I’m observing the terms artificial intelligence (AI) and machine learning being used increasingly in the IT industry. But are organizations actually implementing machine learning and AI use cases to maximize business value, or are they just using these terms in following an IT service management (ITSM) trend?
ITSM.tools AI poll data shows that many companies are at least dipping their toes into the AI waters, and if yours wants to do the same, here are six basic tips for the practical implementation of machine learning and AI.
Here you'll find six basic tips for the practical implementation of machine learning and AI. #MachineLearning #AI #ArtificialIntelligence Click To Tweet1 – Identify the problem statement for artificial intelligence and machine learning
I’ve seen many organizations trying to automate tasks that may not really address the core problem. Hence, it’s important to identify the core problem you are trying to address by implementing AI. Organizations often tend to automate things that are easy to automate. However, this might not get the maximum value from the AI implementation. Understanding the “pulse” of the consumers/business/customers is recommended by conducting a survey or utilizing Business Relationship Management (BRM) techniques so IT is aligned with the business. This will also facilitate the creation of a business case and potentially help with the budget requirements for AI and machine learning.
2- Tool selection
There are many machine learning and AI tools in the market. However, there needs to be a thorough study that includes tool comparison, cost, and value to identify the best-suited tool for your organization’s needs. I would strongly recommend getting testimonials from the existing customers of the tool to get their feedback before finalizing your selection decision. This will also help you to understand the learnings and avoid repeating mistakes during the implementation.
3 – Artificial intelligence and machine learningROI and efforts alignment
It’s crucial to undertake a cost-benefit analysis before implementing an AI and machine learning use case. If the investment made in implementing AI and machine learning is more than the status quo, you need to rethink if your organization actually wants to implement it. The same applies to effort estimation. If the effort required to implement the solution exceeds the status quo, it must be re-analyzed.
From ROI & efforts alignment to KPIs, this article looks at 6 tips to successfully succeed with machine learning & artificial intelligence implementation. #AI #ArtificialIntelligence #MachineLearning Click To Tweet4 – KPIs for artificial intelligence and machine learning
It’s very important to define the pre and post-implementation key performance indicators (KPIs) to justify the value achieved from the implementation of AI and machine learning. For example, pre-implementation, a task took 24 hours. Post-implementation, it takes 10 mins. These KPIs need to be revisited at regular intervals
5 – Business alignment
Understanding the business perspective of value from the AI and machine learning implementation is equally important. Just because the IT service provider feels a specific use case can be automated doesn’t mean the business needs it. Helping the business understand what will change and the business value is crucial.
6 – Organizational change management
In addition to the technical implementation, people management is very important. All the direct and indirect stakeholders need to be fully aligned from the process’s inception until it’s actually implemented. The stakeholders cannot be just kept informed; they need to be part of the entire process, and their inputs should be considered for the success of such initiatives. These stakeholders could be the employees who are currently executing the task manually. They need to understand the value they’ll get from AI and machine learning, and the project sponsor and team need to have a clear roadmap of alternative value-added tasks that the employees would do post-automation.
There are, of course, other aspects of AI implementation to consider. But when your organization is looking to go beyond the trend-based buzzwords to leverage the capabilities of machine learning and AI to grow and support business operations, it’s vitally important to consider the practical value of any initiatives.
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Gururaj A. Kulkarni
ITIL V4 certified, ITIL V3 Expert, Prince 2 Practitioner, ISO 20000 Lead Auditor, COBIT 5 Implementation and Six Sigma GB certified with 18+ years of global experience in IT service delivery.
