Skip to main content

Using Monitoring to Bridge the Gap Between Process and Technology

Ivar Sagemo

For several decades now, IT infrastructure has been the fundamental engine of business processes. Going from the abstract idea of a business process to a smoothly running technical implementation of that process ought to be straightforward, right? But as we know, such is not the case. Technology has improved by leaps and bounds, but our ability to leverage it to our best business effect isn't nearly as well optimized. Too often, IT has become its own little world, all but divorced from the business side and unable to take into account business goals and strategies in the way services are managed.

Consider, for instance, solutions such as Microsoft BizTalk and the way BizTalk-driven processes are typically implemented:

• Company requirements translate into logistic rules and requests. For retail stores for example, packages need to be shipped on time or warehouse stores supplemented adequately. Ideally at the lowest cost and the highest reliability.

• The flow of information is then determined and logical rights are assigned to make that happen. When a company specifies where shipments should go and which are rush orders, as the shipments move towards a destination, updates must be provided to a website. Business logistics such as these take on a technical slant when processes are implemented.

Instead of focusing on whether information is actually getting from one location to another in a timely and accurate manner, monitoring services generally miss the mark and revolve around issues such as the CPU utilization of underlying systems, available storage of associated databases, etc.

Also problematic is that thresholds are typically determined arbitrarily and don't always correlate to actual success or failure of the business process it was derived from. This has the effect of making it harder and slower to solve problems when they occur.

It's also an essentially reactive approach: "Wait until something goes wrong and then fix it." Much better would be: "Anticipate what is likely to go wrong, and ensure that it doesn't."

And what happens when the process changes?

Imagine, for instance, that a new business system is brought in-house such as a new sales tool, involving a whole new data source. How easy or difficult is it for a BizTalk monitoring system to adapt in parallel? Usually, a series of manual modifications are needed — possibly by outside consultants specializing in BizTalk. This is slow, cumbersome, and operationally costly. It also introduces the possibility of inadvertent mistakes that could easily compromise monitoring when the whole point was to improve it.

Building a Better Mousetrap

Instead of that all-too-familiar paradigm, let's imagine something quite different.

• Smart discovery. What if BizTalk monitoring systems, once deployed, could automatically discover the business processes that led to the IT decision such as how information flows, critical dependencies, normal performance at different times and under different conditions — and thus establish accurate thresholds needed to ensure effective performance?

• Intuitive design. What if, instead of having to call in a consultant when things go belly-up, IT people could look at a topological map and understand the issue themselves? What if they could drill down into that map, getting specific technical insight needed to fix the problem quickly?

• Out-of-box best practices. What if your BizTalk monitoring system already knew the kinds of monitoring problems other companies have faced, and the best ways to avoid those problems? What if your organization could benefit from that kind of insight without having to call in a consultant?

While, the goal of every organization is to take those great ideas developed at the process stage and carry them through to the final IT implementation, we know that solutions change after initial deployment: new processes, new partners and changing business demands mean processes shift as the journey toward final implementation moves along. We'd like to think that monitoring could help in that journey.

Ivar Sagemo is CEO of AIMS Innovation.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

Using Monitoring to Bridge the Gap Between Process and Technology

Ivar Sagemo

For several decades now, IT infrastructure has been the fundamental engine of business processes. Going from the abstract idea of a business process to a smoothly running technical implementation of that process ought to be straightforward, right? But as we know, such is not the case. Technology has improved by leaps and bounds, but our ability to leverage it to our best business effect isn't nearly as well optimized. Too often, IT has become its own little world, all but divorced from the business side and unable to take into account business goals and strategies in the way services are managed.

Consider, for instance, solutions such as Microsoft BizTalk and the way BizTalk-driven processes are typically implemented:

• Company requirements translate into logistic rules and requests. For retail stores for example, packages need to be shipped on time or warehouse stores supplemented adequately. Ideally at the lowest cost and the highest reliability.

• The flow of information is then determined and logical rights are assigned to make that happen. When a company specifies where shipments should go and which are rush orders, as the shipments move towards a destination, updates must be provided to a website. Business logistics such as these take on a technical slant when processes are implemented.

Instead of focusing on whether information is actually getting from one location to another in a timely and accurate manner, monitoring services generally miss the mark and revolve around issues such as the CPU utilization of underlying systems, available storage of associated databases, etc.

Also problematic is that thresholds are typically determined arbitrarily and don't always correlate to actual success or failure of the business process it was derived from. This has the effect of making it harder and slower to solve problems when they occur.

It's also an essentially reactive approach: "Wait until something goes wrong and then fix it." Much better would be: "Anticipate what is likely to go wrong, and ensure that it doesn't."

And what happens when the process changes?

Imagine, for instance, that a new business system is brought in-house such as a new sales tool, involving a whole new data source. How easy or difficult is it for a BizTalk monitoring system to adapt in parallel? Usually, a series of manual modifications are needed — possibly by outside consultants specializing in BizTalk. This is slow, cumbersome, and operationally costly. It also introduces the possibility of inadvertent mistakes that could easily compromise monitoring when the whole point was to improve it.

Building a Better Mousetrap

Instead of that all-too-familiar paradigm, let's imagine something quite different.

• Smart discovery. What if BizTalk monitoring systems, once deployed, could automatically discover the business processes that led to the IT decision such as how information flows, critical dependencies, normal performance at different times and under different conditions — and thus establish accurate thresholds needed to ensure effective performance?

• Intuitive design. What if, instead of having to call in a consultant when things go belly-up, IT people could look at a topological map and understand the issue themselves? What if they could drill down into that map, getting specific technical insight needed to fix the problem quickly?

• Out-of-box best practices. What if your BizTalk monitoring system already knew the kinds of monitoring problems other companies have faced, and the best ways to avoid those problems? What if your organization could benefit from that kind of insight without having to call in a consultant?

While, the goal of every organization is to take those great ideas developed at the process stage and carry them through to the final IT implementation, we know that solutions change after initial deployment: new processes, new partners and changing business demands mean processes shift as the journey toward final implementation moves along. We'd like to think that monitoring could help in that journey.

Ivar Sagemo is CEO of AIMS Innovation.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.