Skip to main content

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

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

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

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...