Artificial intelligence (AI) and machine learning (ML) aren’t academic research subjects anymore. Businesses, especially the disruptors and those who are entrenched in digital transformation, have been setting a trend for the adoption of these principles and applying them to yield rich dividends. This trend is now rampant, and it is clearly a favorite when it comes to enriching customer experiences and using data to arrive at smarter decisions, faster deliveries and sustainable businesses.
But here’s the burning question: In a race to get their hands on new-age technology, are technology businesses overlooking the other perks of AI that can accelerate the speed of their IT operations and impact their entire software development life cycle? AI is not limited to automating workflows. When used well by your executive staff and system administrators, AI can make all lives stress-free.
Automating And Augmenting Your IT Department
Now, imagine having sophisticated monitoring and management tools in place that can enable a self-service IT infrastructure. Having infrastructure templates ready for configuration can give you the confidence to scale on demand to support an ever-increasing volume, variety and velocity of deployments. In that scenario, deployments will be as fun as making chocolates out of silicon molds. With AI, you can implement various tools to fit your varied needs, including those for data input and more. HCL Technologies, an Indian multinational company, said their ElasticOps applies AIOps to maintain their managed cloud infrastructure service (a 50,000-instance environment) with 30 engineers.
To name a few tools that can aid AIOPs, I’d start with the cloud. With AI, you can build an automated scaling solution for your cloud platform for future flexibility before taking it live. Monitoring tools can extract utilization metrics of live instances via APIs. Even further, incident management tools can trigger alerts and, in certain instances, pass a percentile threshold, causing pre-scripted response and escalation patterns to be applied, according to the situation. Through all this time, your metric analytics and visualization suite can generate reports based on actionable data. And, these tools don’t even cover half of what can be done with AI and ML during software development.
Managing Your Customer Experience
In addition, there are tools to implement that will help you manage your customer experience. This is particularly helpful, considering the deluge of data that flows in and out of your systems every second — with social media reactions, helpdesk complaints and more. Forward-thinkers can have an APM (application monitoring system) installed to provide real-time insights that help IT teams and the company to avoid revenue-impacting outages.
A few years ago, Netflix found a way to put several experimental machine learning algorithms to good use and started automatically recommending personalized content to subscribers. Their attempts to redeem viewership and constantly gain a new set of subscribers using AI and ML technologies have never let them down. Apparently, the world’s favorite video streaming platform saved and earned big bucks with all these initiatives.
Along the same lines, Amazon acquired Kiva to automate the picking and packing process in their warehouse. According to them, their click-to-ship time went from a peak of 75 minutes to 15 minutes.
To yield such best results, companies should strive for a conversational model that can drive self-service operations to a point where operations professionals can switch their focus to other strategic elements with the enterprise. With a proactive APM, along with automated remediation and declarative provisioning and deployment, employees can address build-level failures, manage pipelines and releases and apply guided code fixes.
Automation: Is It All Or Nothing?
All of this said, the rule of thumb here is that you should never try to automate functions if they aren’t (at least) 80% stable and unchanging. Otherwise, you’d need human interference each time there is a new scenario that requires a change in your scripts. This is not nearly as productive as you’d like it to be, as script maintenance is a huge cost to your company.
When there is a shortage of talent that can draft clear and precise test cases, when there are not enough datasets to train your algorithms continuously, when buying or building the required AI system costs more than the anticipated value or when your functions specifically need general intelligence to address emotional factors, it is nearly useless to bring in AI.
An ideal set of AI solutions will automate your mundane tasks, recognize serious issues at hand, streamline interactions between your various teams and altogether magnify your return on investment. But adopting and investing in these mechanisms is as much a business decision as a technical one. The trick lies in drafting as-is and to-be business process maps, identifying where time is wasted in the current system and focusing on the value that new adoption can bring into the picture. With the right automation tools in place, your workforce can focus on elements that need human intelligence, not artificial intelligence.
Financing AI solutions and machine learning without monitoring and tracking your value stream is a straight path toward failure. To make the transition smoother, aim for incrementalism — slowly adopting one solution at a time. Your executive staff and major stakeholders should familiarize themselves with each solution and its potential and performance within the delivery pipeline. Conducting a value stream mapping exercise can help you identify the waste and the value that come along with each solution, which is especially important if you’re building the solution in-house and will incur development costs.
AI is already working its magic for various e-commerce, retail, health care, banking, logistics and social media giants. It can certainly keep your software development business armed to survive in an automated world.
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