Who Provides AIOps Now, and How Will that Change Over Time?

Analysts and researchers always must make decisions on scope when studying markets of any sort. Which organizations and firms are included within the data set, and what are the selection criteria? 

The AIOps market, generally considered to consist of information technology operations management by using machine learning or artificial intelligence, typically focuses on suppliers of such software capabilities. 

Omdia highlights ServiceNow, Digitate, BMC, IBM and Splunk, LogicMonitor, Sumo Logi, PagerDuty, StackState, VuNet Systems and Netreo as firms it can include within the AIOps market. 

source: Omdia 

Other observers might say the suppliers include :

AppDynamics

BigPanda

BMC

DataDog

DynaTrace

Moogsoft

New Relic

PagerDuty

ScienceLogic

Splunk

source: ITPro 

And yet others--using different criteria-- would add as many as 50 or so additional firms supplying AIOps capabilities defined to include Cloud Management Platforms, Privileged Access Management (PAM), IoT Platforms, Cloud Migration Tools or Hybrid Cloud Storage. 

Researchers also have to make decisions about whether AIOps is a feature of another product sold by a company, or a primary product sold by a company. Eventually, one might well argue, every enterprise software system will include the use of ML or AI to provide operating information, just as all enterprise software will use cloud delivery mechanisms. 

That virtually guarantees there will be changes in market boundaries over time. AIOps will simply become part of the capability set for all enterprise software with performance management requirements. 

So AIOps might be redefined as general purpose tools that are extrinsic to any single application or process. To the extent that ML or AI-assisted analysis and monitoring tools are supplied with any particular app, only suppliers of third party tools that monitor across domains might come to be seen as “true” suppliers of AIOps. 

 

Service Now on Using AIOps to Prevent Outages

IBM Explains AIOps

Yet Another Definition of "Platform"

According to a survey sponsored by CircleCI and Puppet, DevOps and platforms go hand in hand. The survey of 2400 information technology professionals globally looked at “platform models,” defined in yet another way. 

And though many looking at AIOps might consider the use of external computing-as-a-service resources a primary driver of need for AIOps, the survey takes a different approach, limiting “platform” to internal resources only. 

As always, the definition of “platform” really does matter. 

source: CircleCI 

“Broadly speaking, the platform team provides the infrastructure, environments, deployment pipelines and other internal services that enable internal customers — usually application development teams — to build, deploy and run their applications,” the study states.

Likewise, “a digital platform is a foundation of self-service APIs, tools, services, knowledge and support which are arranged as a compelling internal product,” says Evan Bottcher, Thoughtworks Australia head of engineering.  

The report uses the term "internal platform" to mean one that's been built by and for the organization. So AWS or other IaaS offerings would not be thought of as "platforms” in this context.

“In our survey, we defined platform teams as those that are responsible for maintaining a self-service platform other teams use to build and deliver applications or services,” CircleCI says.

41% of Surveyed U.K. IT Staffs Investing in AIOps in 2021

As some observers note,  if all an enterprise is doing is monitoring bare-metal servers and networks in its own data center, “you can probably keep on using your legacy monitoring tools.”

Cloud computing, most would agree, changes matters. Applications running virtually, using cloud service providers plus in-house resources require a new set of monitoring tools. 

In 2021, U.K. technology executives have prioritized information technology operations management  tool investments such as hybrid infrastructure monitoring (51 percent), AIOps (48 percent), and cloud native observability (47 percent). Digital experience monitoring (46 percent) and Application performance monitoring (46 percent) tools are right behind, says OpsRamp, citing research by McKinsey Global. 

source: OpsRamp 

“Siloed tools have historically created problems for hybrid infrastructure management as they offer limited context for assessing the health and performance of an IT service,” says OpsRamp. “Our survey shows AIOps (48 percent) is a focus area for tools consolidation, though it trails network performance monitoring (56 percent) and hybrid infrastructure monitoring (54 percent).”

IBM Buys Turbonomic for Hybrid Cloud AIOps

IBM is acquiring Turbonomic, an application resource management (ARM) and network performance management (NPM) software provider based in Boston, Mass. 

IBM says the acquisition will make it “the only company that will be able to provide customers with AI-powered automation capabilities that span from AIOps (the use of AI to automate IT operations) to application and infrastructure observability, all built on Red Hat OpenShift to run across any hybrid cloud environment.” 

The acquisition will provide businesses with “full stack application observability and management” of containers, virtual machines, servers, storage, networks, and databases, IBM says, while also providing such value at lower cost in hybrid cloud environments. 

The acquisition complements IBM's recent acquisition of Instana for application performance monitoring (APM) and observability, and the launch of IBM Cloud Pak for Watson AIOps to automate IT Operations using AI. 

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Masergy AIOps

Complexity Drives AIOps Interest

Complexity has been one reason for interest in AIOps. 

In the last year, the number of enterprises with over 500 known applications has grown by almost 50 percent, from 32 percent to 47 percent of respondents In parallel, those reporting greater than 20 SaaS applications grew by 37 percent, from 24 percent to 33 percent, according to Aryaka Networks’ State of the WAN report. 

With growth comes complexity, which is still the #1 issue for IT on par with last year at 37 percent, followed by slow application access (33 percent) and performance (32 percent). One area that stood out was cost, identified by 20 percent in 2021 vs 16 percent in 2020, a 25 percent rise. IT Time Sinks: Complexity results in increased resource requirements, spanning slow performance for both the branch and remote workers (44 percent), security breaches (38 percent), integration of cloud and SaaS applications (36 percent), and managing telcos (33 percent).

AIOps for Anomaly Detection

Mid-Size Firm AI Adoption is Likely Mostly Happening as they Adopt Cloud-based Enterprise Apps

For most companies and people, artificial intelligence is not a product, but a capability possessed by a cloud-based business application, such as customer relationship management, enterprise resource planning, supply chain management, or human capital management. 

For that reason, mid-size firms “adopting” AI are largely doing so by adopting cloud-based enterprise applications. 

source: Deloitte 

Some Firms Boosted AI Spending During Covid-19 Pandemic

If the Convid-19 pandemic affected enterprise information technology investments, it arguably has slowed such investments at some firms, which have had to shift support to remote workers. On the other hand, some firms who already have found use cases, and invested more heavily prior to the pandemic, seem to have increased their investment level, a  McKinsey survey found.

Respondents from 61 percent of firms who report success with AI also say their firms increased investment in 2020. Patterns across industries show big variations.

 source: McKinsey

Firms in healthcare, pharma, medical products; as well as companies in the automotive industry were most likely to have increased AI investments in 2020. 

 source: McKinsey

Most enterprises likely have not yet found clear financial benefits from AI deployment, though. A survey of more than 3,000 company managers about their AI spend found just 10 percent had gotten significant financial benefits from their investment so far, a report from MIT Sloan Management Review and Boston Consulting Group found. 

A separate survey of U.K. firms found that 40 percent of 750 surveyed U.K. executives plan to invest in artificial intelligence in 2021, a survey by Fountech Solutions finds. 

  

Some 30 percent of respondents say their firms piloted an AI solution for the first time since the onset of the Covid-19 pandemic. New AI specialists will be hired by 41 percent of respondent firms. Also, 48 percent of respondents say their companies will seek AI training for existing staff.

As a rule, some firms managed to grow revenue and profit during recessions and crises, Boston Consulting Group data suggests. While 44 percent of firms might experience shrinking profit margins and sales growth in a recession or crisis, 14 percent have shown growth in both sales and profits. 

Some 28 percent of firms see lower sales but manage to increase profit margins. About 14 percent of firms see higher sales and lower profit margins. 

Firms in health and consumer staples are most likely to see winners in recessions. Companies in energy, information and communications technology and financial industries are least likely to emerge with higher sales and profits in a recession.

source: Boston Consulting Group 

It is possible--even likely--that firms continuing to invest in AI during the Covid-19 pandemic were already finding themselves gaining market share, increasing sales volume and maintaining or increasing profits. 

The McKinsey survey found, for example, that a small number of respondents at some firms attributed 20 percent or more of their firm earnings before interest and taxes (EBIT) to AI. Those companies planned to invest even more in AI during the COVID-19 pandemic. 

That is in keeping with the BCG data suggesting some firms gain market share and boost sales during recessions and crises. If such gains are attributed to AI, it makes sense that firms would maintain or boost such investments.

ModelOps for Deploying AI

The term ModelOps refers to the governance and life cycle management of all artificial intelligence and decision models, including models based on machine learning, knowledge graphs, rules, optimization, linguistics and agents, according to Gartner. 

“In contrast to MLOps, which focuses only on the operationalization of ML models, and AIOps which is AI for IT Operations, ModelOps focuses on the operationalization of all AI and decision models,” Gartner says. 

source: ModelOp

As this video suggests, ModelOps aims to support scaling of artificial intelligence capabilities across an enterprise.

Some of you might see this as an example of an important business strategy, which is to create a new market. That is one way to avoid being “trapped” in battle aimed at taking market share from other competitors, and instead boosting value and uniqueness by competing in a new and different market. 

It is vendor push, to be sure, but such pushes--as opposed to buyer pulls-- fail if it does not align with an enterprise's need to solve a real problem important to its business model.