Tag Archives: ITSM

AI and Modernization, Service Management

Five critical steps for making a good AI/ITSM decision

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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.

 

 

 

 

 

 

 

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Leadership, Service Management

Nothing will change. Unless you change.

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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.

 

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AI and Modernization, Service Management

Three AI truths with IT Service Management

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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 observability 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.

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AI and Modernization, Service Management

The 3 Pillars of Success for AI-enabled Service Management

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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.

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Service Management

Start with Reporting first *then* Measurement

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What are some common reasons why IT organizations measure and report metrics? Unfortunately, in my experience, the answers are not always the greatest.

  • “We produce this report / measure this indicator because we always have.”
  • “We measure this indicator because everyone else we know does.”
  • “We produce this measure because our tools enable us to.”
  • “We read/heard about this measure in a magazine/training class, and it sounded like something we should be doing.”
  • “My boss expects to see this report.”

Like I said, not the best reasons that I’ve ever heard.

The purpose of measurement and reporting is to enable fact-based decision making. But if no one is making any kind of decisions from your reports, then you’re not capturing and providing the right measures to the right people in your reports. Perhaps you should apply outcome-based thinking and start with reporting first, then measurement!

Are your measures and reports telling the (right) story?

The fact is that regardless of the quality of your measures and reports, a story is being told. But is it the story that needs to be told?

But what you measure, what you report, and the story that is told within those reports is the difference between enabling valuable insights and actions and just noise and confusion. Here are some common measuring and reporting mistakes:

  • Mistaking outputs for outcomes. Many reports contain measures that confuse activity (outputs) with accomplishments (outcomes). In other words, just because you closed the incident record, or answered the telephone within 60 seconds, doesn’t mean that you’ve accomplished any kind of result.
  • Only focused on the Service Desk. Measures that are meaningful and relevant to the service desk have little to no meaning outside of the service desk. No one outside of the service desk really cares about measures like ASA or utilization.
  • Missing business-oriented measures. In today’s digital enterprise, technology is integrated with business processes – so integrated that businesses cannot function without technology. But IT-produced technology-based measures and reports typically do not reflect the business of the business.
  • The infamous “watermelon SLA” report. The measures that IT reports do not reflect the experience of the consumer. IT hits its (self-defined) targets, pats itself on its back, but then wonders why customers aren’t happy. Well…
  • “Customer satisfaction” isn’t about the customer – part 1. First, many IT organizations conflate the terms “customer” and “user.” Remember, a customer defines the requirements for a service and signs a service level agreement (SLA). Users do not sign SLAs. Yet IT organizations send out satisfaction surveys to users and call that “customer satisfaction.”
  • “Customer satisfaction” isn’t about the customer – part 2. Secondly, the surveys that are being sent out to users are often not returned. And if they are returned, it is typically from people who are not happy with the interaction that they just had with IT. Yes, occasionally you get a response from a happy user. Yes, it’s good to know that users have had a poor experience. But regardless of the response, IT continues to send out the same surveys, containing the same questions, reporting the same measures, and nothing changes– good or bad – about user interactions with IT.

Not having consistent, relevant, and meaningful performance measures and reports damages the reputation and value of IT. What’s worse, measurement and reporting are activities often done as an afterthought.

Start with reports first

Starting with a “reports first” mindset will dramatically improve the usability and impact of your reports. How can you apply outcome-based thinking to measurement and reporting? In other words, how to start with reporting first?

Identify the audiences for your reports. Yes, audiences – plural. At a minimum, your audiences will be consumers, IT management, senior executives, and the service desk team. Keep in mind that each audience will have unique reporting requirements. For example, reports that you may provide to senior executives will be different than reports that you may provide to consumers; reports that you may provide to service desk staff will be different than reports that you may provide to other IT colleagues.

What does each audience want to know? Why do they need to know this information? What will they do with the information? What decisions do they need to make? Do they have performance objectives or targets that rely on this information? How frequently do they need the information?

What do you want your audiences to know? Why do you need them to know this information? What would you like them to do with the information? What decisions do you want them to make?

By identifying what your audiences want to know, as well as what you need to tell them, you’ll identify the specific measures that you will need to capture, monitor, and report…but there’s still more to do.

  • Does the data to develop these reports exist?
  • Can the data for each measure be captured?
  • Is the data coming from a source that can be controlled?

If the answer to the above questions is “yes,” you’re on your way to providing the measures and reports your audiences need – and value. But if the answer is “no,” never fear. This just means that you and your audiences will have to do some more work and negotiate what can be done that will meet your audience’s needs.

Make your reports measure up!

Do your reports measure up (pardon the pun!)? Here are my suggested steps for getting your approach to measurement and reporting up to speed.

  • Audit your current reports. Do you know the audience for each of your reports? Are you “missing” an audience for reporting? Do you know what decisions are made from what is reported?
  • Develop decision maps for each published measure. What decisions are enabled by a published measure? Who makes or should be making decisions based on the measure? What may potentially no longer need to be reported?
  • Compare published measures with the organization’s Mission, Vision, Goals, and Objectives (MVGO). Are your measures strictly technology or process measures? What measures are needed to relate the activities of your team or department to MVGO?
  • Review your reports with your audiences and gather feedback. Yes, meet with the receiver of each report and walk through your current reports with them. Ask questions about their job function, business contributions, and goals for their team. Ask them to tell you what measures they need to act upon or make decisions. This does a few things for you. One, it helps you understand how reports are being used. Two, it provides you with insight into another area of the organization. Three, it builds and enhances your reputation as a business-focused (not just technology-focused) colleague.

Effective measuring and reporting don’t happen by chance. Starting with reporting first will result in better measures, better decisions, and better value.

Do your reports measure up?  Do you need reporting that drives good decision-making and tells the right story?  Contact Tedder Consulting today for an no-obligation chat about how to tune-up your reporting and measuring!

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Customer Experience, Service Management

Why your SLAs aren’t helping your XLAs

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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.

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AI and Modernization, Service Management

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

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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.

 

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AI and Modernization, Service Management

You Can’t Automate What You Don’t Understand

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The case for automating workflows is a strong one. There are plenty of reasons why organizations are looking for the right automation tools, including but not limited to:

  • Frees staff from performing tedious, high-volume, low-value tasks
  • Creates cheaper and faster process execution
  • Improves customer experience
  • Makes it easier to scale

I’m not here to argue the case of automation. When done correctly, it can achieve all those benefits above. And many organizations see success when they automate simple, one-step tasks, like password resets.

However, automation can start to feel like a catch-22, especially for those organizations who realize initial success with their simple automated tasks. That’s because they start the automation initiative by looking for the right tools. Many automation conversations in organizations are about the various tool vendors and weighing the features of each tool. And for simple automations, perhaps that’s not a bad way to make decisions.

But if you want to automate multi-step, complex workflows, the tool is the last thing you need to identify. Let’s explore how to make sure you get these multi-step automations correct.

Principles of Good Automation

1. Automation often means orchestration
The term “automation” is often used to describe things that are actually service orchestration. Automation is the act of automating a single task, like password resets. Orchestration refers to automating multi-step processes to create streamlined, end-to-end (and often inter-departmental) workflows. When determining your automation needs, be clear on whether your goal is only to automate or orchestrate.

2.Don’t automate or orchestrate “just because you can”
Every organization has plenty of workflows and tasks from which to choose to automate. But just because you can automate something doesn’t mean that you should, especially in the first stage of your automation initiatives. You want to focus your initial efforts on the tasks that:

    • Are performed on a high-frequency basis, are tedious for people to perform, but are well-defined and produce predictable results.
    • Consume a disproportionate amount of a team’s time. This may indicate that the process is not well-defined to begin with! In this case, be prepared to first invest time into process design.
    • Drive the most ROI for your business. It doesn’t make sense to spend hours and hours defining and automating a task that is only performed on an infrequent basis.

3. Everyone involved must be ready for orchestration for it to work
Creating multi-step, complex workflows almost always involve more than one team or person. You have to have everyone involved in the entire process involved and that requires a level of transparency from everyone in the organization.

Too many organizations begin automation initiatives despite having little insight into the actual steps involved in a workflow—and therein lies the problem. Those organizations are trying to automate work that they don’t understand.

Gaining Transparency is key

The solution for avoiding automation and orchestration missteps is to start by gaining transparency into the work currently being performed – before you start to automate. Here’s how:

  • Get the whole team involved. Automation and service orchestration has to be a collaborative project, or it will never work. People are often resistant to automation initiatives because they do not understand the objectives of the initiative or were not provided with an opportunity to provide feedback. To help overcome this resistance, illustrate how orchestration and automation will not only improve productivity, quality, and efficiency, but will also improve the employee experience by removing toil from daily work.
  • Identify needed business outcomes. Business outcomes are king to all else. You’re going to burn precious resources spending so much time automating tasks and orchestrating procedures that don’t result in measurable and valuable business outcomes. Before automating, first evaluate how a particular workflow achieves business outcomes
  • Understand end-to-end workflows. Does everyone on the team have a shared understanding of each step in a workflow? Is there a clear understanding of how each team contributes to that workflow? Many organizations don’t have this type of insight and it causes massive breakdowns during the execution of a process. Getting insight into the steps involved enables automation. Otherwise, attempts to automate will only result in frustration.

Once you’ve gained transparency into the current work, now you’re ready to evaluate tools. While this may require more time at the outset, doing this foundational work is key to long term success with automation.

Good automation and good service management go together

To be clear, good automation will not fix bad service management. When you try to use automation to address poor service management issues, all that happens is that you screw up faster – and automatically. And your end-users and customers immediately feel the impact of bad service management.

But when good automation is combined with good service management, watch out. Good service management helps you do more with your resources, helps you get everyone on the same page – both from the technology and the business outcomes perspectives, and helps you deliver that differentiated experience. Good service management ensures that you’re taking a holistic approach to delivering IT products and services. And when you start automation efforts by understanding how value is delivered through IT products and services – you’ll automate the things that both make sense and deliver the most value for both the organization and the user.

Tedder’s Takeaway: Why it matters

Tools alone will not make automation work. Automation is only successful when there is a shared and agreed understanding of the resulting business outcomes, combined with having transparency into how work is being done. Augmenting good service management with good automation delivers the differentiated experience for both the organization and the end-user.

Are your automation efforts stuck? Are you not realizing the benefits of service orchestration? Let Tedder Consulting help! From value stream mapping to process design and improvement, Tedder Consulting can enable automation that is both impactful and delivers a great customer experience. To learn more, schedule a free, 30-minute meeting with Tedder Consulting today!

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Service Management

What’s The ROI of Service Management?

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IT service management has typically been seen as yet another cost inside of what is perceived to be a cost center known as “IT”. Why? Because many IT organizations still view service management as operating overhead…and nothing else. The potential business value of good ITSM is ignored.

Many IT organizations could start becoming a strategic organizational partner if they understood the ROI of their work. ROI, or Return on Investment, is an important financial metric that most value centers use to measure success. Unfortunately,  90% of all technical support organizations fail to measure ROI. 

But, a simple shift in thinking about service management and ROI will create major opportunities for IT.

Why is ROI important to IT?

Why should IT leaders care about ROI? Simply put, ROI is the language of the business. Everyone in the C-Suite understands ROI and how important it is in making business decisions. When IT leaders start discussing ROI with peers, they are taking the “techno-speak” out of the discussion.  As a result, ROI makes IT more relatable and understandable to the rest of the business.

Relating IT in terms of ROI within the organization can lead to bigger budgets, better staffing, and improved service relationships. Data shows that top performing IT support organizations produce a ROI of 500% or greater on an annual basis!

Understanding the ROI of Service Management

Measuring the ROI of service management starts with quantifying work. But not in the ways that many typically think, like counting closed tickets or tracking time to resolution. Rather, quantify service management in terms that truly demonstrate business value—measures like savings (or “costs avoided” as an early CFO of mine schooled me about) through better processes, improved productivity, or investments in innovation.  These are the kinds of topics business colleagues care about – not IT operational measures. 

Here are three examples where you can illustrate a business-relevant ROI of good service management. 

ROI Area #1 — Time is Money

According to estimates, the global impact of unplanned downtime is 14.3 billion and employees lose an entire day of productivity due to unplanned downtime. 

Many ITSM leaders measure IT productivity in terms of number of incidents resolved and time to incident resolution. This is a flawed approach.  An incident is not a “value-add”. While there is (limited) value in resolving an incident, the real business value is not having incidents at all. 

So how might good service management practices produce an ROI?  Let’s take an example. Company XYZ implemented service management improvements during quarter two. These changes included improving change enablement practices and developing and publishing self-help knowledge articles regarding the most-frequently encountered issues. 

Q1 Q2 Q3 Q4
12,792 12,374 10,556 9,843

Because of these changes, XYZ saw over 4,700 fewer tickets in Q3 & Q4 than they had in Q1 & Q2. 

Now let’s apply money to the scenario.  Let’s say every ticket costs the company $10 in productivity loss (of course, it’s much more than this!).  By implementing these improvements, IT helped the organization avoid nearly $50,000 of lost productivity. That’s where the real value and the ROI of service management begins to show itself. 

ROI Area #2 — Avoid unnecessary cost with self service 

Another ROI-enhancing area for service management is the concept of “shift left.” Shift left means moving support and enablement activities closer to those doing the actual work.  For example, moving incident resolution or request fulfillment from a desktop support team to the service desk or from the service desk to Level 0 (self-help), can help an organization avoid unnecessary escalation-related costs. Unnecessary escalations result in support costs that are not directly reflected in performance measures.  Because these escalations appear to be just ‘business as usual’, the cost associated with those escalations go unnoticed. 

How many tickets are unnecessarily escalated that could have been solved by Level 1 or by self-help? According to TechBeacon, a typical service desk ticket can cost around $22. But escalating a ticket can cost an additional $69, making the total cost of the ticket $91. If you are handling tens of thousands of tickets, these costs add up quickly.

But when self service offerings are provided for those repeatable and predictable support and enablement activities, your organization avoids the costs associated with ticket escalation.   

ROI Area #3 — Spend on innovation, not support

This last area is perhaps the most important but often the most forgotten. It’s where poor IT service management practices drain resources from innovation.

To understand how good service management facilitates innovation, let’s start by understanding the basis of IT budgets. Generally speaking, IT budgets have three categories of costs:

  • Fixed costs, like salaries, support contracts, and other operating expenses. These costs typically do not change dramatically year over year. 
  • Innovation, in the form of new projects and improvement initiatives.  These costs represent outcomes that the organization would like to realize through investments in technology. 
  • Maintenance and support, which includes application and software updates, responding to incidents and requests, security monitoring and patching, and other day-to-day activities needed to maintain reliability and availability. 

Again, let’s use some easy numbers for illustration. If an IT budget is $1000, then typically fixed costs make up $500.  Innovation is budgeted at $300, and maintenance and support is budgeted at $200. The organization is optimistic about realizing new value through innovation.  Support costs are acknowledged and seem reasonable. 

Until the impact of poor IT service management practices become evident. Poor IT service management practices result in poor change implementations.  Lots of fire-fighting.  Too many meetings to discuss and decide what should be simple requests.  Automation that just doesn’t work well. Confusion regarding how technology enables current organizational outcomes.  Duplicative products and services. 

And suddenly, the IT organization is spending more time in maintenance and support, and less time innovating. And what part of the IT budget absorbs that additional cost?  The budget allocated for Innovation. Innovation is sacrificed to cover the (unnecessarily excessive) cost of simply keeping the lights on.

Good service management preserves that innovation budget, by doing the right things well when it comes to maintenance and support. 

What is the simple shift in thinking that enables service management ROI? 

How is it possible to realize ROI with service management, rather than looking at it as cost? 

The answer is simple.

It starts with a shift in thinking.  Rather than viewing service management as a means of control, begin viewing service management as a business enabler.  

While the IT operational aspects of service management are important, it is not why organizations need to practice good service management.  

Good service management enables organizations to achieve business outcomes.  Good service management enables organizations to realize value from its investments in and use of technology.  And one of the key ways to enable this shift in thinking is to talk about service management in terms of ROI.  

Tedder’s Takeaway – Why It Matters

Shifting how the organization views service management is a critical enabler for discussing the ROI of service management.  Moving the conversation from cost to results, then attaching ROI to those results.  Having the ability to discuss ROI with organizational peers not only makes IT more relatable, it also repositions IT as a strategic enabler, with a tangible way to understand the impact of good service management. 

Is it time to shift your thinking about service management?  Are you reporting operational measures instead of business outcomes?  What would be possible for your organization if you could illustrate the ROI of service management? Let Tedder Consulting help!  For more information, contact Tedder Consulting today.

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Service Management

The Curious Case of the Missing IT Strategy

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IT organizations often get stuck in a vicious cycle of never-ending work. IT implements solution after solution, fixes one problem after another, and no matter how many times they do it, those solutions and fixes never seem to stick. IT often finds itself just trying to keep up with what appears to be a constantly changing business. IT is often seen as the anchor slowing business down and has earned a reputation of being the slowest path toward technology implementation.  And when budget times come around, IT never seems to manage to get its fair share. 

These are signs that an IT organization is missing strategy.  This is a massive problem for both IT and the organizations that IT works within. Why are so many IT organizations missing a strategy?  What can be done to establish a viable IT strategy?

One thing that is certain – the strategy can’t just be “do”.

The Missing IT Strategy

Of course, no leader intentionally avoids developing strategy. It’s usually a consequence of a number of factors. However, in the case of the missing IT strategy, the CIO has to establish a business-technology (not technology alone) strategy, talk and share that strategy with other leaders, incorporate and underpin the larger business strategy, and drive the IT organization forward following that strategy. If a CIO spends too much of their time doing or supervising the day-to-day work within an IT organization instead of delegating, she won’t have the time (or energy) to be strategic. If a CIO has to spend more time supervising the daily activities of IT than ensuring business outcomes and value, that’s usually an indicator of a missing IT strategy. 

The second indicator of a missing IT strategy is the lack of true service management. Why? Because if the service management foundation is not strong or well executed, IT can never be strategic. If IT ignores:

  • Defining services in terms of business value and outcomes
  • Creating workflows that are based on services, not technologies or organization charts
  • Publishing performance reports that are relevant to and meaningful for the business 

then IT is setting itself up for failure.  Many organizations look at service management as just something that a service desk does.  But good service management provides the capability of relating technology investments to business outcomes.  This makes good service management a critical part of the foundation of IT. Having a solid foundation is what keeps IT relevant, reliable, and able to scale to meet business needs. WIthout good service management, IT will waste a lot of time just trying to keep up with service requests and putting out fires instead of enabling the realization of business strategy.  

Finally, the third indicator of a missing IT strategy is a “one thing at a time” mentality. To stay on track, IT organizations often choose to focus on just one initiative at a time. This might help your team feel less overwhelmed, but it often comes with the cost of missing a holistic view of the organization. The ability to see the birds eye view of how the organization relies on technology to create better outcomes for end users and customers is one of the most important skills for an IT leader.  Having this big picture view enables the IT leader to be even more strategic.  

Why does IT need a strategy?

IT operating as only a support team is no longer an option for any business. The speed of business has increased significantly over the last decade, due in a large part to the introduction of new technologies, such as automation, mobile computing, cloud-based services and machine learning. IT has to be the driver and enabler of technology. Whether it’s realized or not, technology has become “baked into” every aspect of the organization. 

The question is “has IT become ‘baked in’ as well?”  Without a well-defined IT strategy, the answer to this question is usually “no”.   

Defining, socializing, and executing a strategy strengthens IT’s role within an organization. It’s what separates the IT organizations that are treated as order takers from the IT organizations that are treated as valued partners. 

How to solve the case of the missing IT strategy

Here are three things that IT leaders can do to solve the case of the missing IT strategy.

What is the business strategy?  How can technology enable realization of business strategy? To shift from a “support only” team to a strategic asset, IT first has to understand the goals and objectives of the overall organization – and how technology can be used to enable realization of those goals and objectives. IT’s strategy must be tied to these business goals and objectives. IT leaders have to take a step back from the inner workings, day-to-day activities of IT and look at the bigger picture of the organization. 

Elevate to real service management, not just some arbitrarily selected processes.  Once IT understands the role of technology in achieving business strategy, IT must then elevate its approach to service management.  Service management is more than just fulfilling requests and resolving outages. An effective approach for elevating service management is to identify and map the value streams of an organization, then identifying how technology underpins those value streams.  Value streams help identify the products and services that IT must deliver. This exercise not only lays out what service management must enable and deliver for the organization, it is also a great way to align what IT is doing to the overall needs of the business. 

Report IT performance in business terms. Once you’ve elevated your service management and understand the goals and objectives of the company, then you’ll be able to produce and publish performance reports that reflect how IT contributions enabled achievement of business goals and objectives.  Having this capability is significant for a number of reasons.  First, it demonstrates that IT truly understands what is important to the organization.  Secondly, it provides the ability to evaluate if IT strategy is meeting business needs.  And lastly, it begins to change the perception of IT as just being a “support team” to a strategic asset.

Thinking and working strategically is transformative for an IT organization. After you’ve seen how IT integrates with the rest of the organization, you won’t be able to go back to working only in a ‘support’ role.  By defining and executing an IT strategy , your entire business will become stronger.  

Need help developing an IT strategy that is aligned with your business objectives?  Let Tedder Consulting help!  Tedder Consulting will first visit your organization to understand your business, goals, and current IT situation.  Tedder Consulting will then conduct an analysis of your IT services and practices to determine how they are operating. Finally, we deliver a plan for aligning your technology strategy to your business goals.  For more information, contact Tedder Consulting today.

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