIO career and shares unique insights on how to leverage AI to achieve both business and career success.  

Topics for discussion include: 
- How CIOs are educating their boards about AI value
- New technology executive roles on the executive committee
- Evolving career paths for CIOs.
- Levering AI leadership needs for Board Director positions.
- And more.

Meet the Speaker
Martha Heller is CEO of Heller, an executive search firm specializing in technology leadership roles. She has placed hundreds of senior technology executives throughout her career. Martha has authored two books on IT leadership - The CIO Paradox: Battling the Contradictions of IT Leadership and Be the Business: CIOs in the New Era of IT – and is a contributor to CIO.com. Her e-newsletter, The Heller Report, has become a must-read for the industry.

The Impact of AI on the CIO Career

CIO Summit

Today's business leaders are racing to adopt Generative AI to streamline operations, transform customer experiences and fuel innovation. But achieving these objectives demands more than technology —success requires a comprehensive GenAI strategy that unites business and IT priorities, establishes robust data governance, and defines clear ethical boundaries. 

In this session, Kader Sakkaria (Head of Global Data Technology at Allied Consulting Group) explores how leaders can strategically implement GenAI across functions to create long-term, sustainable value. Join him to explore common challenges – from data readiness to scaling solutions – and get a strategic framework to overcome them.  

By joining this session, you’ll discover: 
- A strategic framework for aligning GenAI initiatives with business objectives while establishing the governance and ethical guidelines needed to ensure responsible implementation. 
- How to assess organizational data readiness and build the foundational capabilities required to scale GenAI solutions across functions. 
- Best practices for navigating common implementation challenges and measuring ROI as you transition from experimental pilots to enterprise-wide GenAI deployment. 
- And more... 

About the Speaker
Kader Sakkaria is a visionary technology leader with extensive experience leveraging cutting-edge technologies to drive business transformation. As the Head of Global Data Technology at Allied Consulting Group, he has spearheaded innovative AI and data strategies for multinational organizations, focusing on operational efficiency and customer experience. Kader is a recognized expert in enterprise AI adoption, with a strong emphasis on ethical and scalable implementations.

Operationalizing GenAI: From Pilot to Enterprise-Wide Value Creation

Generative AI

IT Operations

Artificial Intelligence

AIOps

Value Creation

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Operationalizing GenAI: From Pilot to Enterprise-Wide Value Creation

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