In recent years, I have seen many customers embark on AI transformations of their IT operations, often struggling to accelerate AIOps (Artificial Intelligence for IT Operations) adoption across their organizations. 

I have observed that many enterprises share a common approach: they bundle their AIOps transformation with their IT outsourcing contract. As a result, it creates an imbalance in the overall offering, with it being skewed more towards labor for managing IT operations while AIOps is perceived as a complementary feature designed to enhance the solution. Since key operational outcomes are already addressed in the outsourcing agreement, AIOps may be perceived as non-essential and more as a nice-to-have in the broader success of IT operations. 

And so, the operational risk is transferred from the customer to System Integrators (SIs), which may affect the distribution of the transformational benefits in the long term. While AIOps creates opportunities for improved efficiency and profitability, the benefits may be distributed across stakeholders, rewarding those who assume higher transformational risks (such as System Integrators), which may lead to more modest financial returns for specific parties (e.g. customer). 

As IT operations are becoming increasingly automated through AIOps, the role of traditional service delivery can shift, which could impact how SIs structure their revenue models.  

(To learn more, see Tribal Knowledge, the Secret Stumbling Block to AIOps Transformation) 

The traditional IT support outsourcing model is going through a transition that is affecting the economics of outsourcing deals, which indicates that AIOps adoption may play an increasingly important role in shaping future operating models. 

In my experience, there are a couple of options for solving this problem:  

Option 1: Enhance the current outsourcing contract 

There is a shift in how IT operations resourcing is being approached, with a growing emphasis on transitioning from labor-centric models to being AIOps-centric. Over the past 30 years, IT organizations have realized operational efficiencies through labor arbitrage, particularly by leveraging offshore talent. While this model has delivered value, AIOps and artificial intelligence (AI) are now creating new opportunities to improve both efficiency and effectiveness in IT operations. 

IT outsourcing deals should have clear, business-driven targets that define how much of IT operations will be managed by AIOps. These targets can be defined by analyzing Standard Operating Procedures (SOPs) in ITOps. Based on an analysis of each SOP, it can be estimated what can be managed by AIOps and how the improvements AIOps will deliver can be quantified, such as improvements in MTTR, incident reductions, compliance, and more.  

While the responsibility for transformation continues to rest with SIs, contracts would include clear expectations for AIOps outcomes. This could involve defining service-level commitments tied to AIOps performance, with corresponding incentives or penalties. IT outsourcing agreements would remain largely the same, with SIs managing IT operations risk—with the shift being from labor to AIOps. 

Option 2: AIOps as a service 

This approach is a departure from traditional outsourcing contract models, shifting toward a value-based model in which software is procured based on the outcomes it delivers. 

In this model, the AIOps software provider assumes risk by being compensated only when specific value metrics are achieved. On the other hand, the customer assumes the operational risk associated with those outcomes not being met. For example, an AIOps compensation might be tied to a use case for a certain number of incidents within an established time frame. If the AIOps solution fails to resolve these incidents, the provider may not be compensated, while the customer must still address any unresolved issues. To mitigate this risk, customers may choose to maintain a small team of Site Reliability Engineers (SREs) and operations, to ensure operational continuity. 

This contract model creates aligned incentives: the AIOps provider is focused on meeting performance targets to be compensated, while the customer aims to realize the business case benefits as efficiently as possible. 

This approach involves a detailed assessment of the outputs required to sustain an effective and efficient IT production environment. 

Different organizations may find different models suitable depending on their structure and priorities. The buyer persona for Option 1 consists of IT executives of large enterprises with well-established, outsourced IT operations. On the other hand, the buyer persona for Option 2 is mid-sized to smaller companies who are looking to adopt AIOps for quick benefits to support their agility goals. 

The key takeaway is that the adoption of AIOps becomes possible when organizations explore measurable, machine-driven IT operations. 

If you’re ready to make AIOps the centerpiece of your IT operations, feel free to reach out to me or Digitate. 

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