Name: Turning AI Investments into Scalable Decisions: A Blueprint for 2026
Start: 2026-02-26T22:00:00Z
End: 2026-02-26T22:01:00.000Z
Location: BrightTALK

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.

Modern infrastructure is a dynamic ecosystem where workloads move fluidly across cloud regions, edge nodes, and on-premises systems. Traditional management methods are no longer sufficient to handle the scale, speed and complexity of these environments. As organizations adopt AI-driven architectures and face increasing regulatory demands, the need for intelligent, autonomous cloud management has become critical. 

At the same time, zero-trust security models are reshaping how businesses protect their systems requiring constant validation of every user, device and transaction. This is a challenge that adds significant operational strain. 

Join Ekambar Kumar Singirikonda, Director of DevOps Engineering at Toyota, to discover actionable strategies for building resilient, predictive, and cost-efficient hybrid infrastructures that align with business priorities.

Key Takeaways:
- Learn how to unify cloud, edge, and on-prem systems with intelligent orchestration platforms that optimize workloads for performance, cost, and compliance.
- Discover how to implement zero-trust security at scale without disrupting operations or user experience.
- Explore AI-driven architectural patterns, including predictive resource management, policy-based orchestration, and self-healing systems.
- Understand strategies to reduce hybrid infrastructure complexity through automation frameworks and unified management tools.
- Gain a roadmap for transitioning from manual oversight to predictive, business-aligned infrastructure operations.

Simplify Hybrid Infrastructure with Autonomous Cloud and Zero-Trust Strategies

Modern CIOs are pivotal drivers of business transformation. As AI reshapes industries, organizations have to embed intelligence into every aspect of their operations to remain competitive. From automating processes and enhancing decision-making to elevating customer experiences, AI's potential to drive change is huge —but unlocking its value requires strategic leadership and a clear roadmap.

This session will explore how CIOs can leverage AI as a catalyst for enterprise-wide transformation, aligning initiatives with business goals and delivering measurable outcomes. 

Join Ekambar Kumar Singirikonda, Director of DevOps Engineering at Toyota, to uncover how to embed AI into your enterprise DNA and lead with confidence in the AI era.

Key Takeaways:
- Discover strategic AI integration techniques that embed AI across enterprise functions to achieve measurable business impact.
- Understand how to evolve as a modern CIO, building executive influence and aligning AI initiatives with business objectives.
- Explore frameworks for evaluating emerging technologies and demonstrating ROI.
- Gain practical approaches to foster innovation, manage change, and upskill teams for AI adoption.
- Learn how to structure multi-year transformation plans that balance innovation with operational stability.

Harnessing AI to Drive Enterprise Transformation and Leadership Excellence

Outdated, fragmented data systems are holding organizations back from unlocking AI’s full potential. Providing accurate, context-driven insights has become essential for staying competitive. Without a scalable, AI-ready knowledge foundation, businesses risk stagnation in an era of rapid innovation.

This session explores advancements in knowledge graph automation and Graph Retrieval Augmented Generation (RAG). Discover how the "flywheel" model enables AI to construct graph structures with human oversight for accuracy, how SHACL ensures data consistency, and how Graph RAG uses large language models (LLMs) to turn natural language into actionable, trustworthy insights.

Join Alan Morrison, Advanced Data Technologies Consultant and Writer to explore success stories from Ernst & Young, Microsoft, and Roche and learn strategies to scale your AI-ready knowledge infrastructure.

Key Takeaways:
- Understand how knowledge graphs enable scalable, AI-ready data foundations.
- Learn how Graph RAG combines LLMs and graph databases for trustworthy insights.
- Explore the "flywheel" model for AI-human collaboration in data validation.
- Gain actionable strategies to future-proof your AI capabilities.

Unlock AI’s Potential with Knowledge Graphs and Graph RAG

CIOs face a dual mandate with AI: they oversee the infrastructure that sustains trust in data and AI systems, but they must also narrate that infrastructure persuasively. Technical excellence means little if boards see only cost centres and regulators see only opacity. The session addresses how to measure and communicate AI value in terms that resonate beyond IT.

Effective measurement requires distinguishing two forms of transparency that executives often conflate. Data lineage describes how information flows through systems: origins, transformations, dependencies. Data provenance records the documented history of specific values: who created or modified them, when, under what parameters. Lineage answers architectural questions relevant to planning and change management; provenance answers forensic questions relevant to audit and dispute resolution. Metrics derived from each capability speak to different audiences. Time to audit readiness concerns board risk committees; value reconstruction time concerns legal and compliance; data preparation hours concern COOs and budget owners.

The session presents a metrics framework that translates governance capabilities into business language. Participants will examine how lineage coverage, provenance depth, error detection time, and downstream impact incidents can be reported in ways that demonstrate value rather than merely documenting activity. The goal is not dashboards for their own sake, but communication that builds confidence among stakeholders who will never examine the underlying systems directly.

Key Takeaways:
- Lineage and provenance serve different purposes and speak to different audiences.
- Metrics must translate technical capabilities into business outcomes.
- Time-based metrics (audit readiness, error detection, value reconstruction) communicate operational value.
- Governance framed as enabling rather than defensive builds broader organizational support.

Measuring AI Value: Metrics That Speak to Boards and Regulators

The CIO role has undergone major transformation since its inception, emerging as a critical driver of business success. 

In 2026, with AI reshaping entire industries, the stakes are higher and CIOs face unprecedented opportunities to lead innovation and expand their influence. However, these advancements come with escalating pressures—balancing transformative strategies against limited resources and organizational readiness. 

This session explores the skills and strategies that distinguish successful CIOs in today’s AI-driven economy. Learn how top-performing CIOs are leveraging AI to meet CEO expectations, accelerate their career trajectories and thrive in a competitive environment. 

Martha Heller, CEO of Heller Search Associates, brings over two decades of expertise in recruiting CIOs and will share actionable insights into the current job market, career pivots and leadership qualities that set tech executives apart. Join her to discover how to navigate the challenges and opportunities of 2026 and position yourself for long-term success.

Key Takeaways:
- Understand the evolving expectations of CEOs for CIOs in the AI era.
- Discover actionable strategies to pivot or accelerate your career.
- Explore the current CIO job market and emerging trends.
- Learn how to balance innovation demands with organizational readiness.
- Gain leadership insights to expand your influence and impact.

Navigating AI Disruption: Elevating CIO Careers in 2026

Modernization presents a critical yet complex opportunity for enterprises striving to balance innovation with operational excellence. Technology leaders often find themselves performing a precarious trapeze act—innovation attracts funding, but operational failures dominate headlines, exposing organizations to significant risks. With 79% of application modernization projects failing and 88% of transformations falling short, the stakes have never been higher.

This session uncovers the root causes of modernization failures, with a focus on applications and data—the foundation of enterprise transformation. Attendees will gain practical strategies to navigate these challenges, ensuring their initiatives align with business goals and deliver tangible results.

Join Jeffrey Tefertiller, Founder of Service Management Leadership, to explore proven approaches that can significantly improve your chances of modernization success.

Takeaways:
- Identify the key reasons behind modernization project failures.
- Align application and data strategies with overarching business objectives.
- Apply actionable frameworks to mitigate risks in transformation initiatives.
- Examine real-world examples of successful modernization efforts.
- Gain insights to future-proof your enterprise technology landscape.

Driving Modernization Success in 2026

Technology initiatives fail most when organisations scale complexity before they’re ready. Teams are asking CIOs to fund ‘enterprise-grade’ platforms for data, AI, security and more before they’re even clear on data ownership, platform priorities and use cases. In turn, organisations invest in high-ticket technologies that teams don’t adopt, yielding unsatisfactory outcomes.

This session introduces a practical framework for starting small and scaling smart when implementing complex enterprise capabilities. Focusing on the real-world example of data catalog and data quality, we’ll explore how starting with small, user-driven solutions allows teams to get to what really matters - ownership, definitions, quality metrics and operational decisions. Rather than focusing on which technology to choose, it’s about sequencing - knowing what can be built first, what can be delayed and understanding when scaling makes the most sense.

The framework has a broad application that CIOs can use for any initiative where tooling, behaviour change and governance need to evolve together. Join Inigo Antolin, Head of Data Management and Governance at the British Council, to receive a practical mental model to reduce risk, preserve optionality and scale with confidence rather than hope.

Key Takeaways:
- Why scaling enterprise platforms too early results in wasted resources and lost trust.
- How starting small can strengthen decision-making across ownership, definitions and quality.
- Discover the Start Small, Scale Fast framework that you can apply across all new tech initiatives.
- How to use constraints as a strategic advantage rather than a limitation.

Start Small, Scale Fast: Hard Lessons from Data Catalog & Data Quality

While many enterprises having successfully initiated AI projects, scaling these initiatives for enterprise-wide impact is still a significant challenge. Despite significant investments, organizations often struggle to realize the transformative value they anticipated. In turn, AI initiatives are kept confined to pilots, proof of concept and isolated use cases with little to no tangible ROI on business operations. This has led to growing scepticism among senior executives, mounting technical debt and missed opportunities for competitive advantage. 

In this session, Obuks Akpede (Co-founder, Datachayn Ltd) explores what it takes to move AI from the edges of the organization into its core operating model. He’ll draw on real-world examples of successful transformations to highlight the execution strategies needed to enable drive measurable business outcomes with AI while retaining resilience, bolstering security and maintaining regulatory compliance.  

Join the session to receive a blueprint for transitioning from experimentation to enterprise-grade AI solutions that harness intelligence to enhance performance, accelerate decision-making and boost competitive differentiation. 

Key Takeaways: 
- Receive a step-by-step framework for scaling AI from pilots to enterprise-wide adoption. 
- Discover strategies for building AI-ready data and technology foundations. 
- Learn proven methods to balance innovation with operational stability and compliance. 
- Explore enterprise-level metrics to measure AI impact and secure ongoing investment. 
- And more..

Scaling AI for Enterprise Impact: Transforming Core Operations with Intelligence

Every enterprise is being told to 'use AI,' but few are being told how to build it. Today’s stacks already include RPA, workflow engines, scripts and orchestration layers. Now AI agents, agentic workflows and automations are being layered on top — often without changing the underlying architecture. That’s how organizations end up with brittle automations, runaway agents and governance problems disguised as innovation. 

Join Eugina Jordan, CEO and Founder of YOUnifiedAI, as she breaks down how enterprises can design automation and agent systems that scale without creating chaos. 

By the end of this session, you will:
- Clearly distinguish between rules-based automation, workflow orchestration and agentic systems — and know exactly where each belongs in your architecture.
- Identify where RPA still delivers value (high-volume, deterministic processes) and where it quietly becomes technical debt as complexity grows.
- Understand how AI agents change system boundaries — including state management, memory and decision authority — and what that means for governance.
- Apply a human-in-the-loop framework to decide when in your workflow human approval is mandatory versus when it introduces unnecessary friction.
- Design control planes for automation and agents, including monitoring, rollback and auditability patterns you can implement immediately.
- Recognize and mitigate real security risks — prompt injection, tool misuse, over-permissioned agents and cross-system data leakage.
- Prevent agent sprawl across cloud, SaaS and internal systems by using architectural guardrails, not policy decks.

Also, we’ll explore:
• A step-by-step execution framework for selecting the right model (automation or agents)
• A deployment playbook you can adapt to your organization
• Real-world case studies showing what worked, what failed and why
This session is for technical and operational leaders responsible for building, securing, and scaling automation in real enterprise environments.

Automation vs. AI Agents: An Execution Framework for Enterprises

Implementing AI capabilities presents a critical yet complex opportunity for enterprises striving to gain a competitive advantage through innovation. Technology leaders feel the immense pressure to implement AI -- in some form or fashion -- but realize the odds are stacked against a value-laden capability. A 2025 MIT study proves how difficult it is to get AI initiatives right. That study found “just 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact.” This is ominous for technology leaders seeking to implement AI in the next two years. 

This session uncovers methods to get more value from your next AI initiative. Attendees will gain practical strategies to navigate these challenges, ensuring their AI initiatives align with business goals and deliver tangible results.

Join Jeffrey Tefertiller, Founder of Service Management Leadership, to explore proven approaches that can significantly improve your chances of modernization success.

Key Takeaways:
- Identify the key reasons behind AI initiative failures.
- Apply actionable methodologies to mitigate risks in AI initiatives.
- Examine real-world examples of successful AI efforts.
- Gain insights to future-proof your AI technology landscape.

Driving AI Success in 2026

Are you struggling to demonstrate the true business value of AI in your service and support operations? Without tracking the right metrics, it's challenging to justify AI investments and scale initiatives effectively.

In this webcast, Jeff Rumburg, Managing Partner of MetricNet, will leverage over 30 years of benchmarking data and expertise to reveal proven KPIs for measuring AI's impact. He'll examine key areas including cost reduction, operational efficiency, customer satisfaction, and performance optimization.

Attendees Will Learn:
- Insights from real-world AI adoption case studies.
- Strategies and challenges in implementing AI solutions.
- Proven ROI, cost, quality, and cycle time benefits of AI.

Measuring AI Impact: Benchmarking Strategies for Service and Support Transformation

AI initiatives often face resistance—not because they lack potential, but because they’re seen as risky, expensive and hard to justify. Leaders are wary of “shiny-toy” projects with fragile ROI stories and unclear success criteria, fearing they’ll be left defending costly, hard-to-explain failures. In a world where attention and budgets are scarce, the real challenge isn’t building better AI models—it’s making them understandable, defensible and aligned with business priorities.

This session redefines GenAI as a practical enabler, not a silver bullet. By acting as a storytelling and translation layer, GenAI can help teams simplify complex AI concepts, design pilots that feel safe and measurable and build trust with finance and leadership teams. Attendees will gain actionable strategies to position AI initiatives for success, from crafting compelling business cases to maintaining alignment through execution.

Join Iñigo Antolin, Head of Data Management and Governance at The British Council, to explore how to secure funding and deliver real-world AI transformation.

Key Takeaways:
- How to position AI initiatives when budgets and attention are limited.
- Strategies for designing pilots and metrics that finance teams trust.
- Using GenAI to simplify decision-making across the funding lifecycle.
- Practical tips for aligning AI initiatives with organizational priorities.

Driving AI Investment Success with GenAI: Practical Strategies for 2026

As Agentic AI moves beyond foundational theory into real-world deployment, the focus shifts to the architectural and operational enablers that make scalable intelligence possible. This session explores the emergence of Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication as critical components for orchestrating distributed, autonomous systems—laying the groundwork for the Agentic Mesh, where agents collaborate, adapt, and self-optimize across domains.

We’ll examine how Agentic AI operates in, on, with, and for storage systems—transforming them from passive data repositories into active participants in decision-making and workflow execution. From client-side perspectives, we’ll discuss how agentic interactions reshape expectations around latency, autonomy, and data locality, and what this means for future-ready infrastructure.

Topics to be covered include:
• Architecting Model Context Protocol (MCP) for scalable coordination
• Enabling Agent-to-Agent (A2A) communication across heterogeneous environments
• Building and managing an Agentic Mesh for distributed intelligence
• Agentic roles in storage systems: embedded, adjacent, cooperative, and service-oriented
• Client perspectives on agentic integration: performance, autonomy, and trust boundaries
• Implications for infrastructure, data governance, and operational models
 
Join us to explore how Agentic AI is redefining the boundaries of intelligent systems—and why storage is no longer just where data lives, but where intelligence begins.

How Agentic AI Transforms the Role of Storage

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

AI-Powered Enterprise Transformation Summit

AI governance presents an architectural challenge that centralized approaches cannot solve. Monolithic review processes create friction that development teams bypass; purely decentralized models produce inconsistency that regulators will not accept. The session presents a federated architecture for AI governance: central functions set policies and standards while domain functions retain authority to adapt implementation within defined thresholds for risk, financial impact, and infrastructure cost.

This federated model rests on five components: policies and standards that define acceptable use; governance structures that distribute authority appropriately; risk assessment processes that scale from self-service questionnaires to expert review; technical infrastructure including model registries and automated policy enforcement; and monitoring systems that track drift, accuracy, and fairness over time. Each component must be designed for the distinctive nature of AI: algorithms that learn and change, rather than fixed logic that can be reviewed once and approved indefinitely.

The session traces a maturity progression from experimentation through restricted deployment to production scale, with governance controls calibrated to each stage. Participants will examine how permission models, audit schedules, and oversight intensity can be tiered to match risk levels, avoiding both the paralysis of excessive control and the exposure of insufficient oversight.

Key Takeaways:
- Federated governance balances consistency with domain-appropriate flexibility.
- Five components (policies, structure, risk assessment, infrastructure, monitoring) form a complete architecture.
- Tiered permission and audit models match oversight intensity to risk level.
- Maturity progression from experimentation to production requires different controls at each stage.

Architecting AI Governance: Federated Models That Scale

Despite significant investments in data platforms, advanced analytics and generative AI, many organizations remain stuck—unable to convert these technologies into actionable, scalable decisions that drive measurable business outcomes. Fragmented data, static dashboards and inconsistent workflows often stand in the way of unlocking the full potential of AI-driven insights.

This session introduces a proven, enterprise-tested framework to overcome these challenges and build an insights-driven operating model designed for 2026 and beyond. Discover how to operationalize generative AI at scale, embed actionable insights directly into decision workflows and strengthen governance without stifling innovation. These strategies will empower your organization to turn data chaos into clarity and achieve sustainable growth in a competitive landscape.

Join Farid Sheikhi, Senior Manager of Analytics, Innovation & Data Enablement at RBCx, to explore how to transform cutting-edge AI investments into a competitive advantage.

Key Takeaways:
- Identify the root causes of fragmented data and inconsistent decision-making.
- Learn how to operationalize generative AI and analytics for real-world impact.
- Balance governance and innovation to scale AI initiatives effectively.
- Embed insights into workflows to drive faster, smarter decisions.
- Gain actionable strategies from real-world enterprise success stories.

Turning AI Investments into Scalable Decisions: A Blueprint for 2026

Artificial Intelligence

Generative AI

Governance

Innovation

Decision Making

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Turning AI Investments into Scalable Decisions: A Blueprint for 2026

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