Tag Archives: ITSM

AI, ITSM

Exposed: Your bad ITSM habits AI won’t ignore

Leave a comment
Share twitterlinkedinmail

As I discussed in another blog, many organizations are rushing to adopt and implement AI-enabled technologies within their ITSM environments. AI can have a significant positive impact on an organization’s ITSM environment.

But if an organization isn’t practicing good ITSM, introducing AI will just make bad ITSM habits worse.

Excuse me…your bad ITSM is showing

In my experience, many organizations that are practicing bad ITSM don’t realize it. Here’s some examples of bad ITSM:

  • Services are not defined. Service definitions describe how people, processes, and technology are used to deliver business value and business outcomes, as well as the specific costs and risks that are managed by IT. But without defined services, there is no shared understanding of the business impact of service interruption, no formal way to determine if existing services can be used to enable new business value, and no way to quantify the contributions of the IT organization – in business terms – to organizational success. The lack of defined services can also be a factor contributing to technical debt.
  • Using the wrong practices and expecting good results. Practices have defined purposes and produce defined results. Using the wrong practices produces unreliable results, as well as unnecessary human effort. For example, practices like registering all contacts to a service desk as “incidents”, then manually reviewing those contacts to determine if they are actually service requests. Or using “service requests” to manage deployments of laptop computers. This just scratches the surface of practice abuses that I’ve encountered.
  • No defined workflows for fulfilling service requests. Many self-service portals are nothing more than a way for consumers to fill out their own service request tickets or initiate an email for requesting service offerings. The result is someone in IT must take manual action to fulfill service requests.
  • Rubber-stamping requests for change (RfCs). I recall reading a blog from Rob England (from his days as The IT Skeptic) wherein he described “change management theater” – entertaining, but nothing really happens. Sadly, this is an appropriate description for what many organizations call “change management”. Changes are pushed through without proper review or the CAB meeting becomes a formality, approving changes without sufficient scrutiny or understanding of potential impacts.
  • Lack of post-action reviews. The step for reviewing the success and impact of a change or an incident or other ITSM event, and learning from any issues, is skipped due to time constraints or perceived lack of value. Never mind that opportunities for learning and improvement are missed.
  • Poor CMDB practices. Updates to the CMDB are done manually and are not integrated with change, release, or deployment management practices, potentially making the information contained within the CMDB suspect. Or an organization will conduct a “discovery” of its computing environment and call that its “CMDB”. Discovery is a way to validate a CMDB and not a way to create and maintain a CMDB. Discovery will never find the logical or non-physical elements of the computing environment that are critical for effective service management.
  • SLAs are not. I’ve discussed the problem with many SLAs before. Not only do many SLAs not discuss services, they also don’t discuss business results and value.
  • Taking a “technology-first” approach – While technology is a needed enabler for ITSM, good service management is more than just implementation of a tool. Taking a technology-first approach typically limits ITSM design to the capabilities of the tool and not based on the requirements of the organization.

Why is bad ITSM a problem for AI?

To become effective, AI solutions must go through a period of learning.  An AI “learns” by identifying patterns through repeated exposure to huge quantities of data and uses algorithms to learn from that data.  The effectiveness of any AI solution is dependent on the quality of the underlying processes and data.

AI solutions must also be made aware of business rules. Business rules define specific criteria and policies that guide the AI system’s decision-making process to ensure alignment with organizational goals and other requirements.

But if IT processes and workflows are not well-defined or are no longer aligned with business needs, AI will do the wrong thing right.  If services and associated SLAs are not defined in terms of business outcomes, that means that business rules are missing within the ITSM environment.  Those training the AI will lack business-based criteria to guide the AI’s decision-making process to ensure alignment with organizational goals and other requirements.

So, when an organization practices bad ITSM and then tries to apply AI to what they call “ITSM”, well…bad things will happen.

  • AI has no rules to follow. An AI system, particularly one designed for automation, needs a clear framework to operate within. If you don’t define a “troubleshooting workflow for a printer issue,” the AI has no way of knowing what steps to take. It can’t classify a ticket, route it, or suggest a solution if it doesn’t understand the process.
  • Automation becomes chaos. Instead of improving efficiency, you risk creating more problems. An AI might take an action that is technically correct but disrupts a broader, unwritten process. Without defined criteria for success or failure, it’s impossible to know if the AI’s actions are helping…or just adding to the mess.
  • No way to measure success or failure. Without defined evaluation criteria, you can’t tell if the AI is working. Is a 10% reduction in average resolution time good? Is it a result of the AI, or something else? If you don’t have a baseline or a goal, you can’t justify the investment or prove the value of the AI solution.
  • Ineffective training of AI models. AI models, especially machine learning models, are trained using historical data. If that data reflects inconsistent or ad-hoc processes, the AI will learn and replicate that inconsistency.

4 things to help clean up your bad ITSM practices

This is not the first time that I’ve discussed the impact that bad ITSM will have on good AI. Here are four things to do to start for cleaning up bad ITSM practices:

  • Define and document the business rules. How is the organization making decisions? What is the criteria for making those decisions? What are the goals and objectives of the organization? The answers to these questions provide decision-criteria that should be used in the design of good ITSM practices.
  • Map value streams. Not just value streams found inside of IT, but organizational value streams. Those value streams become the basis for identifying and defining services.
  • Map workflows. All of them. Start with analyzing historical data to identify and understand the most common incidents and requests. Don’t forget to also go to the Gemba to understand  how work is currently being done to identify where procedures are not meeting the business need.  These workflows form the foundation for process models.
  • Define business-oriented KPIs. How does your organization know what ITSM success looks like? IT metrics alone will not tell the story. You need metrics that reflect business results and value.

Incorporating AI into an organization’s ITSM capabilities offers massive transformative potential. Unfortunately, many expect that AI adoption will magically solve all their ITSM problems. Others are pursuing AI-enabled ITSM solutions because of the fear of missing out. Without first addressing existing ITSM bad habits, AI will amplify, rather than solve underlying issues, resulting in more work for humans and poor business results.

Share twitterlinkedinmail
Human-Centered Design, ITSM

ITSM is failing your customers – here’s why

Share twitterlinkedinmail

When it comes to who is the “customer”, many ITSM implementations are simply confused. And this confusion is why ITSM is failing your customers.

Some IT organizations think the “customer” is someone that contacts a service desk. Others think the “customer” is someone who defines the requirements for a service. But that definition quickly fades once the implementation project is completed and the project sponsor resumes their normal duties. Still other ITSM implementations ignore identifying the “customer”, as these implementations feel that it isn’t necessary to define services in terms of business value and business results.

I recently completed a Humanising IT™ [i] masterclass led by Katrina MacDermid and Wesley Eugene.   During that class, we discussed how so many ITSM implementations, despite the best of intentions,  fall into the “who is the customer?” trap. The Humanising IT approach cuts through this confusion with a simple, but powerful, distinction between the roles of the customer and the user[ii]. The customer is the person deriving value from business services. The user is the person using technology to deliver value to the customer.

Taking this concept of user and customer one step further renders an interesting proposition. IT delivers products and services to a user, who then delivers business value and results to a customer. Is this the reason ITSM is failing your customers?

Why ITSM implementations often fail customers

I must admit that this is a different interpretation of the customer and user roles than I’ve typically followed. However, it could explain how many ITSM implementations have missed the mark when it comes to delivering business value and business results. As I’ve said before, many ITSM implementations are about managing IT, not about delivering or enabling business outcomes.  What could be possible if ITSM implementations shifted focus externally to the humans that use the services provided by the business?

But many ITSM implementations – many IT organizations – haven’t focused on the humans that use the products and services provided by the business. In fact, the products and services that IT delivers are often not built or delivered with humans in mind. Procedures used by IT are often IT-focused, not business-focused. The performance targets and measures for these products and services are defined by IT, not by the people that use the products and services. IT designs products focused on technology “wow factors” (as defined by IT) and less on the people that will be using them.

And because the focus is on IT, and not on the customer, the associated ITSM implementation is basically used to set expectations for the user. Even in that situation, those expectations are defined by IT, usually with little to no input or agreement from users.

How human-centered design can help

The correlation between employee (or user) experience and customer experience has been long established: when organizations enable better experiences for employees (“users”), employees in turn provide better experiences for customers.

When employees feel valued, engaged, empowered, and supported, they are more likely to go the “extra mile” for customers. When employees have the right technologies and solutions, they can resolve customer issues quickly and creatively. Positive employee experiences foster empathy and collaboration, which employees pass on to customers.

What are some things that IT organizations can do to enable a better user experience?

For IT, this means providing users with intuitive and streamlined processes, systems, and products, built with the user in mind. This means listening – and acting on – user feedback. This means providing empathetic support of users.

In other words, make the experience with IT a humanized experience. And the best way for IT to deliver a humanized experience for the user is to include the user as part of the development of solution designs – a core principle of human-centered design.

But getting users involved in solution design is often not so easy. First, it requires a mindset shift within IT to focus first on solving problems, not implementing solutions and technology. Convincing non-IT managers to participate in solution design and decision making can be a challenge. Many non-IT managers are reluctant to allocate resources without a clear return on investment or to take ownership of solution designs. IT often struggles to communicate in non-technical terms, and users often lack the technical understanding needed to contribute to solution design discussions. This results in communication gaps, making it difficult to translate user needs into technology requirements. There are often differences in priorities between IT and users; what’s important to IT may not have the same weight with users. Finally, an organization’s culture may get in the way. If an organization values traditional, hierarchical structures and predictable outcomes, the organization may be hesitant to have users participate in solution designs as it can introduce expected feedback or challenging of existing assumptions.

Three things IT (and ITSM) can do to stop failing the customer

Here are three actions that IT – and ITSM – organizations can take to stop failing the customer.

  • Cultivate an “experience” culture – Promote a culture that values and celebrates collaboration, empathy, and continual learning[iii]. Culture change happens a step at a time, so persistence pays off. When users participate in a solution design, publicize it. Share what was learned. Talk about how the new solution enabled positive employee experiences.
  • Map the internal user journey – An internal user journey map is a visual representation of how employees interact within an organization, including the user’s actions, thoughts, and emotions. From an ITSM perspective, identify when users interact with IT systems, processes, and tools to achieve a specific result. Doing this will identify pain points and improvement opportunities with those systems, processes, and tools.
  • Map the customer journey – Like an internal user journey map, a customer journey map depicts how customers interact with an organization, from initial awareness to post-purchased. Like an internal user journey map, the customer journey map will help identify improvement opportunities for the organization. But the benefit doesn’t stop there for IT and ITSM. Not only does this help IT (and related ITSM practices) understand the customer journey, but also helps IT develop empathy regarding user and customer interactions.

As organizations continue to journey further into the digital economy, a humanized customer experience will become a competitive differentiator. IT organizations, and their associated ITSM implementations, must embrace the benefits of adopting human-centered design in developing solutions. Involving users in solution development results in more humanized outcomes that improve both the employee and customer experiences.

[i] Humanising IT is a trademark of HIT Global.

[ii] Katrina Macdermid, “Human-centred design for IT service management”, Norwich, TSO, 2022, p. 30.

[iii] “Engaged employees Transform Customer Experience. Here’s Why”, https://www.reworked.co/employee-experience/engaged-employees-transform-customer-experience-heres-why/ Retrieved June, 2025.

Share twitterlinkedinmail
ITSM

Is your ITSM approach looking through the windshield…or at the rear-view mirror?

Share twitterlinkedinmail

“In the business world, the rear-view mirror is always clearer than the windshield.”

Sadly, this 1991 Warren Buffet quote applies to many ITSM implementations. Why?

Because the focus of those ITSM implementations is on what has happened, instead of what is happening.

Think about it. Our respective businesses are focused on the view through the metaphorical windshield. The view through the “windshield” represents both what is happening now and the journey ahead. And while the future is unknown, businesses try to create the future by establishing goals and objectives. From a business perspective, the possibilities and opportunities for success are typically found when the organization is looking through the windshield.

Continuing the metaphor, the focus of so many ITSM implementations is the rear-view mirror – a view of what has happened. Make no mistake – trending and performance reports, monitoring tools that deliver event alerts, and recently-written knowledge articles are important contributors to good ITSM. But those reports, tools, and articles are typically inwardly focused, discussing items and topics that are relevant and meaningful only to the IT organization. In other words, those ITSM implementations are more focused on yesterday and less on the future.

The impact of always looking in the rear-view mirror

Why is the “rear-view” perspective an obstacle for ITSM implementations? I would argue that the perspective of continually looking back is not aligned with business goals and objectives. This is one of the factors between ITSM being perceived as a business enabler versus ITSM viewed as a costly expense.

It comes down to this question – what does your business perceive as “value”? Candidly, business value is rarely – if ever – found by looking in the rear-view mirror. In my experience, businesses perceive value when actions taken within the organization result in achieving business  mission, vision, goals, and objectives (MVGO). Businesses perceive value when the data captured, used, and maintained within the organization produces information that enables timely, fact-based decision-making. Businesses perceive value as innovation, responsiveness to the market, increased revenues and profitability, delivering a differentiated experience, and standing out from competitors.

Shifting the ITSM view to the windshield

Does your ITSM implementation enable your business? How does your ITSM implementation help the organization to achieve its MVGO? For many organizations, ITSM is more about IT and less about their businesses. Few organizations (in my experience) develop and maintain a service portfolio, much less a service catalog. I rarely find ITSM implementations reporting measures that relate to the business objectives; rather, most measures and reports align to internally defined IT performance targets.

I’m not suggesting that IT departments stop supporting and delivering the operational aspects of ITSM. I am suggesting, however, that ITSM implementations expand their scope to include the “windshield”. The mindset must shift from seeing ITSM as a means of control or just implementing some tool. The mindset must shift to viewing ITSM as a business enabler.

This means that ITSM implementations must become more strategic from a business perspective. Strategy is about aligning resources and efforts to achieve organizational goals – in other words, looking through the windshield, not just the rear-view mirror.

Shifting the ITSM view to the windshield

Here are some tips for shifting ITSM from just a “rear-view” mirror perspective to also include the windshield.

  • Learn the business of your business. By understanding the business, IT professionals can make informed decisions, improve their communications with non-IT colleagues and become more proactive in developing technology-based proposals for growing and improving business activities.
  • Understand how people, processes, and technology (PPT) enable business outcomes. How does (or can) people, process, and technology enable the organization to achieve its MVGO? What are the vital business functions of the organization? How does PPT enable those business functions?
  • Think and act in terms of business outcomes. How can (or does) ITSM enable or deliver the business results that impact MVGO? Having answers to this question will help shift the perspective and perception of ITSM to a more strategic and business-aligned capability.
  • Measure and report things that are relevant and meaningful to your business. Frankly, no one outside of IT cares how quickly the service desk responds to requests or how many incidents are closed. Identify, measure, and report on metrics that have an impact on the business of the business.
  • Shift SLAs from an IT operational focus to a business focus. In my experience, what many ITSM implementations call a “Service Level Agreement” (SLA) are neither agreed with anyone outside of IT, or discuss the business impacts of IT services. Unfortunately, this is an approach that is deeply engrained within many ITSM implementations. Begin the shift by working with non-IT colleagues to map a frequently followed value stream. Doing this will result in a mutual understanding of the value stream, the business drivers, and success criteria. Use this information to then document and agree a business-focused SLA for that value stream.

In many organizations, ITSM has not achieved its potential. Part of the reason for that is that those ITSM implementations are too focused on the past and only on the IT organization. What could be possible if those ITSM implementations also look ahead rather than just looking behind?

Need some help shifting your ITSM perspective from just the rear-view mirror to what is happening now and ahead?  Let Tedder Consulting and our proven and impactful approach change your ITSM environment to a business enabler.  Contact us today!

Share twitterlinkedinmail
Humanizing IT, ITSM

Four ways that organizations have dehumanized IT – and how to fix it

1 Comment
Share twitterlinkedinmail

Organizations have dehumanized IT.

It’s not a question of “has it happened at our organization?” It is the recognition that it has happened.

Despite the investments organizations have made in technologies and process designs intended to solve business problems, the critical component of the solution has been overlooked. That component? The humans that are interacting with those technologies and process designs.

Four ways organizations have dehumanized  IT

How have IT organizations become dehumanized? Here are a few attributes of a dehumanized IT organization.

  • IT associates think and work in terms of a “ticket.” An IT-related issue is treated as a faceless and voiceless number, rather than as an issue that impacts the productivity of a fellow colleague. IT masks its interactions with the consumer behind technologies, such as email or messaging through a service management tool and neglects the opportunity to connect and empathize with the consumer.
  • IT sends out generic, post-interaction surveys, rather than host face-to-face discussions with consumers. Exasperating the situation is that these surveys typically ask the same questions over and over, oblivious to the humans receiving those surveys. Furthermore, what little data that is captured on those surveys is rarely reviewed, much less actioned.
  • IT organizations do not conduct regular service level review meetings, much less have real SLAs. A service level review meeting should be a great opportunity for face-to-face discussions with consumers and key stakeholders to review service level agreements (SLA) to determine if IT products, services, and performance meet business needs. But what many organizations call a “SLA” is nothing more than some defined configuration parameters used in a service management system. Even worse, those parameters are defined with no input from the consumers served by IT.
  • IT organizations develop new solutions with no involvement from the consumers that will be using those solutions. The frequent approach to new IT solution development is to conduct a few meetings with sponsors and key stakeholders to gather their requirements and gain commitment on budget and resources. Any consumers that will be using the new solution are usually not included in those meetings. In many cases, the “solution” is jammed into an already in-use technology that often is neither fit for purpose or use.

Why is humanizing IT so important

There are many reasons why humanizing IT is so important. First, it’s well known that happy employees deliver better customer service.  A humanized IT approach delivers better human-centered designs and intuitive user-friendly systems and interfaces.

This recent research journal article discusses many benefits that result from humanizing technology teams.  A benefit that may not be obvious is enhanced employee satisfaction and retention. By creating a more human-centric work environment, IT organizations can improve employee well-being, leading to higher job satisfaction and lower turnover rates.

Humanizing IT can also differentiate an organization’s products and services in the marketplace. This recent article discusses how companies that think that business decisions are taken solely on ROI and impact to the bottom are fooling themselves. Embedding human-centered designs within an organization’s products and services encourages emotional connections in B2B relationships, which drives brand loyalty and customer retention.

Enter human centered design

In a world where digitization, automation, and artificial intelligence are driving businesses to invest increasingly in technology, the more that the consumers of that technology value human interactions and connections.[i]   This is where human centered design (HCD) can help.

HCD is an approach for problem solving that starts with understanding what consumers need and arrives at a place where innovative solutions address those needs.[ii]  HCD is about solving problems, not implementing solutions. This means that effective HCD requires a mindset shift within IT from ”problem solver” to “listener and learner”.  By using the HCD approach, IT gains a better appreciation and understanding of consumer challenge, builds better connections with the consumers of solutions, and drives better trust and communication with consumers.

Challenges

While embracing the HCD approach has numerous benefits, organizations are often faced with challenges in adoption.

First, many organizations take a “technology-first” mindset. Rather than first understanding the end-user perspective, organizations identify and implement a technology that seemingly addresses a business problem. On the surface, this “technology first” approach may seem like an easier and quicker fix for business and consumer challenges. But the reality is that technology will only be as good and well-received as the consumers are able to use those technologies easily and successfully.

Convincing senior management can also be a challenge. Traditional metrics, such as efficiency and ROI, may not capture the value of improved user experiences.[iii] Some leaders are concerned that becoming human-centered comes at the expense of ignoring business needs.[iv]

Overcoming deeply entrenched ways of working can be problematic. Organizations have developed ways of working that have evolved over longer periods of time. Employees have been and continue to be evaluated  and rewarded based on these ways of working.

Take some first steps for rehumanizing IT

Adopting a human-centered design approach within IT will not happen overnight. But every journey begins with a few steps. Here are a three steps for starting to rehumanize IT.

  • Ditch those satisfaction surveys. Instead, conduct regular focus group meetings to not only capture consumer feedback, but get direct face-to-face input regarding improvement opportunities.
  • Conduct Gemba walks. Take a page from the Lean methodology and go to where work is being done. Observe, not evaluate, how consumers are interacting with technology. Show respect to consumers by listening to their concerns.
  • Begin participatory designing. Participatory design is a core concept of HCD. It means involving the consumer at the beginning of design activities. Consumers sometimes find it difficult to articulate what their challenges and problems are until they see, feel, and experience those challenges. Involving the consumer from the beginning of design efforts will result in solutions that are more user-friendly, intuitive, and accepted.

HCD can a be a transformative approach for businesses and the IT organizations within those businesses, especially when it comes to the implementation and use of technology. Starting with and including the humans that will be using those technologies in the design of products and services is the key to success in this digital age.

[i] https://www.thinklikeapublisher.com/humanizing-content-as-an-answer-to-ai/ ,Retrieved January 2025.

[ii] https://aircall.io/blog/customer-experience/10-benefits-of-human-centered-design/  Retrieved January 2025.

[iii] https://www.lusidea.com/blog/challenges-in-adopting-human-centric-design-practices , Retrieved January 2025.

[iv] https://www.hrdconnect.com/2023/10/06/human-experience-management-enabling-business-performance-through-human-centered-design , Retrieved January 2025.

Share twitterlinkedinmail
AI, ITSM

Five critical steps for making a good AI/ITSM decision

Share twitterlinkedinmail

There is no question that AI-enabled technologies have the potential for significant positive impact for organizations overall, and for ITSM specifically.  This recent TechTarget article highlights a number of positive business impacts resulting from the adoption of AI-enabled technologies, such as new capabilities and business model expansion, better quality, more innovation, and personalized customer services and experiences.

New and existing ITSM-related vendors are rushing into the space with solutions like AI-powered automation, conversational AI, intelligent chatbots, predictive analytics, and agentic AI (A web search on these terms will return numerous examples!).

And we’re only scratching the surface.  New AI-enabled capabilities are on the horizon, such as:

  • AI agents capable of executing discrete tasks independently based on personal preferences or providing customer service without requiring specific prompts.[i]
  • AI-powered cybersecurity in the form of automated, near-constant backup procedures and AI tools for managing sensitive data to enhance data protection and resilience.[ii]
  • Small Language Models (SLM) that aim to optimize models for existing use cases. SLMs can be trained on smaller, highly curated data sets to solve specific problems, rather than act on general queries (like Large Language Models).[iii]

But just because these rapidly-evolving technologies represent the “latest shiny new thing that really helps” (a tip of the cap to Paul Wilkinson) doesn’t mean that you should succumb to the fear of missing out by just “doing something”. In my experience, a new technology alone rarely (if ever) solves a business challenge.  When it comes to technology investments, it’s better to make a good, informed decision, based upon the unique needs and challenges faced by your organization.

Yet, AI-related technologies can have and are having a significant positive impact on ITSM environments. Many organizations are already benefitting from the use of AI-enabled chatbots, automated ticket management, and service request automation.

The pressure to introduce AI-enabled capabilities to ITSM implementations is real. But which tools?  What capabilities?  How can one decide?

Five critical steps

Here are my five critical steps to making a good AI/ITSM decision.

  • Define overarching goals for using AI within ITSM. It’s easy to become captivated by the latest products and features, especially in today’s AI/ITSM market frenzy. But chasing new products and features usually results in a short-sighted approach to technology adoption that will likely not meet longer term goals and needs. AI within ITSM should not be approached as a point solution; rather, AI should be considered within the broader perspective of ITSM. How will adding AI capabilities address current challenges?  How will adding AI enable the organization to realize future ITSM objectives? Defining overarching goals for AI in ITSM – in business terms – ensures that broader perspective .  Defining overarching goals also establishes the foundation for measuring AI/ITSM success.
  • Conduct a SWOT analysis of the ITSM environment. Conducting a SWOT analysis identifies a company’s internal strengths and weaknesses, as well as external threats and opportunities. Understanding an organization’s ITSM SWOT identifies the critical factors that must be considered before developing an AI strategy. A SWOT is a good way to understand an organization’s readiness and ability to take on an AI initiative.  Having the right stakeholders participate is critical to the success of a SWOT. Include stakeholders (especially non-IT colleagues) that have an interest in both ITSM and in AI capabilities and use.  Include stakeholders that will freely share thoughts and ideas and have a pragmatic understanding of organizational issues and challenges.
  • Develop the AI strategy. What is the approach for bringing in AI into your service management implementation? An effective AI strategy is not about finding places to “plug-in” an AI solution. It’s about understanding the organizational change, data, skills, budget, and infrastructure that will be needed for successfully utilizing AI technologies within the ITSM environment to help achieve the organization’s mission, vision, and goals.  Use the results of the ITSM SWOT as an input to this strategy.
  • Define evaluation criteria. The next step is to define the criteria by which potential AI solutions will be assessed. Defining this criteria up-front helps prevent falling victim to ‘shiny object syndrome’ and identify the solution that is best for your organization. As part of that criteria, consider the solutions alignment with the AI/ITSM strategy, costs (initial, ongoing, and cost effectiveness), the effectiveness of the solution to leverage issues identified in the SWOT, and how the solution enables the pursuit of potential future opportunities.
  • Develop and present the business case. Gaining and maintaining the commitment of senior management is critical for success.  When a potential solution is found, develop and present the business case for that solution. Discuss the technical and cultural challenges that come with AI adoption. Discuss the opportunities that AI with ITSM will provide.  Discuss how a solution will address SWOT and align with the AI strategy.  Discuss the benefits of implementing the solution , how risks will be optimized, and how success will be measured.  Discuss the consequences of doing nothing. Most importantly, ask for management commitment.

Cautions

Before moving forward with introducing AI within an ITSM environment, here are some cautions of which to be aware.

  • Good AI will not fix bad ITSM. The adoption of AI technologies can enable and enhance ITSM capabilities. However,  AI is not a “magic wand” that solves issues like poor process design, inadequate service management governance, and ineffective measurement and reporting.
  • Don’t overlook data quality and governance. Many organizations have data quality and data governance challenges. AI needs data – lots of it – and that data must be accurate, reliable, and trustworthy. Data quality and governance is not just a challenge for ITSM, it is an organizational problem.
  • Is there an ITSM strategy? Many organizations are not achieving the full potential of ITSM adoption. Rather than applying ITSM holistically, many implementations have only focused ITSM implementation on IT operational issues, and not on how ITSM enables business outcomes. Without an overarching ITSM strategy, AI investments risk becoming short-sighted and expensive point solutions that do not address business needs.

Augmenting the ITSM environment with the right AI capabilities can be a huge benefit for the organization, ITSM, and the employees of an organization.  But introducing AI within ITSM is not a decision to be taken lightly. Taking a systemic approach to identifying, justifying, and selecting solutions sets the right expectations with stakeholders and helps ensure successful introduction of ITSM with AI.

[i] https://www.uc.edu/news/articles/2025/01/innovation-experts-predict-top-tech-trends-for-2025.html , Retrieved January 2024.

[ii] Ibid.

[iii] https://www2.deloitte.com/content/dam/insights/articles/us187540_tech-trends-2025/DI_Tech-trends-2025.pdf , Retrieved January 2024.

 

 

 

 

 

 

 

Share twitterlinkedinmail
ITSM, Leadership, Organizational Change

Nothing will change. Unless you change.

Share twitterlinkedinmail

A few years ago, I was invited to conduct an ITSM assessment for an organization. While the request itself wasn’t unusual, it was unusual in that I had conducted an ITSM assessment for that same organization a few years prior. The  IT leadership of the organization had not changed over that time, apart from a different person leading their ITSM adoption efforts. But I was intrigued by the prospect of revisiting a past client engagement to learn whether my previous recommendations had had the positive impact that I had determined was possible.

After conducting interviews, examining their ITSM policies and procedures,  reviewing their IT strategy, and evaluating their ITSM performance reports, I was disappointed to find that there had been no substantial change in their ITSM journey from when I first visited.

I confronted the CIO with my findings. During our conversation, he acknowledged that there had not been much progress in their ITSM journey. He went on to ask if I would simply just tell them exactly what they needed to do, based on my “deep” knowledge of his organization.

I was taken aback. It had been a few years since that first assessment. Over the course of the two engagements,  I had spent about a total of 30 days interacting with the organization – hardly what I would consider a qualification for having a “deep knowledge” of the organization.

So, I took a deep breath, looked the CIO in the eye, and told him that – that I felt that 30 days of engagement over a few years doesn’t constitute a “deep” knowledge of the organization. Further, it was not an issue of not knowing what needed to be done – what needed to be done was clearly outlined in both assessment reports. The issue was that no one – including the CIO – wanted to change.

And then I said it.

“Nothing will change unless there is change.”

And with that, our meeting ended. I packed up my laptop,  left the building, drove away….and  subsequently was not invited back.

Everybody wants change. No one wants *to* change.

I see it all the time. People within an organization get enthusiastic about making a change, improving what is currently being done, expanding and enhancing their capabilities, thinking in terms of possibilities. Excitement fills the discussions within the conference rooms. People leave meetings eager to get started.

And then the time comes for the work that needs to be done to make the change….and sadly, things often go kaput.

What happened?

The 3 U’s of failed change

I’m no psychologist, but from everything that I have read, experienced, and observed about failed change, it seems to come down to the basic human instinct of fear of change. In my experience, that fear of change presents itself in one or more of the following symptoms that I call the “three U’s of failed change”.

  • Unknown – Change pulls people out of their personal comfort zones, where they feel safe. According to this article, this uncertainty feels like failure to our brains, and our brains automatically work to prevent us from failing.
  • Unprepared – Many people resist change because they feel unprepared. Provided training doesn’t really prepare people for the change, and as a result, there is a feeling of loss of mastery. Communications aren’t two-way, so there is no opportunity for feedback or to get answers to questions.
  • Unwilling – Even though people know that processes and systems aren’t working as well as they could, people have become comfortable in their interactions with those processes and systems. They “know” where the issues are, and how to make things work despite those issues. Changes to those processes and systems are perceived as a threat to the personal value of the people doing that work.

These are powerful reasons why change fails, but they are not insurmountable.

How can anything change…unless *you* change?

Is change working through your organization? Are you personally going through change? The answer to these questions is likely “yes”. Organizations are continually changing and evolving. As individuals, we are continually evolving as well. Think about it – what is different about your organization today when compared to two years ago? Compared to two months ago? What events or learnings over that time – both from a professional perspective and a personal perspective – have had an influence on you?

Change is constant – in our lives and in our careers. Here are some tips that I have found useful when experiencing change.

  • Educate yourself. Much of the angst around change is the fear of the unknown. To combat that fear, learn all that you can about what is changing. This will help restore any feelings of loss of mastery.
  • Ask questions. Fill in gaps in your understanding about what is changing. Listen for the “why” – the compelling reason change is necessary, and what success will look like after the change. This will help with any feelings of being unprepared.
  • Try it on. While it takes courage to push through the unknown, leaning into the change and exploring possibilities provides a sense of control. Being a pioneer within the change helps overcome feelings of loss of value. Trying on the change also provides you with valuable insights that you can use to make data-driven decisions about your next steps.

Change is a constant – in our organizations, in our jobs, and in our personal lives. Don’t let change paralyze you – take control. Educating yourself, asking questions, and trying on the change gives the you power and control you need to successfully push through the unknowns associated with change.

 

Share twitterlinkedinmail
AI, ITSM

Three AI truths with IT Service Management

Share twitterlinkedinmail

There’s no question that introducing AI capabilities can have a dramatic impact on IT Service Management (ITSM). Done well, AI adoption will free up ITSM professionals to do the work for which humans are uniquely qualified, like critical thinking, contextual understanding, and creative problem-solving. Furthermore, AI will enable organizations to realize many of the theoretical benefits of ITSM. For example, the use of AI and machine learning can leverage comprehensive in-depth data, not just a small recent sampling, for cause analysis, problem detection, and impact determination of problems. Another example is the use of AI can increase the data of the IT environment and automate the remediation of incidents.

But AI is not a “magic wand” for ITSM.

Before introducing AI capabilities into ITSM, organizations must first consider these three AI truths.

Truth #1 – AI needs good data

For the use of AI to be effective, it needs data. Lots of data. But, if that data is inaccurate, lacks integrity, or is not trustworthy, then the use of AI will only produce inaccurate or poor results.

Data quality is an issue that many organizations will have to tackle before realizing the complete benefits of introducing AI to their ITSM implementations. These means that organizations will have to step up their technology and data governance posture. According to this recent Privacera article, a fundamental principle of data governance is having a high-quality, trusted data source.  Having trusted data sources enables capabilities like ITSM to make accurate and reliable decisions regarding service management issues. But if the data sources used by ITSM tools contain data that is unregulated, the ability to automate responses is significantly hindered.

Truth #2 – AI doesn’t mean process design goes away

The need for effective ITSM processes and procedures doesn’t go away with AI adoption. Machine learning can be used to detect data patterns to understand what was done to resolve an issue. But what machine learning doesn’t do is determine if what is being done is the best approach. Machine learning doesn’t consider organizational goals and objectives with the adoption of ITSM. Machine learning cannot determine what processes are missing or need improvement to gain needed effectiveness and efficiency with ITSM.

Truth #3 – AI doesn’t replace knowledge

“Reducing cost”, often in the form of headcount reductions,  is frequently used as the justification for AI investment, as the use of AI will enable ITSM activities to be automated. And it’s true – many of the ITSM activities currently performed by humans can and should be replaced with AI-enabled capabilities, such as the automated fulfilment of service requests, automated response to incidents, and problem data analysis. But one of the hidden costs of using AI to justify headcount reductions is the form of knowledge loss – the knowledge inside people’s heads walks out the door when their positions are eliminated. And this is the knowledge that is critical for training the chatbots, developing the LLMs needed, and to the continual improvement of AI and ITSM.

While AI can provide the “how” for “what” needs to be done, it cannot answer the “why” it needs to be done.

Good Governance facilitates AI-enabled ITSM

Without governance,  AI can do some serious damage, not just with ITSM, but to the organization. As the role of IT organizations shifts from being data owners (often by default) to being data custodians, having well defined and enforced policies regarding data governance is critical. This means that the frequently found approach to governance consisting of an IT track and a corporate track is becoming untenable. As organizational processes and workflows become increasingly automated, enabled by AI capabilities, governance must become cross-functional[i] , with sales, marketing, HR, IT, and other organizational functions all involved. Organizations must consider and address data-related issues such as compliance with data privacy laws, ethical data use,  data security,  data management, and more.

An effective approach to governance enables organizations to define their digital strategy[ii] to maximize the business benefits of data assets and technology-focused initiatives. A digital strategy produces a blueprint for building the next version of the business, creating a bigger, broader picture of available options and down-line benefits.[iii] Creating a successful digital strategy requires an organization to carefully evaluate its systems and processes, including ITSM processes. And as ITSM processes are re-imagined for use across the enterprise in support of organizational value streams, effective governance becomes essential.

Getting ready for AI-enabled ITSM

What are some of the first steps organizations should take to get ready for AI-enabled ITSM?

  • Formalize continual improvement. One of the most important practices of an effective ITSM implementation is continual improvement. As organizations are continually evolving and changing, continual improvement ensures that ITSM practices evolve right alongside those business changes. And just like service management, AI adoption is not an “implement and forget”; in fact, AI will absolutely fail without formal continual improvement.
  • Answer the “why”. To say that there is so much hype around the use of AI within ITSM would be an understatement. Before jumping into AI, first develop and gain approval of the business case for using AI within ITSM. How will success be determined and measured? What opportunities for innovation will emerge by relieving people from performing those tedious and monotonous tasks associated with the current ITSM environment? What returns will the organization realize from the use of AI within ITSM? What new business or IT opportunities may be available because of the use of AI within ITSM? A good business case establishes good expectations for the organization regarding AI and ITSM.
  • Begin thinking about how AI can be leveraged by ITSM process designs. As discussed in this recent HBR.org article, AI will bring new capabilities to business (and ITSM) processes. With these new capabilities, organizations will need to rethink what tasks are needed, who will do those tasks, and the frequency that those tasks will be performed. The use of AI will enable organizations to rethink their ITSM processes from an end-to-end perspective, considering what tasks should be performed by people and what tasks should be performed by machines.

The concept of augmenting ITSM with AI is a “no-brainer”.  However, success with AI in an ITSM environment requires a lot of up-front thought, good process design, solid business justification, and considering these three AI truths.

[i] https://2021.ai/ai-governance-impact-on-business-functions

[ii] https://www.techtarget.com/searchcio/definition/digital-strategy

[iii] Ibid.

Share twitterlinkedinmail
AI, ITSM

The 3 Pillars of Success for AI-enabled Service Management

Share twitterlinkedinmail

In her book[i], Dr. Kavita Ganesan suggests that any AI adoption be evaluated using three pillars:

  • Model success – Is the AI model performing at an acceptable level in development and production? (In other words, the model performs at the required levels of accuracy, execution time, and other factors.)
  • Business success – Is AI meeting organizational objectives?
  • User success – Are users satisfied with the AI solution and perceive it to be a valid solution?

Many organizations are rushing to incorporate AI-enabled technologies to improve their service management capabilities. AI technologies, such as AI-assistants, chatbots, intelligent process automation, generative AI, and more, can provide a next-level set of capabilities for service management. But are these organizations’ service management practices positioned to fully take advantage of these new capabilities?

Let’s be clear – AI is not a “magic wand.”  AI is a technology. And like any other technology, there are factors that must be addressed if an organization is to realize the benefits that AI can bring to service management.

First, AI needs data – and lots of it. The effectiveness of AI depends on the quantity, quality, relevancy, and timeliness of the data being used by the AI models and algorithms. Any limitations in the data being used by AI will be reflected in the outputs produced by AI – and the use of those outputs by service management processes. The old axiom remains true – garbage in will result in garbage out.

AI cannot be a solution looking for a problem. Just because AI is a “hot topic” now doesn’t mean that it is the solution for every business challenge – especially service management issues. If issues like ineffective workflows, undefined services, poorly defined measures, lack of continual improvement practices, or the absence of high-quality data already exist within the service management environment, the introduction of AI will only exasperate those issues.

Lastly, the use of  good organizational change management practices is critical. There is a lot of FUD (Fear, Uncertainty, and Doubt) surrounding the introduction of AI[ii] within organizations. Yes, there will be impacts to how humans work and interact with technology, but for whatever reason, there is a heightened fear associated with AI-adoption within service management.

Applying the 3 pillars for AI success to AI-enabled Service Management

Before rushing into incorporating an AI solution with a service management environment, let’s adapt and apply Ganesan’s three pillars for success with AI-enabled service management.

The first pillar is business success. How do current service management capabilities support business outcomes and enable value realization? How will the introduction of AI capabilities further enhance the realization of the outcomes and value delivered by service management? If the answers to the above questions aren’t clear, revisiting some foundational elements of service management is in order. Consider the following:

  • Have IT services been defined, agreed, documented, and measured in terms of business value, business outcomes, and the costs and risks associated with the delivery of services? Many IT organizations have defined what they call “services” in terms of
    • what goods and products (like laptops and smart devices) are provided
    • the service actions (like password resets) a service desk will perform, and
    • procedures for gaining access to digital resources (like a cloud-based resource or a shared drive).

Not only does this approach inhibit a mutual understanding of the vital role of technology in business success, but it also commoditizes what IT does. Secondly, this approach fails to establish business-oriented measures regarding results and value.

  • Are non-IT colleagues named as service owners? Are these non-IT colleagues actively involved in the delivery and support of services? This is a significant issue for many service management implementations. In many organizations, IT personnel, not non-IT colleagues, have taken on the role of service owner – the person that is accountable for a service meeting its objectives and delivering the required business outcomes and value. The service owner is critical to understanding what is needed and importantly, how business outcomes and value are realized and should be measured.
  • How might AI adoption enable organizations to consider service management practices that would enhance their business? For example, better service portfolio management would enable better utilization of and data-driven investments in services and technology.

The next pillar is employee success. Frequently (and counterintuitively!), service management practices have been designed and implemented with IT and not the IT service consumer in mind. As a result, interacting with the service desk or a self-service portal can be an exercise in frustration due to the over-technical nature of those interactions. Consider:

  • How might the introduction of AI result in friction-free interactions with services and the fulfillment of service requests? How might AI personalize end-user interactions with service management practices? Consider how AI could shift the burden of interacting with service management practices from the end-user to a personalized and proactive AI-enabled capability.
  • How might the introduction of the AI model result in friction-free interactions with supporting IT services? If consuming IT services present challenges to end-users, it can also be challenging for those that deliver and support those services. Will AI-capabilities enable service management practices to shift from a reactive to proactive stance by identifying and eliminating causes of incidents before they occur? Will AI-capabilities enable better issue resolution by suggesting potential solutions to IT technicians?
  • How might the introduction of AI enable employees to make better, data-driven decisions based on relevant, timely, and accurate knowledge? Knowledge management is among the most significant challenges of a service management implementation, as knowledge is ever evolving and continually being created, revised, and applied. AI may provide a solution – this blog explores how Generative AI could provide organizations (not just IT) with the capability of harnessing its collective knowledge.

The final pillar is AI / service management model success. Frankly, many service management challenges can be resolved through continual improvement activities. Some issues may be resolved through the application of effective and efficient automation. Questions to consider include:

  • How might AI adoption result in better and proactive detection and resolution of issues before those issues impact the organization? How might AI adoption result in improved change implementations through better testing or confirmation of positive business results?
  • Is there sufficient, good-quality data to enable AI-driven service management actions? If AI models are not supplied with sufficient, good-quality data, the results from the model will be suboptimal at best – or worse, just flat-out wrong.
  • What is the required level of accuracy for the model? A “100% accurate” model may be too costly to achieve and maintain; a “75% accurate” model may be perceived as a failure.

Get ready for AI-enabled service management

The introduction of AI to a service management environment can be a game-changer on many levels. Here are four steps to get ready:

  • Make the business case for introducing AI to service management. Think strategically about AI , service management, and how the combination of AI and service management will help the organization achieve its mission, vision, and goals.
  • Communicate, communicate, communicate. The mention of AI adoption may cause concerns among employees. Start open conversations regarding AI-enhanced service management capabilities, incorporate feedback, and proactively address concerns.
  • Identify and define success measures. The mere implementation of AI capabilities within service management is not an indicator of success. Define how the benefits articulated in the business case will be captured, measured, and reported.
  • Begin data governance now. The success of any AI initiative depends on the availability of good quality data. If service management is to leverage AI capabilities, the data being captured must be of good quality. Define and publicize data quality standards for service management practices and ensure compliance through periodic audits.

The introduction of good AI capabilities will not fix bad service management. Applying the three pillars described above will ensure successful introduction of AI capabilities resulting in next-level service management practices for any organization.

Is your service management approach “AI-ready”? An assessment by Tedder Consulting will identify any foundational gaps so your service management environment is “AI-ready”.  Contact Tedder Consulting today for more information!

[i] Ganesan, Dr. Kavita. “The Business Case for AI: A Leader’s Guide to AI Strategies, Best Practices & Real-World Applications”.  Opinois Analytics Publishing, 2002.

[ii] https://www.forbes.com/sites/jenniferfolsom/2024/03/28/meet-your-newest-co-worker-ai  Retrieved April 2024.

Share twitterlinkedinmail
EX, ITSM, XLA

Why your SLAs aren’t helping your XLAs

1 Comment
Share twitterlinkedinmail

It may be hard to believe, but the term “experience economy” is nothing new. The term was first mentioned in this 1998 Harvard Business Review article.  In the article, the authors posited that an experience occurs when a company intentionally uses services as the stage, and goods as props, to engage individual customers in a way that creates a memorable event. In other words, it’s not enough to have great products and services; it’s the experience of the customer that differentiates companies from their competition.

Fast forward to today, and these “memorable events” have become a significant factor in today’s employee-employer relationship, broadly known as employee experience (EX).  Companies providing a good EX can attract and retain top talent, deliver better experiences to their customers, and have employees who are more committed to the company.

What is the experience like when employees are interacting with technologies and services within your company? Is your organization actively measuring and improving those experiences? Is your company committed to a great employee experience?

These are answers that an XLA, or Experience Level Agreement, will reveal.

XLAs provide a different perspective

In IT, there is a tendency to focus on and measure things like technology performance and process execution. Often there is little attention given to how end users perceive the quality and effectiveness of technologies, apart from when an end user reports an incident or makes a service request.

An XLA provides a different perspective. An XLA provides focus to end-users’ experience and needs, by measuring the outcomes and the value of services provided. An XLA seeks to understand how end users feel about their interactions with technology and with those with whom they interact during those interactions.

By understanding the experience, organizations can identify where measures reported by IT do not reflect the end user experience. Understanding the experience also helps identify potential areas for improvement, whether that be with a service, a product, a process, or any other aspect that the end user leverages to get their jobs done.

XLAs are becoming increasingly popular as employers realize that good EX is essential for business success. ” This article from reworked.co discusses the impact of a positive EX:

  • 23% higher profitability
  • 28% reduction in theft
  • 81% reduction in absenteeism
  • 41% reduction in quality defects
  • 64% reduction in safety incidents

Clearly, good EX is good business.

XLA vs. SLA

So, what’s the difference between an XLA and an SLA, or Service Level Agreement?

An XLA focuses on happiness and productivity metrics from the end-user perspective.[i]  XLAs focuses on measuring the quality of the user experience, rather than just technical metrics like uptime or response times.

An SLA is an artifact of many ITSM (IT Service Management) adoptions. An SLA, as described by ITIL®[ii], is a documented agreement between a service provider (typically IT) and a customer that identifies both services required and the expected level of service.[iii] SLAs are intended to manage expectations and ensure both IT and non-IT parts of the organization understand their responsibilities. SLAs should also provide a framework for measuring performance and holding the provider (IT) accountable if they fail to meet their commitments.

SLAs are managed by the service level management practice, which is typically found within IT departments. The purpose of service level management is to set clear, business-based targets for service levels, and ensure that delivery of services is properly assessed, monitored, and managed against these targets. [iv] The SLAs produced should relate to defined business outcomes and not simply operational metrics.

An XLA is not meant to replace an SLA but work alongside SLAs to ensure a holistic view of value and results from the use of IT services.

But wait, isn’t quantifying, reviewing, and discussing business value and results part of SLAs and service level management?

Well, yes. But most organizations that claim to have SLAs, really don’t have SLAs.

The problem with most SLAs

What many companies are calling “SLAs” fall far short of being a service level agreement. Why?

  • Services are not defined and agreed. What and how IT services enable or facilitate business results and business value have not been defined and agreed between IT and non-IT senior managers. Furthering the confusion, what many IT organizations call a “service catalog” only describes technologies and service actions that consumers can request, not business value and outcomes.
  • The so-called “SLA” discusses IT, not the organization. SLAs discuss IT operational performance – typically related to only the service desk – and not business performance. Indeed, many of the issues related to SLAs (for example, the Watermelon Effect) are as a direct result of ITSM tools using the term “service level agreement” as a misnomer for business performance target
  • IT arbitrarily decides its own performance and success metrics. And these metrics are either measures that an ITSM platform administrator used in her last job, or metrics pre-configured within the ITSM platform, or metrics that a senior IT leader picked. Regardless, these performance measures are usually not relevant to anyone in the organization outside of IT.
  • Organizations (including both IT and non-IT leaders) take the wrong approach to SLA. Neither service providers (IT) nor service customers (non-IT managers) invest the time and effort to define services, the relationship and expectations between IT and the non-IT parts of the organization, and agree on business-relevant terms and performance measures. As a result, there is no shared, mutual understanding established regarding the use and importance of technology within the organization.

Close the gaps between SLA and XLA

Understanding how technologies and processes enable business outcomes, as well as what the organization – and the employee – truly value, is critical for a good EX within today’s organizations.

If XLA adoption reveals EX challenges, closing the gaps between SLAs and XLAs will help. Here are some things to try.

  • Define services – in business, not IT terms. Clearly defining and agreeing IT services between IT and non-IT leaders, including service-specific performance measures. Mutual understanding of business value and outcomes from the use of services is foundational for good EX.
  •  Apply Design Thinking. Design thinking is a human-focused method of problem-solving that prioritizes the solution instead of the problem. Identify where EX is falling short, then apply design thinking techniques to redesign the experience to meet both the employee’s and employer’s needs.
  • Are your SLAs really SLAs? If SLAs aren’t documented or agreed with non-IT leaders, or SLAs do not identify clear, business-based measures for quantifying success, then you don’t have SLAs. Treat this as an opportunity to build good business relationships and establish true SLAs, resulting in better business outcomes and EX.

While XLA adoption can be a real revelation for an organization,  it is not a magic wand for instantly improving EX. Like SLAs, XLAs can only be effective through collaboration, leadership, and having a continual improvement mindset across the entire organization. Resolving the gaps between SLAs and XLAs will help.

 

 

[i] https://www.happysignals.com/the-practical-guide-to-experience-level-agreements-xlas

[ii] ITIL is a registered trademark of AXELOS Limited.

[iii] ITIL Foundation: ITIL 4 Edition. Norwich: TSO (2019)

[iv] Ibid.

Share twitterlinkedinmail
AI, GenAI, ITSM, Knowledge Management

AI-enabled Knowledge Management might be low hanging fruit…if we can only reach it

1 Comment
Share twitterlinkedinmail

AI-enabled technologies have captured the imagination of every organization. Organizations (both solution providers and buyers) are rushing to jump on the wave of adopting and integrating AI.

Indeed, AI-enabled technologies have already found their way into IT support. An AI-enabled chatbot of today makes its predecessor chatbot of just a few years ago look… well, archaic. AIOps solutions have increased the IT support organization’s observability capabilities by bringing disparate sources of real-time operational data into a single view, facilitating more proactive actions and automated responses when predefined conditions are met.

But one of the challenges exposed by this initial wave of AI adoption within IT support organizations is the inadequacy of its approach to knowledge management. AI-enabled chatbots and AIOps solutions need both data (lots of it!) and organizational knowledge (lots of this too!) to be effective for use.

Knowledge Management (KM) is a key factor in an organization’s capability for being responsive, for driving efficiency and effectiveness, and for making the best use of limited and precious human resources. I believe that effective KM provides organizations with the capability to adapt, shift, change, and respond appropriately, especially in today’s unpredictable and ever-changing business and technology environment.

But many organizations have found that their KM practices aren’t enabling such a capability. Contributing to this situation are a few factors.

  • Knowledge becomes stale very quickly – if not maintained. The business and technology environment are continually changing. Stale knowledge is not just “stale” – it can be just flat-out wrong, making it unreliable and worthless.
  • In many organizations, it is the IT department that is trying to capture, develop, manage, and use knowledge. Even worse, in many IT departments, it is just the service desk that is investing effort into knowledge management. And many of those service desks, knowledge articles are just a defense mechanism, developed in response to (irate) user demands.
  • IT-authored knowledge articles are usually written in “geek-speak” and often read like a technical manual. Such articles are not helpful with enabling consumers to self-service or self-resolve any technology-related issues.
  • We (IT) just aren’t that good at writing – not just knowledge articles, but anything that doesn’t resemble application code or scripts.

Enter GenAI

Could the use of GenAI as part of an organization’s KM practices be the low-hanging fruit that delivers the transformational return that organizations need?

Generative AI, or GenAI, are algorithms that can be used to create new content.[i]

GenAI adoption has huge potential to address both the challenges in current approaches to KM, as well as enable organizations (not just IT or the service desk) to better capture, manage, and use its collective knowledge. How could GenAI address the challenges organizations have with KM?

  • Overcome that writer’s block. Writing knowledge articles is often viewed as “extra work.” Moreover, those that feel that they are not good writers tend to avoid documenting knowledge in the moment. Using GenAI capabilities and its use of LLMs (Large Language Models), first drafts of knowledge articles can be developed, based on what is entered into systems of record, prior LLM training, and prior curated knowledge articles.[ii] This draft can then be reviewed by experts before being published for use.
  • Finally, self-service! The conversational capabilities of GenAI can replace the cumbersome “search and try it” approach with a conversation-like interaction for self-service. Conversation like responses create a compelling pull for the customer; when it works how they expect it to and gets them back to doing their work more quickly, they will return to using self-service.[iii]
  • Keeping knowledge fresh. Perhaps the most significant challenge of KM is keeping knowledge relevant and current, regardless of where knowledge is created. Frankly, organizations cannot afford to appropriately hire enough staff to perform this critical, yet often tedious, work. Using the machine learning capabilities of GenAI, new knowledge can be created by combining and synthesizing information from various sources.[iv]
  • Making KM an organizational capability. Organizations have long emphasized creating and maintaining documentation, from topics ranging from processes, policies, governance requirements, security, products, applications, and more. There is a wealth of information in different formats for specific needs. LLMs excel at transforming data from one state into another. In the knowledge management use case, this means enabling any knowledge worker to be a knowledge-creation expert.[v]

Warning – challenges ahead

With all the hype and early success around GenAI, it is understandable that an organization may develop a bit of FOMO (Fear Of Missing Out) if they’ve not started adoption. However, FOMO-driven initiatives rarely return any of the expected benefits, and often become money-pits. What challenges do organizations need to address before considering GenAI adoption?

  • Ethics and Integrity. Successful implementation will require a focus on ethics, privacy, and security. Guardrails within services and tools as well as ground rules for acceptable use will separate enterprise success from low-level experimentation. From the IT service desk to the software development pipeline and even outside of IT, generative AI is positioned to impact the way work gets done.[vi]
  • Data Governance. Organizations must realize that when it comes to GenAI and its use of LLM that “Garbage In” results in “Garbage Out” (GIGO). GenAI responses will only be as good as the data that is used to train the AI. Most organizations lack actively defined and enforced data governance policies.
  • Infrastructure impact. The algorithms behind AI are quite complex. LLMs require more computer power and larger volumes of data. The more data available, the better the training of the AI and its associated models. The more parameters defined within a model means the more computer power required. [vii] Investments in infrastructure will be required. AI complexity – LLM require more computer power
  • It’s not just about ROI or cost-cutting. It can be extremely easy to look at the introduction of AI-enabled technologies simply as a way to cut costs, reduce headcount, or increase ROI. AI-adoption requires investment, training, and competent people to have success, so view GenAI-adoption success in terms of reduced costs or reduced headcount. Increasing ROI sounds good but measuring ROI (as with most things technology-related) is often difficult. Success metrics such as scalability, ease of use, quality of response, accuracy of response, explainability, and total cost of ownership[viii] should also be considered.

Get ready for GenAI-enabled KM

As with any emerging technology, GenAI presents potential opportunities and capabilities for many organizations. Here are some suggestions for getting ready for GenAI.

  • Learn. Most every GenAI solution provider offers no-cost learning opportunities through webinars and publications.
  • Review current KM-enabling policies and strategy. What is working well in the current approach to KM? Where are there gaps and resistance? What are knowledge consumers saying about their interactions with knowledge bases? Answers to these questions provide a base for evaluating GenAI solutions for KM.
  • Identify areas where improved KM can impact organizational objectives. Identifying how improved KM capabilities can have a positive impact on organizational strategy and objectives is a critical first step in developing a strong business case for GenAI.

GenAI can provide a means for addressing many of the challenges organizations (not just IT) face with its KM practices. It may just be the key to success for the modern organization in the ever-changing digital world.

[i] https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai , retrieved January 27, 2024.

[ii] https://www.forrester.com/blogs/knowledge-management-id-like-to-introduce-my-new-friend-generative-ai/, retrieved January 22, 2024.

[iii] Ibid.

[iv] Ibid.

[v] Ibid.

[vi] https://www.ciodive.com/trendline/generative-ai/404/?utm_source=CIO&utm_medium=1-2BlastJan18&utm_campaign=GeneralAssembly, retrieved January 22, 2024.

[vii] https://www.ml-science.com/exponential-growth, retrieved January 23, 2024.

[viii] https://ciodive.com/trendline/generative-ai/404/?utm_source=CIO&utm_medium=1-2BlastJan18&utm_campaign=GeneralAssembly, retrieved January 23, 2024.

 

Share twitterlinkedinmail