How to reach the benefits of IA? 5 key practices to achieve it
According to a recent Gartner survey, the difficulty of measuring and understanding the benefits of AI remains one of the main barriers to adoption. Despite its obvious potential, many AI teams face difficulties in adequately communicating and tracking business benefits.
Even so, Artificial Intelligence (AI) has become essential in IT infrastructure management, providing unique opportunities to optimize operations, improve efficiency and reduce costs. In this article, we break down the 5 best practices that, according to this survey, AI-focused data and analytics leaders should implement to capture and monitor the value of their AI projects.
The strategic value of AI in IT infrastructure management
IT infrastructure management has become increasingly complex and demanding in the digital era, where agility, efficiency, and innovation are business imperatives. In this context, AI emerges as an invaluable strategic resource, as organizations can leverage it to:
- optimize operations,
- improve efficiency and
- significantly reduce costs.
This technology has the potential to create substantial business value for organizations, yet teams often find it difficult to achieve and communicate the benefits of AI at a business level.
In a recent Gartner survey, “difficulty measuring value” and “lack of understanding of the benefits and uses of AI” were the top two barriers to implementing this technology. Then come the accessibility and quality of data, lack of skills in artificial intelligence and other factors.
The great differential of this technology is that it can learn and adapt its behavior to complex forms. These are powerful attributes, but they can also make it difficult to predict the performance of AI models.
5 key practices for realizing the benefits of AI
For the business-level benefits of AI to be realized, they need to be managed and monitored before, during and after the implementation of the AI model. That’s why data and analytics leaders need to employ these five benefits-driving best practices outlined in Gartner research.
1. Build an argument for the value of AI
In order to implement any AI project, funding will need to be secured. This is going to require data and analytics leaders to make the case for the benefits of the project.
One path proposed by the Gartner paper is to start from the priorities that stakeholders have, and then move on to the financial and non-financial benefits to the organization. It is also important to highlight the expected results and the KPIs that will define whether the project is successful.
A value story allows connecting not only from hard data, but to evoke the emotions of those who will give course to funding. This is the only way to drive adoption and help AI projects grow.
2. Define a value hypothesis
Establish an assumption about the improvement that the AI project will generate in a specific business KPI.
While it is not certain that this value will be realized with implementation. This allows the AI team to focus on business value and iterate toward a precise target.
3. Develop an action plan
For the benefits of AI to be realized, teams must have a plan to move from an AI result to a set of actions and changes that will then drive the business KPI.
Managing change can be challenging because AI projects often require transformations beyond the direct control of the team building the models. It usually involves:
- process reengineering,
- team skill enhancement,
- resource allocation and
- technology modifications.
4. Test your value hypothesis
It is often difficult to directly associate the effect that an AI project has on the target KPI we defined in the value hypothesis. Especially considering that, outside the AI project, there are many factors that also affect that KPI.
To measure the real impact on a KPI, A/B testing is the most commonly used approach. This way, the new solution is applied on a randomly chosen subset of cases, while a control group is kept to compare performance without the AI project.
Although not always feasible, A/B testing is a standard procedure that, in mature AI organizations, is frequently replaced by methods such as attribution models or latency models.
Regardless of the method selected, testing should be performed as early as possible in the process. This will help identify failures quickly and make adjustments accordingly, avoiding spending a lot of time and resources on the wrong path.
5. Keep track of leading and lagging KPIs
To drive AI value, you need to continuously monitor two types of metrics:
Lagging KPIs: taking the business KPI defined in the value hypothesis, it is necessary to monitor the target business KPIs and analyze possible deviations.
Leading KPIs: these are the metrics that can predict future performance and are useful early indicators of performance issues.
How to apply these steps to reap the benefits of AI in your business
Today, IT infrastructure management demands agile and effective solutions, where Artificial Intelligence (AI) emerges as a key strategic resource. However, the difficulty in communicating and measuring the benefits of AI persists as a significant barrier.
To overcome this obstacle, it is key to have a strategic partner that brings a holistic view on the process. At Wezen, we approach each project from a 360° approach, helping to maximize the value of AI projects.
We can help you get the full benefits of AI. Write to us.
Sources:
- Ramos, L., & Kandaswamy, R. (2023, 7 June). Capture AI Value With These 5 Benefit Realization Best Practices. URL
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