Business technology leaders across various sectors, including construction, insurance, retail, and tech, are placing heightened importance on cloud workload management. The evolving landscape of technology, marked by the integration of artificial intelligence (AI) and cloud computing, underscores the critical need for effective workload analysis and management.
Driving forces behind cloud workload management
The current business landscape, characterized by inflationary pressures, demands a strategic focus on the economics of cloud workload management. Simultaneously, the imperative to reduce CO2 emissions from technology activities places environmental responsibility at the forefront. CIOs are grappling with the challenge of cutting costs while enabling the experimentation and adoption of AI.
As AI becomes integral to business operations, cloud workload management transforms from a technological task into a business practice. It aligns with organizations’ Environmental, Social, and Governance (ESG) commitments, with a particular emphasis on reducing carbon footprints. Companies like Laing O’ Rourke are driving utilization initiatives for cloud and public cloud usage to contribute to their ESG goals.
Real-world impact: Optimizing workloads for cost and energy efficiency
Customer expectations, whether from construction partners or high street consumers, now include a commitment to reduce CO2 emissions. Optimizing workloads, as evidenced by shoe retailer Clarkes switching off data rooms, not only contributes to sustainability but also results in significant power savings.
Rising energy costs, exacerbated by geopolitical events, coupled with a technology skills shortage, are impacting the cost base of businesses. The skills shortage is reported by over half of technology leaders as a barrier to meeting organizational change needs. Efficient cloud workload management not only reduces costs but also enables the redeployment of technology staff to more strategic roles, as observed in the insurance sector.
Strategic considerations in cloud workload management
Cloud workload management is not solely about cost; it requires a nuanced discussion about the strategic placement of workloads. Businesses must evaluate on a use-case basis, considering factors like growth, expansion, and agility. Markerstudy’s CIO emphasizes a complex evaluation based on hard facts to determine the right placement for workloads.
While cloud computing provides agility, the plethora of services and varied cost profiles present challenges. Cloud vendors are investing in cloud economists to ensure businesses receive the promised outcomes and revisit cost analyses periodically.
Future challenges, AI adoption, and data strategies impact workloads
The rise of ChatGPT and the demand for AI adoption at the board level present new challenges. Organizations are establishing groups to coordinate AI across business lines, identifying opportunities for process efficiency. However, the adoption of AI, especially in developing large language models (LLMs), increases cloud workloads and costs, necessitating careful analysis.
As organizations continue to focus on data strategies, including cloud-based data lakes and data warehouses, additional demands are placed on cloud workload management. After completing a three-year Azure-based data warehouse and data platform program, companies like Laing O’Rourke are adapting to the evolving cloud era.
Workload management is indicative of the evolving role of the Chief Information Officer (CIO) in the cloud era. CIOs are now orchestrators, meeting the demands of businesses by digitizing processes, adopting AI, and emphasizing sustainability. The cloud’s role in providing increased agility positions CIOs as leaders in cost-saving and sustainability efforts, driving business and change initiatives.
Effective cloud workload management has become integral to navigating the dynamic technological landscape, enabling organizations to optimize costs, reduce environmental impact, and strategically adopt transformative technologies like AI.
