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To Unlock the Power of AIOps for Digital Transformation, Choose the Right Platform and Use Cases

Ritu Dubey
Digitate

The digital transformation bandwagon is a crowded one, with enterprises of all kinds heeding the call to modernize. The pace has only quickened in a post-pandemic age of enhanced digital collaboration and remote work. Nonetheless, 70% of digital transformation projects fall short of their goals, as organizations struggle to implement complex new technologies across the enterprise.

Fortunately, businesses can leverage AI and automation to better manage the speed, scale and complexity of the changes that come with digital transformation. In particular, artificial intelligence for IT operations (or AIOps) platforms can be a game changer. AIOps solutions use machine learning to connect and contextualize operational data for decision support or even auto-resolution of issues. This simplifies and streamlines the transformation journey, especially as the enterprise scales up to larger and larger operations.

The benefits of automation and AIOps can only be realized, however, if companies choose solutions that put the power within reach — ones that package up the complexities and make AIOps accessible to users. And even then, teams must decide which business challenges to target with these solutions. Let's take a closer look at how to navigate these decisions about the solutions and use cases that can best leverage AI for maximum impact in the digital transformation journey.

Finding the Right Automation Approach

Thousands of organizations in every part of the world see the advantages of AI-driven applications to streamline their IT and business operations. A "machine-first" approach frees staff from large portions of tedious, manual tasks while reducing risk and boosting output. AIOps for decision support and automated issue resolution in the IT department can further add to the value derived from AI in an organization's digital transformation.

Yet conversations with customers and prospects invariably touch on a shared complaint: Enterprise leaders know AI is a powerful ally in the digital transformation journey, but the technology can seem overwhelming and takes too long to scope and shop for all the components. They're looking for vendors to offer easier "on-ramps" to digital transformation. They want SaaS options and the availability of quick-install packages that feature just the functions that address a specific need or use case to leap into their intelligent automation journey.

Ultimately, a highly effective approach for leveraging AI in digital transformation involves so-called Out of the Box (OOTB) solutions that package up the complexity as pre-built knowledge that's tailored for specific kinds of use cases that matter most to the organization.

Choosing the Right Use Cases

Digital transformations are paradoxical in that you're modernizing the whole organization over the course of time, but it's impossible to "boil the ocean" and do it all at once. That's why it's so important to choose highly strategic and impactful use cases to get the ball rolling, demonstrate early wins, and then expand more broadly across the enterprise over time.

OOTB solutions can help pare down the complexity. But it is just as important to choose the right use cases to apply such solutions. Even companies that know automation and AIOps are necessary to optimize and scale their systems can struggle with exactly where to apply them in the enterprise to reap the most value.

By way of a cheat sheet, here are four key areas that are ripe for transformation with AI, and where the value of AIOps solutions will shine through most clearly in the form of operational and revenue gains:

IT incident and event management — A robust AIOps solution can prevent outages and enhance event governance via predictive intelligence and autonomous event management. Once implemented, such a solution can render a 360° view of all alerts across all enterprise technology stacks — leveraging machine learning to remove unwanted event noise and autonomously resolve business-critical issues.

Business health monitoring — A proactive AI-driven monitoring solution can manage the health of critical processes and business transactions, such as for the retail industry, for enhanced business continuity and revenue assurance. AI-powered diagnosis techniques can continually check the health of retail stores and e-commerce sites and automatically diagnose and resolve unhealthy components.

Business SLA predictions — AI can be used to predict delays in business processes, give ahead-of-time notifications and provide recommendations to prevent outages and Service Level Agreement (SLA) violations. Such a platform can be configured for automated monitoring, with timely anomaly detection and alerts across the entire workload ecosystem.

IDoc management — Intermediate Document (IDoc) management breakdowns can slow progress in transferring data or information from SAP to other systems and vice versa. An AI platform with intelligent automation techniques can identify, prioritize, and then autonomously resolve issues across the entire IDoc landscape — thereby minimizing risk, optimizing supply chain performance, and enhancing business continuity.

Conclusion

Organizations pursuing digital transformation are increasingly benefiting from enhanced AI-driven capabilities like AIOps that bring new levels of IT and business operations agility to advanced, multi-cloud environments. As these options become more widespread, enterprises at all stages of the digital journey are learning the basic formula for maximizing the return on these technology investments: They're solving the complexity problem with SaaS-based, pre-packaged solutions; and they're becoming more strategic in selecting use cases ideally suited for AIOps and the power of machine learning.

Ritu Dubey is Global Head of New Business Sales and Market Development at Digitate

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To Unlock the Power of AIOps for Digital Transformation, Choose the Right Platform and Use Cases

Ritu Dubey
Digitate

The digital transformation bandwagon is a crowded one, with enterprises of all kinds heeding the call to modernize. The pace has only quickened in a post-pandemic age of enhanced digital collaboration and remote work. Nonetheless, 70% of digital transformation projects fall short of their goals, as organizations struggle to implement complex new technologies across the enterprise.

Fortunately, businesses can leverage AI and automation to better manage the speed, scale and complexity of the changes that come with digital transformation. In particular, artificial intelligence for IT operations (or AIOps) platforms can be a game changer. AIOps solutions use machine learning to connect and contextualize operational data for decision support or even auto-resolution of issues. This simplifies and streamlines the transformation journey, especially as the enterprise scales up to larger and larger operations.

The benefits of automation and AIOps can only be realized, however, if companies choose solutions that put the power within reach — ones that package up the complexities and make AIOps accessible to users. And even then, teams must decide which business challenges to target with these solutions. Let's take a closer look at how to navigate these decisions about the solutions and use cases that can best leverage AI for maximum impact in the digital transformation journey.

Finding the Right Automation Approach

Thousands of organizations in every part of the world see the advantages of AI-driven applications to streamline their IT and business operations. A "machine-first" approach frees staff from large portions of tedious, manual tasks while reducing risk and boosting output. AIOps for decision support and automated issue resolution in the IT department can further add to the value derived from AI in an organization's digital transformation.

Yet conversations with customers and prospects invariably touch on a shared complaint: Enterprise leaders know AI is a powerful ally in the digital transformation journey, but the technology can seem overwhelming and takes too long to scope and shop for all the components. They're looking for vendors to offer easier "on-ramps" to digital transformation. They want SaaS options and the availability of quick-install packages that feature just the functions that address a specific need or use case to leap into their intelligent automation journey.

Ultimately, a highly effective approach for leveraging AI in digital transformation involves so-called Out of the Box (OOTB) solutions that package up the complexity as pre-built knowledge that's tailored for specific kinds of use cases that matter most to the organization.

Choosing the Right Use Cases

Digital transformations are paradoxical in that you're modernizing the whole organization over the course of time, but it's impossible to "boil the ocean" and do it all at once. That's why it's so important to choose highly strategic and impactful use cases to get the ball rolling, demonstrate early wins, and then expand more broadly across the enterprise over time.

OOTB solutions can help pare down the complexity. But it is just as important to choose the right use cases to apply such solutions. Even companies that know automation and AIOps are necessary to optimize and scale their systems can struggle with exactly where to apply them in the enterprise to reap the most value.

By way of a cheat sheet, here are four key areas that are ripe for transformation with AI, and where the value of AIOps solutions will shine through most clearly in the form of operational and revenue gains:

IT incident and event management — A robust AIOps solution can prevent outages and enhance event governance via predictive intelligence and autonomous event management. Once implemented, such a solution can render a 360° view of all alerts across all enterprise technology stacks — leveraging machine learning to remove unwanted event noise and autonomously resolve business-critical issues.

Business health monitoring — A proactive AI-driven monitoring solution can manage the health of critical processes and business transactions, such as for the retail industry, for enhanced business continuity and revenue assurance. AI-powered diagnosis techniques can continually check the health of retail stores and e-commerce sites and automatically diagnose and resolve unhealthy components.

Business SLA predictions — AI can be used to predict delays in business processes, give ahead-of-time notifications and provide recommendations to prevent outages and Service Level Agreement (SLA) violations. Such a platform can be configured for automated monitoring, with timely anomaly detection and alerts across the entire workload ecosystem.

IDoc management — Intermediate Document (IDoc) management breakdowns can slow progress in transferring data or information from SAP to other systems and vice versa. An AI platform with intelligent automation techniques can identify, prioritize, and then autonomously resolve issues across the entire IDoc landscape — thereby minimizing risk, optimizing supply chain performance, and enhancing business continuity.

Conclusion

Organizations pursuing digital transformation are increasingly benefiting from enhanced AI-driven capabilities like AIOps that bring new levels of IT and business operations agility to advanced, multi-cloud environments. As these options become more widespread, enterprises at all stages of the digital journey are learning the basic formula for maximizing the return on these technology investments: They're solving the complexity problem with SaaS-based, pre-packaged solutions; and they're becoming more strategic in selecting use cases ideally suited for AIOps and the power of machine learning.

Ritu Dubey is Global Head of New Business Sales and Market Development at Digitate

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Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

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