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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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AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...