How AI is Poised to Revolutionize IT Operations
Global spending on artificial intelligence (AI) is forecast to top the $110 billion mark by 2024, according to IDC projections, as part of efforts to remain competitive in a global digital economy.
Two of the leading drivers for AI adoption revolve around delivering a better customer experience and helping employees to get better at their jobs.
AI is changing relationships among IT, business unit leaders and regulatory staff by integrating and automating processes that were previously siloed and executed manually.
As the complexity of enterprise infrastructures rise in response to accelerated technology modernization and business transformation, organizations across industries are exploring the role that AI will play in optimizing IT operations while introducing innovative business operations. As a result, global spending on artificial intelligence (AI) is forecast to top the $110 billion mark by 2024, according to IDC projections.
BizTechReports caught up with Tushar Bajaj and James Moore of IBM to explore the strategic, operational, financial, and technological issues that must be managed effectively to harness the full potential of AI. Here is what they had to say:
James Moore, IBM
One common theme appears to be driving the rapidly growing interest in artificial intelligence and machine learning: complexity.
Executives point to the growing complexity that is emerging across enterprise infrastructures. From an IT operations perspective, institutions have made rapid investments over the past several months in both on-prem and cloud-based technology modernization. Modernizing data centers even as they invest in several public cloud environments -- including AWS, Azure, and more. As a result, it is much harder now to keep track of all the changes going on.” As a result, teams are looking for intelligent automation as a way to better manage complexity and more proactively respond to -- or anticipate and prevent -- disruptions.
From a business process perspective, executives are constantly adjusting their go-to-market strategies while looking for ways to improve customer engagement. Most are exploring data-driven decision-making processes to support these changes. But the complexity of the landscape is matched only by the speed at which new customer demands and competitive factors emerge. This is certainly true in the insurance sector, where the acceleration of adverse weather events has combined with increasing intensity of catastrophic impact to render traditional risk-management and mitigation models moot. Enterprises are looking for AI solutions to provide more comprehensive testing and modeling. More intelligent automation can lead to better and more efficient assessments by selecting the right samples of data to drive the decision-making process.
As business environments across all industries become more dynamic, executives in the roundtable seemed to agree that there is a growing need for continuous -- almost real-time -- monitoring of operational processes and IT operations. This is particularly true in highly regulated sectors, like healthcare and financial services. Many executives note how regulatory functions can no longer operate as an activity that simply “checks” boxes for compliance. “It has to inform business decisions in real-time. They see AI as a tool for providing continuous monitoring and compliance notification that would be beneficial to make sure that they manage risks with minimal resources.
As a result, AI is changing relationships among IT, business unit leaders, and regulatory staff by integrating and automating processes that were previously siloed and executed manually. This allows more value to be derived and shared across the organization in a timely manner. Properly architected and deployed, AI can provide unprecedented context by leveraging the actions of different players to all stakeholders.
Architecture is key, however. While organizations are likely to start their AI initiatives with specific use cases in narrow areas of focus, they should keep the enterprise-wide perspective in mind. Many operations are served by technologies that have been purpose-built for a specific function. For a long time, many organizations have been collecting different data points from stove-piped sources that have been monitored with dedicated tools. They are looking for AI to normalize all of the noise from structured and unstructured data to identify useful correlations that make insights consumable and meaningful. This means keeping data in its native environment. That is the key to harnessing AI for normalizing disparate data sets and sources.
The good news, noted IBM’s Tushar, is that AI is maturing rapidly. “We have seen more and more machine learning models emerge to augment decision-making, and more applications of robotic process automation to replace rule-based algorithms. Moreover, many applications are emerging to augment what has been done in the past, rationalizing and consolidating different tools and technologies.”
IBM’s James, concurred, adding that: “AI can address the inherent messiness associated with having multiple sources of data that are constantly evolving to help organizations derive useful data points.” Beyond optimizing historical investments in technology, however, AI introduces opportunities to leverage predictive analytics and robotic automation to anticipate and address future disruptions and developments in an automated manner. This can free up limited resources to develop the kinds of differentiating operations that will advance important organizational priorities.
For more information on BizTechReport podcast interviews, please contact Melissa Fisher at MFisher@BizTechReports.com.