The early days of cloud computing were about migrating infrastructure paradigms for cost savings. In recent years, however, cloud has become a critical enabler of rapid development and digital innovation.
“The ability to spin up environments on demand, leverage robust service libraries, and access global scalability fundamentally transformed how organizations deliver products and services,” says Joe Nathan, associate principal with The Hackett Group.
Meanwhile, the broader technology landscape has also changed dramatically. “The rise of AI, increasing cyber threats, evolving data requirements, regulatory scrutiny, and the rapid advancement of cloud-native architectures have introduced new levels of complexity — and opportunity,” Nathan says. The wrong moves can set organizations back, while the right ones can enable them to leapfrog rivals.
But CIOs’ increasing experience with the cloud has also led them to rethink where specific workloads perform best.
“Cloud repatriation is a reality,” says Niel Nickolaisen, longtime IT leader and current director of strategic engagements for JourneyTeam. “We move workloads to the cloud only to discover they are not a fit for the cloud, they are too expensive, regulations change, workload demands change.”
As a result, frequent reassessments of cloud strategy are essential. “The pace of change in these areas makes ‘set and forget’ cloud strategies obsolete,” says Joe Topinka, veteran IT leader and founder of CIO Mentor. “This becomes even more urgent during major platform shifts or when emerging technology ambitions outpace current infrastructure.”
Cloud strategies must be continuously realigned with business priorities and new technologies. “The decisions made today will shape long-term agility, compliance posture, cost efficiency, and innovation potential,” Hackett Group’s Nathan says.
S&P Global, for example, which operates in more than 35 countries, has had to integrate compliance with diverse and evolving regulatory requirements into its cloud architecture.
“Without robust cloud governance, organizations risk cost overruns, security vulnerabilities, and missed innovation opportunities,” says Swamy Kocherlakota, chief digital solutions officer at S&P Global.
Following are questions IT leaders should ask about their cloud strategies to ensure they continue to serve their business ambitions.
Do we have an enterprise framework to guide cloud decisions?
Cloud platforms are increasingly procured by non-IT teams. Establishing a unified decision framework that brings together expertise from across the enterprise to guide the cloud lifecycle, from selection to sunsetting, is key.
Without this, “organizations face fragmented architectures, redundant tools, and compliance gaps,” says CIO Mentor’s Topinka, who saw the marketing team at one client select a cloud-based customer engagement platform, only for the legal team to discover later that it introduced serious privacy issues. The company then brought together experts from legal, cyber, privacy, risk, and finance to develop a framework that ensures every cloud decision is evaluated for security, compliance, and scalability, without slowing down business-led innovation.
How can we capitalize on multicloud without sacrificing standardization?
Working with multiple cloud partners can offer negotiating leverage and access to best-of-breed services, but it also compounds complexity and requires a range of expertise.
“To address this, develop clear decision frameworks for selecting cloud providers based on workload requirements, security considerations, and cost models,” S&P Global’s Kocherlakota says.
IT leaders can also establish centers of excellence for each major cloud platform and establish a cross-cloud architecture team to ensure consistency and integration across platforms, he adds.
Do we have the talent and culture to innovate in the cloud?
“Technology alone does not create innovation; people do,” says Topinka. “I have seen teams hesitate to adopt new approaches because they are comfortable with what they know.”
At one client organization, Topinka helped create a small, cross-functional innovation team. These individuals, tapped for their curiosity and fearlessness, were paired with mentors and given permission to experiment, ultimately delivering a new customer-facing capability that expanded the organization’s market reach in ways leadership had not considered.
“The maturity and advancement of cloud solutions depend on the team’s culture and their ability to operate and innovate within the cloud,” Hackett Group’s Nathan adds.
Are we prepared to manage cloud costs at scale?
Developing a three- to five-year cost model is essential to understand the break-even period, steady-state benefits, and total cost of ownership of a cloud solution, Nathan says.
In addition, “many organizations face ‘sticker shock’ due to inefficiencies in resource provisioning and suboptimal architectures,” says S&P Global’s Kocherlakota, who advises implementing robust tagging strategies, adopting automated monitoring tools, and conducting regular optimization reviews.
“Clear visibility into consumption patterns, resource allocation, and usage metrics is essential,” says Nathan, noting that cloud financial management practices help maintain accountability and prevent cost overruns, particularly in multicloud environments.
Allocating cloud costs directly to business units or product teams also increases transparency and encourages more efficient use of cloud resources, according to Kocherlakota.
Is our cloud strategy truly advancing operations and the business?
“While cloud adoption comes with many advantages — scalability, resiliency, and reliability —achieving a strong ROI remains a challenge if the cloud strategy is not comprehensive,” says Krishna Mohan, vice president and global head of the cloud business unit at consultancy TCS.
“Cloud is far more than a technical architecture upgrade,” he adds. “Often in enterprises, the legacy systems hold important core business logic, so legacy modernization on cloud to drive business benefits is key. But it’s more nuanced and complex and time consuming.”
Cloud adoption without attendant legacy modernization can backfire, S&P Global’s Kocherlakota says. “Simply using the cloud as a data center while maintaining legacy applications can lead to cost creep,” he says. “Investing in transforming legacy systems optimizes infrastructure and boosts efficiency.”
The good news, says Mohan, is that generative AI is slashing the time it takes to modernize legacy systems from four to five years to 18 to 24 months, “but it requires accelerated governance, reskilling, and risk management.”
A large organization Topinka worked with was replacing a decades-old mainframe system with a cloud ERP platform. “It was the perfect trigger to revisit their entire cloud strategy,” Topinka says. “Without that reset, they would have simply replicated outdated processes in a new environment, missing opportunities to modernize governance, integration, and innovation approaches.”
How will AI impact our cloud architecture and governance needs?
“AI fundamentally changes compute requirements, storage demands, cost models, and security needs,” says Topinka. “Ignore this, and you will have an expensive, insecure science project.”
One organization he worked with planned to run all AI workloads in a single public cloud region until they realized that data residency regulations would make that option non-compliant. A hybrid approach addressed regulatory requirements and kept the project on track.
“Even with the best intentions, AI programs will stall or misfire if they are not grounded in a cloud architecture built for scale, compliance, and secure operations,” Topinka says.
Another of Topinka’s clients had minimal cloud experience and quickly committed to an aggressive AI program. “Their data pipelines, security model, and governance structures were not ready for AI-scale workloads,” Topinka explains. “Revisiting the cloud strategy before scaling AI saved them from costly rework and allowed the AI program to deliver business value faster.”
Access to the high-quality data required for AI models — particularly large language models — is a must.
“Data pipelines are a foundational requirement because they are the fuel that drives the AI models and enables continuous learning and feedback loops to improve model outputs,” says Dinakar Hituvalli, CTO at enterprise software and project management firm Deltek.
To help a large logistics company develop a cloud strategy aligned with its AI and data transformation objectives, Hackett Group’s Nathan says they began with a reassessment of cloud goals before turning to AI readiness, ensuring the infrastructure could support data centralization, model training, and scalable inference, while addressing regional data residency requirements to support the company’s expansion into Europe and Asia-Pacific.
Does our cloud strategy align with our sustainability goals?
“As enterprises increasingly prioritize environmental responsibility, it’s important to request and compare detailed environmental impact data from cloud providers,” says Kocherlakota, especially as cloud providers vary in their commitments to renewable energy and carbon neutrality.
IT leaders can factor energy efficiency into their cloud decisions by hosting workloads in regions powered by renewable energy and optimizing workloads to minimize consumption. “This not only supports sustainability goals but can also enhance brand reputation and align with corporate social responsibility initiatives,” Kocherlakota says.
Should our cloud-first strategy be cloud-only?
In short, no, says Mohan of TCS. Today’s organizations may find that hybrid or distributed cloud designs may make sense in situations with complex business, security, regulatory, or operational spending requirements. It’s about the right workload placement.
AI is also having an impact on these decisions. “Traditionally, enterprises pulled data to a centralized location for orchestration, production, and deriving value,” says Mohan. “With AI now being applied to wherever the data resides across the IT estate, organizations can drive more impactful edge use cases and outcomes.”
As such, CIO’s cloud strategies should consider hybrid and edge solutions. “With AI and cloud working together, CIOs can balance out their return on investment, since edge cloud reduces latency, improves reliability, lowers data transfer, and enables real-time decision-making, directly improving customer experience and ROI,” Mohan says.
What happens if we need to move to a different service or provider?
“In a time of rapid change and massive uncertainty it is critical for technology leaders to avoid technology dead ends. Sadly, it is hard to predict the form of those dead ends,” says Nickolaisen. “Anything in our world may be a barrier to agility — that includes cloud architecture and decisions.”
Whereas in the past a cloud decision may have been primarily made based on cost and elasticity, leaders must now consider obsolescence and portability of workloads, contracts, and skills. IT leaders should work through what they can do to reduce “switching costs” because they might need to change technologies, providers, tools, and skills quickly.
“How can we loosely couple cloud services from other services so that we can find a reasonable balance between replaceability and reusability? Even things like contract terms might need to change to roll with the uncertainty punches,” says Nickolaisen, who has been able to migrate from one cloud provider to another, and migrate to or repatriate from the cloud, in hours. “This becomes essential when conditions are changing quickly, as they are now,” Nickolaisen says.
As Topinka urges: “Your cloud strategy needs to be a living plan, reviewed often, or it will age out quickly.”
Read More from This Article: 9 cloud strategy questions every IT leader must answer
Source: News