Tag Archives: business case

Suffering from AI fever? Here’s why the big picture still matters

August 3, 2025 Doug Tedder Leave a comment

Is your organization exhibiting “AI fever”? “Symptoms” include people asking questions like:

“How can our organization take advantage of AI-enabled capabilities?”
“Where can we leverage AI to automate our workflows?”
“How can we use AI to improve efficiency and reduce costs?”
“What happens if our competitors beat us to the market with an AI-based solution?”

These are legitimate questions. Effective adoption of AI-capabilities is changing the way that companies work, by driving workflow efficiencies and automation, providing data analysis and insights that weren’t before possible, improving work quality, personalizing experiences, and more.[i] The market is crowded with solutions, all of which make it seem so simple to fully take advantage of AI capabilities. It’s easy for organizations to get lured in.

But the potential results from AI adoption don’t excuse organizations from doing the needed critical thinking before making solution decisions. Before jumping on the AI bandwagon, organizations must answer questions like:

What problem will AI solve for the organization?
Do customers want an AI-based solution, and if so, how should it help them?
Is the necessary foundation in place within the organization to benefit from AI adoption?
How will AI fit into the “big picture” of the organization?

The big picture must come first

Understanding the big picture must come first before considering any AI solution. Why is having a holistic (or big picture) approach so important, especially with AI adoption?

First, AI adoption can have a significant impact on the entire organization. Many organizations only think of AI as something that will only impact IT or technology, but the impact can be much more than that. AI impacts multiple areas of an organization, and having a holistic approach and strategy ensures that AI initiatives are aligned across all parts of the organization.[ii]

Without taking a holistic approach to AI adoption, organizations risk implementing a point solution within a workstream that is neither sustainable nor enhances organizational efficiency. While there may be local benefits, the actual outcome is that the overall workstream becomes less efficient. Implementing a solution in this way would be like speeding up only the middle of a conveyor belt. While widgets may move along faster on the conveyor belt, the constraints at the beginning and end of the conveyor belt become highlighted and problematic.

Data and ethics are two of the more significant considerations for the effective use of AI. AI needs data – lots of it – and that data must be of high quality and integrity if results are to be trusted and reliable. The use of data also presents ethical considerations, with issues like data privacy, bias, and appropriate use. These factors mean that data quality and data governance are also part of the AI big picture.[iii]

To fully use the capabilities engineered into AI solutions, these solutions typically require training to function effectively. People within an organization must develop skills and competencies to use the solutions. But here’s perhaps an unexpected twist – not only do the people using the solutions require training, but the solution itself requires training as well. These solutions must be trained using the datasets provided by the organization and tailored for organizational-specific tasks and use cases. Having people with the skills and knowledge for data preparation, tuning algorithms, data visualization, and problem-solving are needed for training the AI.

AI adoption challenges

While AI can bring a lot of capabilities to enhance an organization’s performance, it is far from a “magic wand.” Here are a few challenges organizations are facing with AI adoption.

People. Will people feel threatened by AI adoption? Or will they feel enabled? The best AI capabilities are worthless unless they enhance the ability of people to do their work. Effective and comprehensive organizational change management as part of AI adoption is critical.[iv]
Good AI will not fix broken processes. Having well-defined processes is the foundational structure for AI adoption. It’s straightforward: bad processes result in poor AI adoption and poor outcomes. Without clearly defined workflows, how can an organization identify valuable use cases or strategically expand AI-enabled capabilities, such as automation, or agentic AI.
Missing the skills needed for success. This article from ARM highlights the impact of the lack of in-house AI talent is having on AI adoption. Nearly one-half of leaders feel that the lack of skilled talent is a primary barrier to successful AI implementation.

How to treat AI fever

Do you have that big picture view of your organization? Is your organization prepared to take advantage of the right AI capabilities that elevate organizational performance, efficiency, and capability? Now is the time to get ready. Here are four tips for ensuring a holistic approach to AI adoption.

Map your value streams. One of the most significant challenges to adopting and exploiting AI capabilities is that organizations don’t understand their value streams, much less have mapped those value streams. Value stream maps depict the big picture of how work and value flow through an organization. It also identifies where constraints may be within the organization. With this holistic understanding of its value streams, organizations can better figure out if AI solutions will enhance performance…or enhance constraints.
Focus on ongoing learning and development. “Training as an event” cannot keep pace with the rapid changes in AI capabilities. The rapid pace of AI enhancements demands a continual learning approach to training and development. Adaptive learning is an educational approach that tailors educational experiences to meet an individual’s unique needs, skills, and learning pace, facilitating a continual learning experience.[v]
Invest in your current staff. The best source of knowledge about how work is currently done is the people that are doing the work. This is the exact knowledge that is needed to train AI. Invest in reskilling or upskilling current employees in AI concepts and technologies, enabling them to take on new career opportunities that will emerge with AI adoption.
Answer the “why.” Building a business case not only answers how and why AI adoption will address a business challenge, but it also drives the needed commitment from senior management for making it happen.

AI capabilities are evolving at a dizzying pace. Having a holistic or big picture view of the organization is the best way of ensuring that your organization can take advantage of the right AI solutions at the right time to deliver the right results.

[i] “Artificial Intelligence is changing how companies work”, https://www.deloitte.com/ch/en/Industries/technology/perspectives/artificial-intelligence-Is-changing-how-companies-work.html , retrieved April 2024.

[ii] “Why adopting AI needs a holistic approach”, https://betanews.com/2025/02/03/why-adopting-ai-needs-a-holistic-approach-qa/ , retrieved April 2024.

[iii] Ibid.

[iv] “Adopting AI-driven Change Management: Key strategies for Organizational Growth”, https://voltagecontrol.com/articles/adopting-ai-driven-change-management-key-strategies-for-organizational-growth/ , retrieved April 2024.

[v] “Understanding Adaptive Learning: How AI is revolutionizing personalized education”, https://elearningindustry.com/understanding-adaptive-learning-how-ai-is-revolutionizing-personalized-education , retrieved April 2024.

AI, ITSM

Five critical steps for making a good AI/ITSM decision

January 14, 2025 Doug Tedder Leave a comment

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.

AI, ITSM

Three AI truths with IT Service Management

August 5, 2024 Doug Tedder Leave a comment

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

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

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

Truth #1 – AI needs good data

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

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

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

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

Truth #3 – AI doesn’t replace knowledge

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

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

Good Governance facilitates AI-enabled ITSM

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

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

Getting ready for AI-enabled ITSM

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

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

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

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

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