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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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 $600 Billion Wake Up Call

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Breaking Down Agentic AI Fragmentation and Complexity with Governance

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

Building AI Agents That Build Trust

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...