Artificial intelligence is accelerating cloud adoption, infrastructure demand, and spending, forcing enterprises to replace periodic optimization reviews with continuous management.
In this Q&A with Channel Insider, Joaquim Alfaro Camps, global director of cloud advisory and FinOps services at Syntax, explains how the shift is also changing the role of cloud consultants—giving them new tools to identify risks, control costs, and guide decisions in real time.
How has AI impacted the way companies think about their cloud infrastructure?
AI has turned cloud infrastructure from a planning and capacity challenge into a game of continuous management.
Even before AI, cloud environments were growing more complex. Multicloud strategies, distributed workloads, and decentralized ownership across business units made governance, visibility, and cost management increasingly difficult.
AI accelerates all of those dynamics and brings different needs. AI services and applications can be deployed faster than mission-critical applications. As a result, cloud costs can escalate quickly and resource utilization changes constantly.
Given this, organizations can no longer rely on periodic optimization exercises or quarterly governance reviews. They need continuous visibility, intelligent automation, and real-time optimization to ensure performance, control costs, maintain security, and keep AI initiatives aligned with business objectives.
What do those changes mean for the providers and consultants who have historically advised on best practices?
Traditional cloud advisory has always been built on deep expertise, but it has largely been driven by periodic assessments, consultant-led analysis, and point-in-time recommendations. That approach worked when cloud environments changed at a manageable pace, but today’s multicloud, AI-enabled environments evolve far too quickly for static assessments to keep up.
The good news is AI is also transforming cloud advisory by augmenting—not replacing—human expertise. Instead of relying solely on documentation reviews, manual analysis, and point-in-time assessments, advisors can now leverage AI to rapidly analyze massive volumes of technical, operational, and financial data.
This enables them to identify patterns, detect emerging risks, and uncover optimization opportunities with far greater speed and consistency.
The result is a more proactive advisory model. AI can help forecast resource consumption, identify cost and performance anomalies before they become business issues, and continuously monitor cloud environments as they evolve.
Rather than making recommendations based only on historical snapshots, advisors can deliver ongoing, data-driven insights that help organizations optimize operations, strengthen governance, and make smarter cloud decisions in real time.
We’ve shifted from advisory based on historical analysis to advisory driven by augmented intelligence.
How can the addition of AI tooling optimize different phases of the cloud lifecycle?
The biggest opportunity isn’t using AI as another point solution but rather embedding it across the entire cloud lifecycle. When you do that, every phase becomes faster, more informed, and more proactive.
For example, during cloud readiness assessments, AI can analyze far more environment data than a person could manually, helping uncover risks, dependencies, and modernization opportunities much earlier.
As organizations move into architecture design, AI can evaluate design patterns, identify potential tradeoffs, and help advisors recommend architectures that are better aligned with both technical requirements and business goals. And once workloads are running, AI continuously analyzes usage, spending, and resource consumption to identify optimization opportunities before unnecessary costs begin to accumulate.
The same is true for governance and security. AI can continuously monitor for policy violations, configuration drift, and compliance risks across increasingly complex cloud environments, giving organizations much better visibility than periodic reviews alone.
The end result is a more intelligent, adaptive cloud operating model where AI delivers
continuous insight and automation, while experienced advisors provide the strategic judgment and business context needed to turn those insights into better decisions.
Are there any components of legacy cloud advisory conversations that are still relevant in the AI era?
The foundations of the advisory conversation remain the same. We are still talking about IT services that support business processes, business outcomes, and operational resilience.
Organizations still need to understand what they are trying to achieve, which workloads and capabilities are most critical, what risks they must manage, and how cloud decisions support their broader strategy.
What has changed is the speed, scale, and risk profile of the environment. AI increases the velocity at which new services are adopted, data is consumed, infrastructure demand changes, and decisions need to be made. This makes traditional advisory topics such as governance, architecture, security, cost management, and operational accountability even more important, not less important.
Security is also a much larger concern in the AI era. The use of AI introduces new questions around data exposure, access control, model governance, compliance, and the potential impact of security breaches. Advisory conversations must therefore continue to focus on strong foundations: clear ownership, well-defined controls, secure architectures, and disciplined operating models.
In that sense, legacy cloud advisory does not disappear. It evolves. The same core questions remain relevant, but they must now be answered with more continuous visibility, faster decision cycles, and stronger alignment between technology, risk, and business priorities.