AI in 2024: Trends, Challenges and Opportunities

Andreas Grabner
Learn more about Dynatrace

It's no secret that artificial intelligence (AI) is transforming every aspect of our lives — from healthcare and entertainment to education and business. AI has become central to how organizations drive efficiency, improve productivity, and accelerate innovation.

Conversational generative AI chatbots, such as ChatGPT and Google Bard, can transform the way we work by automating various organizational tasks. Organizations are now recognizing the significant benefits of these technologies when delivering digital services, specifically in development, operations, and security. Generative AI-based solutions allow organizations to automate tasks such as writing software code, creating dashboards, and enabling users to query data through natural language.

Clearly, generative AI will usher in advantages within various industries. However, the technology is still nascent, and according to the recent Dynatrace survey, The state of AI 2024: Challenges to adoption and key strategies for organizational success, there are many challenges and risks that organizations need to overcome to use this technology effectively.


Source: Dynatrace

Organizations Will Accelerate AI Investments

The survey's findings indicate that organizations are already recognizing the vast potential of AI. Nearly two-thirds (61%) of technology leaders say they will increase investment in AI over the next 12 months to speed up software development.

Additionally, survey respondents said AI is benefiting other areas of their organization, too. Other use cases technology leaders identified include enabling business users to easily customize dashboards (54%) and to build interactive queries for analytics (48%). This means AI will affect not only IT and back-office support functions, but also front-line staff in customer-facing roles.

Deploying AI to Reduce Multicloud Complexity

Organizations are building and running millions of applications in the cloud, creating vast amounts of data and complex environments that are difficult to manage. To solve this, technology leaders are turning to AI — 87% of technology leaders say AI-powered issue prevention and remediation are critical to managing multicloud complexity.

Organizations will increase AI investment over the next 12 months to address this complexity by delivering predictable, trustworthy, and precise answers in real time. For example, 73% of technology leaders are investing in AI to generate insight from observability, security, and business events data.

This will create greater productivity among individual teams. DevOps teams, for example, can now focus on strategic projects and innovation instead of tedious manual work. According to the survey, nearly three-quarters of IT operations, development, and security teams plan to use AI to become more proactive in executing their work.

Technology leaders believe AI will also transform the following core DevOps use cases:

■ threat detection, investigation, and response (82%)

■ automating complex operations tasks (63%)

■ eliminating false alerts and the manual effort of validating code deployments (58%)

Minimizing AI Risk Is a Top Priority for Technology Leaders

While the advantages of AI technology are clear, many technology leaders are concerned that generative AI could be susceptible to unintentional bias, error, and misinformation; according to the report, 98% of respondents cited this as a concern. To address this, DevOps teams need to engineer AI prompts that contain detailed context and precision. In doing so, they can achieve meaningful, AI-generated responses that users can trust and avoid inaccurate or inconsistent statements.

Additionally, there are security and compliance risks. According to the survey, 95% of technology leaders are concerned that using generative AI to create code could result in data leakage, as well as improper or illegal use of intellectual property.

AI models must be managed with sufficient guardrails in place to prevent accidental exposure of sensitive information. This will drive demand for purpose-built AI platforms with built-in security and privacy requirements.

AI Will Benefit Employees Throughout Organizations

According to the report, AI will improve workforce satisfaction throughout organizations. Nontechnical workers can make informed, data-driven decisions with easier access to analytics through natural language queries and virtual assistants.

However, to fully take advantage of the benefits of AI, technology leaders agree that a composite AI approach is needed. This entails pairing generative AI with other forms of AI — such as generative, predictive, and causal AI — and different data sources, such as observability, security, and business events. This approach brings precision, context, and meaning to AI outputs. Ultimately, this context enables teams to use this AI-enabled data for better and more efficient decision making.

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AI in 2024: Trends, Challenges and Opportunities

Andreas Grabner
Learn more about Dynatrace

It's no secret that artificial intelligence (AI) is transforming every aspect of our lives — from healthcare and entertainment to education and business. AI has become central to how organizations drive efficiency, improve productivity, and accelerate innovation.

Conversational generative AI chatbots, such as ChatGPT and Google Bard, can transform the way we work by automating various organizational tasks. Organizations are now recognizing the significant benefits of these technologies when delivering digital services, specifically in development, operations, and security. Generative AI-based solutions allow organizations to automate tasks such as writing software code, creating dashboards, and enabling users to query data through natural language.

Clearly, generative AI will usher in advantages within various industries. However, the technology is still nascent, and according to the recent Dynatrace survey, The state of AI 2024: Challenges to adoption and key strategies for organizational success, there are many challenges and risks that organizations need to overcome to use this technology effectively.


Source: Dynatrace

Organizations Will Accelerate AI Investments

The survey's findings indicate that organizations are already recognizing the vast potential of AI. Nearly two-thirds (61%) of technology leaders say they will increase investment in AI over the next 12 months to speed up software development.

Additionally, survey respondents said AI is benefiting other areas of their organization, too. Other use cases technology leaders identified include enabling business users to easily customize dashboards (54%) and to build interactive queries for analytics (48%). This means AI will affect not only IT and back-office support functions, but also front-line staff in customer-facing roles.

Deploying AI to Reduce Multicloud Complexity

Organizations are building and running millions of applications in the cloud, creating vast amounts of data and complex environments that are difficult to manage. To solve this, technology leaders are turning to AI — 87% of technology leaders say AI-powered issue prevention and remediation are critical to managing multicloud complexity.

Organizations will increase AI investment over the next 12 months to address this complexity by delivering predictable, trustworthy, and precise answers in real time. For example, 73% of technology leaders are investing in AI to generate insight from observability, security, and business events data.

This will create greater productivity among individual teams. DevOps teams, for example, can now focus on strategic projects and innovation instead of tedious manual work. According to the survey, nearly three-quarters of IT operations, development, and security teams plan to use AI to become more proactive in executing their work.

Technology leaders believe AI will also transform the following core DevOps use cases:

■ threat detection, investigation, and response (82%)

■ automating complex operations tasks (63%)

■ eliminating false alerts and the manual effort of validating code deployments (58%)

Minimizing AI Risk Is a Top Priority for Technology Leaders

While the advantages of AI technology are clear, many technology leaders are concerned that generative AI could be susceptible to unintentional bias, error, and misinformation; according to the report, 98% of respondents cited this as a concern. To address this, DevOps teams need to engineer AI prompts that contain detailed context and precision. In doing so, they can achieve meaningful, AI-generated responses that users can trust and avoid inaccurate or inconsistent statements.

Additionally, there are security and compliance risks. According to the survey, 95% of technology leaders are concerned that using generative AI to create code could result in data leakage, as well as improper or illegal use of intellectual property.

AI models must be managed with sufficient guardrails in place to prevent accidental exposure of sensitive information. This will drive demand for purpose-built AI platforms with built-in security and privacy requirements.

AI Will Benefit Employees Throughout Organizations

According to the report, AI will improve workforce satisfaction throughout organizations. Nontechnical workers can make informed, data-driven decisions with easier access to analytics through natural language queries and virtual assistants.

However, to fully take advantage of the benefits of AI, technology leaders agree that a composite AI approach is needed. This entails pairing generative AI with other forms of AI — such as generative, predictive, and causal AI — and different data sources, such as observability, security, and business events. This approach brings precision, context, and meaning to AI outputs. Ultimately, this context enables teams to use this AI-enabled data for better and more efficient decision making.

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Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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