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Artificial Intelligence is often celebrated for its ability to enhance customer experiences like personalized recommendations, intelligent chatbots, & interactive avatars, etc. But what if we look into the aspects of AI transforming the way enterprises operate internally, creating efficiencies that ripple outward to benefit customers? By deploying AI into internal operations, enterprises can unlock operational efficiencies and reimagine how they do business or “keep the lights on”
While there are countless prospects of AI touching key areas of business, let’s focus on how it can transform traditional IT operations like device management and network management
How or why do you even introduce AI/ML in device management? This all starts with the very first step of ordering devices. Traditionally, large enterprises work with a “guesstimate” to come up with a number to satisfy their device needs for the quarter or year. These devices are laptops and desktops for new hires to be productive on day 1 of their jobs. The problem with this approach is the “guesstimate” – historical numbers, growth projections from the finance team, & scenario planning, etc. This will always create a scenario where you will end up ordering over or under the actual required quantity of devices. This not only disrupts the budget and the operations where there are more than expected employees hired or sudden surge in requirements from existing employees, but it also creates overheads like – inventory costs (takes shelf space to keep devices), expiring warranty costs (devices start their warranty from the day you buy them), & human capital costs (employees required just to manage the inventories by discarding out of warranty devices), etc.
If you have been operating the business for a few years now and have kept data like the inventory data, time-series data of new hires & ordered devices, and expiring warranty of existing devices, you could deploy a device forecasting model using the very reliable opensource forecasting model – Prophet (by Meta). You can aggregate this data at a quarterly level and train the Prophet model to detect underlying trends, seasonality patterns, and anomalies. You can also improve this better by adding the aspects of device types like MacBook and PCs (if you offer the choices to your employees) and get the forecasting by device type, further enhancing your operations.
This will transform your operations and save a lot of overhead & hidden costs mentioned above. With just-in-time predictions, you will ensure that you are ordering the devices per the anticipated needs backed by data and machine learning. Not only this, but you can also use this forecasting to work with device vendors to directly ship devices to your new and existing employees which provides additional benefits like – eliminating a major portion of inventory costs & reducing 2-way shipping costs where you pay shipping for getting the devices to your sites and paying again when you ship to the employees, and this will also result in reducing carbon emissions right away.
How about network management? Traditionally, enterprises relied heavily on tools like SNMP and off-the-shelf network management software. These are used to collect metrics like uptime, bandwidth usage, & error rates, etc. Once the thresholds are breached, the pagers would go off for engineers to pore over a myriad of data and trends to play catch-up! By the time they spot an issue, it usually has already impacted the users. While traffic management relies on static rules, the configuration changes and troubleshooting are very labor intensive – requiring engineers to analyze the logs and make manual adjustments, making educated guesses and manually tweaking settings. And it gets even worse when it comes to the maintenance windows – which on most occasions (if not planned properly) disrupts business operations!
On the other hand, modern networks are playing on a whole different level! Complex cloud architecture with numerous changes every day and services with traffic patterns that change faster than we can blink, all have made the traditional methods obsolete. The reliance on static thresholds with periodic data collection often results in blind spots, missing transient issues, and (the worst of all) unexpected spikes in demand. These issues creates an opportunity for AI/Ml solutions which can enable real-time monitoring and dynamic traffic management – switching gears from a reactive firefight into a proactive function!
To solve these challenges, enterprises can rely on their existing wealth of operational data – device logs, bandwidth usage, & incident reports, etc. Once the objective is defined for the AI/ML implementation, they can pilot some projects with open-source models like DeepAR to forecast network traffic and anticipate peak loads. These models can capture underlying trends and seasonality, to help in allocating bandwidth effectively and avoid potential overloading. When it comes to anomaly detection, Isolation Forest, Local Outlier Factor, PCA, or Autoencoders could be trained to quickly identify deviations from normal usage patterns and flagging potential performance bottlenecks before they escalate. And like any other AI/Ml solution, the key is to integrate these in your existing monitoring systems, creating a feedback loop that continuously refines and improves predictions. There are also SaaS solutions provided by companies like Dynatrace, LogicMonitor, Juniper Networks, Auvik, & Cisco, etc. that can accelerate this journey.
This approach would be the first step in transforming the traditional network management to an advanced data-driven operation. This is not just about avoiding downtimes, it enhances your resource utilization, scaling seamlessly, and most importantly, keeping the customer trust with enhanced user experience.
As we look to the future, it’s clear that the most successful enterprises will be the those that embrace and harness the full potential of AI, not just for customer-facing products and services, but for transforming their internal operations as well.
