Unlocking AIOps, Part 2: The build-vs.-buy decision

Hello again! Continuing from our previous blog, in today's blog we will delve into a crucial decision that organizations often face when considering AIOps implementation—the build vs. buy dilemma. During our LinkedIn Live event on Mapping the impact of AIOps for CIOs, CTOs, and IT managers, we explored the factors to consider when making the build-versus-buy decision and how it impacts an organization's journey toward efficient IT operations. AIOps is undoubtedly a game-changer, but the path to implementing it varies based on an organization's unique needs, resources, and capabilities. The decision to build an in-house AIOps solution or buy a solution from a vendor involves careful evaluation of multiple factors.

To build or not to build your AIOps solution

Building an in-house AIOps solution demands substantial investments in data infrastructure, talent, and time. Organizations need to assess their technical capabilities, available resources, and long-term maintenance costs. This approach offers customization but often comes with significant capital and operational expenditure. Some of the challenges in building your own solution include:

• R&D costs: Heavy research and development (R&D) investments for AIOps can strain organization's IT budget.
• Expertise: Building AIOps solutions requires highly skilled AI, ML, and big data implementation experts with a good understanding of the mathematical models deployed. Advanced analytics talent helps reduce the margin of error for AIOps's AI and ML models.
• Data governance: Data quality and quantity is a key determinant of an AIOps solution's effectiveness and accuracy. A carefully constructed data collection mechanism from well-structured data lakes, meshes, and warehouses is required for full coverage data ingestion of the AIOps engine.

So, should I buy my AIOps solution?

Again, this requires careful evaluation of multiple factors, including your organization's needs, budget, and timelines to make an informed decision. On the other hand, buying an AIOps solution does offer quicker deployment, pre-built models, and ongoing support. In our previous blog, we discussed an example of an organization that adopted AIOps and enhanced its data collection and monitoring capabilities. This example highlighted the potential benefits of buying a solution from a trusted vendor. To learn more, check out our detailed whitepaper on AIOps, which deep-dives into the pros and cons of building or buying your own AIOps solution.

The decision-making process

The decision-making process involves evaluating core competencies, costs, and long-term goals. It's not just about the immediate implementation; it's about the sustainability and scalability of the chosen approach. While building an AIOps solution may offer customization, it requires significant technical expertise and resources. On the other hand, buying a solution can provide quicker access to AI capabilities and ongoing support. Ultimately, the decision should align with the organization's strategic objectives. As we highlighted during the event, there's no one-size-fits-all answer. Organizations must carefully weigh their options and consider their technical expertise, resources, and long-term goals. That's it for this part. Check out a snippet from our LinkedIn Live session where we discussed the decision-making process that goes into the build versus buy decision of AIOps. In the next blog, we'll shed light on the business impact model of AIOps, illustrating how AIOps contributes to overall organizational success. Stay tuned for more insights!