ITIL® 4 AND
ARTIFICIALINTELLIGENCE
Julie L. MohrWhite Paper
February 2019

Contents
1

Introduction

03

2

What is artificial intelligence?

03

3

Artificial intelligence and ITSM

03

4

AI and reliable ITSM data

03

5

Machine learning and ITSM

04

6Internet of Things and ITSM

04

7High velocity IT and business agility

05

8Optimize and automate and artificial intelligence05

9Artificial intelligence and the general management practices of ITIL 4

05

10 Artificial intelligence and the service management practices of ITIL 4

07

11 The benefits of AI-driven ITSM practices

10

12 Focus on improving the value chain with artificial intelligence

10

13 About the author

11

14 About AXELOS

12

15 Trade marks and statements

12

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1. Introduction
Artificial intelligence (AI) is an exciting and emerging field with a rapid pace of change and innovation. Theintersection of AI with IT service management (ITSM) holds great potential to improve service management
practices and drive significant innovation in the industry. This white paper highlights, the advancements ofartificial intelligence, along with AI’s current adoption in ITSM and its potential for the future.

2. What is artificial intelligence?
Artificial intelligence is a scientific field of study focusing on the development of computer systems that can
execute functions traditionally performed exclusively by humans. Research areas include speech, sight,
decision-making, interpretation, and even human-like motor tasks. Major innovations have developed inimage recognition, machine vision, text to speech and speech to text, machine learning, natural language
processing, robotics, and expert systems. The areas of scientific advancements most significant to ITSM aremachine learning, natural language processing, and speech.
Artificial intelligence is not new. Research in the field of artificial intelligence began in the mid-1950s. The
current explosion in this field is largely due to the lower cost of technology and data storage, improved
processing speed, scalability using cloud services, and the availability of large masses of data collected via
multiple channels, including social media and the internet.
Companies in many vertical markets are excited about the potential of artificial intelligence and areinvesting in new strategies in this rapidly evolving field. For example, artificial intelligence is being usedin mobile devices such as cell phones and tablets, and common home devices. The intelligence of theseordinary devices is reflected in their ability to provide answers to questions and to help manage our lives
more effectively. However, this intelligence is mostly limited to existing programmed responses to common
questions or using the gathered data to provide relevant and useful information to consumers. Over time,these devices will continue to amass volumes of data that drive the learning process and new innovations
in how artificial intelligence integrates into our daily lives.

3. Artificial intelligence and ITSM
From an ITSM perspective, the application of artificial intelligence to service management practices has
great potential. Collecting data about our services, customer requirements, and end-user issues andsolutions fuels the opportunity to develop systems that utilize artificial intelligence to drive improvements
to service design, delivery, and support. Adding artificial intelligence into the ITSM domain allows service
providers to use software to make data-driven judgements and decisions based on pattern recognition in
the data collected across multiple channels and systems.
The potential for AI is significant, allowing the service provider to improve the customer experience,
improve services in meaningful ways to the business, and shift the IT workforce from repetitivetransactional work to innovative and creative work.
ITIL 4 supports organizations in developing an overall strategy for using artificial intelligence acrossthe ITIL management practices. AI can drive major service provider advantages and benefits in all ITIL
practices, particularly in the following: information security management, knowledge management,
measurement and reporting, workforce and talent management, incident management, monitoring andevent management, problem management, service design, service desk, service request management,
and software development and management.

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4. AI and reliable ITSM data
An important consideration in developing a strategy to leverage artificial intelligence in ITSM is theavailability of data. Although IT services and service management practices focus on collecting data, thedatasets may not have the veracity or quality needed to drive the predictive analysis required in artificial
intelligence. The primary sources of data available include events from monitoring, incidents, requests,
problems, known errors, workarounds, and changes. The data is collected either through monitoring
systems or by using humans to enter information while working through transactional work with customers.
The collection of data using humans is an error-prone process with a high degree of variability. Learningfrom an unreliable set of data requires the ability to filter meaningful information that can then be used todrive intelligence. Traditional methods of analysis require humans to spend significant time cleaning up
datasets and running reports and analysis to glean relevant insights. Using computers, the analysis process
is not only freeing up valuable time of resources but also can be an automated and repeatable process that
improves or learns over time.
While many ITSM providers have a knowledge management strategy to capture and retain institutional
knowledge, the knowledge database is often a source of unreliable, non-relevant, and out-of-date
information. Significant efforts are applied to capturing knowledge from subject matter experts, but due
to poor knowledge management practices, the knowledge is not maintained. Once the dataset is deemed
unreliable by its users, it quickly stagnates and is unreliable to support decisions.
The service provider must place an emphasis across functional silos that knowledge is the core asset ofthe organization and therefore a priority. All service management practices must contribute to effective
knowledge management to ensure that data is collected and maintained as a native activity within eachpractice. A focus on knowledge will drive the collection of data that can then be used in automation,
service design, testing, predictive analysis, auto-recovery, self-service and other service management areas.
Another important consideration is the variety of data collected across the entire user experience. Artificial
intelligence requires not just an isolated set of data but multiple data sources. For example, a user asks forhelp from a chatbot to resolve an issue with a laptop. The answer may be generated from prior incidents,
requests, known errors, knowledge articles, and using information obtained from the laptop. Any device
connected to the internet is a potential source of data that can be leveraged in ITSM artificial intelligencestrategies. The efforts of the service provider must focus on collecting data across the entire value chain
and include data from the customer experience and all service management practices.

5. Machine learning and ITSM
A significant area of importance in artificial intelligence are the advancements that have been made inmachine learning. Machine learning focuses on feeding computer systems large quantities of data andinformation to help the computers to learn, act, and think like humans do autonomously.
Machine learning is an integral part of developing IT services and ITSM tools. However, successfulmachine learning requires a large amount of data. Traditional applications use knowledge gained about aprocess or business requirement to produce a specific, predictable outcome. Machine learning is wheredata about IT services and processes is used to learn from the collected data without pre-programming
the outcome. Machine learning typically utilizes advanced statistical techniques and computing systems
to improve ITSM tasks over time. With machine learning, the application can write additional programs
to interpret input and predict outcomes. Advancements in machine learning are at the root of the current
explosion of artificial intelligence.

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From an ITSM perspective, machine learning is the ability of IT services and service management tools
to learn from the data collected. From a value stream perspective, the organization does not just define
the activities, workflows, controls, and procedures needed to achieve the business objectives, but canalso learn from these and create new ones or improve existing outcomes without or with limited humanintervention.

6. Internet of Things and ITSMInternet of Things (IoT) is the use of technology that allows common devices to collect and share data overthe internet. Data collected from smart devices is communicated both to the consumer and the productmanufacturer or service provider using applications that are also stored on the devices. In addition, theintelligent devices often provide remote control capabilities.
From an ITSM perspective, smartphones, tablets, and other end user devices can be used to gather data
about the end-to-end customer experience. The data can be very beneficial in improving IT services andbetter aligning those services with business expectations to create an improved value stream.
As the service provider designs new systems, it will become increasingly important for the components
within the service to communicate and share meaningful information. The service management
organization needs to design IT services that provide a high availability but also use gathered data fromsmart devices to identify required maintenance, recognize and remove problems proactively, and automate
common requests.

7. High velocity IT and business agility
Changing demands within business markets require that IT organizations work with greater velocity insupport of business agility. Agile software development practices provide an iterative approach to gathering
requirements and producing business outcomes quicker with shorter development cycles. Agile methods
can introduce significant risk especially with organizations that do not have mature service management
practices. At the same time, mature service management practices can stifle agile takeup, and thus
a balance is needed, underpinned by an organizational philosophy of continual improvement. Agiledevelopment is just one way in which IT organizations must react and adapt quickly to changing businessneeds.
Artificial intelligence has great potential to assist organizations in understanding and reacting to current
service needs, and to help predict future needs. Rather than wait until humans can analyze data todiscover patterns in behaviors within the value stream, AI can filter out relevant data, identify patterns, andproactively take actions to address identified needs or problems within the value stream.

8. Optimize and automate and artificial intelligence
To maximize the value of work performed by human resources, artificial intelligence can pave the way
for successful optimization of IT services and service delivery models. Optimization of services helps theorganization to automate predictable and documented work practices. The organization first focuses on
capturing knowledge of how to perform repetitive tasks as thoroughly as possible. The captured knowledge
is then automated within the service or service management tool, thus reducing the need for humans toperform the work.
In addition to optimizing the use of limited organizational resources, automation using artificial intelligencecan learn from the automated processes and adapt as needed when new variables present themselves.

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Across the organization and the service management practices, artificial intelligence can be used to identify
opportunities to improve efficiencies, reduce costs, eliminate errors and waste, and improve overall value.

9. Artificial intelligence and the general management practices of
ITIL 4Of the ITIL 4 general management practices, ITSM organizations can make use of artificial intelligence tohelp manage security and knowledge, and to support decision making. These are just a few examples, andother practices can also potentially benefit hugely from AI.
With the introduction of artificial intelligence, organizations will also need to focus on developing
appropriate analytical and knowledge management skills as existing resources transition from performing
mostly transactional work to higher valued work within the service value chain.

9.1 INFORMATION SECURITY MANAGEMENT
Artificial intelligence can be used to assist with the prevention, detection, and correction of security related
incidents within organizations. Integrating AI into information security management can improve theorganizations ability to verify and predict potential threats and automate responses, thus reducing risks
associated with poorly trained or unmotivated staff. As new services are designed, artificial intelligenceshould always be a consideration, as it can provide great benefits for the security management of thatsystem. For example, imagine a service that can use internal sensors to understand and prevent security
breaches based upon a comparison to normal conditions.
Information security management is critical to the business and management of business risk. Using AI
to detect anomalies within ITSM data, identify vulnerabilities, and prioritize issues for staff intervention is
only the beginning. Improving security is not just about using artificial intelligence in the detection of, andrecovery from security related incidents; organizations must begin to build artificial intelligence into theentire value chain to predict, prevent, detect, and learn from security breaches.
A software development company creates a software application using artificial intelligence that
detects potential bugs and vulnerabilities, identifies the potential risk for a security breach, andautomatically develops an effective protection against any potential threat.

9.2 KNOWLEDGE MANAGEMENT
Artificial intelligence requires data. Data is derived from service management practices, business services
and processes, customer interactions, ITSM tools and monitoring systems, and many other data points
across the value chain. Organizations must develop an overall knowledge management strategy that
focuses on data collection throughout service management practices.
Structured datasets that reside in relational databases where the structure is well defined and understoodare easily queried against using traditional reporting methods or business intelligence tools. With little
effort, the organization can use this structured data to support business decisions. However, businesses
are increasingly accumulating massive amounts of unstructured data in images, documents, audio,
video, and other data files where traditional analytical methods are not as effective. Organizations can use
machine learning and natural language processing to find patterns and derive insights from unstructured
data where it was impossible to do so in the past.

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Developing a successful knowledge management practice requires a shift in the focus of the organization
to understand that the capture of knowledge is not something done as an added service management
practice but is in fact embedded into every practice. To be successful, organizations must develop aknowledge sharing culture where knowledge is viewed as the core asset of the organization and critical for
long-term survival.
Along with a well-defined knowledge management practice, artificial intelligence can be used to auto createknowledge articles from data, suggest articles for human intervention or use, and even suggest knowledge
articles directly to customers through self-service. Organizations can use artificial intelligence to learn how
to exploit existing knowledge and avoid knowledge gaps
IBM’s Watson uses natural language processing to analyze and gain insights from large volumes of
unstructured data in many different vertical markets.

9.3 MEASUREMENT AND REPORTING
Artificial intelligence also has significant potential to enhance the measurement and reporting practice.
Predictive analytics uses the data collected in ITSM value stream and applies traditional statistics anddata modeling techniques along with machine learning and artificial intelligence. The main objective ofpredictive analytics is to understand the likelihood of future events. Predictive analytics can benefit acrossthe value chain, helping the organization to make better management decisions, produce new insights
about customers and services, and assist with better data analysis.
Customer data and predictive analytics are also key enablers of disruption within the business. Using
artificial intelligence with a 360-degree view of the customer across all channels of support will help drive
improvements in service delivery and support and provide insights for new innovative services.
A service company uses predictive analytics to develop a model that identifies customers who aremost likely to not renew at the end of their contract and devise a marketing plan that provides
incentives most likely to retain those customers for an additional term.

9.4 WORKFORCE AND TALENT MANAGEMENTMedia coverage of artificial intelligence has increased in society, so has the fear that artificial intelligence
will replace humans and eliminate jobs. While changes are possible and probable, ITSM leaders willneed to focus on developing the existing workforce for a future where transactional activity is managed bymachines and creative and knowledgeable staff are needed to drive innovation and deal with exceptions
which are identified but not managed by artificial intelligence.
The adoption of artificial intelligence requires different skillsets in the workplace to properly build AI- driven
systems and develop and maintain the data required to fuel AI systems. ITSM staff will need to program
and refine the way that artificial intelligence works as well as ensure that the business processes andpractices successfully interface with the artificial intelligence built into business services. Staff will shift
their focus to value-drive activities across the value chain, including managing knowledge, assisting withthe training of AI-driven systems, dealing with complex situations or situations not previously encountered,and using productivity gains to work on project-based work. AI can also support training, education andknowledge sharing among ITSM professionals, as well as identifying the correct resource for 		
handling exceptions.

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10. Artificial intelligence and the service management practices of
ITIL 4
While there is significant potential to implement artificial intelligence across the general management
practices of ITIL 4, many organizations are already benefitting from its use in their service management
practices. Using machine learning and natural language processing, organizations can improve themanagement of events, requests, incidents, and problems, and improve the overall customer experience.
Again, this is not an exhaustive list, and many other service management practices can also benefit fromthe use of artificial intelligence.

10.1 INCIDENT MANAGEMENT
The incident management practice primarily focuses on restoring service as quickly as possible. As
organizations attempt to reduce costs, the incident management process is often a place where headcountis kept at minimum levels. With a lack of resources, organizations struggle to be proactive in meetingcustomer needs. In the early stages of adoption, artificial intelligence is most often used to improve incident
categorization, issue prioritization, pattern recognition for commonly occurring incidents across services,auto resolution of incidents, and workforce optimization.
An organization that is in the early stages of artificial intelligence adoption should focus on ensuring thatdata collected in the incident management practice is structured consistently and maintained as it is usedwithin the process. The focus on knowledge creation and maintenance will be instrumental in improving
machine learning.
Artificial intelligence can help to identify a resolution for a customer query by using natural
language processing to understand the intent of the customer query and matching it to solutions
stored in the knowledge base. In many cases, solutions can be automated, thus reducing the needfor any human intervention.

10.2 MONITORING AND EVENT MANAGEMENT
The collection of data across value streams includes the monitoring of business services and businessprocesses, as well as IT services and service management processes. Automated event correlationand filtering is improved through applying artificial intelligence and training the system with existing
documented rules, workflows, and automated responses.
Artificial intelligence can also be used to:zz monitor systems and watch for previously unseen eventszz use events which have already been resolved to determine the best approach to handle subsequent eventszz drive the development of new automation of commonly occurring events.

The pattern recognition capabilities in artificial intelligence can be used to analyze data and identify trends
in events that may indicate a more systemic issue not yet identified as an exception. Early detection
and alerting of staff enables a more proactive approach to managing services. If we use human efforts
to analyze the same data source, it is likely to require lengthy timeframes with little or limited success.
Artificial intelligence is ideal for analyzing large data sets and identifying correlations within the data
While the primary goal of artificial intelligence integrated in to monitoring and event management is tobe proactive in eliminating service disruptions, AI can also provide information that will help humans to
identify and correct incidents and problems more effectively. Artificial intelligence can recognize patterns

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that can be assessed for potential risk, and create, categorize, and prioritize incident tickets. This allows theuse of resources on other areas of importance to be maximized
Artificial intelligence is used to identify patterns in previous monitoring data to proactively balance
workloads in real time and thus reduce any capacity related issues with a service.

10.3 PROBLEM MANAGEMENT
In addition to identifying patterns in events, artificial intelligence is also incredibly important when spotting
trends in incidents and problems, repeat problems, corrective actions, and automatic recovery, andpreventing future occurrences of systemic incidents and problems.
Through analysis of data and the use of machine learning, artificial intelligence can analyze patterns inincidents, identify potential areas for future problems and alert staff of these areas. Using a large datasetof previously documented incidents, problems, and resolutions, artificial intelligence and machine learning
can help to ensure that problems are handled more efficiently and prioritized and categorized with lesserror. When appropriate, the system can also automate workarounds, perform preventative maintenance,
apply fixes, and auto generate change tickets with risk assessment and impact analysis.
Problem management is one of the most labour-intensive practices to perform effectively, and thevalue of using artificial intelligence to perform these types of analysis is significant. Artificial intelligencecan automate the analysis of data and augment the knowledge of ITSM professionals, while also
troubleshooting more complex problems and reducing the time taken to identify the root case 		and resolution.
An ITSM solution identifies and logs a recurring issue by studying patterns and anomalies inincident, problem, and event data. Based upon the insights gained through analysis, the ITSM
solution identifies a potential fix and corrective action.

10.4 SERVICE DESKNatural language processing combined with machine learning can also be used to enhance customer
interactions. The systems can collect and analyze data across multiple channels, including social media
and self-service, to gain insight into the customer, their work processes, and changing needs. Using humanlanguage interactions, the AI-driven systems can understand and interpret the spoken or written word andpersonalize experiences for customers.
Artificial intelligence is used to provide self-service knowledge directly to customers through systems
such as chatbots. Using data captured in the incident management and request fulfillment processes, thechatbots can be programmed to handle previously defined workflows for the management of common
issues. An area of importance is the use of natural language processing within a chatbot. If a customer
poses a question that is not contained in the rules tables, the system can then try to understand the intent
of the customer and provide an appropriate answer.
Natural language processing can be either text-based interactions using a chatbot or voice interactions
like those used in many smart devices in the home. The natural language processing engine attempts toprocess customer questions by breaking the spoken or written text into its parts, and then comparing thetext with registered responses to help understand the specific words and word sequences, and the intent of
the customer.
The emerging field of self-service through AI-driven technology is a frequent entry point into the use of
artificial intelligence in ITSM. The cost of entry is lower than many other forms of artificial intelligence, and
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many high-tech firms provide free access to decision-based agents. The adoption cycle is also made easierdue to the importance of knowledge captured and used in service desks as a means of shifting knowledge
directly to the customer to avoid additional calls for services or support.
A higher education institution uses prior incidents and requests to develop a chatbot specificallyfor handling of all students issues during the onboarding process at the beginning of the school
year for incoming freshman. The chatbot manages the entire process, keeps students connectedwith campus resources, and also proactively notifies students of all upcoming deadlines and any
potential exceptions.

10.5 SERVICE REQUEST MANAGEMENT
Much like events, incidents, and problems, artificial intelligence can be used to automate commonlyoccurring service requests without the need for human intervention. Automation can reduce the burden
on human resources and the costs associated with fulfilling service requests. Artificial intelligence canhelp with the process of automating new requests by learning from previously documented requests andcreating new workflows based upon pattern recognitions identified in requests that have been fulfilled.
Artificial intelligence can also be used to analyze patterns in prior service requests to anticipate potential
future requests, assist with reducing errors in the assignment of requests, and predictively manage requests
to prevent service level agreement breaches. Most organizations have a significant volume of requests, andwhen artificial intelligence is used the organization can improve the overall customer experience, achievesignificant cost reductions, improve processing times, and eliminate traditional delays.
A virtual agent is available 24x7 to handle basic service requests such as password resets, softwareor hardware requests, purchases, downloads, and the allocation of licenses.

11. The benefits of AI-driven ITSM practices
Using artificial intelligence to support automation can be highly valuable for service management
professionals, and help to optimize the use of available resources. A service organization making use of
artificial intelligence can receive major benefits, including improvements in practices and productivity,
better utilization of resources, reductions in operational costs, and improved service levels. Artificial
intelligence can also lead to business improvements in the customer experience, higher availability ofbusiness services, high productivity, lower costs, and increased business scalability and agility.

12. Focus on improving the value chain with artificial intelligence
The potential application of artificial intelligence in the ITIL 4 service management practices is vast,
but it is important to focus on solving business problems rather than just technology. When using
artificial intelligence, organizations should focus on improving the value stream, and not just theservice management processes. The adoption of artificial intelligence will not always run smoothly, andorganizations that begin this process earlier can expect to have failed attempts along the way. The goal is touse artificial intelligence to improve the ability of humans to manage service across the value chain.
Artificial intelligence is no longer an isolated domain of study but has grown into an interdisciplinaryscience crossing over into many different areas of research and design, including manufacturing, sales andmarketing analytics, automation, healthcare, and automotive industries, to name just a few. The

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ITSM industry is identifying ways to embed artificial intelligence not only into ITSM practices but also intobusiness processes, driving disruption and innovation.
Dr. Ng of Stanford University said in 2016 that artificial intelligence is the new electricity. His statement
indicates the potential of artificial intelligence but also the possible magnitude of its impact on our society.
Artificial intelligence will continue to impact the evolution of service management for years to come.

13. About the author
Julie L. Mohr is a dynamic, engaging change agent who brings authenticity,
integrity, and passion to practitioners worldwide. Through her books, articles,
speaking, consulting, and teaching, her purpose is to spark change in theworld with thought-provoking dialog and interaction. Julie has a B.S. degree incomputer science from The Ohio State University, a MaED from the University of
Phoenix, and is currently pursuing her Ph.D. in Management and Organizational
Leadership in Information Systems & Technology from the University of Phoenix.
Julie is a certified ITIL Expert and a Certified Governance IT Professional. Julie
captivates audiences at conferences worldwide on topics of authentic leadership,business strategy, artificial intelligence, the disruption economy, knowledge
management, organizational culture, and innovation.

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14. About AXELOSAXELOS is a joint venture company co-owned by the UK Government’s Cabinet Office andCapita plc.
It is responsible for developing, enhancing, and promoting a number of best practicemethodologies used globally by professionals working primarily in project, programme andportfolio management, IT service management and cyber resilience.
The methodologies, including ITIL®, PRINCE2®, PRINCE2 Agile®, MSP®, RESILIA®, and its
newest addition AgileSHIFT® are adopted in more than 150 countries to improve employees’skills, knowledge, and competence in order to make both individuals and organisations work
more effectively.
In addition to globally recognized qualifications, AXELOS equips professionals with a wide
range of content, templates and toolkits through the CPD aligned My AXELOS and our online
community of practitioners and experts.
Visit www.AXELOS.com for the latest news about how AXELOS is ‘Making organizations
more effective’ and registration details to join AXELOS’ online community. If you have specificqueries, requests or would like to be added to the AXELOS mailing list please contact
Ask@AXELOS.com.

15. Trade marks and statements
AXELOS®, the AXELOS swirl logo®, ITIL®, PRINCE2®, PRINCE2 Agile®, MSP®, M_o_R®,
P3M3®, P3O®, MoP®, MoV®, RESILIA® and AgileSHIFT® are registered trade marks of
AXELOS Limited. All rights reserved.
Copyright © AXELOS Limited 2019.
Cover image credit: caiaimage/Robert Daly/Getty
Reuse of any content in this White Paper is permitted solely in accordance with the permission
terms at https://www.axelos.com/policies/legal/permitted-use-of-white-papers-and-case-studies
A copy of these terms can be provided on application to AXELOS at Licensing@AXELOS.com
Our White Paper series should not be taken as constituting advice of any sort and no liability
is accepted for any loss resulting from or use of or reliance on its content. While every effort ismade to ensure the accuracy and reliability of information, AXELOS cannot accept responsibility
for errors, omissions, or inaccuracies. Content, diagrams, logos, and jackets are correct at timeof going to press but may be subject to change without notice.
Sourced and published on www.AXELOS.com

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About this White Paper:
ITIL 4 supports the development of an overall
strategy for using AI across ITIL practices, bringing
advantages in: information security management,
knowledge management and others.

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