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

Redis Monitoring 101: Key Metrics You Need to Watch

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Beyond Traditional Autoscaling: The Future of Kubernetes in AI Infrastructure

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI Drives Surge in Data Budgets

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned Architecture Causes Service Disruptions, High Operational Costs and Security Challenges

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

How GenAI Can Save Time for the NetOps Team

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

Will AI Solve the Growing Data Divide?

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

Top Concerns for Tech Decision Makers

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

Gartner: Top Trends Shaping the Future of Cloud

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

The Great SaaS Hangover (and the Cure Nobody Is Talking About)

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

OpenShift Monitoring: 5 Things You Need to Keep an Eye on

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

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

Redis Monitoring 101: Key Metrics You Need to Watch

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Beyond Traditional Autoscaling: The Future of Kubernetes in AI Infrastructure

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI Drives Surge in Data Budgets

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned Architecture Causes Service Disruptions, High Operational Costs and Security Challenges

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

How GenAI Can Save Time for the NetOps Team

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

Will AI Solve the Growing Data Divide?

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

Top Concerns for Tech Decision Makers

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

Gartner: Top Trends Shaping the Future of Cloud

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

The Great SaaS Hangover (and the Cure Nobody Is Talking About)

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

OpenShift Monitoring: 5 Things You Need to Keep an Eye on

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...