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Enterprise leaders across industries face a growing challenge; traditional infrastructure systems — built for predictable, static workloads — are increasingly misaligned with the dynamic, high-throughput demands of AI and data-driven operations.
The National Institute of Standards and Technology (NIST) has quantified key challenges to AI deployment, including compute constraints, latency sensitivity, and workload volatility driven by frequent model drift and retraining cycles. These limitations can not only stall innovation, but also can contribute to the widespread failure of AI systems to scale beyond pilot phases.
Meanwhile, the National Institutes of Health (NIH) has underscored the growing computational burden in modeling complex biological processes, citing the need for scalable infrastructure to support high-performance computing in drug discovery and biomedical research.
Emerj Artificial Intelligence Research Founder and Head of Research Daniel Faggella had a conversation with Deborah Golden, U.S. Chief Innovation Officer at Deloitte, about how enterprise leaders are rethinking infrastructure in the age of AI. Their discussion explores how infrastructure can evolve from a static backbone to a dynamic enabler of innovation — balancing performance, governance, and cost control at scale.
This article lays out strategies that can help turn infrastructure into a foundation for trustworthy, scalable AI in the enterprise:
- Treat infrastructure as a strategic asset: Viewing infrastructure as a dynamic, intelligent ecosystem that drives innovation beyond traditional cost-centered approaches.
- Build cross-functional teams as co-owners: Creating integrated teams across business, compliance, and IT to break down silos and accelerate meaningful AI deployment.
- Design infrastructure for real-time adaptability and safety: Embedding governance, self-correction, and ethical guardrails directly into infrastructure to handle AI’s inherent volatility.
Listen to the full episode below:
Guest: Deborah Golden, U.S. Chief Innovation Officer, Deloitte
Expertise: Enterprise Innovation, Security Leadership, Change Management, Cross-Industry Risk
Brief Recognition: Deborah Golden is the U.S. Chief Innovation Officer at Deloitte, leading enterprise-wide innovation strategy and transformation initiatives. Prior to her current role, she served as the U.S. Cyber and Strategic Risk leader, driving large-scale security and resilience programs across sectors. Deborah earned her Master’s degree in Information Technology from George Washington University and is widely recognized for her leadership in inclusive innovation, systems thinking, and cultural change.
Treat Infrastructure as a Strategic Asset – Moving Beyond Cost to Business Value
Throughout her podcast appearance, Deborah stresses that enterprise leaders should shift their view of infrastructure from a static cost center to a dynamic strategic asset critical for AI success. Instead of thinking of infrastructure as just hardware or basic IT support, leaders should see it as an adaptable, intelligent platform designed to evolve with business goals and drive measurable outcomes.
To make the mindset she describes more practical, Golden recommends treating infrastructure like a product with clearly defined business-focused KPIs. Leaders can start by asking:
- What specific business outcomes should infrastructure enable? (e.g., faster decision-making, reducing AI errors, improving customer experience)
- How do we currently measure infrastructure’s impact on these outcomes?
She advises building a cross-functional infrastructure roadmap owned by business, IT, compliance, and finance teams that:
- Defines clear Service Level Agreements (SLAs) tied to business metrics, not just technical uptime
- Embeds governance and risk management processes early to anticipate AI volatility and model drift
- Includes regular retrospectives to review performance, risks, and opportunities for optimization
Golden also highlights that AI infrastructure should be designed to adapt and self-govern in real time, because AI models and data environments are inherently unpredictable. The goal is a living infrastructure that can:
- Monitor model performance continuously and trigger automated corrections
- Track data lineage and compliance in real time to reduce risk and accelerate audits
- Optimize compute resources dynamically to balance cost, latency, and sustainability
By adopting a ‘product mindset’ and embedding these capabilities, organizations can help ensure infrastructure investments deliver not only technical reliability but also measurable business value — turning infrastructure into a true enabler of AI-driven innovation.
“In the past, infrastructure was simply about physical components like servers and cables, designed to support known environments. But AI fundamentally disrupts this model by operating in unpredictable spaces where models constantly drift, inputs mutate, and outputs continually surprise us.
The old approach of just scaling faster won’t work; instead, we need an intelligent infrastructure that can dynamically adapt, govern itself, and continuously optimize in real-time.”
– Deborah Golden, U.S. Chief Innovation Officer at Deloitte
Build Cross-Functional Teams as Co-Owners – Aligning Business, Compliance, and IT
One of the core barriers to scaling AI and infrastructure is organizational misalignment. Deborah points out that when infrastructure remains siloed in IT or operations, it can blindside the business, causing friction, cost overruns, and failure to scale. A potential solution lies in forming cross-functional teams that include business leaders, compliance officers, CFOs, and IT as true co-owners rather than occasional advisors.
Golden stresses that these teams should be embedded in day-to-day decisions, not just convened monthly, to ensure alignment on risk, governance, and business value. The ensuing collective ownership can drive fast return on investment and move projects beyond “pilot purgatory” to meaningful deployment and impact.
“If infrastructure continues to live in a silo, it doesn’t just slow deployment, it can blindside the business. If one person or one owner is driving that, they may not see that complement of upside and downside. The most advanced organizations aren’t just chasing speed. They’re building coherence across data, across compute, across risk, across ethics.”
– Deborah Golden, U.S. Chief Innovation Officer at Deloitte
Design Infrastructure for Real-Time Adaptability and Safety – Embed Governance Early
AI infrastructure faces unique challenges: constant model drift, hallucination, and rapidly escalating cloud costs. Deborah highlights that organizations can no longer bolt on safety, explainability, or governance after deployment; these should be integral from the beginning.
She states that “if your systems can flex, adapt, and govern in real time, you’re winning.” Infrastructure should be intelligent and proactive — able to self-correct, track data lineage, detect shadow AI risks, and embed ethical guardrails dynamically.
The approach Deborah describes is one she insists can also reduce operational drag, better control escalating costs, and build enterprise trust, positioning organizations to scale AI securely and sustainably:
“You can’t fix AI infrastructure challenges with policy, because we don’t know where these AI models are going to go. We know they’re only going to continue to increase. Whether that’s in cloud or on-prem, these costs will continue to scale at a higher exponential rate. So if that’s the case, how do you actually then build in these types of safeguards? It can’t be an afterthought post-deployment. You’re just going to continue to increase your cost. Risk and oversight require informed decisions in the moment, not something designed after you’ve actually moved forward with that deployment.”
– Deborah Golden, U.S. Chief Innovation Officer at Deloitte
The following principles distill the core of the proactive approach Deborah describes throughout the episode:
- AI infrastructure should be designed up front — not retrofitted after deployment.
- Governance, safety, and explainability should be embedded from the start.
Deborah emphasizes that designing AI infrastructure isn’t just a technical exercise — it’s about embedding resilience, accountability, and adaptability from the start. She points to several emerging risks that leaders should keep top of mind:
- Model drift and hallucinations can compromise the reliability of AI outputs over time.
- Cloud costs can spiral quickly without proactive monitoring and scaling strategies.
- Shadow AI deployments—tools launched outside IT’s oversight—undermine governance and security.
To manage these risks, infrastructure should do more than serve the model; it should actively govern and adapt in real time. Deborah underscores the importance of systems that:
- Self-correct as conditions shift,
- Maintain data traceability across pipelines, and
- Enforce ethical and compliance standards without slowing innovation.
Her broader message to the executive podcast audience is clear: effective infrastructure can enable AI systems to make better decisions — and to do so continuously and at scale, without creating downstream risk for the business.
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