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Embracing AI Can Support Business Resiliency During Economic Uncertainty

Ritu Dubey
Digitate

During economic uncertainty, enterprises want improved business uptime, productivity gains, and revenue assurance. To be best positioned to achieve these objectives, it is vital to have a resilient IT and business infrastructure in place. However, with pressure on cost control, reducing and optimizing budgets, companies can't simply hire more support staff, so other optimization avenues need to be explored.

Looking back at previous recessions or economic downturns, investment in technology was one of the first areas to be affected by budgetary cuts and, while it may be tempting for management to look at IT budgets as an area to cut back on for short-term savings, in the long term this can damage a company on a much more fundamental level. Failure to invest in IT could, for example, see an organization left behind technologically while its customers and competitors forge ahead, eroding competitive advantage.

The past few years of the COVID-19 pandemic, which saw business operating models forced to radically shift, and quickly, have helped to demonstrate the importance and value of technology as the backbone of modern business. Investment in technology during this time kept businesses connected, communicating, and operating in the most logistically challenging times. As a result, there is now a greater understanding and appreciation at the management level of what technology brings to the table.

Having said that, of course, investments in IT operations still must show demonstrable business value, especially in testing economic conditions, and that has been historically challenging. In fact, at a November 2022 keynote event in London, Gartner stated that research they conducted found that just 17% of organizations are consistently able to demonstrate the business value of IT. That percentage must improve significantly, and that's where enterprises look to automation to increase IT resilience and optimize performance across the enterprise, without increasing costs. Automating IT tasks can help with business scaling and create sustainable competitive differentiation, which can be a key element during uncertain times.

How AI and AIOps Drive Intelligent Automation

The autonomous enterprise will define the future of corporations, driven in no small part by AI, which enables organizations to streamline and intelligently automate some of the most essential and elemental IT operations tasks, such as monitoring, alerts, root cause analysis, incident management, service request automation such as managing employee onboarding and offboarding. Intelligent automation not only makes organizational IT systems stronger but also frees up skilled IT staff to focus on higher value projects.

By integrating AI and machine learning (ML), enterprises can leverage AIOps to add even more value to IT operations. Embedding historic baseline data, ML works with AI to pull new, deep data from right across an organization as directed, apply intelligent analysis to that data, and add context and meaning. The result is actionable intelligence, which can help management to better understand their business, the economic impact of any downturn, uncover operational inefficiencies and where there may be room for reviews and improvements. As AIOps builds intelligence and knowledge across an organization, it enables proactive and predictive monitoring, so potential problems can be identified and assessed, alerts raised and proactive repairs options given, based on data analysis and historic event profiles and scenarios.

As more enterprises digitize their operations, AIOps are no longer limited to supporting traditional tech workflows, but are now employed across a variety of mainstream business processes, from finance to sales to sourcing and procurement (S&P).

AIOps Delivers Tangible Benefits in Business Performance Monitoring

To illustrate the value of AIOps to an organization, let's consider business performance monitoring. Within today's modern enterprise, business applications and systems, and the supporting IT infrastructure are incredibly complex. Monitoring the health of critical business processes across an organization is vital for seamless business transactions and robust business continuity. As more organizations embrace digital transformation to become fully automated enterprises, historic dependency on labor intensive, inefficient and error-prone manual monitoring and issue resolution is removed. Instead, end-to-end visibility, allied to the correct tools can automatically detect potentially disruptive incidents early and, through AIOps, recommend remedial action. This frees up IT team expertise to focus on higher value, more complicated tasks.

If we look at the retail industry, for example, AIOps is deployed to proactively monitor the performance or health of technology operations across stores, e-commerce sites, and other channels such as mobile apps. Analyzing business processes, applications, middleware, infrastructure, and devices, AIOps applies data analysis, context, and intelligence to automatically detect, visualize, flag, and diagnose anomalies, highlighting root causes and providing automatic resolution of the issues. AIOps can deliver a wealth of benefits to retailers, not only in terms of improved operational KPIs such as Mean-Time-To-Detect, Mean-Time-To-Resolve, time to triage and overall system efficiency, but Business KPI's through optimized inventory, smoother retail operations, improved customer experience and retention, and reduced downtime, leading to higher revenue.

IT Investment is a Multi-faceted Win-Win

The bottom line is that technology is continuously evolving, with AI, ML, automation and cloud-native products and platforms deployed to drive both ROI and positive business outcomes. Strategic investment in IT is really an investment in preparation, business resilience, and advancing competitive positioning. Embracing automation drives the tangible business value of IT operations and encourages the mindset that IT be seen as a business driver as opposed to a cost center. AI and AIOps deliver a wealth of operational improvements, including significantly better system performance and uptime, predictive and preventative intelligence, along with enhanced security and compliance.

The agility and business flexibility that all these technologies provide can help enterprises to better understand challenges, changing market conditions and uncertainty by embedding greater intelligence to decision making through the application of data science, improving efficiencies and better positioning businesses to support future growth — all without breaking the bank.

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

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APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

Embracing AI Can Support Business Resiliency During Economic Uncertainty

Ritu Dubey
Digitate

During economic uncertainty, enterprises want improved business uptime, productivity gains, and revenue assurance. To be best positioned to achieve these objectives, it is vital to have a resilient IT and business infrastructure in place. However, with pressure on cost control, reducing and optimizing budgets, companies can't simply hire more support staff, so other optimization avenues need to be explored.

Looking back at previous recessions or economic downturns, investment in technology was one of the first areas to be affected by budgetary cuts and, while it may be tempting for management to look at IT budgets as an area to cut back on for short-term savings, in the long term this can damage a company on a much more fundamental level. Failure to invest in IT could, for example, see an organization left behind technologically while its customers and competitors forge ahead, eroding competitive advantage.

The past few years of the COVID-19 pandemic, which saw business operating models forced to radically shift, and quickly, have helped to demonstrate the importance and value of technology as the backbone of modern business. Investment in technology during this time kept businesses connected, communicating, and operating in the most logistically challenging times. As a result, there is now a greater understanding and appreciation at the management level of what technology brings to the table.

Having said that, of course, investments in IT operations still must show demonstrable business value, especially in testing economic conditions, and that has been historically challenging. In fact, at a November 2022 keynote event in London, Gartner stated that research they conducted found that just 17% of organizations are consistently able to demonstrate the business value of IT. That percentage must improve significantly, and that's where enterprises look to automation to increase IT resilience and optimize performance across the enterprise, without increasing costs. Automating IT tasks can help with business scaling and create sustainable competitive differentiation, which can be a key element during uncertain times.

How AI and AIOps Drive Intelligent Automation

The autonomous enterprise will define the future of corporations, driven in no small part by AI, which enables organizations to streamline and intelligently automate some of the most essential and elemental IT operations tasks, such as monitoring, alerts, root cause analysis, incident management, service request automation such as managing employee onboarding and offboarding. Intelligent automation not only makes organizational IT systems stronger but also frees up skilled IT staff to focus on higher value projects.

By integrating AI and machine learning (ML), enterprises can leverage AIOps to add even more value to IT operations. Embedding historic baseline data, ML works with AI to pull new, deep data from right across an organization as directed, apply intelligent analysis to that data, and add context and meaning. The result is actionable intelligence, which can help management to better understand their business, the economic impact of any downturn, uncover operational inefficiencies and where there may be room for reviews and improvements. As AIOps builds intelligence and knowledge across an organization, it enables proactive and predictive monitoring, so potential problems can be identified and assessed, alerts raised and proactive repairs options given, based on data analysis and historic event profiles and scenarios.

As more enterprises digitize their operations, AIOps are no longer limited to supporting traditional tech workflows, but are now employed across a variety of mainstream business processes, from finance to sales to sourcing and procurement (S&P).

AIOps Delivers Tangible Benefits in Business Performance Monitoring

To illustrate the value of AIOps to an organization, let's consider business performance monitoring. Within today's modern enterprise, business applications and systems, and the supporting IT infrastructure are incredibly complex. Monitoring the health of critical business processes across an organization is vital for seamless business transactions and robust business continuity. As more organizations embrace digital transformation to become fully automated enterprises, historic dependency on labor intensive, inefficient and error-prone manual monitoring and issue resolution is removed. Instead, end-to-end visibility, allied to the correct tools can automatically detect potentially disruptive incidents early and, through AIOps, recommend remedial action. This frees up IT team expertise to focus on higher value, more complicated tasks.

If we look at the retail industry, for example, AIOps is deployed to proactively monitor the performance or health of technology operations across stores, e-commerce sites, and other channels such as mobile apps. Analyzing business processes, applications, middleware, infrastructure, and devices, AIOps applies data analysis, context, and intelligence to automatically detect, visualize, flag, and diagnose anomalies, highlighting root causes and providing automatic resolution of the issues. AIOps can deliver a wealth of benefits to retailers, not only in terms of improved operational KPIs such as Mean-Time-To-Detect, Mean-Time-To-Resolve, time to triage and overall system efficiency, but Business KPI's through optimized inventory, smoother retail operations, improved customer experience and retention, and reduced downtime, leading to higher revenue.

IT Investment is a Multi-faceted Win-Win

The bottom line is that technology is continuously evolving, with AI, ML, automation and cloud-native products and platforms deployed to drive both ROI and positive business outcomes. Strategic investment in IT is really an investment in preparation, business resilience, and advancing competitive positioning. Embracing automation drives the tangible business value of IT operations and encourages the mindset that IT be seen as a business driver as opposed to a cost center. AI and AIOps deliver a wealth of operational improvements, including significantly better system performance and uptime, predictive and preventative intelligence, along with enhanced security and compliance.

The agility and business flexibility that all these technologies provide can help enterprises to better understand challenges, changing market conditions and uncertainty by embedding greater intelligence to decision making through the application of data science, improving efficiencies and better positioning businesses to support future growth — all without breaking the bank.

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

Hot Topics

The Latest

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...