AI and Automation: The Cornerstone of Cloud Investments in 2024
Navigating 2024’s tech landscape where Cloud, AI, and Automation converge.
Stefan Georgiev, senior product manager at Nerdio, unveils crucial considerations for IT leaders diving into AI and automation in the cloud era. Strategize effectively for transformative success.
In the current technological landscape, AI and automation are becoming primary motivations for cloud investments. This is driven mainly by what IT leaders see as the transformative power of these dynamic technologies. The challenge heading into 2024, however, is not just in adoption but in strategically aligning AI and automation with organizational plans, fiscal projections, staffing, and talent acquisition. This article details key considerations IT leaders must address when contemplating or optimizing their AI and automation strategies and initiatives.
1. Business and cloud-specific goals
IT leaders are responsible for ensuring that AI and automation initiatives resonate with the organization’s goals. It’s essential to evaluate if suggested initiatives or use cases for AI hold strategic value — especially considering that in 2024 we’ll start seeing AI implemented daily and yield real outcomes. For example, can employees use AI tools like Copilot to automate specific tasks? What training will be available for them? Will only one team begin to use AI to test it out before the entire organization can benefit? These questions are crucial to determining strategic goals before AI implementation.
Simultaneously, alignment between AI, automation strategies, and explicit cloud objectives becomes non-negotiable. Defining the precise business challenges the proposed AI and automation initiative aims to address is a must. From there, IT leaders must define whether AI and automation are an end solution or a tool. This distinction becomes even more important considering the unique complexities associated with AI compared to traditional IT endeavors.
2. ROI and TCO analysis
Before venturing into AI and automation initiatives, IT leaders must fully analyze Return on Investment (ROI) and Total Cost of Ownership (TCO). The TCO for AI and automation encompasses more than just the implementation expenses. It must cover the nuances of cloud storage fees, data transfer charges, and the costs linked to cloud-centric machine learning services. Furthermore, IT leaders must be aware of the ambiguities and risks AI and automation initiatives bring to predict and measure ROI accurately. The recommendation is to tackle AI and automation initiatives via iterative and adaptive change management methodologies. The “fail-fast” principle is essential to maintain TCO and ensure ROI. Additional attention must be spent on fine-tuning configuration and data retention, as minor changes can balloon cloud costs. Finally, IT leaders must manage their organizations’ expectations around ROI and TCO by leveraging tools for predicting costs and labor.
3. Data quality and availability
The success of any AI and automation initiative lies in ensuring that the organization has enough data and that this data is available or easily migratable to the cloud. If an organization doesn’t have enough data, IT leaders must prioritize initiatives around structuring and gathering this data.
Further, when an organization chooses to build AI and automation solutions instead of leveraging off-the-shelf solutions, data accuracy and preparation must be evaluated, as those will impact the quality and duration of training. From there, this will impact and be a key factor in the success of any AI and automation initiative. Finally, in the cloud paradigm, the emphasis on data availability, residency, backups, cost, and adherence to compliance become areas that IT leaders must address.
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4. Technology selection
In AI and automation, selecting a vendor specializing in these areas is a key consideration. One of the factors to consider is if the organization already has a vendor or partner for other initiatives they can work with. This often helps with procurement situations and imploring objective experts, but keeping an open mind and evaluating new vendors and technologies is important. Next, IT leaders must review their organizations, talent, and budget and choose between off-the-shelf or in-house tools and technologies. A key part of that decision in modern IT operations is determining if the organization should leverage open-source technologies.
Another thing to consider is the needs of the workforce. For example, 2024 will likely see the continuation of both remote and hybrid work, requiring a flexible tech stack of technologies like cloud-delivered desktops to allow employees to work from anywhere. A notable development is the integration of AI-powered anomaly detection to oversee the performance of these technologies. This advancement enables AI to actively monitor resource utilization among the workforce, troubleshoot issues, pinpoint performance bottlenecks, and identify deviations from the established baseline. This advancement will provide IT leaders and administrators with recommendations for applications and resources to define the desktop experience on the individual level for users – which will inherently improve overall organizational productivity and performance.
At each step of this decision-making process, IT leaders must keep in mind the alignment of tools and technologies with the organization’s existing technology stack, long-term goals, partners, and customers. Suppose the organization decides to build its tools and technologies in-house. In that case, IT leaders must remember that industry leaders – Microsoft, Google, and AWS – are quickly and constantly introducing features and products that render custom-built and open-source AI products obsolete.
5. Outages, scalability, security, and contingency plans
AI and automation, while predominantly housed on reliable cloud platforms, are not immune to service interruptions. IT leaders should focus on ensuring any initiative’s scalability while ensuring existing systems are noticed. Recognizing that AI and automation initiatives have their own set of challenges, from technical hiccups to data security breaches, IT leaders must have robust risk mitigation processes. Considering cloud compliance challenges and data sovereignty, a multi-tiered security strategy must be in place. Additionally, a repeatable and automated deployment process for any AI and automation initiative must be viewed from the first design step to completion. This automation will become highly valued during unforeseen disruptions while ensuring scalability and integration agility.
As we head into 2024, the integration of AI and automation is poised to play a pivotal role in enhancing security measures. Organizations will increasingly use AI-driven solutions like Microsoft’s Copilot to identify and automate various security tasks efficiently. These tasks encompass critical activities such as patching vulnerabilities, continuous monitoring for potential threats, swift responses to security incidents, and enforcing robust security policies. By leveraging the capabilities of AI and automation, businesses aim to fortify their defenses, preemptively address vulnerabilities, and bolster their overall security infrastructure while utilizing their current tech stack.
6. Talent and expertise
Team expertise and training are required for an organization to successfully execute its AI and automation activities, especially in the cloud. IT leaders are tasked with guaranteeing that their teams embody the specialized skills to design, execute, maintain, and review AI and automation in a cloud of choice or even multi-cloud. In 2024, as organizations begin to use AI, use-case-specific training with prompt engineering will be crucial to helping employees develop the skills needed to use AI and ensure organizations ultimately see ROI.
Continually evaluating existing talent, identifying expertise gaps, and strategizing on upskilling recruitment or external consultations is vital. Further, the presence or lack of expertise will be the key driver when deciding to build in-house or use off-the-shelf tools and technologies.
7. Ethical and compliance considerations
There are overlapping concerns around ethics and compliance within AI and automation initiatives. Navigating data privacy intricacies and potential algorithmic biases will become the main focus for IT leaders. As AI and automation initiatives move from idea to implementation and productization, organizational ethics and compliance must be considered at every step. Additionally, when running these initiatives in the cloud environment, further nuanced challenges are added: data security, availability, cost, retention, secure transit, and access auditing become central considerations.
Navigating the Complex Landscape
The mix of cloud, AI, and automation leads to a complex matrix of considerations that IT leaders must navigate in 2024. Each dimension warrants strategic forethought, from strategic alignment to data integrity, tool selection to talent cultivation, and ethical considerations to the deployment of best practices. As the technological realm continually shifts, the clarion call for organizations and IT leaders is evident: harness the transformative essence of AI and automation in the cloud, underpinned by a well-informed, strategic, and ethically responsible methodology.
How are you strategizing AI and automation in your cloud journey? Share your insights on why these technologies matter for your organization’s success on Facebook, X, and LinkedIn. We’d love to hear from you!
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