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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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

Related Links

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Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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