Tag Archives: Metrics

4 things IT can do to improve Business-IT Alignment – and enable AI success

Share twitterlinkedinmail

A few years ago, I thought that we had finally moved beyond the conversation of “business-IT alignment”.  I thought that business processes and technology had finally become integrated; if not integrated, then at least the boundary between business processes and technology was significantly blurred.

Well, I was wrong. Business-IT alignment – or the lack thereof – is still a thing.

We’re still struggling with business-IT alignment

This recent CIO.com article discusses seven hard truths of business-IT alignment.  Here are a few of those hard truths:

  • “The business is not your customer.” I agree. For IT to act like non-IT colleagues are ‘customers’ simply drives a wedge between the IT department and the rest of the organization. This behavior also provides IT with an excuse for not understanding the business of the business.
  • “Like it or not, you are responsible for business outcomes.” That’s true. The real value from the use of technology is for the organization to realize business outcomes and value. But too often, IT sees and measures success in terms of projects getting done, or laptops being delivered, or contacts at the service desk being resolved.
  • “The business really does need to understand what you do.” That’s also true. While the IT department is responsible for the installation and maintenance of digital technology, IT must be more than just a technology caretaker. IT organizations must help the rest of the organization understand how the use of technology supports business strategy.
  • “You’re probably talking about the wrong things.” Couldn’t agree more. Many of the measures and reports that are being produced by IT are only because the tools being used by IT make it easy to produce these measures and reports. Do these measures have any meaning or relevancy to the rest of the organization?

Why is this a problem?

Business-IT alignment is not just a catch phrase or buzzword. The digital era is amplifying the importance of having strong business-IT alignment. But within many organizations, business-IT alignment is missing. How does the lack of alignment impact IT and the rest of the business?

First, IT is unable to respond to business demands at the speed of business. Consider the challenge that every modern business faces – serving the digital customer. The digital customer is demanding that businesses provide services at anytime from anywhere. In response, businesses want to leverage emerging technologies such as chatbots and GenAI to meet that demand. But because IT hasn’t been involved in those business strategy conversations, it is forced to play “catch up” to meet these demands – demands for which IT is usually unprepared. IT is not prepared because no one has been trained, much less involved in the selection of this technology – but then IT is expected to make it work as well as fit with existing systems and infrastructure. When IT is forced to play catch up, in-flight projects get delayed as IT resources are shifted to meet new demands.

But this behind-the-scenes work is rarely visible to the rest of the business. To the rest of the business, IT is a barrier to responding to the digital customer.

Secondly, the rest of the organization continues to look at IT as just a cost center. What those outside of IT may not realize is that IT must deliver warranty (security, resiliency, continuity, capacity, performance) as part of its services – regardless if that’s been communicated or specifically requested. Delivery of an expected level of warranty costs money – costs that may not be apparent to non-IT colleagues.

Why ITSM hasn’t helped

Wasn’t ITSM adoption supposed to address issues like the above and align the IT organization with the rest of the business? True, business-IT alignment is a goal of ITSM adoption…but for many organizations, it didn’t happen. Why?

  • ITSM was (and continues to be) an IT initiative with little to no involvement from non-IT colleagues. The initial ITSM project focused internally on IT processes and infrastructure management and excluded defining services and business-IT strategy. Making things worse, IT didn’t map how what it does supports business results or delivers business value. There was (and is) no link established between ITSM goals and objectives and organizational goals and objectives.
  • The ITSM initiative only focused on implementing a tool. This is a suboptimal approach for two reasons. A technology-only focus excludes how ITSM impacts people – both within and external to IT, as well as processes, suppliers, and partners. Secondly, the IT organization only took actions that facilitated use of the tool, not necessarily align with business needs.
  • ITSM is only focused on IT operations – or even worse, just the IT service desk. ITSM is viewed only as a way to deal with end-users of IT products and systems, never considering how technology could be used strategically to deliver business value or results. As a result, not only is ITSM not aligned with the business, IT is not internally aligned.

Successful AI adoption requires Business-IT Alignment

Businesses continue to experience the impact of the digital economy. In the digital economy, the “store” is always open, and customers expect that systems are “always on”.  Customers can (and will) do business whenever and from wherever they want – using any internet-accessible device. Customers expect a differentiated, frictionless experience that provides value. Encountering system downtime or a poor experience is simply out of the question.

And organizations are turning to new capabilities enabled by emerging technologies, like chatbots, GenAI, intelligent automation, and more to meet this ever-increasing customer demand. In the digital economy, the technology managed and delivered by IT is the crucial connector between a business and its customers.

What does this mean for IT? IT can no longer play a back-office role within digital organizations. IT has a critical role as a business operates within the digital economy – and strong alignment between business and IT is required.

The successful use of chatbots, GenAI, automation, and other emerging technologies starts with having strong business-IT alignment. So how do organizations seize this opportunity, avoid the mistakes of the past (as with ITSM adoption), and realize true business-IT alignment?

First, ensure that any AI initiative has clearly defined objectives that are aligned with business strategy.

Second, successful adoption of AI requires strong involvement of business leaders[i]. Successful use of AI-enabled capabilities depends on the AI understanding the business of the business. It’s business leaders that have the knowledge that AI needs.

IT organizations must make the investment in building skills and competencies, in both AI technologies and in understanding the business of the business. Technology-only skills are no longer sufficient. IT must become that trusted advisor to help guide business leaders as the organization navigates the challenges of an AI-enhanced digital economy.

Lastly, good ITSM is an enabler for AI adoption. Good ITSM means aligning activities with business goals and objectives, defining services to ensure a shared understanding how technology delivers business value and outcomes, and providing business-relevant metrics and reporting.  As a result, good ITSM enables fact-based decisions regarding AI adoption, such as where intelligent automation would improve a customer or employee experience.

Nothing will change – unless there is change!

Let’s be clear. Business-IT alignment challenges will not just go away, nor will they fix themselves. It’s up to IT to align with the rest of the organization, not the other way around. And it’s not just the CIO alone that can drive business-IT alignment – the entire IT organization must also drive it as well.

It’s time to break the pattern. Here are some suggestions for breaking through those alignment barriers– all of which can be initiated by IT.

  • Establish and nurture the guiding coalition. To demonstrate its commitment to overcoming the challenges of business-IT alignment, IT must form a team to drive change. This early step in Kotter’s 8-step model demonstrates IT’s commitment to driving improvement in business-IT alignment.
  • Map business value streams – plus. Value Stream Mapping is a great way to identify how value flows through an organization. But don’t stop there – identify and map how technology supports each step within each value stream. Review these value streams with all IT personnel to raise awareness of how IT enables business success. Then, take it one step further. Review value stream maps with non-IT stakeholders and decision-makers. Not only will this illustrate the role of IT in business success, but those stakeholders may also even be surprised to see how technology enables value flow through the business!
  • Look at what you’re reporting to whom. If IT is sending reports full of technology metrics to business colleagues, then IT is reporting the wrong things! Identify and define ways to measure and report on metrics that directly reflect the organization’s mission, vision, and goals. By measuring and reporting on metrics that are important to the business, IT demonstrates how its contributions lead to business success.
  • Get serious about continual improvement. IT organizations can positively influence non-IT colleagues by fixing those things that cause constant irritation when interacting with IT products, processes, and services. Establishing a regular and on-going continual improvement practice to remove these irritants – then publicizing those efforts – will begin to change the perception of IT.

Business-IT alignment has long been a critical success factor for the modern, digital-age organization. Success with AI adoption is raising the need for alignment to a new level. Taking these first steps will set you on the path of business-IT alignment – and AI success.

Does your IT organization continue to struggle with alignment to “The Business”? Let Tedder Consulting help you establish the strong foundation you need so that your organization will realize the business results required from its investments in and use of technology.  Contact Tedder Consulting today for a no-obligation discussion about how we help!

[i] https://www.forbes.com/sites/forbestechcouncil/2023/10/06/why-business-leaders-should-understand-ai-alignment, Retrieved April 2024.

Share twitterlinkedinmail

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

Start with Reporting first *then* Measurement

Share twitterlinkedinmail

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!

Share twitterlinkedinmail