Digital Transformation Needs Intentionality

Larry Dragich

With the convergence of technology finding its way from the corporate world to our personal devices and home appliances, meeting the expectations of a quality customer experience is a formidable challenge.

Digital Transformation seems to be on everyone's radar but if there is no intentionality from the IT Executive who is sponsoring the program it becomes more of a loose correlation of technology initiatives under an IT strategy banner.

Consider some of the initiatives: Data/Analytics, Mobile Technology, Private Cloud, Artificial Intelligence (AI), Machine Learning, and the Internet of Things (IoT). All have their own unique role to play that is intrinsic to a Digital Transformation program. Although, when you step back and consider how to measure the success for such a program, things can get a little murky.

Digital Transformation requires more than just the latest technology, it's a mindset that iterative change is on the way and should be embraced. This also requires us to factor in the people and process parts of the equation and find ways to measure the end-user-experience (EUE).

One way to do this is to sponsor an Application Performance Monitoring (APM) initiative that can provide visibility to the business, help communicate the progress, and highlight the impacts to the organization.

Meaningful metrics can be difficult to obtain without a specific focus on business impact (transactions) and a concise way to collect them. Consider that a strong APM solution opens the door for better clarity on how each technology initiative affects the EUE, providing key metrics for a Digital Transformation program.

I recommend including all three monitoring factions within an APM strategy, Wire Data Analytics, Synthetic Transactions, and Agent Code Instrumentation.

1. Wire Data Analytics- Discover and decipher application performance data as it traverses the network.

2. Synthetic Transactions- Web robots that execute specific transactions for location-based availability and act as a barometer for measuring application performance.

3. Agent Code Instrumentation- Lightweight agents monitoring the application code as it executes from the Web and App servers. To gain visibility at the edge, script injection is often used for client render time.

Utilizing these overarching delivery mechanisms to provide input into a Machine Learning and/or AI solution has the potential to dramatically improve application delivery and performance across a variety of IT disciplines. This also lays the ground work to support a DevOps culture, providing an amplified feedback loop that is so desperately needed.

Once you develop a strategy on the best way to bring together the 3 monitoring factions, APM becomes "table stakes" on the digital transformation front because you can't improve what you don't measure.

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Digital Transformation Needs Intentionality

Larry Dragich

With the convergence of technology finding its way from the corporate world to our personal devices and home appliances, meeting the expectations of a quality customer experience is a formidable challenge.

Digital Transformation seems to be on everyone's radar but if there is no intentionality from the IT Executive who is sponsoring the program it becomes more of a loose correlation of technology initiatives under an IT strategy banner.

Consider some of the initiatives: Data/Analytics, Mobile Technology, Private Cloud, Artificial Intelligence (AI), Machine Learning, and the Internet of Things (IoT). All have their own unique role to play that is intrinsic to a Digital Transformation program. Although, when you step back and consider how to measure the success for such a program, things can get a little murky.

Digital Transformation requires more than just the latest technology, it's a mindset that iterative change is on the way and should be embraced. This also requires us to factor in the people and process parts of the equation and find ways to measure the end-user-experience (EUE).

One way to do this is to sponsor an Application Performance Monitoring (APM) initiative that can provide visibility to the business, help communicate the progress, and highlight the impacts to the organization.

Meaningful metrics can be difficult to obtain without a specific focus on business impact (transactions) and a concise way to collect them. Consider that a strong APM solution opens the door for better clarity on how each technology initiative affects the EUE, providing key metrics for a Digital Transformation program.

I recommend including all three monitoring factions within an APM strategy, Wire Data Analytics, Synthetic Transactions, and Agent Code Instrumentation.

1. Wire Data Analytics- Discover and decipher application performance data as it traverses the network.

2. Synthetic Transactions- Web robots that execute specific transactions for location-based availability and act as a barometer for measuring application performance.

3. Agent Code Instrumentation- Lightweight agents monitoring the application code as it executes from the Web and App servers. To gain visibility at the edge, script injection is often used for client render time.

Utilizing these overarching delivery mechanisms to provide input into a Machine Learning and/or AI solution has the potential to dramatically improve application delivery and performance across a variety of IT disciplines. This also lays the ground work to support a DevOps culture, providing an amplified feedback loop that is so desperately needed.

Once you develop a strategy on the best way to bring together the 3 monitoring factions, APM becomes "table stakes" on the digital transformation front because you can't improve what you don't measure.

Related Links

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Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

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Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

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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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

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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 ...

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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.

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The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...