AI & ML: Driving the Next Generation of Innovation in DevOps and Workload Automation

IT & DATA MANAGEMENT RESEARCH,
INDUSTRY ANALYSIS & CONSULTING
Stephen D. Hendrick
Research Director and Principal Investigator
Enterprise Management Associates
shendrick@enterprisemanagement.com
How AI/ML Is Changing DevOps
With a deep dives into:
Event and Incident Management
Workload Automation

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Featured Speaker
3 © 2020 Enterprise Management Associates, Inc.
Steve Hendrick, Research Director, EMA
Steve has over 20 years of experience as an industry analyst and 10 years of experience
across a variety of application development roles. He specializes in application development
and deployment topics, including DevOps, lifecycle management, application platforms,
decision analytics, middleware, and the broad spectrum of tools and technologies that support
professional developers and IT managers. Steve brings advanced quantitative and qualitative
research techniques as an industry analyst, providing vendors with deep insight into the
markets where they compete.

Sponsors
Thanks to our sponsors who
made this research possible.

Agenda
The State of DevOps
1
Enterprise Involvement in AI/ML,
Expected DevOps Benefits and
Challenges
2
The Impact of AI/ML on Event and
Incident Management
3
Best Practices for Success in
DevOps with AI/ML
5
The Impact of AI/ML on Workload
Management
3
Questions
6

Research Scope
Objectives
What is the state of workload automation
in the enterprise today?
Better understand the impact that AI/ML is
having on DevOps, workload automation, and
the enterprise
Identify best practices in
workload automation
How EMA approached these objectives
Worldwide survey of large end-user
enterprises (N=289)
Respondents included directors, VPs, and C-
level execs in app dev, IT, IT Ops
Strong respondent familiarity with AI/ML and
workload automation
Survey fielded in August 2020
MoE is +/- 5% @ 90% confidence

DevOps Maturity in Large Enterprises
We have DevOps plans
but have not begun to
implement them, 1%
We are just beginning
our DevOps journey,
3%
We have automated
some DevOps
activities, 16%
We have automated
many DevOps
activities, 48%
We have automated
nearly all DevOps
activities, 32%
What is your enterprise’s approach to DevOps?

Large Enterprises See AI/ML as a High Priority
Not, minimally
& partially
challenging,
43%
Mostly &
Extremely
challenging,
57%
How challenging is it to implement an
enterprise AI/ML strategy?
Not, Low,
Medium
priority,
13%
High,
Extremely
high priority,
87%
Is AI/ML a priority for your enterprise?
Undefined,
minimally &
partially
defined, 14%
Mostly &
Extremely
well defined,
86%
How well defined is your enterprise
AI/ML strategy?

Expected DevOps Benefits From AI/ML
0% 10% 20% 30% 40% 50% 60% 70%
Application performance
Speed in delivering new applications
Application reliability
Application quality
OpEx efficiencies in IT
Speed in updating existing applications
Employee productivity
Workload automation efficiency
Customer satisfaction (external)
Application innovation and design
Ability to leverage cloud services
Customer satisfaction (internal)
Business competitiveness - revenue growth
Cost controls
Business competitiveness - brand protection
Business process optimization/efficiencies
Ability to transition to microservices
Ability to address regulatory or compliance requirements
Business requirements for new products or services
Event & incident management capabilities
Senior management decision-making
Where do you expect to see improvements when using DevOps tools with AI/ML capabilities?
Segmented by Automation of DevOps activities
Some
Many
Nearly All
DevOps Activities
Automation

Expected DevOps Benefits From AI/ML
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65%
Application performance
Speed in delivering new applications
Application reliability
Application quality
OpEx efficiencies in IT
Speed in updating existing applications
Employee productivity
Workload automation efficiency
Customer satisfaction (external)
Application innovation and design
Ability to leverage cloud services
Customer satisfaction (internal)
Business competitiveness - revenue growth
Cost controls
Business competitiveness - brand protection
Business process optimization/efficiencies
Ability to transition to microservices
Ability to address regulatory or compliance requirements
Business requirements for new products or services
Event & incident management capabilities
Senior management decision-making
What improvements do you expect when using DevOps tools with AI/ML capabilities? Segmented by Role of Respondent
App Dev
IT
IT Ops
IT Role

How is AI/ML Impacting DevOps?
0%
10%
20%
30%
40%
50%
60%
No change Little change Moderate
change
Significant
change
Transformative
change
Percent
of
Enterprises
by
DevOps
Segment
How are your tools with AI/ML capabilities impacting your DevOps processes and workflows?
Segmented by DevOps Automation
Beginning (3%)
Some (16%)
Many (48%)
Nearly All (32%)
DevOps Activities
Automated
(Percent of Sample)

Challenges in Using DevOps tools with AI/ML
44%
43%
39%
38%
38%
37%
35%
28%
23%
23%
21%
17%
Evaluating how effective a tool's AI/ML capabilities are
Integrating new AI capabilities into our DevOps toolchain and workflows
Configuring DevOps tools with AI/ML capabilities for maximum utility
Understanding the many ways that AI/ML can drive value across DevOps
Understanding how the vendor tool's AI/ML capabilities add value
Determining where next to implement DevOps tools with AI capabilities
Finding staff with the skills needed to leverage DevOps tools with AI/ML
capabilities
Finding time to leverage new DevOps tool AI/ML capabilities
Changes in how our staff address DevOps because of these tools
Justifying budget for DevOps tools with AI/ML capabilities
We don't have budget to acquire the tools with AI/ML capabilities that we
need
Demonstrating the value add of DevOps tools with AI/ML capabilities
What challenges are you facing when using or adopting DevOps tools with AI/ML
capabilities?

How Challenging is AI/ML?
0%
10%
20%
30%
40%
50%
60%
Extremely
challenging
Mostly
challenging
Partially
challenging
Minimally
challenging
Not
challenging
Percent
of
enterprises
by
DevOps
segment
How challenging is it to implement an enterprise AI/ML strategy?
Segmented by automation of DevOps activities
Some (16%)
Many (48%)
Nearly All (32%)
DevOps Activities
Automated
(Percent of Sample)

Current Approach to EIM
What best describes your enterprise's approach to event and incident management?
Segmented by DevOps Automation of Activities
0%
5%
10%
15%
20%
25%
30%
35%
No specialized
tools to
identify
incidents, do
RCA,
remediate
Specialized
tools to
identify
incidents
Specialized
tools to
identify
incidents and
remediate
Specialized
tools to
identify
incidents and
do RCA
Specialized
tools to
identify
incidents, do
RCA, &
remediate
Single
platform to
identify
incidents, do
RCA, and
remediate
incidents
Single
platform to
identify
incidents & do
RCA, but
another tool to
auto
remediate
Single
platform to
identify
incidents, do
RCA, and
auto
remediate
Percent
of
Enterprises
by
DevOps
segment
Some
Many
Nearly All
DevOps Activities
Automated

How AI/ML Can Improve EIM
34%
29%
29%
25%
23%
23%
21%
18%
18%
17%
15%
11%
8%
8%
6%
4%
Increased IT staff productivity
Improved collaboration between IT and business
Higher customer (end-user) satisfaction
Enablement for digital transformation initiatives
Better collaboration across relevant IT groups
Reduced # of routine incidents reaching help desk (incident deflection)
Reduced # of major incidents due to better visibility/incident avoidance
New or improved cross-organizational services
Reduced mean time to resolve (MTTR)
IT cost savings
More effective and predictable major incident response
Improved support for IoT
Revenue protection due to reduced downtime
Reduced unplanned IT work
Reduction in unplanned downtime
Headcount savings
What are the three most important ways that AI/ML capabilities can improve
incident management tools?

58%
51%
50%
44%
43%
41%
40%
39%
27%
Real time telemetry data including events, logs, traces, and
metrics from ongoing application and system operations
Historical telemetry regression data for baseline and
training models
Information about the efficacy of automated actions taken
Business rule data
Project status information
Project team timesheet data
Data describing human decisions made
Information about the efficacy of human actions taken
Accurate inventory of services including owners and
functionality
What types of data are needed to drive event and incident
management tools with AI/ML capabilities?
Data Needed to Drive EIM Tools with AI/ML
HRD in
use,
61%
HRD not
in use,
39%
Percent of sample using Human
Response Data (HRD)
HRD data is “in use”
When one or both
responses are selected

Current Business Motivation for EIM Investment
55%
54%
50%
41%
42%
33%
42%
33%
37%
31%
17%
21%
0% 10% 20% 30% 40% 50% 60%
Reducing the volume of events and incidents
Protect revenue and reduce risk
Reducing the amount of time spent diagnosing and
troubleshooting problems
Improving the quality of the software delivered
Reducing the cost of event and incidents
Reducing headcount by eliminating manual processes
Reinvesting cost savings into innovation activities (net-
neutral cost)
Generating data to understand where improvements
need to be made in people, process, or tools
We have no current investment plans for our incident
and problem management solution
Percent of segment responding
What is your enterprise's current business motivation for investing in event
and incident management?
Segmented by need for human decision/action data
HRD in use (61%)
HRD not in use (39%)

6%
13%
22%
23%
34%
When developing apps, we address
batch/event-driven workload automation
outside the current DevOps process
batch/event-driven workload automation as a
specific ops process
informally in our DevOps process
formally in our DevOps process
When developing apps, batch/event-driven
workload automation is as important as
infrastructure and business logic automation
in our DevOps process
To what extent are WLA activities a
part of your DevOps processes?
1%
8%
18%
37%
35%
They are not included
They are addressed as an ops responsibility
before the application is promoted to
production
They are addressed informally and depend on
the application
They are formally addressed as part of
application development
They are formally addressed for new
application development and application
changes
To what extent are WLA activities a part of
your application development processes?
Is WLA a Part of DevOps & App Dev?
9%
73%
20%
58%

Enterprise WLA Activities Vary by DevOps Maturity
0%
10%
20%
30%
40%
50%
60%
70%
We address
batch/event-driven
workload
automation
outside the current
DevOps process
We address
batch/event-driven
workload
automation as a
specific ops
process
We address
batch/event-driven
workload
automation
informally in our
DevOps process
We address
batch/event-driven
workload
automation
formally in our
DevOps process
Batch/event-driven
workload
automation is as
important as
infrastructure and
business logic
automation in our
DevOps process
Percent
of
enterprises
by
DevOps
segment
To what extent are workload automation (WLA) activities a part of your DevOps processes?
Segmented by automation of DevOps activities
Beginning (3%)
Some (16%)
Many (48%)
Nearly All (32%)
DevOps Automation
(Percent of Sample)
Total Responses: 6% 13% 22% 23% 34%

AI/ML Top 3 Business Benefits for Workload Mgmt
0% 10% 20% 30% 40% 50% 60% 70% 80%
Increased IT staff productivity
Higher customer/end-user satisfaction
Faster provisioning and deployment
Improved optimization of public cloud resources
Redeploy human resources to address higher value work
Improved collaboration between IT and business
Superior DevOps efficiencies
Accelerated levels of innovation
Enablement for digital transformation initiatives
Improved support/management of hybrid or multi-cloud
Improved revenue due to reduced downtime
Better collaboration across relevant IT groups
New or improved cross-organizational services
Improved IT cost savings
Reduction in unplanned downtime
Reduced unplanned IT work
Rank order the top 3 business benefits that AI/ML capabilities are expected to bring to workload management.
Segmented by Top 3 rank order
1st
2nd
3rd
Rank order

AI/ML Top 3 Functional Benefits for Workload Mgmt
0% 10% 20% 30% 40% 50% 60% 70% 80%
Machine learning to auto-remediate job processing issues
Profiling and performance analysis for acceptance and load testing
Managed file transfer
Augmented policy-driven scheduling optimization
Automated testing and proactive health checks
Automated application discovery and dependency mapping
Autoscaling for workload optimization
Dynamic resource allocation and optimization
Automation in support of data assimilation and reconciliation
RPA orchestration
Runbook or IT process automation
Virtual agents
Rank order the top 3 functional benefits that AI/ML capabilities are expected to bring to workload management.
Segmented by Top 3 rank order
1st
2nd
3rd
Rank order

Best Practices in AI/ML for DevOps, WLA, & EIM
Don’t delay
Best Practices for Enterprises
Use AI/ML to drive benefits in quality and
automation
Demand more and partner with your
software vendors
Develop a Center of Excellence for AI/ML
Build a foundational understanding of AI/ML
Transition from EIM point tools to a single
platform
Tools/platforms that best leverage HRD drive
significant EIM improvements
AI/ML makes EIM products smarter driving
productivity, customer sat, & brand protection
WLA needs be a formal part of your DevOps
and application development process
AI/ML makes WLA tools more intelligent,
reliable, and performant
1
2
3
4
5 10
8
9
7
6

Questions?
Get the Report
http://bit.ly/3r1GPHK

AI & ML: Driving the Next Generation of Innovation in DevOps and Workload Automation

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