The Essential Tools to Support Digital Transformation - Part 3

APMdigest asked experts from across the IT industry — from analysts and consultants to users and the top vendors — for their opinions on the essential tools to support digital transformation. Part 3 covers analytics, AI and machine learning.

Start with The Essential Tools to Support Digital Transformation - Part 1

Start with The Essential Tools to Support Digital Transformation - Part 2

Advanced IT Analytics (AIA)

While there are many critical areas of technology innovation currently evolving in IT, the most powerful and transformative are advanced analytic capabilities, often integrated with insights into service (application/infrastructure) interdependencies. What EMA calls advanced IT analytics (AIA), and many in the industry call "AIOps," is an arena of fast-paced innovation in many dimensions, with diverse options for investment, and benefits ranging from improved IT-to-business alignment, improved business performance, dramatic values in toolset consolidation and unifying IT, as well as core strengths in dramatic reductions in mean-time-to-repair, as just some examples. Whether AIA can properly be called a tool or not, it typically helps to assimilate many different toolsets into a new, cross-domain layer designed for proactive rather than reactive IT management and planning.
Dennis Drogseth
VP of Research, Enterprise Management Associates (EMA)

Artificial intelligence (AI)

Artificial intelligence (AI) is becoming a mission-critical tool to support digital transformation. New development platforms like cloud and microservices enable enterprises to reach new market opportunities faster. On the flip side, more than three-quarters of CIOs around the world believe these new applications are so complex that IT is becoming almost unmanageable. As many small teams work together, getting consistent end-to-end visibility is more challenging, but also more important. Many companies try to solve this problem by growing their operations team, leading to a higher time investment and eventually increased costs. When looking at the problem more closely, it becomes obvious that most time is spent in analyzing data in the context of the impact on the business. This is where artificial intelligence (AI) can help. AI-based systems can find the root cause of problems in milliseconds no matter how complex a system, ultimately resolving application problems before customers are impacted. The next step is to use AI-based virtual assistants, which understand natural language and can provide actionable answers to complex digital performance questions in real-time. And, by simplifying conversations that can be held over voice or chat, AI help expand the use of operational data beyond IT experts.
Alois Reitbauer
Chief Technology Strategist, Dynatrace

MACHINE LEARNING

The most important tool to support digital transformation is a modern, scalable, and fast data analytics platform with machine learning built-in. Unencumbered by legacy databases, digital economy companies disrupt traditional industries with agile approaches and modern, open analytical platforms to derive insight from heavy volumes of data right now — and not later after they have missed their opportunity. Traditional industry players are equally data driven, moving as quickly as they can to modernize their data warehouses and analytical stores and avoid disruption and minimize customer churn. Start-ups with fresh rounds of funding and 100-year old banks each understand a modern data analytical platform with machine learning built-in is imperative to digital transformation.
Jeff Healey
Senior Director of Vertica Product Marketing, Micro Focus

KPI ADVANCEMENT TOOLS

A company's ability to differentiate and win now rests largely on how expeditiously they can respond to changing business needs by rolling out high-performing, innovative online products and services. Businesses need highly productive development teams that excel across three areas: quality, velocity and efficiency. Development leaders need KPI advancement tools leveraging empirical data to guide smart decisions that drive improvements in all of these areas. Many organizations continue to rely heavily on mainframe processing. Therefore, digital transformation requires tools that go beyond just integrating the mainframe more fully into developer environments, to actually amplifying developer productivity on the platform.
Sam Knutson
VP of Product Management, Compuware

WORKSPACE ANALYTICS

The most important tool that an organization needs to drive digital transformation is access to workspace analytics. To improve the performance of its end users, an organization must have visibility into how issues experienced at the endpoint are impacting productivity. Analytics can link client-side end-user facing data regarding VDI sessions with the usage of guest resources within infrastructure environments. This collected data can then be tied back into the VDI session, presenting IT with telemetry they can then use to monitor, analyze and optimize endpoint performance within their end-user computing environments.
Simon Clephan
VP of Business Development and Strategic Alliances, IGEL

monitoring integration as a service (MIaaS)

Digital transformation has the tendency to create monitoring blind spots. You've got one foot in the cloud, one foot on prem. You're wading into DevOps and real-time analytics. You need something that's going to bring it all together for you. That's why I recommend a monitoring integration as a service (MIaaS) platform.
Moria Fredrickson
Director of Marketing, Blue Medora

Read The Essential Tools to Support Digital Transformation - Part 4, covering communication and collaboration.

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Signs It May Be Time to Reassess Your IT Infrastructure Strategy

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Enterprise Edge AI Reaches Inflection Point

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 ...

AI Is Hitting Operational Limits

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 ...

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Your Observability Stack Has a Telemetry Pipeline Problem

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 ...

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The Essential Tools to Support Digital Transformation - Part 3

APMdigest asked experts from across the IT industry — from analysts and consultants to users and the top vendors — for their opinions on the essential tools to support digital transformation. Part 3 covers analytics, AI and machine learning.

Start with The Essential Tools to Support Digital Transformation - Part 1

Start with The Essential Tools to Support Digital Transformation - Part 2

Advanced IT Analytics (AIA)

While there are many critical areas of technology innovation currently evolving in IT, the most powerful and transformative are advanced analytic capabilities, often integrated with insights into service (application/infrastructure) interdependencies. What EMA calls advanced IT analytics (AIA), and many in the industry call "AIOps," is an arena of fast-paced innovation in many dimensions, with diverse options for investment, and benefits ranging from improved IT-to-business alignment, improved business performance, dramatic values in toolset consolidation and unifying IT, as well as core strengths in dramatic reductions in mean-time-to-repair, as just some examples. Whether AIA can properly be called a tool or not, it typically helps to assimilate many different toolsets into a new, cross-domain layer designed for proactive rather than reactive IT management and planning.
Dennis Drogseth
VP of Research, Enterprise Management Associates (EMA)

Artificial intelligence (AI)

Artificial intelligence (AI) is becoming a mission-critical tool to support digital transformation. New development platforms like cloud and microservices enable enterprises to reach new market opportunities faster. On the flip side, more than three-quarters of CIOs around the world believe these new applications are so complex that IT is becoming almost unmanageable. As many small teams work together, getting consistent end-to-end visibility is more challenging, but also more important. Many companies try to solve this problem by growing their operations team, leading to a higher time investment and eventually increased costs. When looking at the problem more closely, it becomes obvious that most time is spent in analyzing data in the context of the impact on the business. This is where artificial intelligence (AI) can help. AI-based systems can find the root cause of problems in milliseconds no matter how complex a system, ultimately resolving application problems before customers are impacted. The next step is to use AI-based virtual assistants, which understand natural language and can provide actionable answers to complex digital performance questions in real-time. And, by simplifying conversations that can be held over voice or chat, AI help expand the use of operational data beyond IT experts.
Alois Reitbauer
Chief Technology Strategist, Dynatrace

MACHINE LEARNING

The most important tool to support digital transformation is a modern, scalable, and fast data analytics platform with machine learning built-in. Unencumbered by legacy databases, digital economy companies disrupt traditional industries with agile approaches and modern, open analytical platforms to derive insight from heavy volumes of data right now — and not later after they have missed their opportunity. Traditional industry players are equally data driven, moving as quickly as they can to modernize their data warehouses and analytical stores and avoid disruption and minimize customer churn. Start-ups with fresh rounds of funding and 100-year old banks each understand a modern data analytical platform with machine learning built-in is imperative to digital transformation.
Jeff Healey
Senior Director of Vertica Product Marketing, Micro Focus

KPI ADVANCEMENT TOOLS

A company's ability to differentiate and win now rests largely on how expeditiously they can respond to changing business needs by rolling out high-performing, innovative online products and services. Businesses need highly productive development teams that excel across three areas: quality, velocity and efficiency. Development leaders need KPI advancement tools leveraging empirical data to guide smart decisions that drive improvements in all of these areas. Many organizations continue to rely heavily on mainframe processing. Therefore, digital transformation requires tools that go beyond just integrating the mainframe more fully into developer environments, to actually amplifying developer productivity on the platform.
Sam Knutson
VP of Product Management, Compuware

WORKSPACE ANALYTICS

The most important tool that an organization needs to drive digital transformation is access to workspace analytics. To improve the performance of its end users, an organization must have visibility into how issues experienced at the endpoint are impacting productivity. Analytics can link client-side end-user facing data regarding VDI sessions with the usage of guest resources within infrastructure environments. This collected data can then be tied back into the VDI session, presenting IT with telemetry they can then use to monitor, analyze and optimize endpoint performance within their end-user computing environments.
Simon Clephan
VP of Business Development and Strategic Alliances, IGEL

monitoring integration as a service (MIaaS)

Digital transformation has the tendency to create monitoring blind spots. You've got one foot in the cloud, one foot on prem. You're wading into DevOps and real-time analytics. You need something that's going to bring it all together for you. That's why I recommend a monitoring integration as a service (MIaaS) platform.
Moria Fredrickson
Director of Marketing, Blue Medora

Read The Essential Tools to Support Digital Transformation - Part 4, covering communication and collaboration.

Related Links

Hot Topics

The Latest

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

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

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

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 ...

5 Security Principles Every Entrepreneur Should Apply to Leadership

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 ...

Signs It May Be Time to Reassess Your IT Infrastructure Strategy

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 ...

Enterprise Edge AI Reaches Inflection Point

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 ...

AI Is Hitting Operational Limits

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 ...

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

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 ...

Most Enterprises Think They Can Switch AI Vendors in a Month ... Most Who've Tried Couldn't

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 ...

Your Observability Stack Has a Telemetry Pipeline Problem

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 ...

Operator to Orchestrator: 80% of IT Pros See Shift in Role as AI Permeates Workflows

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 ...