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AI Marketplace & Practical AI

The opening discussion examined how AI has moved from experimentation to execution, reshaping industries and redefining enterprise strategy. The session explored the accelerating pace of adoption—Generative AI reaching 65% penetration in under two years and Agentic AI forecast to exceed 80% within five - as organizations evolve toward automation, reasoning, and collaboration between human and digital agents.

A key theme was alignment over experimentation. Enterprises are focusing on clearly defined business value, using the four ROI levers of cost reduction, productivity, revenue growth, and risk management to guide their AI portfolios. WWT's Practical AI framework—"buy where available, build as appropriate"—was positioned as a structured model for achieving measurable outcomes.

Real-world examples illustrated tangible impact: AI tools driving efficiency, improved proposal velocity, and over $200M in new revenue opportunity. The overarching takeaway reinforced that success depends on connecting AI investment to purpose, not just possibility.

This session focused on how organizations advance from isolated pilots to scalable AI maturity. Many remain in the early phases—experimenting without structure or measurable outcomes. The discussion centered on what separates success from stagnation: governance, prioritization, and cultural readiness.

Through the AI Maturity Curve, attendees examined the critical transition from experimentation to operationalization. The session demonstrated how structured frameworks like AI Studio accelerate this journey by providing repeatable processes, clear metrics, and governance discipline that ensure each new use case compounds enterprise value.

Highlights included the growing impact of internal AI adoption programs and the creation of AI champions driving collaboration across business units. The key takeaway underscored that accelerating results requires not only advanced technology, but a coordinated effort to embed AI within operating models and decision-making culture.

This discussion explored the next evolution of AI—systems capable of acting with autonomy, context, and coordination. Attendees examined how Agentic AI moves beyond prompting into structured reasoning and task orchestration, allowing AI agents to collaborate and deliver outcomes independently.

WWT's internal examples demonstrated multi-agent architectures that integrate diverse data sources and workflows, achieving higher accuracy and speed while reducing manual effort. Participants saw how these architectures are transforming operations across industries, from document-heavy processes to complex decision environments.

The takeaway emphasized that enterprises are entering an era of human-agent collaboration, where digital agents extend human capability, optimize time-to-decision, and establish new levels of operational intelligence.

The conversation examined the organizational foundations required to scale AI responsibly and deliver measurable productivity. Attendees explored adoption challenges such as tool sprawl, uneven enablement, and the absence of clear success metrics - all of which can dilute value realization.

The focus was on structured adoption frameworks - governance, training, and persona-based deployment models that embed AI into daily workflows. Practical examples demonstrated how aligning data management, access control, and learning pathways can produce measurable gains in productivity and employee experience.

The session reinforced that true ROI comes from turning experimentation into durable capability. When adoption is intentional, supported, and measured, AI transitions from novelty to necessity - delivering sustained business performance and cultural acceptance.

This session addressed the infrastructure and operational readiness required to scale AI at enterprise level. As AI investments accelerate, organizations face new constraints in compute performance, energy capacity, and hybrid deployment complexity.

Attendees explored the shift toward High-Performance Architecture (HPA) -  integrated compute, networking, and storage systems engineered for parallel workloads and low-latency AI processing. The AI Factory concept was highlighted as a scalable model for aligning architecture, governance, and workload optimization to maximize ROI while minimizing operational risk.

Key insights underscored that scaling AI requires intentional design, not incremental upgrades. Organizations must plan for hybrid AI environments that balance performance, sustainability, and cost efficiency - ensuring agility as demand evolves.

The final session underscored that innovation without security undermines trust, governance, and long-term viability. The discussion introduced the ARMOR framework (AI Readiness Model for Operational Resilience) as a comprehensive structure for securing AI systems across governance, infrastructure, and data integrity.

Key elements included lifecycle protection—from data classification and model guardrails to adversarial testing and zero-trust segmentation. Real-world examples demonstrated how the framework supports scalable, compliant, and resilient AI operations in complex enterprise environments.

The session closed with a clear principle: responsible AI must be secure AI. As organizations accelerate adoption, security, governance, and ethics must advance in parallel - ensuring AI innovation is sustainable, compliant, and trusted.

Customer Panel: Fireside Chat

Enterprise leaders shared how they realized tangible business value through AI adoption and collaboration.

Wherever you are in your AI journey, WWT can help get you there faster and more efficiently. Check out these offerings from our AI and data consultants, engineers and architects designed to help you achieve long-term AI success!