Communicate multicloud’s strategic business value
Multicloud isn’t optional anymore. Most medium to large enterprises are already operating across multiple cloud platforms, whether they’ve planned for it or not. So, the question is no longer “Should we go multicloud?” It’s “How do we make multicloud a competitive edge?”
This starts with the clarity of your vision. If you’re leading IT, you need to be able to explain, in one page or less, how multicloud investments tie directly to business outcomes, speed, cost control, risk reduction, and innovation. Skip the slides. Just tell people plainly: here’s how multicloud cuts costs, moves faster, avoids vendor lock-in, and gives us room to scale with fewer blockers. That narrative needs numbers. Show how uptime improved. Show how it saved you from pricing shocks. Prove the point.
Jon Alexander, SVP of Product in Cloud Technology at Akamai, said it well: “AI is accelerating this divide, automating configurations and predicting infrastructure needs in ways that give multicloud-native companies an increasing advantage.” He’s right. It’s not just about infrastructure anymore. It’s about how far ahead your competitors are getting because they’re already using AI to make multicloud work harder for them.
We’ve seen what happens when businesses bet too heavily on a single ecosystem. Sharad Kumar, an experienced entrepreneur and investor, pointed to the Broadcom-VMware fallout as a cautionary tale, companies locked into one cloud provider are deeply exposed when the terms of that relationship suddenly change. Whether it’s pricing, capabilities, or strategic direction, you don’t want all your mission-critical assets at the mercy of one partner’s next decision.
Multicloud is the clear path forward to avoid being boxed in. But it only adds value if leadership talks about it the right way, with proactive goals and business-oriented metrics. If your team doesn’t understand what success looks like here, they won’t build it.
Align multicloud strategies with generative AI (genAI) initiatives
Your cloud strategy likely predates the current wave of generative AI. That’s fine, but now it needs an upgrade. GenAI is introducing new demands for compute power, data access, and model diversity that your existing cloud architecture was probably never designed for.
If you want your organization to innovate with genAI, multicloud isn’t just helpful, it’s essential. It gives teams access to specialized AI tools that aren’t always available in a single ecosystem, and it enables faster experimentation across different models. Nandakumar Sivaraman, SVP and Chief Architect of Enterprise Data at Bridgenext, put it clearly: “AI/ML introduces new drivers for multicloud adoption” by giving organizations access to diverse foundation models and region-specific data capabilities.
The key is tying this technical opportunity back to business impact. That means asking the right questions: What’s the value in using OpenAI through Azure vs. Google Vertex AI? How can we build faster or better if we’re not locked into one?”
Michael Ameling, Chief Product Officer of SAP Business Technology Platform, adds to this with a sharp directive: “Every aspiring IT leader must consider how they will harness new technology, especially genAI, to drive innovation and create lasting impact.” He’s pointing out the shift in mindset that leadership needs. It’s not about managing infrastructure anymore, it’s about designing environments that let your teams create transformative products.
C-suite leaders should also keep an eye on zero-ETL strategies and no-code/low-code integrations emerging from SaaS players tightly aligned with cloud providers. These integrations remove friction and expose opportunities that weren’t visible a year ago. Every delay in adapting your multicloud plan to accommodate genAI is a delay in delivering business innovation.
Optimize secure, high-performance multicloud networking
A lot of companies end up in multicloud by accident, mergers, acquisitions, department-led cloud deployments. That’s fine, but working with multiple platforms without a clear networking strategy causes problems fast. Poor latency, inconsistent security policies, unpredictable performance, it all adds up.
The public internet isn’t built for secure, high-performance communication between cloud environments. As multicloud architectures evolve, prioritizing private, direct interconnects is no longer a luxury. It’s foundational. These connections avoid unnecessary hops across the public web, reducing latency, cutting exposure to external threats, and improving service performance end-to-end.
Bratin Saha, Chief Product and Technology Officer at DigitalOcean, nailed it: “Most organizations use multiclouds, yet the ability to seamlessly and securely interconnect these environments remains a significant challenge.” He highlights the growing support from cloud providers who now offer native cross-cloud interconnect solutions, AWS Direct Connect, Azure ExpressRoute, Google Cloud Interconnect. These are built for organizations that value speed and security without expensive workarounds.
If you’re running serious workloads, real-time transaction systems, AI model training, data replication across platforms, then the performance and resiliency of your cloud-to-cloud networking must be dialed in. Leaders must evaluate not just raw bandwidth but network design principles: redundancy, encryption, segmentation. These aren’t things to patch later. They’re core to keeping operations running when it counts.
When outages hit or throughput drops, nobody blames the virtualization layer, they blame leadership for weak infrastructure strategy. Take the time now to solve for performance and secure connectivity across environments. It shortens incident response times and directly strengthens service delivery.
Standardize identity and security policies across clouds
Security grows more complex the moment you’re working across more than one cloud. Each environment has its own identity management system, access policies, encryption defaults. That’s a recipe for gaps, human errors, permissions out of sync, and missed detections.
Centralizing identity and access management is the move. One unified layer across all providers brings clarity. It brings control. And it keeps your organization aligned on what users and systems can, and cannot, do across cloud and hybrid deployments. Any other method adds friction and creates audit headaches.
Jimmy Mesta, Co-founder and CTO at RAD Security, said it directly: “Without federated identity, unified telemetry, and real-time policy enforcement, you’re flying blind.” And he’s right. You can’t enforce zero-trust models, respond to threats, or meet compliance if your visibility is fragmented or your controls are loosely tied together.
This becomes an even bigger issue when your dev teams are pushing apps at speed, your data teams are training models across zones, and your operations teams are managing runtime environments in multiple clouds. Everyone needs secure access. Nobody wants security slowing down delivery. That’s where automation helps. Automate least-privilege policies across all platforms. Set up centralized detection and incident response workflows that operate in real time and don’t break across platforms.
For executives, the takeaway is this: if you want to scale innovation without scaling risk, identity and security must be unified. Not just monitored, controlled. Not just standardized, automated. That’s what protects IP, data, and reputation.
Strategically place workloads to balance performance, compliance, and cost
Multicloud only delivers real value when workloads are placed deliberately. Every workload, whether it’s an internal system, customer-facing app, or AI training pipeline, has unique requirements. Performance, cost, compliance, proximity to users or partners, and workload portability all play into where it should run.
The cloud landscape now includes thousands of instance types. AWS offers over 850 EC2 variations alone. Azure and Google Cloud follow suit with extensive combinations rooted in compute, memory, and specialized hardware needs. When you also consider options like reserved vs. spot instances, serverless compute, and edge deployments, it’s clear that keeping this chaotic menu in check requires structured evaluation.
Don’t leave placement decisions solely to engineers optimizing for convenience or historical patterns. Set clear criteria. Include key indicators like latency tolerance, data residency laws, peak vs. baseline usage requirements, and levels of inter-service communication. Unstructured placement leads to performance bottlenecks, ballooning costs, or worse, compliance violations.
Avishai Sharlin, Division President of Product and Network at Amdocs, explains why this matters: “Edge functions reduce latency and support real-time, location-based updates.” Those are major drivers for telecommunications, media, IoT, and financial services where responsiveness is a business imperative.
Leaders need visibility into where applications operate and why. A workload might benefit from GPU-optimized regions for AI inference today, and tomorrow it might need to shift to remain compliant with new data laws in a specific region. Containerization, abstraction layers, and cloud-agnostic platforms make that kind of flexibility realistic, when planned properly.
Work placement isn’t a hardware discussion. It’s a business architecture choice. Make sure you treat it that way.
Strengthen data governance for GenAI integration
Data governance isn’t optional anymore, especially not in a multicloud environment where private large language models (LLMs), AI agents, and enterprise data stacks are converging. AI doesn’t generate business value without data. And data doesn’t deliver that value unless it’s properly governed, classified, secured, and accessible across platforms.
The organizations doing this right aren’t just setting up access control lists. They’re building frameworks that define who can use what data, how long it’s retained, the sensitivity level, and terms for sharing across clouds. They’re applying continuous controls with clear visibility across teams developing, training, and deploying AI models.
Bart Koek, EMEA Field CTO at Immuta, underscored a measurable approach here: “Track key performance indicators like the percentage of data covered by access policies, the reduction in data breach incidents, and the improvement in data accessibility for authorized users.” These aren’t theoretical metrics. They show risk containment, regulatory alignment, and faster AI development velocity.
From the security side, Tim Morris, Chief Security Advisor at Tanium, makes it clear: without real-time, continuous visibility into your IT estate, any talk of “AI readiness” is premature. He advises looking at both how data is being used and why. Contextual metadata, user behavior, and storage location all factor into risk-based governance models that scale with your environment.
If you’re running AI across multicloud, then lazy or static data security practices will slow you down or expose you to regulatory consequences, sometimes both. A dynamic, integrated governance layer lets your teams move fast while staying in control of data that is fueling your business’s future capabilities.
Accelerate architectural agility and application modernization
Legacy systems didn’t break overnight, but they’re now one of the largest barriers to speed and innovation in enterprise IT. And they don’t play well with multicloud. If your organization is serious about operating across multiple clouds, you have to modernize your applications and architecture to match the pace of change.
Agile architecture isn’t about rewriting everything at once. It’s about designing systems that can evolve. This means prioritizing component-based architectures, feature flagging, observability from the ground up, and adopting continuous delivery pipelines. GenAI can support this shift. Use it to accelerate testing, automate refactoring tasks, and streamline platform migration. If you’re still approaching app modernization as a batch migration or tech debt clean-up exercise, you’re already behind.
Vikram Murali, VP of Application Modernization and IT Automation at IBM, points out the imbalance clearly: “While enterprises rapidly adopt AI for development, operational management lags across SDLC, devops, ITSM, and IT operations.” This is the problem. Most enterprises push AI into development workflows but leave critical operational environments stuck on outdated tools and manual processes. As a result, deployments slow down, risks accumulate, and opportunities are missed.
C-suite leaders must understand that modernization is not an infrastructure task, it’s a business enabler. Applications influence agility, customer experience, productivity, and even talent retention. Aging platforms with long release cycles tie your teams down. Modern application architecture frees them to experiment, scale, and build with confidence.
Funds spent on modernization avoid future instability. Time spent aligning teams with agile architecture reduces complexity. It’s not a one-time upgrade, it’s a culture shift with measurable business output.
Integrate FinOps to manage multicloud costs proactively
More clouds mean more spending. But more spending doesn’t have to mean more waste. FinOps, the financial discipline of managing cloud spending in alignment with engineering and business needs, has to be embedded across multicloud operations.
Multicloud introduces cost complexity fast. Each platform measures usage differently, billing models and discounts vary, and overprovisioning becomes easy if left unchecked. Proactive FinOps means making cost visibility native to the development process, not something tacked on after the bills arrive.
Ananth Kumar, a FinOps leader at ManageEngine, summed it up: “Integrating FinOps practices early in development can prevent cost debt accumulation.” He recommends right-sized environments, usage-based alerts, and automated tagging during both dev and test phases. This shift-left mindset saves you from rebuilding projects because the financial architecture wasn’t sustainable.
Executives need to do three things: integrate cost observability into engineering tools, build shared accountability between finance and tech teams, and measure cloud usage not just in dollars but in value delivered. Knowing peak vs. typical usage patterns helps optimize autoscaling and ensures teams aren’t budgeting for theoretical load patterns.
Multicloud doesn’t need to be expensive. But if you don’t manage it actively, it will be. FinOps transforms cost control from a reactive cleanup operation into a planning tool that directly supports innovation, margin improvement, and environmental goals like reduced consumption and emissions. You want that control in real time, not quarterly.
Embrace AIops for enhanced multicloud IT operations
Multicloud environments have added layers of complexity across platforms, toolsets, and workflows. Managing incident responses, performance metrics, and service reliability without automation is becoming unsustainable. This is where AIops, AI-driven IT operations, steps in. It’s not a future idea. It’s already determining how efficiently teams resolve issues, scale services, and reduce operational noise.
AIops platforms correlate alerts across infrastructure, application, and service layers, reducing redundant signals and surfacing actionable insights. That kind of correlation doesn’t just save time, it prevents outages. It also improves service-level objective (SLO) adherence when full-stack observability is otherwise buried under siloed logs and fragmented events.
Jonathan LaCour, CTO at Mission, explains the trajectory well: AIops will move from assisting operators to autonomously taking action. This isn’t just a labor-saving advantage, it expands strategic capacity. As LaCour emphasized, “AIops platforms will become increasingly autonomous… allowing IT engineers to focus on critical thinking, problem solving, and automation.” That shift is already underway in organizations that prioritize automation maturity across their CI/CD pipelines and cloud orchestration tools.
For C-level leaders, the point is simple: if IT still functions primarily as a reactive support function, it won’t scale with your multicloud ambition. You need platforms that enforce consistency, accelerate incident resolution, and make knowledge reusable. Systems should learn from past events, predict known failure patterns, and automate preventive actions. That’s the promise of AIops, and it’s aligned with what modern, distributed digital infrastructure demands.
Invest in centralized monitoring platforms that support AIops across providers. Couple it with configuration as code, container orchestration, and continuous delivery workflows. It produces measurable reliability gains while freeing up engineers to focus on transformation, not just uptime.
Build a culture of continuous learning and collaboration
As cloud and AI evolve, the skills required to operate these environments change just as fast. Your teams will fall behind if continuous learning isn’t built into your company’s operating model. This isn’t just about technical certification, it’s about creating a culture where cross-functional collaboration and skill agility are seen as core competencies.
Combining application development, security, infrastructure, and financial operations requires alignment across disciplines. It also requires teams to rethink their roles and adopt new tools, GenAI included. Professionals in operations roles now need a working understanding of model lifecycle management, security integration for LLMs, and intelligent automation tools that didn’t exist two years ago.
Anant Adya, EVP at Infosys Cobalt, points directly to this challenge: “Professionals working in operations roles must develop a mix of technical and analytical AI skills, including learning the basics of AI, machine learning, and deep learning.” It’s a practical recommendation, not a theoretical one. If your ops teams can’t manage AI workloads and understand how those models behave in production settings, it limits your ability to use AI anywhere in the business at scale.
Executives must lead with investment and accountability. That means structured training programs, frequent hands-on labs, certifications, and internal communities of practice that share learnings quickly. It also means executive support for experimentation, encouraging people to test new methods, challenge assumptions, and deliver outcomes beyond narrow job titles.
Transformation doesn’t come from tools alone. It comes from people who are motivated, capable, and aligned. A multicloud future demands continuous learning and cross-team fluency. Build that into your strategy now, or risk operational fragmentation later.
Recap
Multicloud isn’t a technical preference, it’s a business decision with strategic consequences. When done well, it improves resilience, unlocks access to specialized AI capabilities, reduces long-term costs, and protects against vendor risk. But the architecture alone doesn’t guarantee the benefit. It’s the decisions around governance, team culture, operations, and financial control that define whether multicloud becomes an advantage or an overhead.
As a leader, your role isn’t to micromanage cloud choices, it’s to set clarity around value, risk posture, and velocity. That means aligning your teams to shared outcomes, funding modernization deliberately, and driving a culture where continuous learning and experimentation aren’t just encouraged, they’re operational needs.
The organizations that move fastest in the next decade won’t be the ones spending the most, they’ll be the ones making the smartest use of complexity. Multicloud is already here. The only question is whether you’re shaping it to serve your business or simply reacting to its demands.
