Top 5 questions to ask before choosing an AI process automation platform

IDC estimates the growth of the Global AI Market in 2021 to 16.4% year over year in 2021, to $327.5 billion. “By 2024, the market is expected to break the $500 billion mark with a five-year compound annual growth rate (CAGR) of 17.5% and total revenues reaching an impressive $554.3 billion”, says the study.

The pandemic had a tremendous influence because it boosted the necessity to work from home, putting a strain on security, forcing even more cross-department collaboration in remote mode, and engulfing more data sources.

Questions to ask before choosing an AI platform for your IT operations - Top 5

The AI platform providers' landscape will become more and more crowded with various companies. AI-powered implementations offer analytics solutions ready to help humans with the decision-making processes while also performing security alert triage and taking automatic actions regarding some aspects which require standard responses. Here are five questions to ask before deciding who gets on the shortlist.

1. Does the solution use centralized, historical, and real-time data to make decisions?

AI process automation platforms adoption translates to data-driven solutions. The first step is to gather the necessary data in a centralized manner and then analyze it. The best platform designs are domain-agnostic so that they work regardless of the data source and the format. This feature helps them operate across different verticals and industries, not only IT, although it is ideally suited for this sector.

Data either comes from historical records or is recorded and transmitted in real-time (that is an approximation which means data about a second old). An AI-powered solution should be able to handle both, analyze, correlate and take decisions accordingly. This provides resolutions based on past experience and considers the current situation, similar to how the human mind works.

2. Can an AI solution provide ROI in a reasonable time frame?

Most AI-based solutions are not cheap, but they get to the breakeven point very fast and towards a great ROI in less than a year, considering that through operations monitoring, the company aims to fill in the gaps between the current and the desired state, and there is a clear flow for these.

Note that the solution is not economically efficient during the learning phase. Still, as soon as the machine learning algorithm has been trained and calibrated, it is ready to offer great insights and excellent quality leaps. It can directly affect metrics such as mean time to resolution, human time spent on notifications, and human time spent solving tickets. All these can be translated into savings.

For an accurate evaluation, you need to assess every process you hope to automatize and think if it is viable to eliminate the human part and replace it with AI.

The good news is that there will be some quick wins as soon as the ai and ml algorithm is trained, and you will see a boost in productivity right away. The bad news is that this change rate will slow down as the AI solutions become more stable. Detection quality will improve with use, so the sooner you get it, the better.

3. Will an AI-powered process automation solution work well with my current systems?

AI deployment does not come on a blank slate. It needs to integrate seamlessly with legacy systems and make full use of these. When selecting an AI platform provider, be sure to ask about connectivity and compatibility with your current configuration.

Ask about the provider's ability to connect to your existing infrastructure and retrieve data points from it in real-time. The AI platform needs to be able to manage the system to implement the decisions it takes immediately, with less or without human intervention. If your infrastructure has special requirements such as containers, serverless systems, or other special items, be sure to mention these and include them in the conditions.

Make a list of the applications for which you want performance monitoring if any. A simple application of AI for this is thread restarting without restarting the entire application.

4. Will such a solution help manage the organization more efficiently?

CIOs choose AI-powered platform deployment when their human teams become overwhelmed, especially regarding alert fatigue. Too many notifications, too many items to handle simultaneously.

This is an excellent tool to take on some of the work of NOC and SOC teams. Instead of letting humans do all the hard lifting, including repetitive tasks like ticket creation, information collection, and alert analysis, these can be left to AI. Simultaneously, the operations teams focus on finding solutions for those issues that don't have an automatic handling protocol.

The alert triage feature of some such systems aims to drastically reduce the number of alerts that are escalated to the engineers and cut through the alert noise.

One of the latest tools, the Arcanna.ai Alert Triage - part of Siscale’s AI-powered process automation platform, promises to correlate and suppress alerts up to the bare minimum. NOC and SOC engineers will have more time to find creative ways to resolve issues after the tool performs an automated check-up. Siscale’s AI system - Arcanna.ai, is bringing a revolutionary approach: a learning system formed - like Anton Chuvakin was saying in one of his mind-blowing articles, by “a combination of human brains confirming the alerts that are prepared by the machines in a way that's optimal for human decision” (The Uphill Battle of Triaging Alerts, Darkreading.com, 2019).

5. Does the platform offer high-security features?

A good AI-powered platform can also function as an additional security layer. It can help organizations be proactive about threats by identifying, isolating, and managing breaches. It can identify threats by performing anomaly detection and shut down systems or block access to resources if there are any concerns.

In addition to the platform's features, check if it connects well with other security measures such as firewalls, antivirus software, VPNs, and more.

Using machine learning, your organization can also analyze data and classify threats. This is the basis to create automated remedies and put in place automatic responses when a cybersecurity alert is fired.

This is a simple framework to help you structure your requirements when considering AI platforms adoption in your organization. For each department and each process, there are numerous other questions to ask, and the AI works best on a lean and streamlined workflow, so be sure to strategize before you digitalize.

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