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The Forrester Wave™: Process-Centric AI For IT Operations (AIOps), Q2 2023
The Forrester Wave™: Process-Centric AI For IT Operations (AIOps), Q2 2023


Wave Report
The Forrester Wave™: Process-Centric AI For IT Operations (AIOps), Q2 2023
June 25, 2023
The 11 Providers That Matter Most And How They Stack Up
June 25, 2023
CCCarlos Casanova
with Glenn O'Donnell, Sara Sjoblom, Kara Hartig


Summary
In our 30-criterion evaluation of process-centric artificial intelligence for IT
operations (AIOps) providers, we identified the most significant ones and
researched, analyzed, and scored them. This report shows how each provider
measures up and helps IT operations professionals select the right one for their
needs.


TOPICS

AIOps Capabilities Are Not Guaranteed By Adding AI To Existing Technologies



Evaluation Summary




Vendor Offerings


Vendor Profiles










Evaluation Overview





Supplemental Material











AIOps Capabilities Are Not Guaranteed By Adding AI To Existing Technologies
The shift to complex IT landscapes with hybrid-multicloud and on-premises
operations continues to fuel the need for comprehensive business insights.
Organizational approaches to AIOps, such as process-centric versus
technology-centric vendor solutions, share a goal of improved insights, but each
attacks this challenge differently. The process-centric vendors that we
evaluated demonstrated extensive analytical and automation skills underpinned by
data collection capabilities heavily reliant on third parties, sometimes even to
the extent of filtering the raw data prior to analysis. Reliance by vendors on
third parties for analytics and data processing, however, is not a desirable
feature for AIOps tools because it limits the potential of proactive actions.
Demonstratable execution of eight use cases provided insight into vendor
capabilities beyond discrete independent functions. Digital experience
monitoring (DEM) is an important aspect of delivering comprehensive insight but
still lags as a capability when it shouldn’t, given the plethora of third-party
options that can be integrated.
As a result of these trends, process-centric AIOps customers should look for
providers that:
   
 * Offer more than third-party data filtering and outsourced analytics.
   Organizations must be proactive and able to act on data issues before they
   become IT alerts and events. Vendors that take in filtered data or offload
   key processing of raw data leave customers without complete insights and a
   limited ability to act before business issues arise. Third-party connectors
   are important as a method to provide broader raw data collection coverage.
   Avoid this approach as a permanent option when its use significantly or
   exclusively limits what proactive actions can be taken. Critical preventative
   measures like anomaly detection and mean time to identify (MTTI) are often
   severely limited if not outright impossible when the raw environmental data
   is unavailable for on-platform processing and analysis.
 * Provide a full series of discrete actions sequenced for real AIOps value.
   Vendors varied in their origins, producing a clear disparity in solutions
   evaluated. Offerings from adjacent areas such as incident response and IT
   service management (ITSM) could not always execute the full series of actions
   needed to deliver the complete capability tested, natively or with
   third-party integrations. For a technology to be considered as an AIOps
   solution, it cannot ignore core functions necessary for business insights
   with no alternative option provided. Organizations acquire technologies to
   provide a complete solution to their needs. AIOps is no different. Whether
   natively, third-party, or some combination of both, organizations today must
   handle use cases and scenarios in their entirety, not in pieces or with gaps.
 * Augment strong automation capabilities with robust remediation. Most
   solutions have strong capabilities for the automation of processes,
   notifications, and other procedural actions. Transitioning these automations
   to autoremediation, however, highlighted a key distinction among vendor
   capabilities. Expediting notifications, approvals, and various other
   procedural actions was critically important for yesterday’s needs, but the
   real need today is autoremediation. Current IT landscape complexity and the
   speed of business require AIOps solutions to execute remediations when
   needed. Low-risk, definitively detectable issues with clearly articulated
   resolutions must be cleared from human work queues and handled exclusively
   with technology. Several evaluated vendors were simply not able to
   demonstrate the ability to meet these needs, or their capabilities were
   severely limited in their efforts.

Evaluation Summary
The Forrester Wave™ evaluation highlights Leaders, Strong Performers,
Contenders, and Challengers. It’s an assessment of the top vendors in the
market; it doesn’t represent the entire vendor landscape. You’ll find more
information about this market in The Process-Centric AIOps Landscape, Q1 2023.
We intend this evaluation to be a starting point only and encourage clients to
view product evaluations and adapt criteria weightings using the Excel-based
vendor comparison tool (see Figures 1 and 2). Click the link at the beginning of
this report on Forrester.com to download the tool.
Figure 1
Forrester Wave™: Process-Centric AIOps, Q2 2023
Figure 2
Forrester Wave™: Process-Centric AIOps Scorecard, Q2 2023
Vendor Offerings
Forrester evaluated the offerings listed below (see Figure 3).
Figure 3
Evaluated Vendors And Product Information
Vendor Profiles
Our analysis uncovered the following strengths and weaknesses of individual
vendors.
Leaders
   
 * BMC Software delivers an AI-enabled platform atop decades of operational
   experience. With deep roots in enterprise-class solutions, the new BMC
   Software AI-enabled operations platform called Helix handles complex
   hybrid-multicloud operational environments. Its superior vision focuses on
   unifying service and operations management, leveraging AI/ML for proactive
   insights, fostering cross-team collaboration, and enabling a business-centric
   preventative IT approach. A comprehensive roadmap further enhances its
   support for dynamic, public cloud, and software-defined data centers. Also in
   the future is enhancing predictive service health, historical resolution,
   insights-driven recommendations, and proactive automation. Public pricing
   details, however, are not readily available.
   
   The blueprint-based service modeling capability uses a modular design to
   stitch together topology from DEM, application performance management (APM),
   IT infrastructure management, and network performance monitoring tools. It
   works across major cloud providers and on-premises infrastructure to enable
   business insights. BMC combines native automation with AI/ML to continuously
   detect the state of the infrastructure and recommend actions. Extensive
   native data collection capabilities and a comprehensive assortment of
   third-party collectors reinforces its history of supporting diverse
   environments in global enterprises. Reference customers raved about how easy
   it was to integrate data sources. One noted that an internal team was “blown
   out of the water” by the noise reduction and predictive analytics
   capabilities. BMC is a good fit for enterprises with complex and diverse
   environments that span mainframe to cloud and everything in between.
 * Aisera excels with data ingestion, service mapping, and operational use
   cases. Aisera is bold and unapologetic in its effort to predict and prevent
   75% of issues on the journey to its goal of zero unplanned outages. For a
   six-year-old company in Series D funding, Aisera is not shy about taking on
   established global players head-on. Leadership speaks openly of humans’
   inability to scale and wants to show how changes in the technology stack can
   help make predictions. Aisera also wants to bring natural language processing
   (NLP) and natural language understanding (NLU) to IT operations. Its AI
   observability copilot tackles this challenge by leveraging three dynamics of
   intelligence: human, machine, and predictive. Delivering on its solid vision,
   however, will take investments in its partner ecosystem that is lacking a
   services approach, formal training, and a certification structure to ensure
   that partners can support Aisera’s audacious posture.
   
   Aisera has an all-in commitment to use AI/ML in every aspect of its offering.
   Predictive and probability capabilities use large-language-model-based NLP
   and deep learning techniques to identify complex patterns with dimensionality
   reduction that helps discover anomalies. NLP and unsupervised NLU help
   identify entities from the incoming alerts and incidents, which are
   reconciled and form the basis of its probabilistic service maps. Aisera
   relies on third-party APM tools and currently only offers a partial
   distributed tracing capability. Aisera is a good fit for enterprises that
   want to be aggressive in their approach to modernizing IT operations by
   investing in the latest AI/ML approaches and are striving for a zero
   unplanned outage environment that even Aisera recognizes as unrealistic.
 * PagerDuty minimizes disruptions’ impact with 700-plus integrations and
   automation. PagerDuty goes beyond alerting to automate processes and
   workflows that accelerate resolutions. It works in real time to compress and
   correlate event data and urgently mobilize the right teams. PagerDuty
   integrates into a customer’s heterogeneous environments with open APIs and
   more than 700 integrations. A product-led growth strategy that engages
   customers as design partners in an early-access program delivers value
   rapidly. Detailed 12- and 24-month roadmaps bring enhancements to alert
   grouping, incident enrichment, automated human-in-the-middle interactions,
   and event-orchestration-driven automation. Neither roadmap includes an
   on-premises option — a current gap. Global alert grouping helps responders
   identify related issues, blast radius, and root-cause analysis for incidents
   across all corporate services.
   
   Normalizing event data and enriching it with remediation-specific data powers
   automation workflows throughout the PagerDuty platform. PagerDuty mobilizes
   teams during an incident by automatically adding chat-driven operations
   (ChatOps) channels, setting up conference bridges, and sending status
   updates. Powered by AI, intelligent alert grouping, flexible time windows,
   and autopause incident notifications reduce event noise by using a
   probabilistic model with textual preprocessing, previewing noise compression
   for services, building recommended time windows using ML models, and
   enriching events with contextual information. Reference customers praised
   PagerDuty’s event noise reduction, with one calling its Event Intelligence
   “very powerful.” PagerDuty is a good fit for enterprises with diverse
   technologies that will remain in place or must integrate into a common
   platform that can drive automation and eliminate low-value work.
 * ServiceNow builds on a trusted platform, but the partner ecosystem can be
   challenging. Enterprise service management (ESM) stalwart ServiceNow expanded
   its platform capabilities with automation and AI/ML operations into yet
   another market segment, an example of its strong vision and roadmap. As an
   add-on capability to its global installation base, ITOM AIOps Enterprise
   could eclipse even the largest of its competitors. Some organizations may
   consider the additional ITOM AIOps commitment to be of concern with regard to
   single-vendor lock-in. However, one customer reference noted the seamless
   interoperability of the installed ServiceNow ITOM AIOps and ITSM offerings as
   a benefit. ServiceNow has made several AIOps-related acquisitions, which are
   integrated, but Lightstep and Era Software are separate from its AIOps
   offering and hence were not evaluated. ServiceNow implementations can be
   overwhelming for organizations that do not have the proper resources to
   support it.
   
   Adding modern-day AI/ML and automation capabilities to decades of workflow
   operational capabilities is a powerful statement. Strong business service
   insights combined with event noise reduction and suggestive alerting can
   greatly reduce workloads and improve business outcomes. This can be seamless
   for existing customers with a platform already natively aware of incidents,
   changes, and the configuration management database. A reference customer
   stated, “Anomaly detection is statistically correct but hard to
   operationalize.” Another stated, “You need to really understand the partner
   ecosystem, or you will grind to a halt.” Reliance on the customer community
   for help instead of a weak help desk is also necessary. ServiceNow ITOM AIOps
   Enterprise is a serious choice for larger organizations that have already
   adopted its ESM solution.

Strong Performers
   
 * BigPanda’s flexibility, scalability, and visibility overcome a lack of native
   remediation. BigPanda is a flexible and scalable platform that is designed to
   provide unified visibility, control, and automation to its customers. Its
   open data strategy and technology-agnostic approach enable it to function as
   a central hub between the noisy IT environments generating volumes of data
   and the people and workflows that need to operate it. Planned investments in
   broadening its operational data set reach, algorithms to extract more
   insights, and efforts to deliver an easier and more prescriptive solution
   will serve its customers well. Potential customers can try out the product
   without engaging professional services because of a straightforward
   go-to-market approach, a self-service option that includes a starter-pack
   feature, and a consumption-based pricing model.
   
   BigPanda offers extensive automation capabilities that can easily trigger
   third-party remediations. Its lack of a native remediation capability,
   however, could be a challenge for organizations seeking technical debt
   reduction or help with the complexity of using third-party remediations that
   has become overly burdensome. Utilizing existing features and adding new
   tools is easy and straightforward according to reference customers. They also
   stated, “The platform is designed to scale horizontally to support
   large-scale complex environments with ease.” A unified analytics approach
   enables exploration and visualization of interrelated metrics that provide
   insights, increase productivity, and help standardize incident response
   workflows. BigPanda is a great option for organizations looking for a
   technology-agnostic solution that automates operational workflows across a
   wide assortment of technologies in the landscape.
 * SolarWinds application performance monitoring uplifts weak reporting and
   dashboards. SolarWinds, based on its most recent financial results, appears
   to have recovered from the 2020 hack that drove many customers away and
   forced potential customers to hold off on any new deals. Over the past 18
   months, it has worked to build the core of an AIOps platform. It handles data
   ingestion, provides a unified approach for future capabilities, and supports
   its current observability and service management offerings. The evolution of
   the platform has been slow, and specific roadmap dates were not provided. It
   is projected to allow users to observe, analyze, and act in a manner that
   delivers intelligent incident management via standalone offerings. The
   strength of its service management platform will provide easy integration for
   incident response, change management, and root-cause detection.
   
   SolarWinds is expanding on its platform that operates seamlessly to
   accelerate efforts from mean time to detection through mean time to repair
   (MTTR). APM and database monitoring strengths come together through diverse
   deployment capabilities enabling greater value to customers. A reference
   customer mentioned the intuitive and user-friendly operation but described
   reporting and dashboards as “too light” and “insufficient for advanced work,”
   areas noted in the roadmap. When available, entity topologies are used to
   find correlations and concurrences of events, while ML algorithms are used
   for heuristic maps when they’re not. SolarWinds had one of the most extensive
   container and orchestration offerings of all vendors. Enterprises looking for
   an AIOps solution built into a common service management and observability
   platform should consider SolarWinds. SolarWinds declined to participate in
   the full Forrester Wave evaluation process.

Contenders
   
 * UST’s SmartOps platform has solid foundations upon which it improves its
   capabilities. UST, a privately held firm established in 1999 as a systems
   integrator, leveraged years of experience to build UST SmartOps — an
   AI-powered cognitive automation platform. Its strong vision regards IT as a
   business process that an evolution from business and digital transformation
   services drives. Its partner ecosystem lacks managed service providers. A
   customized pricing model, posted only in the Amazon Web Services marketplace,
   will make the consideration of the solution challenging for some
   organizations. The UST roadmap lacks specific feature-delivery timelines.
   Strategically, UST seeks long-term relationships with a smaller number of
   clients with which it can grow. This is evident by an average client tenure
   of 13 years with an annual growth rate of 17% over the past five years. It is
   an outcome-driven, unified platform powered by intelligent automation, and
   predictive algorithms are deployed on-premises or in the cloud.
   
   UST prides itself on analyzing critical alerts/incidents to try and predict
   outages at least 30 minutes before they occur. It utilizes AI, ML, and NLP
   algorithms throughout its operations for predictive and probability
   processing, a key feature noted by a reference customer. But gaps in
   demonstrating core capabilities and use cases exist. UST collects data via a
   native agentless approach supplemented by agent-based monitoring via
   third-party integrations. Its agnostic integration hub (iHub) approach to
   underlying tools enables it to provide a unified view of the IT landscape
   across multiple channels. An ML operations integration with the SmartOps
   platform can detect model/data drifts and dynamically adjust thresholds. UST
   is a good fit for organizations looking for a long-term partner to work
   closely with throughout their AIOps journey.
 * Everbridge is strong in data optimization but must add some capabilities for
   full AIOps. Everbridge is an established IT alerting and incident response
   vendor with tight integrations into IT processes through its acquisitions.
   AIOps, however, demand that vendors go deeper into and wider across the
   technology stack than IT alerting and incident response are intended to go.
   Everbridge’s approach to the market as an incident response vendor inevitably
   excludes efforts to process and analyze raw sensory and telemetry data from
   which predictive alerting can be derived — an AIOps staple. More than 20
   years of resilience-oriented solutions drive Everbridge to solve existing
   technology issues before they become business problems by using automation,
   analytics, and AI technologies as a force multiplier. Everbridge’s
   command-and-control approach focuses on incident commanders and resolvers.
   
   Everbridge automates initial triage efforts, and when automated remediations
   cannot be performed, it initiates automation of the on-call management.
   Real-time monitoring and observability are not natively available. Everbridge
   relies exclusively on third parties for all aspects associated with data
   collection, processing, noise suppression, and anomaly detection at the
   sensory and telemetry level. A visual drag-and-drop workflow designer helps
   design automations for signal normalization, communications, automated
   remediation, and toolchaining. Real-time reports with insights present the
   flow of signals to alerts, incidents, notifications, and automations, a
   strong capability noted by a reference customer. Twenty-three predefined
   roles far surpass what is available out of the box (OOTB) from all other
   vendors. Everbridge well serves organizations that more directly focus on the
   incident response aspects of AIOps.

Challengers
   
 * Sumo Logic’s data ingestion shines, but the impact of its recent acquisition
   is unknown. Sumo Logic is a cloud-native vendor founded in 2010 with a focus
   on analytics and log management. It has since established itself as a Strong
   Performer in security analytics utilizing the same platform. Sumo Logic is
   intent on providing an analytics platform that ensures application
   reliability, secures cloud-native applications, and enables deep insights
   into cloud infrastructures. Continued support for and contribution to the
   OpenTelemetry project will help enable further interoperability of its
   platform at the telemetry layer. Sumo Logic’s pricing structure is
   straightforward, easy to understand, and posted publicly on its website. In
   May 2023, Francisco Partners acquired Sumo Logic for $1.7 billion and took
   the company private. With other complementary and overlapping vendors in the
   Francisco Partners stable, it is unclear what will change in Sumo Logic’s
   offerings.
   
   A strong data ingestion layer enables Sumo Logic to take in a broad spectrum
   of telemetry to use in its analytics engine to improve resilience. Native
   real user monitoring and third-party DEM integrations provide insights that
   reach out to determine user and customer experience. A customer noted the
   products’ scalability and suitability for teams across the operational
   landscape. Another customer stated that Sumo Logic was inconsistent with its
   messaging about utilizing a proactive model. Sumo Logic is a good choice for
   organizations seeking to address security analytics challenges and IT
   operational issues on the same platform. Sumo Logic declined to participate
   in the full Forrester Wave evaluation process.
 * Freshworks has a solid workflow platform but is still developing its AIOps
   capabilities. Freshworks was a Strong Performer in the Q4 2021 ESM Forrester
   Wave but is still in the process of developing all the AIOps capabilities
   needed to compete against AIOps Leaders. The 12- and 24-month roadmap lacked
   feature details and specific timelines for delivery. Its product vision
   articulated many important AIOps capabilities, but most Leaders are already
   delivering those capabilities and looking to the next generation. Its partner
   ecosystem is well established as part of the broader offerings and includes
   important aspects such as training and guidance on how to build better
   relationships with customers. Freshworks is available to all Freshservice
   customers that subscribe to the Pro and Enterprise plans.
   
   Freshworks infrastructure and operations management capability utilizes
   intelligent alert management, on-call management, cloud discovery, and
   orchestration to identify and route issues. Its Freddy AI uses proprietary
   algorithms to correlate alerts and study historical data to find patterns.
   Freshworks was unable to demonstrate capabilities related to three use cases:
   real-time monitoring and observability, operational anomaly and root-cause
   detection, and infrastructure and operations management. While some core
   features within those use cases are available in Freshworks, the full use
   case could not be completed as prescribed. Organizations already leveraging
   or considering acquiring the Freshservice ESM product should consider
   Freshworks to take advantage of mutual benefits the solutions offer.
 * Moogsoft’s intelligent and suggestive alerting overshadows its lack of data
   ingestion. Moogsoft is well established in the delivery of AI-driven
   solutions, specializing in IT operations management and incident resolution.
   A data-agnostic posture supports its perspective that data ingestion at the
   infrastructure and application layer is less important than the algorithms
   needed to make the data actionable. Moogsoft focuses on automating
   operational processes with a zero-touch approach and enabling self-healing to
   minimize MTTR. In 2020, it aggressively pivoted to a multitenant
   software-as-a-service (SaaS) model but has not yet brought all its
   on-premises features to the SaaS offering. That early pivot positioned
   Moogsoft for longer-term strategic growth, while it works on delivering
   additional observability capabilities.
   
   Moogsoft clusters alerts and events to reduce event noise, streamline the
   incident management process, and make the resulting situations actionable. It
   highly relies on third-party tools to provide it with the necessary data to
   execute its mission effectively and efficiently. Its analytics reduce
   troubleshooting time and help identify probable root causes. Moogsoft’s Next
   Steps features build on situations that were determined to have historical
   similarities and power intelligent and suggestive alerting. Moogsoft lags
   Leaders in the quantity of OOTB integrations it provides because they have
   hundreds more. Customers noted that the learning curve and complexity of
   setting up Moogsoft could be challenging for organizations. Enterprises
   should consider Moogsoft if they want to coalesce disparate data sources to
   bring insights forward to their operational teams. Moogsoft declined to
   participate in the full Forrester Wave evaluation process.


Evaluation Overview
We grouped our evaluation criteria into three high-level categories:
   
 * Current offering. Each vendor’s position on the vertical axis of the
   Forrester Wave graphic indicates the strength of its current offering. Key
   criteria for these solutions include ingestion and service mapping,
   visualizations and experience, detection and insights use cases, and
   operational use cases.
 * Strategy. Placement on the horizontal axis indicates the strength of the
   vendors’ strategies. We evaluated the vision, innovation, roadmap, partner
   ecosystem, adoption, and pricing flexibility and transparency.
 * Market presence. Represented by the size of the markers on the graphic, our
   market presence scores reflect each vendor’s product revenue and number of
   customers.

Vendor Inclusion Criteria
Each of the vendors we included in this assessment has:
   
 * Annual revenues of greater than $10 million.
 * A single code base or multiple code bases with one UI.
 * A process-centric, not technology-centric, or an operationally aligned AIOps
   solution.
 * Relevance to Forrester clients based on the volume of client interest in the
   AIOps solution.

Supplemental Material
Online Resource
We publish all our Forrester Wave scores and weightings in an Excel file that
provides detailed product evaluations and customizable rankings; download this
tool by clicking the link at the beginning of this report on Forrester.com. We
intend these scores and default weightings to serve only as a starting point and
encourage readers to adapt the weightings to fit their individual needs.
The Forrester Wave Methodology
A Forrester Wave is a guide for buyers considering their purchasing options in a
technology marketplace. To offer an equitable process for all participants,
Forrester follows The Forrester Wave™ Methodology to evaluate participating
vendors.
In our review, we conduct primary research to develop a list of vendors to
consider for the evaluation. From that initial pool of vendors, we narrow our
final list based on the inclusion criteria. We then gather details of product
and strategy through a detailed questionnaire, demos/briefings, and customer
reference surveys/interviews. We use those inputs, along with the analyst’s
experience and expertise in the marketplace, to score vendors, using a relative
rating system that compares each vendor against the others in the evaluation.
We include the Forrester Wave publishing date (quarter and year) clearly in the
title of each Forrester Wave report. We evaluated the vendors participating in
this Forrester Wave using materials they provided to us by March 2023 and did
not allow additional information after that point. We encourage readers to
evaluate how the market and vendor offerings change over time.
In accordance with our vendor review policy, Forrester asks vendors to review
our findings prior to publishing to check for accuracy. Vendors marked as
nonparticipating vendors in the Forrester Wave graphic met our defined inclusion
criteria but declined to participate in or contributed only partially to the
evaluation. We score these vendors in accordance with our vendor participation
policy and publish their positioning along with those of the participating
vendors.
Integrity Policy
We conduct all our research, including Forrester Wave evaluations, in accordance
with the integrity policy posted on our website.

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