After AI Hype: 4 Priorities to Move AI from Pilots to Safe Production
IT budgets are largely flat, but AI spend is rising, which means the rest of the portfolio will face tighter investment. In this environment, the differentiator is not experimentation, but allocation and measurement. Leaders need to decide what to scale, what to stop, and what “success” means in business terms. Below are the four priorities that stood out most during discussions at the Trask Future Insight conference.
Executive Snapshot
AI spend is rising while IT budgets stay flat, forcing tougher portfolio choices. Winners focus on revenue-generating outcomes (not cost savings), put AgentOps controls in place, and invest in the foundations: provable context plus cloud, security, and identity to run AI safely in production.
Priority 1: Prove outcomes, not just productivity
Most organizations see productivity gains, but only a minority achieves measurable financial impact. Too often, ROI is defined only as cost savings and FTE reduction. The strongest results come when AI can improve customer experience (NPS), often by compressing processing times, improving decision quality, or enabling personalization that shifts customer value.
What resonated strongly at Trask Future Insight is a simple practical governance checkpoint: after every AI project, can we prove measurable outcomes that confirm the original business case, using hard metrics like time, quality, cost, and risk (not only “better experience”)?
Published AI case studies highlighted by Gartner show what “transformative outcomes” look like in practice. For example, Hiscox reduced risk assessment from 3 days to 3 minutes. Scotiabank cut AML adverse media false positives by 95% and saved $4M per year. DigitalOwl accelerated life insurance claims decisions with 60–70% time savings.
Priority 2: Put AgentOps controls in place as agents specialize
Agent deployment is shifting from universal assistants to specialized agent solutions. At the same time, “Shadow AI” grows wherever governance and tooling do not keep up. Gartner expects that by 2028, 80% of real profits from AI agents will come from specialized agent solutions, not universal agents.
As autonomy increases, operating controls move to the center. “AgentOps” becomes non-optional because leaders will be asked the same questions they face with any critical system.
Why did it decide this? Can we observe what it is doing and evaluate it over time? Will it alert when it drifts, and can we roll changes out safely with versioning and controlled deployment? In regulated environments, continuous improvement without losing control is the requirement.
Priority 3: Build provable context with provenance and multimodal data
Modern AI requires more than high-quality datasets. It requires contextual, traceable, and interpretable information that can stand behind decisions. The data layer increasingly needs to support digital provenance, meaning origin, ownership, integrity, semantics, and transformations behind data used in decision flows. It also needs multimodal data fabrics that unify application data, documents, sensor feeds, external sources, and synthetic data into a single contextual layer.
Reliable, scalable AI requires provable context, digital provenance and a multimodal data fabric that agents can trust. We see fast momentum here: data architectures designed for AI reasoning, decision flows and agentic behavior, rather than traditional reporting.
— Jan Antoš, CTO Trask
Two shifts are accelerating this. Streaming and data-in-motion architectures are reshaping enterprise systems. AI-accelerated data platform modernization is speeding up projects from assessment to migration and adoption, supporting multiple phases of the lifecycle.
Priority 4: Get cloud, security, and identity ready for safe production
The underlying layers of enterprise technology are undergoing their own transformation. Cloud economics is evolving to support AI-heavy workloads. Security teams now defend not only data and infrastructure, but model behavior, training pipelines, and agent decision logic. Identity frameworks are expanding to include workloads, services, and autonomous agents acting on behalf of people or organizations.
AI readiness is increasingly cloud readiness. Success doesn’t come from having the best model, but from deploying AI quickly and safely to production, and keeping it improving continuously.
— Renata Švarcová, Senior Manager, AWS Competence Team, Trask
These shifts require new governance, risk models, and operating practices. We also see momentum toward more autonomous vulnerability remediation as environments become more dynamic.
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