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

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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 ...

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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 ...

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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 ...

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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

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 ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

5 Security Principles Every Entrepreneur Should Apply to Leadership

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...