2024 AI Predictions - Part 5

With a focus on GenAI, industry experts offer predictions on how AI will evolve and impact IT and business in 2024. Part 5, the final installment in this series, covers the advantages AI will deliver.

Start with: 2024 AI Predictions - Part 1

Start with: 2024 AI Predictions - Part 2

Start with: 2024 AI Predictions - Part 3

Start with: 2024 AI Predictions - Part 4

Go to: predictions about AIOps

Go to: predictions about AI in software development

DATA INTEGRATION

Generative AI will become increasingly important for resolving complicated data integration challenges, essentially providing a natural-language intermediary between data endpoints. Instead of requiring complex, fragile data mappings, both endpoints will be able to converse as though the other endpoint were human.
Jason Bloomberg
President, Intellyx

DATA MANAGEMENT

Historically, data management is a bit of a black box with highly technical skills required to create a strategy and manage data efficiently. With the help of LLMs, modern data management will change its framework, allowing users to participate in the entire data stack in a fully governed and compliant manner.
Vasu Sattenapalli
CEO, RightData

REAL-TIME ANALYTICS

AI-powered real-time data analytics will give enterprises far greater cost savings and competitive intelligence than before by way of automation, and enable software engineers to move faster within the organization. Insurance companies, for example, have terabytes and terabytes of data stored in their databases, things like documentation if you buy a new house and documentation if you rent. With AI, in 2024, we will be able to process these documents in real-time and also get good intelligence from this dataset without having to code custom models. Until now, a software engineer was needed to write code to parse these documents, then write more code to extract out the keywords or the values, and then put it into a database and query to generate actionable insights. The cost savings to enterprises will be huge because thanks to real-time AI, companies won't have to employ a lot of staff to get competitive value out of data.
Dhruba Borthakur
Co-Founder and CTO, Rockset

AI to drive real-time intelligence and decision making — 2024 will be foundation for the next phase of AI. We'll see a number of new innovations for AI, but we're still years away from the application of bigger AI use cases. The current environment is making it easy for startups to build and prepare for the next hype cycle of AI. That said, 2024 is going to be the year of chasing profitability. Due to this, the most important trend in 2024 will be the use of AI to drive real-time intelligence and decision-making. This will ultimately revolutionize go-to-market strategies, de-risk investments, and increase bottom-line value.
Mike Carpenter
CEO and Co-Founder, XFactor.io

DOMAIN-SPECIFIC KNOWLEDGE

The evolution of generative AI across industries will focus on advancements in domain-specific knowledge and expertise, making specialized talent increasingly competitive. The advent of ChatGPT this past year showcased the potency of large language models (LLMs) in understanding and generating human-like text, which has accelerated investments and innovations in generative AI. Moving into 2024, I anticipate a continuous maturation of generative AI technologies, particularly emphasizing domain-specific knowledge and real-time adaptation to evolving scenarios. This convergence of generative AI with domain expertise will facilitate more nuanced and valuable insights, making AI a quintessential partner in decision-making processes across industries. With this, the demand for AI and machine learning talent will continue to surge in 2024, as businesses increasingly integrate AI not just into their products, but into their operational frameworks. Apart from foundational skills in machine learning, statistics, and programming, I expect to see an increased demand for expertise in domain-specific AI applications and AI governance.
Evan Welbourne
Head of AI and Data, Samsara

In 2024, there will be an increased focus on integrating domain expertise into applications using generative AI and LLMs to develop specific answers that can be applied to real-life situations. Companies must provide the necessary domain expertise and context to make the application more specific and significantly more accurate, which will prove crucial as the technology matures. In addition, there will be a growing demand for explainability and traceability of AI-generated outputs, with AI pointing out how it produced a particular answer, referencing source literature, for example, or sending generated source code through an independent system to provide a detailed explanation of its functionality.
Bjorn Andersson
Senior Director, Global Digital Innovation Marketing and Strategy, Hitachi Vantara

ORGANIZATIONAL WORKFLOWS

In 2024, Applied AI will seamlessly integrate into organizational workflows, enhancing human capabilities and improving operational efficiency. AI technologies will be user-friendly and adaptable, aligning with existing human behaviors and operational processes to facilitate easy adoption and immediate benefit realization. AI will be designed to complement existing workflows, promoting efficiency without causing disruption or necessitating significant changes in work patterns. This approach will ensure smooth transitions, quick adoption, and immediate productivity improvements. By aligning with human behaviors and enhancing current processes, AI will enable organizations to be more responsive and agile, easily adapting to changing conditions and evolving needs. In 2024, the focus of Applied AI will be on practical integration, ensuring that AI technologies work harmoniously within existing organizational structures to drive innovation and success.
Nick King
CEO and Founder, Data Kinetic

WORKFORCE MANAGEMENT

In HR specifically, AI will take an active role in how companies attract, hire, manage, and retain top talent at a global scale. I also think generative AI will be an effective tool for providing continuous knowledge and risk mitigation when it comes to global workforce management. If you are not leveraging generative AI, you will get run over by the competition.
Siddharth Ram
CTO, Velocity Global

USER EXPERIENCE

In the evolving landscape of 2024, generative AI will transform our daily online experiences in an AI-driven world, which emphasizes the critical need for responsible AI usage. User experience research teams can now evaluate user experiences they couldn't afford or didn't have the time to assess in the past. Understanding your target audiences will be more important than ever as generative AI continues to dominate online experiences and generate content. Providing directional insights to product, marketing and design teams in order to vet digital experiences for potential audiences will become key to business success.
Nitzan Shaer
Co-Founder and CEO, WEVO

MODERNIZING LEGACY TECHNOLOGY

After a year of GenAI practice, legacy businesses are starting to understand that GenAI interest is not just driven by hype, and instead could be truly transformative for their sector. Therefore, in 2024, we can expect even more traditional businesses to deploy the technology to help evolve legacy systems and modernize their technology stack. Typically, traditional companies are not amenable to change or agile enough to adopt the latest in new technology. Many companies are tied to legacy software due to a combination of outdated procurement processes, familiarity, or concerns about data loss or disruption, making modernization inaccessible. The key here is that GenAI can assist with migrating off old code bases and technology stacks to modern programming languages and platforms. However, GenAI could bridge this gap by allowing companies previously locked into legacy systems to access a more modern workforce's knowledge and work practices. GenAI also makes some modern tools far more user-friendly, and therefore more likely to be deployed across businesses.
Greg Benson
Chief Scientist, SnapLogic, and Professor of Computer Science at the University of San Francisco

VERTICAL USE CASES

Expect to see an increase in vertical use cases for AI and a tight race between incumbents and emerging vendors to solve more nuanced, complex problems for these users. There's already a race for incumbent players to infuse AI into every facet of their platforms. At the same time, we're seeing several new emerging apps coming onto the scene that are purpose-built for vertical use cases within the business — like Sales, Marketing, Legal, and IT. As AI models become more robust and sophisticated, they will be able to handle the nuanced and complex tasks needed for these vertical teams. This will ultimately enable better integration between systems and processes and lead to improved operational efficiencies, as well as cost savings.
Stephen Franchetti
CIO, Samsara

Related Links

Hot Topics

The Latest

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

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 ...

Observability Costs Rising Faster Than Value

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

2024 AI Predictions - Part 5

With a focus on GenAI, industry experts offer predictions on how AI will evolve and impact IT and business in 2024. Part 5, the final installment in this series, covers the advantages AI will deliver.

Start with: 2024 AI Predictions - Part 1

Start with: 2024 AI Predictions - Part 2

Start with: 2024 AI Predictions - Part 3

Start with: 2024 AI Predictions - Part 4

Go to: predictions about AIOps

Go to: predictions about AI in software development

DATA INTEGRATION

Generative AI will become increasingly important for resolving complicated data integration challenges, essentially providing a natural-language intermediary between data endpoints. Instead of requiring complex, fragile data mappings, both endpoints will be able to converse as though the other endpoint were human.
Jason Bloomberg
President, Intellyx

DATA MANAGEMENT

Historically, data management is a bit of a black box with highly technical skills required to create a strategy and manage data efficiently. With the help of LLMs, modern data management will change its framework, allowing users to participate in the entire data stack in a fully governed and compliant manner.
Vasu Sattenapalli
CEO, RightData

REAL-TIME ANALYTICS

AI-powered real-time data analytics will give enterprises far greater cost savings and competitive intelligence than before by way of automation, and enable software engineers to move faster within the organization. Insurance companies, for example, have terabytes and terabytes of data stored in their databases, things like documentation if you buy a new house and documentation if you rent. With AI, in 2024, we will be able to process these documents in real-time and also get good intelligence from this dataset without having to code custom models. Until now, a software engineer was needed to write code to parse these documents, then write more code to extract out the keywords or the values, and then put it into a database and query to generate actionable insights. The cost savings to enterprises will be huge because thanks to real-time AI, companies won't have to employ a lot of staff to get competitive value out of data.
Dhruba Borthakur
Co-Founder and CTO, Rockset

AI to drive real-time intelligence and decision making — 2024 will be foundation for the next phase of AI. We'll see a number of new innovations for AI, but we're still years away from the application of bigger AI use cases. The current environment is making it easy for startups to build and prepare for the next hype cycle of AI. That said, 2024 is going to be the year of chasing profitability. Due to this, the most important trend in 2024 will be the use of AI to drive real-time intelligence and decision-making. This will ultimately revolutionize go-to-market strategies, de-risk investments, and increase bottom-line value.
Mike Carpenter
CEO and Co-Founder, XFactor.io

DOMAIN-SPECIFIC KNOWLEDGE

The evolution of generative AI across industries will focus on advancements in domain-specific knowledge and expertise, making specialized talent increasingly competitive. The advent of ChatGPT this past year showcased the potency of large language models (LLMs) in understanding and generating human-like text, which has accelerated investments and innovations in generative AI. Moving into 2024, I anticipate a continuous maturation of generative AI technologies, particularly emphasizing domain-specific knowledge and real-time adaptation to evolving scenarios. This convergence of generative AI with domain expertise will facilitate more nuanced and valuable insights, making AI a quintessential partner in decision-making processes across industries. With this, the demand for AI and machine learning talent will continue to surge in 2024, as businesses increasingly integrate AI not just into their products, but into their operational frameworks. Apart from foundational skills in machine learning, statistics, and programming, I expect to see an increased demand for expertise in domain-specific AI applications and AI governance.
Evan Welbourne
Head of AI and Data, Samsara

In 2024, there will be an increased focus on integrating domain expertise into applications using generative AI and LLMs to develop specific answers that can be applied to real-life situations. Companies must provide the necessary domain expertise and context to make the application more specific and significantly more accurate, which will prove crucial as the technology matures. In addition, there will be a growing demand for explainability and traceability of AI-generated outputs, with AI pointing out how it produced a particular answer, referencing source literature, for example, or sending generated source code through an independent system to provide a detailed explanation of its functionality.
Bjorn Andersson
Senior Director, Global Digital Innovation Marketing and Strategy, Hitachi Vantara

ORGANIZATIONAL WORKFLOWS

In 2024, Applied AI will seamlessly integrate into organizational workflows, enhancing human capabilities and improving operational efficiency. AI technologies will be user-friendly and adaptable, aligning with existing human behaviors and operational processes to facilitate easy adoption and immediate benefit realization. AI will be designed to complement existing workflows, promoting efficiency without causing disruption or necessitating significant changes in work patterns. This approach will ensure smooth transitions, quick adoption, and immediate productivity improvements. By aligning with human behaviors and enhancing current processes, AI will enable organizations to be more responsive and agile, easily adapting to changing conditions and evolving needs. In 2024, the focus of Applied AI will be on practical integration, ensuring that AI technologies work harmoniously within existing organizational structures to drive innovation and success.
Nick King
CEO and Founder, Data Kinetic

WORKFORCE MANAGEMENT

In HR specifically, AI will take an active role in how companies attract, hire, manage, and retain top talent at a global scale. I also think generative AI will be an effective tool for providing continuous knowledge and risk mitigation when it comes to global workforce management. If you are not leveraging generative AI, you will get run over by the competition.
Siddharth Ram
CTO, Velocity Global

USER EXPERIENCE

In the evolving landscape of 2024, generative AI will transform our daily online experiences in an AI-driven world, which emphasizes the critical need for responsible AI usage. User experience research teams can now evaluate user experiences they couldn't afford or didn't have the time to assess in the past. Understanding your target audiences will be more important than ever as generative AI continues to dominate online experiences and generate content. Providing directional insights to product, marketing and design teams in order to vet digital experiences for potential audiences will become key to business success.
Nitzan Shaer
Co-Founder and CEO, WEVO

MODERNIZING LEGACY TECHNOLOGY

After a year of GenAI practice, legacy businesses are starting to understand that GenAI interest is not just driven by hype, and instead could be truly transformative for their sector. Therefore, in 2024, we can expect even more traditional businesses to deploy the technology to help evolve legacy systems and modernize their technology stack. Typically, traditional companies are not amenable to change or agile enough to adopt the latest in new technology. Many companies are tied to legacy software due to a combination of outdated procurement processes, familiarity, or concerns about data loss or disruption, making modernization inaccessible. The key here is that GenAI can assist with migrating off old code bases and technology stacks to modern programming languages and platforms. However, GenAI could bridge this gap by allowing companies previously locked into legacy systems to access a more modern workforce's knowledge and work practices. GenAI also makes some modern tools far more user-friendly, and therefore more likely to be deployed across businesses.
Greg Benson
Chief Scientist, SnapLogic, and Professor of Computer Science at the University of San Francisco

VERTICAL USE CASES

Expect to see an increase in vertical use cases for AI and a tight race between incumbents and emerging vendors to solve more nuanced, complex problems for these users. There's already a race for incumbent players to infuse AI into every facet of their platforms. At the same time, we're seeing several new emerging apps coming onto the scene that are purpose-built for vertical use cases within the business — like Sales, Marketing, Legal, and IT. As AI models become more robust and sophisticated, they will be able to handle the nuanced and complex tasks needed for these vertical teams. This will ultimately enable better integration between systems and processes and lead to improved operational efficiencies, as well as cost savings.
Stephen Franchetti
CIO, Samsara

Related Links

Hot Topics

The Latest

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

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 ...

Observability Costs Rising Faster Than Value

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...