Sekhar Koduri, Senior Director, Enterprise Offerings, DMIJanuary 13, 20255 Min ReadAleksia via Alamy StockGlobal spending on public cloud services will reach $805 billion this year and double by 2028. This exponential growth is being driven, in part, by a growing interdependence between artificial intelligence innovation and cloud infrastructure.AI systems demand tons of computational power and data. Generating this data in an on-premises data center is extremely expensive and impractical for most organizations. Conversely, the cloud provides a scalable and adaptable environment for AI to thrive.For instance, cloud platforms provide on-demand, fixed, and ephemeral compute resources for advanced AI processing. These resources are crucial for rapid prototyping and experimentation in AI innovation.However, unexpected costs, security, and regulatory concerns prevent cloud investments from reaching their full potential. Without a solid, sustainable cloud infrastructure, strong data foundation, and enterprise governance, organizations will not be able to reap the many benefits that AI has to offer.Fortunately, many prevalent challenges surrounding data management, cybersecurity, enterprise governance, cost containment, and change management are solvable.Strong Data Foundation Drives Business OutcomesAI applications demand vast amounts of data for training, testing, and validation, necessitating robust data storage solutions. As such, organizations must strengthen their data governance, integration, preparation, scalability, and financial policies to prepare a cloud environment for AI innovation.Related:Cloud platforms provide scalable computing power for AI workloads, eliminating the need for physical servers. With cloud-native object storage and distributed frameworks, organizations can efficiently process and store large datasets to ensure scalability and optimize performance. Also, modern semantic databases can provide the data foundation for ever-growing generative AI workloads.Organizations can use the pay-as-you-go model to eliminate upfront costs and dynamically scale resources based on demand. Additionally, multi-tier storage options optimize costs by storing data based on access patterns, and serverless platforms can provide cost-effective solutions for storing and analyzing petabytes of data.A robust data foundation not only supports scalability and cost-efficiency but also ensures ethical alignment and operational excellence.Security at the CoreFor organizations looking to incorporate AI-powered tools into their cloud environments, it's imperative to ensure the environment is secure beforehand.Related:Concerningly, 80% of data breaches in 2023 involved data stored in the cloud. In dynamic environments like the cloud, a zero-trust philosophy is a necessity.Several key components of zero-trust can fortify an organization's cloud security. For instance, asset discovery and misconfiguration monitoring can ensure organizations maintain visibility into their cloud environment. Further, cloud identity and entitlement management can ensure users can only access the minimum resources and permissions necessary to perform their tasks.No cyber efforts are foolproof -- even with these strategies in place, threat detection and incident response tools remain critical in case a malicious actor does breach the network. These should include continuous monitoring, vulnerability scanning, and guided remediation across cloud assets, workloads, and identities. Once security is assured, organizations can focus on other pressing challenges.Robust Enterprise Governance Framework Is Essential In response to the rapidly evolving field of AI, including generative AI, organizations should establish a multidisciplinary team dedicated to integrating AI within rigid regulatory frameworks, like the NIST AI Risk Management Framework.Related:Organizations should adhere to best practices like scalability, data management, and automation to create a secure, ethical, and efficient environment for AI deployment. Luckily, cloud providers offer cloud-native capabilities to navigate compliance requirements. For instance, some providers offer model bias, explainability features, and generative AI safeguards.Overcoming governance challenges through structured frameworks ensures that AI systems align with organizational goals and societal values, paving the way for responsible AI innovation.FinOps Keeps Ballooning Cloud Costs Under ControlOne of the most prevalent cloud migration and management concerns is cost. According to a McKinsey report, the average company spends 14% more than they intend to on cloud migration each year, and 75% of organizations exceed their planned budget. Cloud financial operations, or FinOps, provide a technological and organizational solution.The technological aspect enables cost observability through dashboards, regular reporting and alerts for cost overruns. These tools provide visibility into current and future costs and enable proactive management, so organizations aren't surprised by cloud invoices. Additional FinOps procedures and policies include approval processes for resource changes that impact costs and ongoing cloud cost forecasts.Implementing FinOps solutions and procedures drives financial accountability, efficiency, and overall cost control in cloud environments. As a result, organizations can optimize resources and investments.OCM Unites People, Processes & TechnologyAny technology or procedure is only as effective as the people using it. Organizations tend to underestimate the role of their workforce in ensuring a successful and sustainable cloud deployment.Leadership must focus on the employee perspective and experience to prevent delays and ensure successful cloud deployments. Organizational change management, or OCM, is a fundamental part of the transformational journey to the cloud.Leadership can ensure a smooth transition to the cloud with effective change communication, stakeholder collaboration, transparency, and thorough education. Organizations must account for the triumvirate of people, processes and technology throughout cloud migration, deployment and management.When the three work in harmony, organizations can significantly improve their operations, maximize the value of their cloud infrastructure, and capitalize on the boundless potential of AI.About the AuthorSekhar KoduriSenior Director, Enterprise Offerings, DMISekhar Koduri, DMIs lead for the data and analytics practice under the DMI CTO/EO office, spearheads transformative initiatives by leveraging the power of advanced analytics, data science, and AI for DMIs customers. He designs and implements robust data-driven strategies and AI workloads, enhancing operational efficiency and service delivery across sectors while ensuring AI safety and transparency. He is currently engaged in initiatives that explore the practical applications of Generative AI and large language models in the rapidly evolving AI landscape. He unlocks business utility and transformative capabilities by integrating these GenAI capabilities into customers operations.See more from Sekhar KoduriNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also LikeWebinarsMore WebinarsReportsMore Reports