2024 AI Predictions - Part 2

With a focus on GenAI, industry experts offer predictions on how AI will evolve and impact IT and business in 2024. Part 2 covers the stakeholders that will drive AI.

Start with: 2024 AI Predictions - Part 1

Go to: predictions about AIOps

Go to: predictions about AI in software development

CHIEF AI OFFICER (CAIO)

We'll see the emergence of new C-suite roles, like Chief AI Officer, who will partner with CIOs to ensure AI adoption continues to grow and emerging regulations are adhered to across the enterprise.
John Cannava
Chief Information Officer, Ping Identity

In 2024, organizations will increasingly appoint leaders to ensure that they are prepared for the security, compliance, and governance implications of AI. As employees become more accustomed to using AI in their personal lives through exposure to tools such as ChatGPT, they will increasingly look to use them in the workplace to boost their productivity. Organizations have already realized that if they don't empower their employees to use these tools officially, they will do so without consent. They will, therefore, appoint a chief AI officer (CAIO) to oversee their use of these technologies in the same way many have a security executive on their leadership teams. The CAIO's role will be centered on developing policies and ensuring the workforce is educated and empowered to use AI safely, to protect the organization from accidental noncompliance, intellectual property leakage, or security threats. This will pave the way for widespread adoption of AI in the enterprise. As this trend progresses, AI will ultimately become a commodity, as the mobile phone has.
Bernd Greifeneder
CTO and Founder, Dynatrace

CTO

The Chief AI Officer will disappear. Rather than the Chief AI officer, the Chief Technology Officer (CTO) will be the natural choice for steering AI strategy. This is not a deprioritization of AI but rather an acknowledgment that AI requires a more cohesive integration to broader technological and business strategies. The CTO will educate and guide the rest of the c-suite on the value of AI, a strategic shift that places AI at the heart of more business decisions.
Prince Kohli
CTO, Automation Anywhere

In 2024, I anticipate the CTO role will evolve as technology leaders will play a central role in fostering collaboration between security and legal departments as AI regulation, legislation, and policy discussions continue to take shape. Drawing on their comprehensive knowledge of the dynamic technology landscape and how technologies can best be harnessed for business success, CTOs have a holistic grasp of the implications of AI deployment, making them instrumental in leading AI regulation discussions. By collaborating with legal and HR teams, CTOs can enhance their organizations' readiness to navigate and comply with emerging AI regulations.
Rob Juncker
CTO, Code42

DATA TALENT

The continued prevalence of AI will lead to an influx of data talent and the need for AI skills. As businesses continue to embrace AI, we're going to see not only an increase in productivity but also an increase in the need for data talent. From data scientists to data analysts, this knowledge will be necessary in order to sort through all the data needed to train these AI models. While recent AI advancements are helping people comb through data faster, there will always be a need for human oversight — employees who can review and organize data in a way that's helpful for each model will be a competitive advantage. Companies will continue looking to hire more data-specific specialists to help them develop and maintain their AI offerings. And those who can't hire and retain top talent — or don't have the relevant data to train to begin with — won't be able to compete.
Brian Peterson
CTO and Co-founder, Dialpad

TECH-SAAVY WORKFORCE

Closing the tech gap — How GenAI is fostering a tech savvy workforce of the future: Throughout history, entry level workers have often been tasked with mundane projects for the first several years of their career. In the near term, we will see many of those early career tasks be automated, freeing up time for entry level employees to spend more time on those "big learning moments" that typically come by being in meetings with leaders and participating in complex, strategic tasks. By empowering entry level workers to do more, they will not only accelerate their career paths, but feel a greater sense of accomplishment and belonging in their roles.
Joe Atkinson
Chief Products and Technology Officer, PwC

AI CONSULTANTS

In the year ahead, there will be consulting services designed to help organizations understand what form of AI is the right AI for their particular needs and specific applications. Not everybody needs the top-of-the-pack GPT-5, if it comes out next year. To determine which AI model is the best match for them, businesses will work with AI experts that provide advice based on their specific use cases to help them keep costs low in the long run. Addressing costs upfront is important because, for POCs, the cost differentials between various AI models are minimal. But as you scale the model to thousands or millions of calls across users, you will pay a hefty price because these models run on costly computer and high-end storage.
Prem Balasubramanian
CTO, Hitachi Digital Services

MANAGED SERVICE PROVIDERS

With the growing technical complexity and tightening budgets, enterprises will increasingly rely on Managed Service Providers (MSPs) to manage and monitor AI technologies. MSPs, with their expertise in AI technologies, will play a key role in spotting errors and ensuring the smooth integration of AI into IT workflows, allowing enterprises to focus on business growth. AI, in turn, will play a crucial role in enhancing network security by providing advanced monitoring, analysis, and error detection capabilities.
Renuka Nadkarni
Chief Product Officer, Aryaka

DIGITAL WORKFORCE

The digital workforce and the human workforce will coincide – Employees across industries are fearful that AI with automate their jobs and displace them. While it's reasonable to suspect that AI will alter jobs (many technological advancements have), in 2024 we'll see that generative AI is making jobs easier and output stronger (companies are already starting to see massive ROI from implementing the new tools). More and more jobs will soon be enhanced by AI —  as it streamlines tasks and offers easy access to knowledge — and employees will find that tedious work has been simplified for them. Rather than replace existing employees, organizations will need to leverage their expertise to determine how new AI tools can best supplement their positions. Over the next few years, businesses and employees will learn to leverage this new "digital workforce" alongside their human workforce, without making the latter feel underutilized or ignored.
Hubert Palan
Founder and CEO, Productboard

Start with: 2024 AI Predictions - Part 3, covering the technologies driving AI.

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The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

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Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

AI Is Making Incident Management More Complex - Bad Data Makes It Harder

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

MEAN TIME TO INSIGHT Podcast - Episode 26: Network Compliance

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

2024 AI Predictions - Part 2

With a focus on GenAI, industry experts offer predictions on how AI will evolve and impact IT and business in 2024. Part 2 covers the stakeholders that will drive AI.

Start with: 2024 AI Predictions - Part 1

Go to: predictions about AIOps

Go to: predictions about AI in software development

CHIEF AI OFFICER (CAIO)

We'll see the emergence of new C-suite roles, like Chief AI Officer, who will partner with CIOs to ensure AI adoption continues to grow and emerging regulations are adhered to across the enterprise.
John Cannava
Chief Information Officer, Ping Identity

In 2024, organizations will increasingly appoint leaders to ensure that they are prepared for the security, compliance, and governance implications of AI. As employees become more accustomed to using AI in their personal lives through exposure to tools such as ChatGPT, they will increasingly look to use them in the workplace to boost their productivity. Organizations have already realized that if they don't empower their employees to use these tools officially, they will do so without consent. They will, therefore, appoint a chief AI officer (CAIO) to oversee their use of these technologies in the same way many have a security executive on their leadership teams. The CAIO's role will be centered on developing policies and ensuring the workforce is educated and empowered to use AI safely, to protect the organization from accidental noncompliance, intellectual property leakage, or security threats. This will pave the way for widespread adoption of AI in the enterprise. As this trend progresses, AI will ultimately become a commodity, as the mobile phone has.
Bernd Greifeneder
CTO and Founder, Dynatrace

CTO

The Chief AI Officer will disappear. Rather than the Chief AI officer, the Chief Technology Officer (CTO) will be the natural choice for steering AI strategy. This is not a deprioritization of AI but rather an acknowledgment that AI requires a more cohesive integration to broader technological and business strategies. The CTO will educate and guide the rest of the c-suite on the value of AI, a strategic shift that places AI at the heart of more business decisions.
Prince Kohli
CTO, Automation Anywhere

In 2024, I anticipate the CTO role will evolve as technology leaders will play a central role in fostering collaboration between security and legal departments as AI regulation, legislation, and policy discussions continue to take shape. Drawing on their comprehensive knowledge of the dynamic technology landscape and how technologies can best be harnessed for business success, CTOs have a holistic grasp of the implications of AI deployment, making them instrumental in leading AI regulation discussions. By collaborating with legal and HR teams, CTOs can enhance their organizations' readiness to navigate and comply with emerging AI regulations.
Rob Juncker
CTO, Code42

DATA TALENT

The continued prevalence of AI will lead to an influx of data talent and the need for AI skills. As businesses continue to embrace AI, we're going to see not only an increase in productivity but also an increase in the need for data talent. From data scientists to data analysts, this knowledge will be necessary in order to sort through all the data needed to train these AI models. While recent AI advancements are helping people comb through data faster, there will always be a need for human oversight — employees who can review and organize data in a way that's helpful for each model will be a competitive advantage. Companies will continue looking to hire more data-specific specialists to help them develop and maintain their AI offerings. And those who can't hire and retain top talent — or don't have the relevant data to train to begin with — won't be able to compete.
Brian Peterson
CTO and Co-founder, Dialpad

TECH-SAAVY WORKFORCE

Closing the tech gap — How GenAI is fostering a tech savvy workforce of the future: Throughout history, entry level workers have often been tasked with mundane projects for the first several years of their career. In the near term, we will see many of those early career tasks be automated, freeing up time for entry level employees to spend more time on those "big learning moments" that typically come by being in meetings with leaders and participating in complex, strategic tasks. By empowering entry level workers to do more, they will not only accelerate their career paths, but feel a greater sense of accomplishment and belonging in their roles.
Joe Atkinson
Chief Products and Technology Officer, PwC

AI CONSULTANTS

In the year ahead, there will be consulting services designed to help organizations understand what form of AI is the right AI for their particular needs and specific applications. Not everybody needs the top-of-the-pack GPT-5, if it comes out next year. To determine which AI model is the best match for them, businesses will work with AI experts that provide advice based on their specific use cases to help them keep costs low in the long run. Addressing costs upfront is important because, for POCs, the cost differentials between various AI models are minimal. But as you scale the model to thousands or millions of calls across users, you will pay a hefty price because these models run on costly computer and high-end storage.
Prem Balasubramanian
CTO, Hitachi Digital Services

MANAGED SERVICE PROVIDERS

With the growing technical complexity and tightening budgets, enterprises will increasingly rely on Managed Service Providers (MSPs) to manage and monitor AI technologies. MSPs, with their expertise in AI technologies, will play a key role in spotting errors and ensuring the smooth integration of AI into IT workflows, allowing enterprises to focus on business growth. AI, in turn, will play a crucial role in enhancing network security by providing advanced monitoring, analysis, and error detection capabilities.
Renuka Nadkarni
Chief Product Officer, Aryaka

DIGITAL WORKFORCE

The digital workforce and the human workforce will coincide – Employees across industries are fearful that AI with automate their jobs and displace them. While it's reasonable to suspect that AI will alter jobs (many technological advancements have), in 2024 we'll see that generative AI is making jobs easier and output stronger (companies are already starting to see massive ROI from implementing the new tools). More and more jobs will soon be enhanced by AI —  as it streamlines tasks and offers easy access to knowledge — and employees will find that tedious work has been simplified for them. Rather than replace existing employees, organizations will need to leverage their expertise to determine how new AI tools can best supplement their positions. Over the next few years, businesses and employees will learn to leverage this new "digital workforce" alongside their human workforce, without making the latter feel underutilized or ignored.
Hubert Palan
Founder and CEO, Productboard

Start with: 2024 AI Predictions - Part 3, covering the technologies driving AI.

Related Links

Hot Topics

The Latest

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

AI Is Making Incident Management More Complex - Bad Data Makes It Harder

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

MEAN TIME TO INSIGHT Podcast - Episode 26: Network Compliance

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ...