AI Lives Up To The Hype (In The Enterprise)

Ram Chakravarti is the chief technology officer for BMC.


Over the past few months, I’ve been traveling across the globe having strategic conversations with customers about artificial intelligence (AI). I’ve shared my perspective on AI in the enterprise world and gotten an understanding of where AI has been progressing in global organizations. There is a resounding consensus for the support of automation in IT operations with different kinds of AI, and there’s a term that’s come to the forefront: AIOps.

In Melbourne, I heard from companies that are looking to make government services and telecommunications services smarter—more attuned to end customer and constituent needs without taxing IT teams. In New York and London, financial services companies expressed the need to make the business run faster and manage technology resources to support their business requirements.

Sao Paulo and Chicago triggered conversations about the manufacturing space. In the Middle East, energy and natural resources organizations wanted ways to innovate without hiring and training more people. All of these needs can be addressed using existing technology investments and leveraging data better with the teams already in place.

In Singapore and Hong Kong, I spoke with systems integrators who are looking for ways to marry the value of existing investments with the risk of emerging technologies. They are seeing organizations—from government to commercial and consumer businesses—wanting to harness their internal data to build and deliver new services, exceed constituent and consumer expectations, and find ways to do more with less.

Enter AI. More importantly, generative AI using large language models with reliable, trustworthy data—your data.

Think of it this way: Your organization generates, stores and often analyzes immense amounts of proprietary data. Your employees’ and customers’ requests and actions generate incredible amounts of data that are only growing each year. Then, your company assets (computers, servers, IoT endpoints, processors, etc.)—all of the machinery and applications that make your business run—complement the people data.

This is everything you need to improve service requests and fulfillment, ensure technology optimization, and predict needs to operate IT more proactively. All you need to do is use the learning and intelligence models to ingest, manage and apply the correct data for those insights.

For example, a large global ratings business relies on its technology to provide the information and services that subscribers need to make informed monetary decisions. Its online services shape the way markets behave. It can’t afford to have impacts on digital service delivery or even a lag that could impact decision-making.

This means its IT team—a lean and efficient organization—must be able to constantly monitor and account for the millions of data sources ingested, the technology that correlates the data for insights, and the delivery vehicles and digital channels while keeping the company operating at the speed of the market.

AI models that are embedded into the IT operations software present a viable approach to addressing these business mandates. With AI incorporated into these observability solutions, they can both predict and proactively optimize technology operations for the company. Ultimately, it means fewer people’s hours are needed on the monitoring side to ensure a constant, reliable source of insights. Instead, they can spend their time deploying new services that generate revenue.

This story is unique, but the value realized is replicable in every industry. The greatest hindrance is the reluctance and hesitation to deploy AI models throughout the organization—and rightly so. There are cautions and concerns that should be needed when it comes to this massively disruptive technology.

The ethical concerns with AI—and, in particular, with generative AI—are real. Hallucinations are a legitimate challenge to overcome, and the world of deepfakes only reinforces the need for greater diligence and caution. However, there is an opportunity to apply generative AI in the enterprise today.

What if you could harness the power of your enterprise data? I am a huge fan of open-source data and learning sets, but when it comes to your company, there are specific datasets that are useful and relevant. For example, knowing what my grandmother’s daily routine is won’t be of much help in clustering service tickets to faster resolution.

More isn’t necessarily better when it comes to generative AI in your organization. The right data is far more important, and that often means using learning models that will continue to learn from your organization’s specific needs. In short, generative AI can create even greater value for the data in your organization.

AI belongs in the enterprise and provides incredible value today. There are ways that generative AI can create immense value for the IT organization and beyond in its capacity to predict, resolve and proactively address IT operations needs and requirements—from autonomous operations to self-service operations to DevOps and beyond. It all starts with the data you trust—your own.
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