business case - Innovative Service Management Consulting and Training: Tedder Consulting

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.

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

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Five critical steps for making a good AI/ITSM decision