Transformations in Generative Artificial Intelligence (AI) will be the focus in the segment, in addition to the importance of data observability
THE Dynatrace, a leader in unified observability and security, reveals technological trends for 2024. In a world in which Artificial Intelligence (AI) plays a central role, organizations are redefining their strategies to maximize the value of this technology that is already transforming the entire global corporate landscape .
“Businesses are recognizing that, in isolation, AI will not deliver significant long-term value. We are witnessing the transition to a more holistic approach, comprised of other intelligences. The impact resulting from this combination promises to be truly surprising for business”, says Bernd Greifeneder, Chief Technology Officer (CTO) at Dynatrace.
Check out the top seven trends for 2024 according to Dynatrace experts:
1) The world will adopt a composite approach to Artificial Intelligence (AI): Generative Artificial Intelligence (AI) enters the later phases of its hype cycle and organizations will realize that the technology, while transformational, cannot deliver significant value on its own. As a result, they will move towards a composite intelligence approach that combines Generative Artificial Intelligence (AI) with other additional data types and sources. This approach will allow for more advanced reasoning and bring precision, context and meaning to the results produced. For example, development teams (DevOps) will combine Generative with Causal and fact-based Predictive to supercharge digital innovation, predicting and preventing problems before they occur and generating new workflows to automate the Software delivery lifecycle.
2) Codes generated by Artificial Intelligence (AI) will create the need for digital immune systems: More organizations will face major disruptions to digital services due to quality issues or program codes that are insufficiently overseen. Developers will increasingly use AI-powered generative autonomous agents to write programming code for them, exposing their organizations to greater risks of unexpected issues that impact customer and user experiences. This is because the challenge of maintaining autonomous agent-generated code is similar to preserving code created by developers who have left an organization, resulting in the situation where none of the remaining team members fully understand the code. Therefore, no one can quickly resolve issues in the code when they arise. Furthermore, those who attempt to use Generative Artificial Intelligence (AI) to review and resolve issues in code created by autonomous agents will encounter a recursive problem, as they will not yet have the fundamental knowledge and understanding to manage it effectively. These challenges will drive organizations to develop digital immune systems, combining practices and technologies for software design, development, operations and analytics to protect their internally, ensuring code resiliency by default. To enable this, organizations will leverage Predictive Artificial Intelligence to automatically predict issues in code or applications before they occur, emerging and triggering an instant, automated response to protect the user experience. For example, development teams can design applications with self-healing capabilities. These features enable automatic rollback to the latest stable version of the codebase if a new version is buggy, or automated provisioning of additional cloud resources to support an increase in demand for computing power.
3) Organizations will appoint a director of Artificial Intelligence (AI) to oversee the safe and responsible use of AI: Companies will increasingly appoint senior executives to their leadership teams to ensure preparedness for the security, compliance and governance implications of Artificial Intelligence (AI). As employees become accustomed to using this technology in their personal lives, through exposure to tools like ChatGPT, they will be more willing to use Artificial Intelligence (AI) to increase their productivity at work. Organizations have already realized that if they do not officially train their employees to use Artificial Intelligence (AI) tools, their use will continue to be carried out without consent. Therefore, a director of Artificial Intelligence (AI) will be appointed to oversee the use of these technologies, in the same way that many companies already have a security executive on their leadership teams. This professional will focus on policy development, education, and workforce training to safely use Artificial Intelligence (AI) to protect the organization against accidental noncompliance, intellectual property leaks, or security threats. These practices will pave the way for the widespread adoption of Artificial Intelligence (AI) in organizations. As this trend advances, the use of this technology will become a commodity, just as it was with cell phones.
4) Data observability will be mandatory: Organizations are looking to drive smarter automation and enable faster decision-making as the volume of data continues to double every two years and organizations look to ingest and analyze it faster and on a larger scale. However, the cost and risk of poor quality data is more significant than ever. One Dynatrace research indicates that 57% of DevOps professionals say that the lack of data observability makes it difficult to conduct automation in a compliant manner. As a result, organizations will increasingly require solutions that provide data observability, enabling them to quickly and securely ingest reliable, high-quality data ready for on-demand analysis. Increasing data observability will enable users such as IT operations and business analytics teams to understand data availability, structure, distribution, relationships and lineage of that information across all sources, including different platforms in hybrid distributed environments It is multicloud. This understanding is essential for generating insights that users can trust, ensuring data is up to date, identifying anomalies, and eliminating duplicates that can lead to errors.
5) Organizations will extend observability to more enterprise use cases as senior management seeks to support sustainability and FinOps objectives: The combined pressure of adopting more environmentally sustainable business practices and dealing with rising cloud costs will drive observability from an IT priority to a business requirement. The increased use of Artificial Intelligence (AI) by organizations will be one of the main drivers of this trend, as it increases the consumption of Cloud resources, resulting in an increase in the carbon footprint. However, observability data analytics powered by Artificial Intelligence (AI) can help organizations address these challenges and mature their FinOps and sustainability practices, revealing actionable insights and leveraging intelligent automation to address critical business points. inefficiency in Cloud environments. The increased use of Artificial Intelligence (AI)-powered observability will enable organizations to automatically orchestrate their systems for optimal resource utilization, reducing emissions and the cost of operating their Cloud environments. As a result, we will see growing interest in observability use cases beyond the IT department as the broader enterprise begins to take note.
6) Platform engineering will become mission critical: Organizations will recognize that a secure, well-functioning Software delivery pipeline is as vital to business continuity as the quality and security of the digital services that end users and Customers rely on. We will therefore see a shift towards 'productization' of the tools used to drive best practices in DevOps engineering, website security and reliability. This will bring platform engineering to the forefront as organizations codify the know-how and capabilities needed to automate secure software delivery pipelines. As this trend takes hold, software delivery, security and operations processes will be driven through application programming interfaces (APIs) that automate these tasks based on real-time insights from observability data.
7) Organizations will phase out legacy SIEM solutions as security teams look for smarter threat analysis: Next-generation threat intelligence and analysis solutions will phase out security information and event management (SIEM) systems. These modern solutions enable security teams to extend capabilities beyond log analysis to access context provided by a broader range of data modalities and different types of Artificial Intelligence (AI), including generative, causal, and predictive techniques, whenever working together. As a result, organizations will have access to deeper, more accurate, intelligent and automated threat analysis, helping to protect their applications and data against increasingly sophisticated threats.
These trends not only reflect imminent changes in the technology landscape, but also point to the opportunities and challenges that organizations will face as they incorporate these advanced technologies into their operations.
