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

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When Automating Forever Becomes Faster Than Doing It Once

People want to be doing more engaging work, yet their day often gets overrun by addressing urgent IT tickets. But thanks to advances in AI "vibe coding," where a user describes what they want in plain English and the AI turns it into working code, IT teams can automate ticketing workflows and offload much of that work. Password resets that used to take 5 minutes per request now get resolved automatically ...

Smarter Systems for Disinformation: How Data-Centric Design Could Transform Online Trust

Governments and social platforms face an escalating challenge: hyperrealistic synthetic media now spreads faster than legacy moderation systems can react. From pandemic-related conspiracies to manipulated election content, disinformation has moved beyond "false text" into the realm of convincing audiovisual deception ...

The 5 Pillars of Cross-Platform Observability

Traditional monitoring often stops at uptime and server health without any integrated insights. Cross-platform observability covers not just infrastructure telemetry but also client-side behavior, distributed service interactions, and the contextual data that connects them. Emerging technologies like OpenTelemetry, eBPF, and AI-driven anomaly detection have made this vision more achievable, but only if organizations ground their observability strategy in well-defined pillars. Here are the five foundational pillars of cross-platform observability that modern engineering teams should focus on for seamless platform performance ...

What's Blocking Scalable AI?

For all the attention AI receives in corporate slide decks and strategic roadmaps, many businesses are struggling to translate that ambition into something that holds up at scale. At least, that's the picture that emerged from a recent Forrester study commissioned by Tines ...

Not All Networks Are Built for the Edge

From smart factories and autonomous vehicles to real-time analytics and intelligent building systems, the demand for instant, local data processing is exploding. To meet these needs, organizations are leaning into edge computing. The promise? Faster performance, reduced latency and less strain on centralized infrastructure. But there's a catch: Not every network is ready to support edge deployments ...

Why Observability Is the Missing Piece in Your Business Growth

Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...

Gartner: Regular AI System Assessments Triple the Likelihood of High GenAI Value

Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...

Beyond Autoscaling: What True Kubernetes Optimization Actually Requires

Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...

Is Your Network Ready for the AI Boom? 5 Overlooked Stress Points

Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...

Keeping Your Business Stable When Going Through an IT Disaster

The more technology businesses invest in, the more potential attack surfaces they have that can be exploited. Without the right continuity plans in place, the disruptions caused by these attacks can bring operations to a standstill and cause irreparable damage to an organization. It's essential to take the time now to ensure your business has the right tools, processes, and recovery initiatives in place to weather any type of IT disaster that comes up. Here are some effective strategies you can follow to achieve this ...

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

When Automating Forever Becomes Faster Than Doing It Once

People want to be doing more engaging work, yet their day often gets overrun by addressing urgent IT tickets. But thanks to advances in AI "vibe coding," where a user describes what they want in plain English and the AI turns it into working code, IT teams can automate ticketing workflows and offload much of that work. Password resets that used to take 5 minutes per request now get resolved automatically ...

Smarter Systems for Disinformation: How Data-Centric Design Could Transform Online Trust

Governments and social platforms face an escalating challenge: hyperrealistic synthetic media now spreads faster than legacy moderation systems can react. From pandemic-related conspiracies to manipulated election content, disinformation has moved beyond "false text" into the realm of convincing audiovisual deception ...

The 5 Pillars of Cross-Platform Observability

Traditional monitoring often stops at uptime and server health without any integrated insights. Cross-platform observability covers not just infrastructure telemetry but also client-side behavior, distributed service interactions, and the contextual data that connects them. Emerging technologies like OpenTelemetry, eBPF, and AI-driven anomaly detection have made this vision more achievable, but only if organizations ground their observability strategy in well-defined pillars. Here are the five foundational pillars of cross-platform observability that modern engineering teams should focus on for seamless platform performance ...

What's Blocking Scalable AI?

For all the attention AI receives in corporate slide decks and strategic roadmaps, many businesses are struggling to translate that ambition into something that holds up at scale. At least, that's the picture that emerged from a recent Forrester study commissioned by Tines ...

Not All Networks Are Built for the Edge

From smart factories and autonomous vehicles to real-time analytics and intelligent building systems, the demand for instant, local data processing is exploding. To meet these needs, organizations are leaning into edge computing. The promise? Faster performance, reduced latency and less strain on centralized infrastructure. But there's a catch: Not every network is ready to support edge deployments ...

Why Observability Is the Missing Piece in Your Business Growth

Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...

Gartner: Regular AI System Assessments Triple the Likelihood of High GenAI Value

Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...

Beyond Autoscaling: What True Kubernetes Optimization Actually Requires

Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...

Is Your Network Ready for the AI Boom? 5 Overlooked Stress Points

Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...

Keeping Your Business Stable When Going Through an IT Disaster

The more technology businesses invest in, the more potential attack surfaces they have that can be exploited. Without the right continuity plans in place, the disruptions caused by these attacks can bring operations to a standstill and cause irreparable damage to an organization. It's essential to take the time now to ensure your business has the right tools, processes, and recovery initiatives in place to weather any type of IT disaster that comes up. Here are some effective strategies you can follow to achieve this ...