According to a recent HBR article, “All too often, we are bombarded by news on the wonders created by artificial intelligence (AI)—what it’s going to do for us and how it’s going to change our lives. But that coverage misses a critical point: For any business wanting to leverage the benefits of AI, what truly matters is not the AI models themselves; rather, it’s the well-oiled machine, powered by AI, that takes the company from where it’s today to where it wants to be in the future.”

So, what’s the key to achieving this? The authors argue that it is building, integrating, testing, releasing, deploying, and managing the system to transform results from AI models into actionable insights for end users. This practice, better known as AIOps, is key to achieving AI at a large, reliable scale and doing it right starts with the right environment.

As the HBR piece puts it, “To unlock the value of AI, you need to start with a well-designed production environment…You want a setting in which software and hardware work seamlessly together, so a business can rely on it to run its real-time daily commercial operations.” The article outlines the three critical criteria that underpin a good product environment and, in turn, a successful AI project:

  1. Dependability. The authors write, “Avoiding data bottlenecks is important to creating a dependable environment. Putting well-considered processing and storage architectures in place can overcome throughput and latency issues.” AIOps teams should also prepare contingency plans so that technical issues don’t crash the entire AI system—a critical and challenging undertaking given the large amounts of data and processing speed associated with AI applications.
  2. Flexibility. The old adage that change is the only constant is certainly true when it comes to business objectives. While these may fluctuate on an ongoing basis, “Everything needs to run like clockwork at a system level to enable the AI models to deliver their promised benefits: data imports must happen at regular intervals according to some fixed rules, reporting mechanisms must be continuously updated, and stale data must be avoided by frequent refreshing.” In this environment, it’s critical that production be flexible and allow for efficient system reconfiguration and data synchronization without impacting performance.
  3. Scalability and Extendability. Many IT systems have different performance, scalability, and extendability characteristics, and issues can quickly arise when they cross system boundaries. Introducing AI models into the mix only complicates things further. As such, it’s important that AIOps teams are prepared to address challenges with legacy infrastructure as part of their expansion into AI. As the HBR piece puts it, “The success depends greatly on the ability of the team to constantly adjust, tinker, and test the existing system with the new proposed solution, reaching equilibrium through functionality of old with new systems.”

Organizations have taken different approaches to achieve the three components outlined above, and the HBR piece outlines these in greater detail. But with AI poised to deliver competitive advantage in virtually every sector, it’s clear that investing in AIOps is the best path forward.

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