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APM, Observability and AIOps - a Way Forward

Ron Williams
Gigaom

What's coming in operations management tooling? In a nutshell, a shift from observability to intelligent operations and the longer-term move towards AI-enabled operations in support of the business, but application performance management (APM) still has a place.

Let's break these pieces down. First, APM could be perceived as becoming passé, in tooling terms. All larger companies use it, and tools vendors pull it into their observability suites. Companies still need APM as a starting point if they are unready for the integration heavy lifting, coordination between multiple departments, and political capital that more advanced solutions require.

Many vendors recognize this, selling APM at a reasonable cost with bundled access to other features — but there's a catch. Historically, APM licensing has been based on users, rather than data consumed. But now, vendors are using data as the driving factor for cost. The focus now is on data consumption models: If you're consuming a certain volume of logs, telemetry, and traces, these will drive your cost.

This means less predictability. If someone is temporarily consuming a lot of data, even legitimately (for example, for a new project), they'll have a blip in their billing. In addition, a user can say, "Oh, I can use this feature too," meaning they consume more data, which makes more money for vendors. APM is almost the gateway drug to observability, feature by feature.

Some companies make it easier for you to add another of their little tools because it's convenient. One company has 26 products — if you use one, you can access the others. Suddenly, finance goes, "Wait a minute, why do we suddenly have this big cost increase?" And you have to go back and look and realize, "Oh, George added this one, Sarah used that one, and Sam used the other one, and wow, our bill just quadrupled."

We're also seeing the rise of generative AI in Ops. Predictive AI and machine learning have long been in the mix, but this is the first year that genAI will appear in products. I expect every vendor will offer something related, but the offerings will almost universally be bad. It's not the vendors' fault, but nobody knows what we can, or should be doing with this capability. So vendors will include the feature, whether or not it's useful or really answers the questions businesses have.

For this reason, I'm updating one of my models. Historically, I have shown the evolution from monitoring to observability to awareness. This year, I'll change from monitoring to observability to intelligence. Under "intelligence" I have questions such as:

Is the business OK?

What was the result of last month's marketing campaign?

Sales has a new initiative; what will impact our services and support?

Unless you're in the business of IT, your real questions are not about IT but the business. If you fly people from point A to point B, you want to ask questions about that, not whether the revenue management system is working.

Observability didn't look to answer these questions, but now that we have more intelligence in tools, we must address them. You want to ask your chat interface that connects to your AIOps that question, rather than going over to revenue management and then going over to this group, that group, or the other group, for the answers.

These tools still have the same problems with AI: choosing the right algorithm at the right time, explainable AI, and AI bias — these are not going away. Let's say I train my AI on all my data … stop there, I don't have all my data because, for example, the guys over in desktop support didn't want to give me their data, but the guys over in networking did. I've trained the models on network data, and the AI now knows networking. So, what is every problem going to be? You guessed it, a networking problem.

Being able to train the AI and getting beyond its biases are going to be challenging. Additionally, generative AIs can hallucinate, presenting nonsense data as fact. Trusting AI as we train it to learn our businesses and help us run more efficiently is part of the new paradigm in business operations.

That'll set the scene for 2024: I expect them to have something, but it won't really help. It may be a little more focused in 2025, but by year three and on — that's when I really believe the AI they're putting into some of these tools will be truly useful. That is, it can answer questions about the condition of the enterprise, not the condition of IT.

That's the direction I see the industry taking, and I'm pushing to see how vendors will impact how the entire business operates. In three years, we should see the hype turn into real changes. For now, the nascent large language models show promise; but with planning and focus, generative AI won't be another promise broken.

Ron Williams is an Analyst at Gigaom

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APM, Observability and AIOps - a Way Forward

Ron Williams
Gigaom

What's coming in operations management tooling? In a nutshell, a shift from observability to intelligent operations and the longer-term move towards AI-enabled operations in support of the business, but application performance management (APM) still has a place.

Let's break these pieces down. First, APM could be perceived as becoming passé, in tooling terms. All larger companies use it, and tools vendors pull it into their observability suites. Companies still need APM as a starting point if they are unready for the integration heavy lifting, coordination between multiple departments, and political capital that more advanced solutions require.

Many vendors recognize this, selling APM at a reasonable cost with bundled access to other features — but there's a catch. Historically, APM licensing has been based on users, rather than data consumed. But now, vendors are using data as the driving factor for cost. The focus now is on data consumption models: If you're consuming a certain volume of logs, telemetry, and traces, these will drive your cost.

This means less predictability. If someone is temporarily consuming a lot of data, even legitimately (for example, for a new project), they'll have a blip in their billing. In addition, a user can say, "Oh, I can use this feature too," meaning they consume more data, which makes more money for vendors. APM is almost the gateway drug to observability, feature by feature.

Some companies make it easier for you to add another of their little tools because it's convenient. One company has 26 products — if you use one, you can access the others. Suddenly, finance goes, "Wait a minute, why do we suddenly have this big cost increase?" And you have to go back and look and realize, "Oh, George added this one, Sarah used that one, and Sam used the other one, and wow, our bill just quadrupled."

We're also seeing the rise of generative AI in Ops. Predictive AI and machine learning have long been in the mix, but this is the first year that genAI will appear in products. I expect every vendor will offer something related, but the offerings will almost universally be bad. It's not the vendors' fault, but nobody knows what we can, or should be doing with this capability. So vendors will include the feature, whether or not it's useful or really answers the questions businesses have.

For this reason, I'm updating one of my models. Historically, I have shown the evolution from monitoring to observability to awareness. This year, I'll change from monitoring to observability to intelligence. Under "intelligence" I have questions such as:

Is the business OK?

What was the result of last month's marketing campaign?

Sales has a new initiative; what will impact our services and support?

Unless you're in the business of IT, your real questions are not about IT but the business. If you fly people from point A to point B, you want to ask questions about that, not whether the revenue management system is working.

Observability didn't look to answer these questions, but now that we have more intelligence in tools, we must address them. You want to ask your chat interface that connects to your AIOps that question, rather than going over to revenue management and then going over to this group, that group, or the other group, for the answers.

These tools still have the same problems with AI: choosing the right algorithm at the right time, explainable AI, and AI bias — these are not going away. Let's say I train my AI on all my data … stop there, I don't have all my data because, for example, the guys over in desktop support didn't want to give me their data, but the guys over in networking did. I've trained the models on network data, and the AI now knows networking. So, what is every problem going to be? You guessed it, a networking problem.

Being able to train the AI and getting beyond its biases are going to be challenging. Additionally, generative AIs can hallucinate, presenting nonsense data as fact. Trusting AI as we train it to learn our businesses and help us run more efficiently is part of the new paradigm in business operations.

That'll set the scene for 2024: I expect them to have something, but it won't really help. It may be a little more focused in 2025, but by year three and on — that's when I really believe the AI they're putting into some of these tools will be truly useful. That is, it can answer questions about the condition of the enterprise, not the condition of IT.

That's the direction I see the industry taking, and I'm pushing to see how vendors will impact how the entire business operates. In three years, we should see the hype turn into real changes. For now, the nascent large language models show promise; but with planning and focus, generative AI won't be another promise broken.

Ron Williams is an Analyst at Gigaom

The Latest

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...