Five Building Blocks for Successful AI-Enabled IT
Advances in machine learning and NLP have cracked the flood gates open and AI is now emerging as the definitive transformational technology of our times. A recent IDG study found 93% of IT professionals have already deployed or started to explore AI to augment their ITSM and ITOM modernization efforts. Clearly, AI is making its way into our workplaces faster than we imagined.
With all the hype and enthusiasm surrounding AI-enabled ITSM solutions, it’s tempting to jump on the bandwagon and rush to implementation and adoption. But more often than not, such implementations fail to deliver expected business results; simply because businesses are not aware of the scope of new technology. In order to turn AI aspirations into realistic outcomes, IT practitioners need to fully understand the potential of AI and where its limitations lie.
This blog outlines the essential elements for introducing and scaling AI within your organization. From setting realistic expectations to choosing the right AI vendor, we’ve pinned down the five building blocks that can help you accelerate your AI journey.
1. Identify the right use cases and the outcomes you intend to achieve
It is important to understand the current state of your service desk operations and also know what needs to be improved. This evaluation helps in identifying the right use cases and prioritizing the ones to go after. This also ensures your technology adoption strategy aligns with the overarching business goals and objectives of the organization. A common myth with AI projects is that building AI systems is expensive and can be a complex exercise. To counter this fallacy, organizations look for cheaper and faster ways to implement without a well-thought-out use case or implementation strategy, thus failing to maximize the potential of AI-enabled solutions. To avoid such mishaps, technology buyers must have a clear understanding of the business outcomes that they wish to achieve through this technology implementation and pick the right use cases.
2. Recognize the right AI options for your ITSM environment
While it’s important to embrace new technologies to drive innovation, it’s equally important to know what AI-enabled ITSM is supposed to do for you. For example, applying AI technologies to suboptimal or poor processes will not result in an optimal service desk solution. Instead, organizations will only get a suboptimal outcome more quickly. So, the solution is to switch to a modern ITSM tool rather than deploying AI solutions to fix an inefficient legacy system.
3. Set realistic expectations
Deploying a new technology like AI has its own hits and misses. While AI can empower your support team to work smarter and faster with intelligent recommendations, it’s important to note that it needs the right infrastructure, right use cases, and relevant data to support your end goals. Having a good understanding of both the promise of AI and where its limitations lie, will help you set realistic expectations to meet your business needs. Start small with simple use cases and see success and continuously optimize before you dive into large-scale and complex use cases.
4. Have a robust data management practice
A key challenge to most AI projects starts with having the right and relevant data sets in place. While collecting data has become simple, managing and having trusted high-quality and relevant data continues to be a challenge for organizations. AI requires a wealth of data to learn and make intelligent recommendations to your support team. Lack of robust data management and governance practices restricts accessing siloed data and fails to successfully support implementing AI and ML projects. Businesses must see that a well-defined and well-managed data strategy and practices are in place before rushing into AI adoption.
5. Choose the right AI vendor to partner with on this journey
According to a recent IDG study, speed of implementation and integration with existing technology is touted as one of the major challenges to applying AI technologies in ITSM. Most organizations land themselves in a complex AI integration process by choosing a vendor platform that requires advanced coding and scripting skills to integrate with existing systems. Organizations spend several weeks to go from initial deployment to full operations. To avoid such setbacks, businesses must ensure they do their research and choose the right AI vendor. If you foresee integration-related challenges, pick a vendor who has a no-code/low-code integration setup. Vendors, today, predominantly offer out-of-the-box capabilities/workflows and common IT plug-ins. Modern vendors help you quickly integrate AI solutions with existing systems to reap the benefits of AI from day one.
To conclude, be sure to set realistic goals, list your requirements, understand the current state of your IT landscape and pick the right vendor. These building blocks can help you transition from an opportunistic, tactical route to AI to a more strategic one.
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