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3 Factors Shaping the AI-Driven Enterprise of the Future

Melissa Burroughs
Alteryx

What will the enterprise of the future look like?

If we asked this question three years ago, I doubt most of us would have pictured today as we know it: a future where generative AI has become deeply integrated into business and even our daily lives.

Yet, according to a study by Alteryx, a whopping 82% of the 2,800 IT and business decision makers we surveyed across the globe said that AI is already impacting what their organizations can achieve. While AI has been around for decades, and many have rightfully predicted its potentially lasting impact, this sudden, widespread adoption caught even the best of us off guard.

So, what can business leaders do to help their enterprise prepare for the unknown?

The study, titled Defining the Enterprise of the Future, uncovers market factors significantly impacting organizations, responsible AI adoption, and how the IT workforce should upskill in the future.

1. Dynamic Market Conditions

If the generative AI curveball wasn't enough, now couple this with business challenges we haven't seen in years — high inflation and rising interest rates — as well as ever-increasing data breaches and international conflicts. Businesses of all sizes must now make even more complex decisions to survive today's volatility. Thankfully, this is where AI can help, with 52% stating they will invest in advanced technologies such as AI to respond to the changing market environment.

Embracing the power of accessible AI technology will help enterprises reinfuse transformations to navigate ever-changing economic and tech disruptions. Quickly adopting new technologies like AI may confer first-mover advantages that help capture added revenue, improve customer satisfaction, enhance employee experience, and increase value gained from existing systems and data.

No doubt, boards and senior leaders across industries feel this allure strongly. But before jumping right in to deploying AI across the business, it's important to consider how to do so responsibly.

2. Responsible AI Adoption

While many agree that AI can no longer be ignored, there are still several concerns with the technology, which respondents listed as: data privacy (50%), transparency (41%), data governance (41%), and accountability (36%). IT's strategic management of risks such as these is critical to ensure business resilience in general, and it becomes especially important when rapidly adopting new technologies.

Not adopting AI responsibly could lead to significant damage to your company's reputation, which is why 80% say that AI security, ethics, and governance are key to the success of their organization as they prepare for the future. Ensure you have principles and frameworks in place that help guide how you deploy the technology and integrate it into your portfolio. Leading enterprises and NGOs worldwide have begun publishing their AI operating principles, which often include such aspects as fairness, safety, explainability, social benefit, and human oversight.

3. Upskilling for the Future

Technology alone isn't enough to succeed in the future, and enterprises are looking to hire talent with the right skills for the rise in AI. But the skills needed today will likely not be the same ones needed in three years. In fact, 45% say that while their IT department currently has a need for AI talent, this figure falls to 40% three years from now.

So, what should practitioners look out for to ensure they have the right skills in place?

For starters, they should experiment with new and multiple disciplines, as 72% of business leaders say it is more important for their employees to be multi-skilled than to specialize in one area. Specializing in one area will not be enough in the future — especially for those with the top five skills that many predict will become obsolete: network engineering (29%), repetitive coding (24%), database administration (23%), systems administration (21%), and application support (20%).

While AI is changing everything, it also renews focus on the human side of business. We must determine how to leverage new technologies such as AI to make the best decisions for our organizations, ensure the right policies are in place to support positive outcomes, and upskill talent to contribute meaningfully in a tech-driven world.

Melissa Burroughs is Director of Product Marketing at Alteryx

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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.

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 ...

3 Factors Shaping the AI-Driven Enterprise of the Future

Melissa Burroughs
Alteryx

What will the enterprise of the future look like?

If we asked this question three years ago, I doubt most of us would have pictured today as we know it: a future where generative AI has become deeply integrated into business and even our daily lives.

Yet, according to a study by Alteryx, a whopping 82% of the 2,800 IT and business decision makers we surveyed across the globe said that AI is already impacting what their organizations can achieve. While AI has been around for decades, and many have rightfully predicted its potentially lasting impact, this sudden, widespread adoption caught even the best of us off guard.

So, what can business leaders do to help their enterprise prepare for the unknown?

The study, titled Defining the Enterprise of the Future, uncovers market factors significantly impacting organizations, responsible AI adoption, and how the IT workforce should upskill in the future.

1. Dynamic Market Conditions

If the generative AI curveball wasn't enough, now couple this with business challenges we haven't seen in years — high inflation and rising interest rates — as well as ever-increasing data breaches and international conflicts. Businesses of all sizes must now make even more complex decisions to survive today's volatility. Thankfully, this is where AI can help, with 52% stating they will invest in advanced technologies such as AI to respond to the changing market environment.

Embracing the power of accessible AI technology will help enterprises reinfuse transformations to navigate ever-changing economic and tech disruptions. Quickly adopting new technologies like AI may confer first-mover advantages that help capture added revenue, improve customer satisfaction, enhance employee experience, and increase value gained from existing systems and data.

No doubt, boards and senior leaders across industries feel this allure strongly. But before jumping right in to deploying AI across the business, it's important to consider how to do so responsibly.

2. Responsible AI Adoption

While many agree that AI can no longer be ignored, there are still several concerns with the technology, which respondents listed as: data privacy (50%), transparency (41%), data governance (41%), and accountability (36%). IT's strategic management of risks such as these is critical to ensure business resilience in general, and it becomes especially important when rapidly adopting new technologies.

Not adopting AI responsibly could lead to significant damage to your company's reputation, which is why 80% say that AI security, ethics, and governance are key to the success of their organization as they prepare for the future. Ensure you have principles and frameworks in place that help guide how you deploy the technology and integrate it into your portfolio. Leading enterprises and NGOs worldwide have begun publishing their AI operating principles, which often include such aspects as fairness, safety, explainability, social benefit, and human oversight.

3. Upskilling for the Future

Technology alone isn't enough to succeed in the future, and enterprises are looking to hire talent with the right skills for the rise in AI. But the skills needed today will likely not be the same ones needed in three years. In fact, 45% say that while their IT department currently has a need for AI talent, this figure falls to 40% three years from now.

So, what should practitioners look out for to ensure they have the right skills in place?

For starters, they should experiment with new and multiple disciplines, as 72% of business leaders say it is more important for their employees to be multi-skilled than to specialize in one area. Specializing in one area will not be enough in the future — especially for those with the top five skills that many predict will become obsolete: network engineering (29%), repetitive coding (24%), database administration (23%), systems administration (21%), and application support (20%).

While AI is changing everything, it also renews focus on the human side of business. We must determine how to leverage new technologies such as AI to make the best decisions for our organizations, ensure the right policies are in place to support positive outcomes, and upskill talent to contribute meaningfully in a tech-driven world.

Melissa Burroughs is Director of Product Marketing at Alteryx

Hot Topics

The Latest

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.

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 ...