Why You Need to Develop an AI Strategy for IT Operations Now — Before Your Next Crisis Event Hits
Countless enterprise plans have been disrupted in 2020 as a direct result of the ongoing global pandemic. Many organizations have decided to put off key technology investments in order to focus on sustaining their current value chain. However, organizations considering delaying an artificial intelligence (AI) capability for IT should rethink that decision. Holding off on adopting AI for IT Operations can mean putting your entire organization at a disadvantage.
Many organizations have already embraced AI or are currently racing to put AI projects in place. 14% of organizations say they already have an active AI project running, and a full 48% of organizations say they plan to have one in place by the end of 2020.
In the realm of IT Operations, having access to AI-powered analytics is just as important as being able to support other departments’ AI use. Artificial intelligence can allow IT teams to more readily identify persistent problems as well as the opportunities that can address them. AI tool sets enhance the work IT teams already do, giving them access to information faster while making the processes they use more efficient. Importantly, AI platforms can allow IT teams to address major sources of business disruption, including the ability to calculate change-related operations risk and identify vital predictors of potential major incidents.
But knowing about these advantages and benefits is not enough. IT leaders and key organizational stakeholders must be prepared with an AI strategy that can make implementation quicker, cheaper, and more effective at achieving the desired business goals. With a plan to adopt AI — and, ideally, upscale its capabilities over time — IT organizations can reduce threats to AI success while enabling their AI platforms to do more for them on a shorter timeline.
What Is Artificial Intelligence (AI), and How Can It Benefit IT?
Artificial intelligence (AI) encompasses a broad range of technologies, systems, and engineered solutions.
A textbook definition of AI could read as follows (courtesy of Gartner):
“Applying advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take action.”
Looking specifically at AI opportunities for IT, AI tools can add new features and services to the systems and workflows IT already uses.
For example, AI can be used to enhance IT analytics through the following capabilities:
- Applying structure to unstructured data, such as through the use of Natural Language Processing (NLP)
- Analysis of datasets and data features to create optimized interpretations, such as through the generation of incident topic clusters
- Auto-identification of key metrics that can indicate risk through the use of machine learning (ML)
- Trend analysis and predictive analytics
- Intelligent automation of processes, workflows, and low-level problem/risk remediation
As an example, a major incident risk prediction engine can combine with IT analytics to evaluate the use of hundreds of performance metrics at a time. These metrics are considered for their ability to correlate to the rise of major incident factors. Metrics with a strong predictive capability are favored, and over time the ML engine is able to develop a model capable of measuring the current level of major incident risk in near-real-time.
How IT Organizations Can Combine Their AI and Business Strategy for Maximum Effectiveness
AI adoption can’t produce miracles. While that may seem to run counter to the messaging of many hyped AI discussions, the truth is that AI is a tool like any other. It must be chosen based upon the specific tasks it will need to perform, and its effectiveness is constrained or empowered by the decisions the larger enterprise environment makes.
With this in mind, IT leaders and key organizational stakeholders must be judicious about what AI solutions they adopt and how those solutions are implemented. Since more change can be perceived to add more risk, organizations should know that they can start with the foundations of AI adoption today using the technologies and processes they already have in place. Then, they can expand the role AI plays as the organization adds new AI tools and implements AI in a greater proportion of IT activities.
Factors to Consider Within Your IT Organization’s AI Strategy
IT leaders and other key stakeholders can use the following factors to help them identify opportunities for AI-backed improvements, decide upon the right AI solutions for their business needs, and implement AI with the maximum chances of success:
Start With Business-Driven Problems
Step one should be to identify the central sources of pain within IT Operations and it’s customers. Is it related to recurring problems? Are assignment groups not nimble enough when responding to new types of incidents? Are operations teams not proactive enough in preventing change-related problems? Does IT lack a clear view of the enterprise technology ecosystem as a whole?
Compile a list of challenges and areas of sub-optimal performance that you would want AI technologies to address. Then, you can objectively compare which AI solutions offer the most promise to resolve these pain points.
Determine Which Tasks and Processes AI Can Help With
The challenges you identify directly define the AI solutions that can solve them. This is a much more valuable perspective than comparing specific vendor-advertised AI features, which may or may not apply to the given situation.
For example, enterprises struggling with unexpected service disruptions could benefit from an AI engine that helps them score change risks while expediting operations workflows related to change acceptance and remediation. IT is having difficulty making informed decisions because of data silos, then an AI-powered IT analytics solution can help make the right data and insights more readily available.
Be Prepared with a Data Infrastructure That Can Support Your AI Initiative
Many organizations need to set about cleaning up their data stores and improving their data pipeline. Without the right technology in place to import data, transform it using a canonical data model, and analyze it with appreciable speed, then AI technology performance will be limited by one or more bottlenecks.
The good news is that cloud-based solutions for data analytics mean that you can improve your data infrastructure and processes without having to grapple with physical hardware at scale. Modern IT analytics solutions can make the most of containerization and edge computing to minimize the digital footprint of analytics and AI solutions while still making capabilities performance-ready.
At the same time, be aware of the criteria your chosen AI solution must meet in order to be capable of acting in response to your enterprise data. Any chosen solution should be ready with data adapters for key systems of record, for example, so that all major platforms like Jira, ServiceNow, Azure, Office365 etc. can have their data readily accessed and analyzed.
Educate Teams, and Give Them the Tools to Propel Their Own Success
One key factor to remember is that AI and analytics talent doesn’t have to come from an outside source. You can invest in upskilling and educating your existing IT human assets in how to use analytics to guide their decisions. This gives your top value-producing employees greater ownership over your value chain.
Research shows that employees who are more satisfied with their job environment tend to go above and beyond their job duties more often. This can definitely be the case when you make your existing team a part of your AI projects.
It also works the other way: weaving AI into your existing teams’ daily workflows can prompt more adoption and more benefits from your technology investments. At one Numerify customer’s enterprise, for example, AI-backed analytics reporting allowed each team member to act as a business analyst. Having access to self-service reporting, dashboards, and visualizations allowed them to proactively identify solutions to nagging problems that generated significant organizational pain.
For more information on adopting and implementing AI strategically within your IT organization, read our recent report: “Artificial Intelligence in IT Service & Change Management: A Primer“
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