With AI, organizations are unlocking the full potential of their hybrid and multicloud investments, achieving cost efficiency and operational excellence.

The rise of hybrid and multicloud environments has been a game-changer for businesses, offering unprecedented scalability, flexibility, and pathways to innovation. Yet, this adoption has also brought to light a significant challenge in managing costs associated with cloud services.

As companies dive deeper into the multicloud environment, they're finding that managing the associated expenses is more complex than anticipated. Resource usage can become a significant challenge. Achieving comprehensive visibility into resource usage, performance, and costs across hybrid and multicloud environments is difficult because companies often lack comprehensive insights and control over their diverse IT landscapes. This leads to overrun budgets and underutilized resources, highlighting a critical gap in effective financial management within cloud environments.

Enter artificial intelligence (AI) and machine learning (ML). These technologies are proving to be invaluable tools for companies looking to get a handle on their cloud costs in a hybrid/multicloud environment. AI and ML dive deep into data on resource usage and business operations, uncovering insights that can lead to more informed decision-making. With these technologies, businesses can predict demand more accurately, optimize costs automatically, and monitor performance more effectively. This isn't just about cutting costs; it's about optimizing the entire cloud environment to ensure that every dollar spent is driving value, maintaining performance, and adhering to compliance standards in a risk-managed way.

Common Challenges to Managing Hybrid and Multicloud Programs

Driven by the business need for greater flexibility, resilience, and access to best-in-class services across different cloud providers, multicloud and hybrid cloud strategies are on the rise, creating a labyrinth of services and pricing models. This complexity is not just a matter of navigating different platforms; businesses also struggle to integrate these services so they function as a cohesive unit. The diversity in operating and billing structures across cloud providers complicates the management process.

Another challenge is the lack of overall visibility and control over cloud resources. In the absence of comprehensive monitoring tools and clear governance policies, organizations struggle to get a holistic view of their cloud spending and usage patterns. This lack of transparency can lead to inefficiencies, as decision-makers might not have the insights necessary to make choices about current resource usage, allocation, and cost management. Without a detailed understanding of where and how cloud resources are being consumed, it becomes nearly impossible to identify areas of waste or opportunities for optimization.

Navigating Cloud Complexity with AI

AI/ML offers a pathway to not just mitigate the complexities of today’s hybrid cloud environments but to transform them into opportunities for optimization and cost savings. These technologies predict and respond to demand, refine cost management and enhance performance monitoring with a level of precision and foresight previously unattainable.

  • Predictive Analytics for Demand Forecasting: AI/ML excels in analyzing vast amounts of historical data and identifying patterns to forecast future cloud usage with high accuracy. For instance, Wipro's AI-driven solutions have enabled businesses to anticipate demand surges, such as those experienced during online sales events or seasonal peaks in user activity.
    By accurately predicting these changes, companies can adjust their cloud resource allocation in advance, ensuring they're neither over-provisioned nor caught off-guard by sudden demand. This strategic adjustment prevents waste and ensures that customer experiences remain seamless, which is a critical factor in maintaining competitive advantage.
  • Automated Cost Optimization: Applying AI/ML solutions extends the capabilities to the meticulous examination of cloud usage data, identifying not just current inefficiencies but also predicting future cost-saving opportunities. For example, by analyzing usage patterns, AI algorithms can provide a consolidated view of underutilized servers or recommend switching to more cost-effective cloud services or pricing plans.
    This proactive approach to cost management can automate the scaling of resources, significantly reducing operational costs without impacting performance. Businesses can reduce the need for manual oversight, allowing teams to focus on strategic initiatives rather than day-to-day cost management.
  • Enhanced Performance Monitoring: Leveraging AI/ML for performance monitoring means businesses can continuously oversee cloud service levels and automatically adjust resources to meet demand. Wipro's solutions in this area have empowered companies to dynamically allocate computing power where it's needed most, ensuring optimal performance across the cloud environment.
    For instance, in a case where a client faced challenges with application performance due to unpredictable workloads, Wipro implemented an AI solution that automatically scaled resources in real time, significantly improving service delivery and customer satisfaction. This level of responsiveness is crucial for maintaining the high standards of service delivery that customers expect, addressing performance bottlenecks before they impact the user experience.
Putting It All Together

To be successful in integrating AI into cloud financial management and instilling financial prudence, businesses must further prioritize the accessibility of quality data, invest in training for their teams, and establish clear governance and oversight mechanisms. AI's potential to transform cloud cost management is amplified when it is fed high-quality, comprehensive data, enabling more accurate forecasting and decision-making. Furthermore, fostering a culture that values data-driven insights and continuous learning can empower teams to leverage AI tools more effectively, driving innovation and efficiency. Establishing robust governance structures ensures that AI's integration aligns with the organization's dual strategic objectives: cost optimization and operational excellence.

The integration of AI into IT financial planning for hybrid/multi cloud environments marks a critical step forward for businesses seeking to optimize their cloud investments. While this shift may seem daunting to less digitally mature organizations, it is essential for harnessing the full potential of cloud in a rapidly evolving digital landscape. By embracing a change in mindset, prioritizing strategic integration, and leveraging AI's capabilities, organizations can achieve a more dynamic, predictive, and cost-effective approach to cloud management. As a larger more strategic approach to managing costs organizations the practice of cloud economics can also come into play to maximize business value. For businesses, this journey will continue to unfold and will require insight and collaboration to navigate these changes successfully. By taking the right steps organizations can realize significant cost savings but also gain a competitive edge through enhanced agility and strategic foresight.

About the Author

Gaurav Parakh
Global Head of Hybrid Cloud Strategy & Emerging Offerings, Wipro

Gaurav is a Senior Hybrid Cloud Strategist at Wipro with over 24 years of experience in IT consultancy and advisory services. He specializes in digital transformation, go-to-market and business development.

At Wipro, Gaurav engages with Fortune 500 clients and partners at the leadership level, drawing on his experience designing and implementing cloud solutions, operating models, and strategies for FinOps, open source and generative AI to drive success.

He also has a background in entrepreneurship, having built tech startups in 3D printing, education and AI from inception to exit.