5 Keys to Empower Your AI-Enabled ITAM Tool Decisions

Forrester predicts that this is the year organizations will lay down their pie-in-the-sky ideas about Artificial Intelligence (AI) and become more pragmatic, embracing the “no pain means no AI gain” motto. This is a level-headed approach that recognizes that any kind of change can be a bit uncomfortable but that the rewards far outweigh the initial discomfort.

However, this discomfort and change must be in the service of business goals instead of just the desire to jump on the AI bandwagon for the coolness factor. Use the five keys below to make wise choices as you embark on your journey to AI-enabled IT Asset Management (ITAM).

1. Enabling innovation

There are AI solutions that are very good for specific, narrow tasks, but even these can be easily tricked. These solutions might be intelligent, but they can’t yet rival the human brain. When analyzing business priorities and assessing the options for AI-enabled improvements, look at where current roles can benefit from AI, rather than trying to identify roles that could be fully replaced by AI. In today’s complex and highly competitive business environment, the chances are that your organization is focusing on innovation rather than cost-saving as its first priority. So, make use of AI as a tool that can help the teams innovate.

2. Focus on business value

You can increase the likelihood of finding useful application areas for and real benefits from AI if you spend some time figuring out the answer to “What are the insights I don’t currently have that would help me deliver more value?” There are Machine Learning algorithms that can search your data to deliver insights based on previously defined questions, and some can spot patterns in large data-sets and provide completely novel insights to questions you haven’t even thought of. There is a high risk of the AI initiative being seen as a technical vanity project by other budget holders if the first steps cannot be clearly connected to business value.

3. Data you can trust

To avoid mistaken insights and bias in decision-making, you need data you can trust. This is one of the challenges of AI, and it is a critical one. You most likely already have a lot of data that can be used for training algorithms. Some of that data might be labeled, and some are likely rather messy. In the world of Machine Learning (ML), the saying “garbage in, garbage out” applies with painful consequences. Training datasets have a major impact on how trustworthy the AI-based insights will be. It might well be that you need to spend time collecting additional real-world data after you’ve identified a potentially beneficial domain for AI in your organization.

4. Make use of current offerings

Because experts need to train AI, and because it follows a long learning curve, it doesn’t make sense to try to build everything AI-related in-house. In fact, try to avoid building AI capabilities from scratch while assessing potential business benefits and specific application areas. Instead, use cloud-based AI services. Use enterprise software platforms with AI capabilities built-in. Unless you’re stuck in the on-premises software world, it’s highly likely your vendors have already introduced AI-powered functionality into their tools. Use those to learn more about what can be achieved with AI.

In addition, as you consider what the best uses of AI will be for your organization, you will most likely find that there are already industrialized offerings available. Building your own is unlikely to make sense unless you’re working for a company that is selling an AI solutions platform.

5. Have important conversations

AI means different things to different people. That’s why it’s critical to have detailed discussions with current and potential software vendors to better understand their approach to using AI in the tools and services they offer. When you have a trusted relationship with a vendor and a shared understanding of what you both mean by “AI,” then have a conversation in these terms. However, with many vendors, a better approach is to focus on specific aspects of AI to avoid falling into a marketing trap.

In a telling article, the Financial Times revealed that a hefty number of vendors claiming to use AI capabilities in their product and service offerings don’t actually have any AI there. So, try to keep it buzzword-free. When you talk with your vendors, ask them about their automation capabilities. Ask them about their ML capabilities. Ask them about their product roadmaps for both. Be specific about what you expect from them.

Don’t Be Fooled

Magic beans may have worked for Jack, but AI is no magic bean. Vendors sometimes try to present AI in this way, but because you now know what to look for, you’re far less likely to be duped into wasting money on the wrong solution. You will instead be focused on the business value that the solution brings. Equipped with the keys listed above, you will have a solid decision-making foundation from which to choose the AI solutions that will propel your organization forward.

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