AI Trends and Predictions 2024 From Industry Insiders

What should we expect from AI in 2024? IT leaders and industry insiders share their AI and automation trends and predictions for this year and beyond.

Rick Dagley

January 17, 2024

47 Min Read
robot pressing AI button

We at ITPro Today have made our tech predictions for 2024, as well as some "anti-predictions" — trends that many in the IT field expect to play a significant role in 2024 but our tech experts think otherwise.

Not surprisingly, with all the hype surrounding generative AI in 2023, many of these predictions — and anti-predictions — revolve around AI and automation.

Now it's the industry's turn. Below are (in no particular order) 68 AI and automation predictions from those who are part of the technology industry — covering everything from privacy and security concerns to AI's effect on innovation and the bottom line. But first check out their predictions for other areas of tech:

What the Tech Industry Expects From AI in 2024

Multi-modal Generative AI: The Next Frontier

Related:Top 10 AI Stories of 2023

In the coming year, we will see the emergence of multi-modal generative AI — the next frontier in AI innovation. GPT-4 can seamlessly integrate image and text, and this trajectory will soon expand into additional modes, including voice, video, music, and other sensory inputs like sensor data. Multi-modal generative AI promises to provide a more comprehensive and immersive grasp of our world, deepening the way we interact with and perceive information through AI." Abhishek Gupta, Principal Data Scientist, Talentica Software

Generative AI Will Focus on Personalization

In 2024, there will be a shift in the focus of generative AI toward personalization. In this transformative phase to address emerging concerns about data privacy and security, we will see generative AI zero in on the development of domain-specific chatbots, while ensuring safeguards for data privacy at the organizational level. This strategic approach will serve as a pivotal driver to further accelerate the adoption of generative AI by catering to the needs of both organizations and individual users." — Abhishek Gupta, Principal Data Scientist, Talentica Software

Addressing AI Privacy Concerns

Companies are rightly concerned with questions of data security, risk and compliance, AI ethics, and stewardship of customer data amidst the frenzy of LLMs and AI in general. Anyone with those concerns (or those who appreciate the competitive advantage of training models on secured proprietary data for training models) will invest in AI platforms that enable them to build and deploy models ethically and safely in-house. It is likely these new workloads will live on-prem or in private-cloud architectures until the industry evolves to manage the risks. — Brian Weiss, Field CTO, Hyperscience

Related:Gartner Breaks Down GenAI: Is It Overhyped or a Game-Changer?

GenAI Will Become a Key Technology That Powers Organizations

According to Gartner, generative AI will increasingly be featured in everyday software products and will make a strong impact on enterprises over the next five years. The technology will go from being merely a cost-saving tool to a fundamental aspect of companies' operations, with benefits such as revolutionizing supply chain processes and delivering more tailored products to customers. Right now, organizations are funding generative AI from other departments' budgets, most notably from data science and analytics. We will see a shift in how organizations allocate funds, with generative AI getting its own budget and a designated leader to oversee integration. However, as AI does require training and customization to reach its maximum capability, full integration will occur gradually over the course of several years, not in 2024 alone. — Ajay Kumar, CEO, SLK Software

AI Brings With It the Risk of 'Gray Work'

In 2024, as more companies apply AI to address real business issues and automate processes, they'll quickly discover how unprepared they are. AI models are constantly learning and if the infrastructure and data supporting them is not solid, it will be an expensive lesson on many levels. Feeding AI models poor quality data will lead to a loss of productivity as employees either make bad decisions based on insufficient or erroneous insights, or they're forced to waste time looking for the right information and double check their work. This wasted productivity time, also known as "gray work," has been a drain on companies for years. The root cause is critical information being in a variety of tools and apps instead of being centralized, accurate, and available in real time. AI, if not properly integrated, brings the risk of exacerbating gray work in 2024. This will be obvious as companies that have successfully optimized AI will gain significant competitive advantage.  — Debbi Roberts, SVP of Product Management, Quickbase

GenAI and Predictive Analytics Will Pave the Way for Innovation at a Rapid Pace

GenAI and predictive analytics help bring greater value and insights to customers and IT leaders, aiding in making workflows and business operations more efficient. Predictive analytics, for example, can detect problems before we can identify them, and using predictive maintenance ensures all office technology and IT resources are up-to-date. Companies will soon prefer products and solutions with predictive tools to improve IT and procurement protocols. High-capacity scanners, for example, help combat the acceleration in document digitalization. AI will reach offices, becoming more integrated into the IT workflows and assisting in managing devices. — Fernando Maroniene, Senior Director of Product Marketing, Brother International

2024 Won't Be the Year That AI Radically Changes

We've seen the hype around blockchain and Web3 in years past, and generative AI is no different. While much of the "AI hype" occurred in 2023 and LLMs have been around for nearly a decade, we can't expect any groundbreaking use cases and broader adoption for quite some time — in the next 10 years or so. Instead, generative AI will have the greatest impact on individual productivity, such as marketing. In 2024, use cases for internal processes versus external ones for the benefit of customers will be the primary focus. We, of course, need to be aware of the distinction. — Danny Allan, CTO, Veeam

The Frontrunners Will Crack the Productivity Code

We'll see "first mover advantage" disappear, as best practices emerge midyear and gain widespread adoption later in the year. By the end of 2024, those who have adopted AI-assisted code tools and cracked the code on how to use AI well will be outperforming companies that are not. For DevOps teams already on this bandwagon, information sharing and increased productivity will become standard, enabling them to monetize and scale their successes. Wing To, General Manager, Intelligent DevOps,

Businesses Use AI to Gain a Competitive Advantage Amid Unpredictable Markets

The most crucial AI trend in 2024 will be the rise in utilizing AI to gather contextual information, which will enable organizations to better understand their environment and gain a leg up amid unpredictable markets, supply chain challenges, and talent management issues. Organizations, drowning in dashboards, will leverage natural language processing to unlock and transform data into actionable insights, empowering leaders to predict risks, manage costs, and enhance operational effectiveness. Jeff Moloughney, CMO,

Generative AI Will Create New Job Opportunities in 2024

Innovation and automation will transform how individuals perform certain tasks, yet open new roles across various industries. In fact, ServiceNow's research with Pearson found that 1 million additional full-time roles will be created to support the implementation of emerging technologies in the U.S. retail industry. That's an amazing opportunity for cashiers or sales floor associates, with solid foundational knowledge of the industry to be reskilled for the technical roles that will be needed. As new generative AI solutions and tools emerge, organizations should provide more learning pathways for all workers to grow their skills and technical expertise. — Amy Regan Morehouse, Senior Vice President, Global Education and RiseUp, ServiceNow

Unexpected and Transferrable Soft Skills Will Be in Most Demand Throughout 2024

The demand for "human" skills will only continue to rise because of emerging technology and automation. Holistic skills — communication, collaboration, analytical thinking, and innovation — will be critical for new technical roles created by AI. Curiosity will also rise as the No. 1 skill needed. According to ServiceNow's research with Pearson, 71% of U.S.-based admin assistants are strong candidates for help desk agents and similar roles. Bringing together information to solve challenges and asking the right questions will be critical as generative AI and prompting will continue to be more prevalent in the new year. Companies should offer flexible learning models and curriculum for individuals to upskill or reskill and build in-demand, adaptable skill sets rather than just earning traditional credentials. — Amy Regan Morehouse, Senior Vice President, Global Education and RiseUp, ServiceNow

AI Tools Become Strategic Part of CIOs' Playbook

AI tools that help teams predict risk, manage costs, or operate more efficiently will allow CIOs to take information and convert it into business-ready insights. Jeff Moloughney, CMO,

GenAI Transforms App Development Timelines

Thanks to generative AI, app development will go from months to minutes. AI will also be incorporated by using your natural language to speak to apps in order to populate data in your mobile app forms. — Shash Anand, SVP of Product Strategy, SOTI

Embracing AI for Automated Underwriting, Sales, and Support

2023 was the year of realization that the world would change forever with GenAI. The insurance industry specifically understood that with GenAI a lot of automation can take place, especially in the underwriting, sales, and support domains. GenAI can take an insurance submission, automatically map the risks, match the submission to the internal guidelines of the insurance products, and then automatically respond to the agents/direct of the eligibility and policy details. — Elad Tsur, co-founder and CEO, Planck

Normalizing Conversations with GenAI

In 2024, the population will get used to chatting with GenAI models. People won't be surprised when getting responses from LLMs when they expect to speak to a human representative. Those interactions will also normalize GenAI to ease the overall acceptance and adoption of the technology. — Elad Tsur, co-founder and CEO, Planck

AI Will Be Increasingly Personalized

Companies like Google and Apple will evolve their AI-based tools (Siri and Bard) to enable digital assistants that can have extensive dialogues via voice commands with consumers. The banking industry will follow suit, developing AI-based technology that will help financial institutions deliver highly personalized, proactive, and consultative digital interactions with customers. — Daniel Haisley, EVP of Innovation, Apiture

Embedded Finance and AI in Financial Advisory

The integration of embedded finance and AI will bring significant changes to the financial advisory sector in 2024. This combination has the potential to reshape the role of the financial advisor by allowing them to make more informed decisions and increase efficiency, thus providing an opportunity to take on more customers. As embedded finance gains more capabilities with AI, the advisory landscape will undergo a transformative shift, emphasizing automation and data-driven insights. — Phill Rosen, Global CTO, MoneyLion

Overcoming Generative AI Supply Challenges

While AI adoption in data centers is still in the early stages, the industry must prepare for potential challenges going into 2024. For starters, a projected 1.6 million to 2 million H100 GPU shipments are being put into production in 2024, with AMD just announcing 300K-400K of their latest MI100 GPU to deliver this year. Neither Nvidia nor AMD can keep up with demand — customers want more. This will only add to the demand pressures that the data center industry is experiencing now, in addition to organic cloud growth. An estimated 80-90% of these shipments will land domestically in the U.S., adding 2.5 to 3.0 gigawatts of demand pressure on the market. With the recent OpenAI corporate drama highlighting existential concerns about AI's potential to save or destroy the world, the physical bottlenecks present a nearer-term concern as AI innovations may stall if capacity can't be delivered to enable AI hardware to operate. — Tom Traugott, SVP of Strategy, EdgeCore Digital Infrastructure

The Next Wave of Companies Adopting AI

Automation-heavy industries with returns inversely proportional to response time will be the fastest to grow their adoption of AI and ML. Though many companies in finance and fraud detection have already blazed a trail for AI long before AI made its mark, we expect to see another wave of companies in these real-time industries adopting the technology to make critical split-second, automated decisions. — Naren Narendran, Chief Scientist, Aerospike

More Focused, Smaller LLM Models Coming

Though LLMs are impressive in their generality, they require huge amounts of compute and storage to develop, tune, and use, and thus may be cost-prohibitive to the overwhelming majority of organizations. Only companies with vastly deep resources have the means to access them. Since there needs to be a path forward for making them more economically viable, we should expect to see solutions that decentralize and democratize their use. We should anticipate more numerous, more focused, and smaller models that consume less power becoming more readily available to a wider range of users. These focused models should also be less susceptible to the hallucination effects from which LLMs often suffer. — Naren Narendran, Chief Scientist, Aerospike

GenAI: Less Talking, More Doing

In 2023, financial services and technology firms alike raced to adopt generative AI capabilities primarily to provide information and knowledge to users in a natural, conversational interface. In 2024, with advances in LLMs to do things like function calls, we will see generative AI technology do more actions on behalf of users, instead of just spitting out information. LLMs will be used to orchestrate various API calls to complete complex workflows for users. — Joseph Lo, Head of Enterprise Platforms, Broadridge

Shift Toward Client-Facing Applications

Many applications of generative AI in 2023 were for productivity enhancements within firms. This is just the first step. In the coming year, firms will increasingly have better expertise with the technology and feel more comfortable launching customer-facing applications, while safeguarding data accuracy, brand reputation, and legal and compliance needs. It's an exciting time to be a customer. — Joseph Lo, Head of Enterprise Platforms, Broadridge

More Multi-modal Capabilities (Vision, Voice)

The state of the art in models from companies like OpenAI continues to advance. Last November, OpenAI launched GPT-4 Vision, DALL-E3, and enhanced versions of Whisper (voice transcription) and Voice Synthesis models. These further revolutionize how humans interact with computers and their digital lives. Today we've been primarily interacting with models via text — but in 2024 we will see more use cases that involve inputting and generating images/video/audio. — Joseph Lo, Head of Enterprise Platforms, Broadridge

Procurement Teams Will Embrace the AI Wave

As AI tools become more prominent and accessible in 2024, organizations will invest in analytics and insights tools, automation, and AI-driven optimization of purchasing decisions to improve efficiency. In our Amazon Business State of Procurement report, 98% of decision-makers globally are already planning investments or upgrades in analytics and insights tools, automation, and AI-driven optimization of purchasing decisions in the next few years. The same amount are interested in discovering or learning about new digital tools that could scale up their procurement operations. These investments will not only reduce manual work and allow departments to spend less time on tasks that can be consolidated or automated, but they will also reduce spending, streamline processes, and ensure organizations meet employee and stakeholder needs. — Doug Gray, Chief Technology Officer, Amazon Business

AI Adoption Will Continue, but More Methodically

In 2023, AI was said to be a tool that propels organizations to a new productivity height. However, 2024 will be a year where organizations try to understand how to strategically align their business to the technology.

— Skip Levens, Media and Entertainment Marketing Director, Quantum

Companies to Develop Own AI Capabilities

In 2024, a lot of companies will develop their own AI capabilities using the components available because they believe it will be a competitive differentiator. And if it's a competitive differentiator, they're better off developing their own rather than buying something off the shelf that anyone can buy. Over the decades, trends in technology purchasing have swung back and forth — from ERP to best of breed development, then to platform, back to best of breed, and now back to ownership. — Guy Yehiav, President, SmartSense by Digi

AGI Will Step Into the Limelight

Artificial general Intelligence (AGI) will begin taking the spotlight in 2024. This means AI will learn to accomplish the intellectual tasks that you and I can do. Even more, AGI technology will enable machines to surpass human capabilities in most tasks we define as economically valuable today. AGI will be the connective tissue between people, machines and data — changing the way we work. Remedial tasks will increasingly be handled by AI — allowing more meaningful jobs to be fulfilled by humans, ultimately increasing efficiency and producing higher-quality business outcomes. — Umesh Sachdev, CEO, Uniphore

The World Will Go Hypermodal in Its Approach to AI

In 2024, generative AI enters the later stages of its hype cycle, and organizations will realize that the technology, while transformational, cannot deliver meaningful value by itself. As a result, they will move toward a hypermodal AI approach that combines generative AI with other types of artificial intelligence and additional data sources. This approach will enable more advanced reasoning and bring precision, context, and meaning to the outputs produced by generative AI. For example, DevOps teams will combine generative AI with fact-based causal and predictive AI to supercharge digital innovation by predicting and preventing issues before they occur and generating new workflows to automate the software delivery lifecycle. — Bernd Greifeneder, Chief Technology Officer and Founder, Dynatrace

AI Will Improve the Standard of Living for All

The democratization of AI is helping to level the playing field, providing anyone with an internet connection with equal access to tools and information. In 2024 and beyond, individuals of all backgrounds and across geographies can use AI to their advantage, from streamlining work to enhancing education. We will see companies and governments of every size benefit from AI. While smaller entities may have traditionally been unable to embrace modern technologies quickly due to budget and resources, with the democratization of AI they will be able to innovate, scale, and streamline operations. — Lloyd Adams, President, SAP North America

No Industry Will Be Left Untouched by AI

As organizations realize the benefits of AI, we will not only witness a surge in AI adoption in 2024 but fundamental differences in how every industry — from retail to utilizes to automotive — operates. Predictive analytics, for example, is already playing a key role in retail and manufacturing by providing businesses with valuable insights and trend forecasting. In the year ahead, organizations will have better insight into consumers' preferences and shopping habits, allowing them to make more personalized recommendations and increase sales. In financial services, we will see AI-driven chatbots and applications that provide consumers with personalized financial advice and stock-price forecasting, changing the way consumers, investors, and brokers manage finances. — Lloyd Adams, President, SAP North America

AI's Biggest Risk

In 2024, the biggest risk from AI is not "foom" or "doom," but the excessive consolidation of power by the cohort of trillion-dollar companies that have monopolies on crucial data in their respective markets. The ability to train foundation models, natural data monopolies, and concentration of AI/ML technical talent at the world's largest technology companies will pose significant risks for enterprises whose business concerns overlap. IT leaders should proceed with caution —the more you invest in one CSP (under the guise of AI specialization or otherwise), the more difficult it will be to avoid lock-in and eventual cannibalization. — Spencer Kimball, co-founder and CEO, Cockroach Labs

AI Policies Will Evolve Rapidly to Keep Pace With the Market

The benefits AI brings in the form of productivity gains, data and analytics efficiencies, and competitive advantages are impossible to ignore. However, it is important as a society and industry that we learn how to embrace the technology safely to move generative AI tools quickly from the shadows into daily operations under corporate security control. Simply put, we need strategies to minimize risks while optimizing benefits — and that's where corporate policies will come in. — Manny Rivelo, CEO, Forcepoint

Democratization of AI & 2024 Uses — Get Ready

"Open-source AI will continue to improve and be taken into widespread use. These models herald a democratization of AI, shifting power away from a few closed companies and into the hands of humankind. A great deal of research and innovation will happen in that space in 2024. And whilst I don't expect adherents in either camp of the safety debate to switch sides, the number of high-profile open-source proponents will likely grow." — Andy Patel, Researcher, WithSecure

AI Will Create Disinformation to Sway Elections

AI will be used to create disinformation and influence operations in the run-up to the high-profile elections of 2024. This will include synthetic written, spoken, and potentially even image or video content. Disinformation is going to be incredibly effective now that social networks have scaled back or completely removed their moderation and verification efforts. Social media will become even more of a cesspool of AI and human-created garbage. — Andy Patel, Researcher, WithSecure

Rise of Personal Assistants Will Lead to Security Risks

Personal assistants — like the new Humane device — will creep into the mainstream during 2024, whether they be in the form of wearables or smartphone functionality. AI will be integrated all over the place, and it will be given limited agency in the form of the ability to perform web searches, read and send email, read and edit calendars, and similar. The first adopters of this technology will likely include some high-value targets. Personal assistants may be the motivating factor that encourages real-world attacks against AI, such as prompt injection and inference attacks. Other techniques, such as search engine optimization, will also factor into adversarial plans. Tricking an AI into malfunctioning or giving misleading results will be a tempting proposition for an attacker. And when the interface is driven by voice commands and the user can't mouse over to check what it is they are about to open, it'll be much easier to trick folks into opening malicious links or documents. — Andy Patel, Researcher, WithSecure

Explainable AI Will Play a Key Role in the Acceptance and Trust of AI Systems

The next frontier in AI for physical operations lies in the synergy between AI, IoT,  and real-time insights across a diversity of data. In 2024, we'll see substantial advancements in predictive maintenance, real-time monitoring, and workflow automation. We may also begin to see multimodal foundation models that combine not just text and images, but equipment diagnostics, sensor data, and other sources from the field. As leaders seek new ways to gain deeper insights into model predictions and modernize their tech stack, organizations will become more interested in explainable AI (XAI). XAI is essential for earning trust among AI users — it sheds light on the black-box nature of AI systems by providing deeper insights into model predictions, and it will afford users a better understanding of how their AI systems are interacting with their data. Ultimately, this will foster a greater sense of reliability and predictability. — Evan Welbourne, Head of AI and Data, Samsara

Evolution of GenAI Across Industries Will Focus on Advancements in Domain-Specific Knowledge and Expertise

The advent of ChatGPT this past year showcased the potency of large language models in understanding and generating human-like text, which has accelerated investments and innovations in generative AI. Moving into 2024, there will be 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. — Evan Welbourne, Head of AI and Data, Samsara

Adopting AI for Pragmatic Progress

In 2024, generative AI will transition to the "slope of enlightenment" phase of the technology hype cycle from its current position somewhere between the "peak of inflated expectations" and "trough of disillusionment" phases. The initial excitement around AI enablers will give way to more pragmatic expectations, focused on targeted applications that offer tangible value as enterprises move beyond experimentation to focus in on practical implementations. Overall, this marks a pivotal moment, signaling a more mature and purpose-driven era for generative AI. With this, attention will shift from "enablers" to application, highlighting how enterprises are directing investments toward initiatives that drive efficiency and meaningful change. — Cameron van Orman, Chief Strategy Officer, Planview

Robust Governance Frameworks Will Drive Enterprise-Wide GenAI Adoption

In 2024, we'll see enterprises advance their governance frameworks to unlock broad benefits and productivity gains from meaningful application of generative AI. Executive buyers understand how ungoverned deployment of generative AI can damage their organization and reputation. Unsurprisingly, the top two reasons for not yet implementing generative AI came down to data privacy concerns and lack of trust in generative AI results. Therefore, any governance framework must give executive buyers confidence that it can effectively manage the risks associated with the AI applications, including their embedded LLMs, the end users of those applications, and the exchanges between the first two. — Suresh Vittal, Chief Product Officer, Alteryx

AIOps' Fall Will Be LLMs' Triumph

In 2024, more companies will reach a breaking point with AIOps and shift their focus toward the potential of LLMs. While AIOps was a laudable concept when introduced, in practice it has failed to live up to its promise. The idea that you could train a model on data emitted by apps that change every day is nothing more than a pipe dream. Large language models appear to be a far more promising alternative because they attack the problem differently and help users make more intelligent decisions. Companies are waking up to this fact, but many more will begin to act on it in the new year. — Jeremy Burton, CEO, Observe

LLMs: The Second Coming of AI in Observability

In 2024, it will become apparent to almost everyone that LLMs/GPT deliver meaningful productivity improvements in the world of observability. From simple help to writing RegEx's and queries, LLMs and the friendly GPT interface will enable new users to get up to speed faster and resolve incidents faster than ever before. At the same time, AIOps (the first generation of AI) will continue to fall out of favor as those that implemented it realize that the promised benefits like root cause detection just aren't there. — Jeremy Burton, CEO, Observe

SMEs, Not Just Big Business, Will Embrace AI

Big companies embraced AI first, but smaller companies will get more in on the action in the coming year. SMEs will be expected to collect data better and hear the same demands for transformative AI as the big companies and tech leaders. Managed service providers will use this change as an opportunity to drive growth and find clients. — Richard Ricks, founder and CEO, Silver Tree

Further Development of AI-Based Solutions

Based on data and patterns, in 2024 we will start seeing useable AI products in high-level automated B2B and B2C decision-making or business management processes as a result of AI's prominent capability of detecting patterns/ trends, strengths/ weaknesses of any technological systems or analyzing behavioral patterns of team performance or consumer data. — Agur Jõgi, CTO, Pipedrive

Improved Quality Assurance of SaaS Products

AI will increase the quality assurance of SaaS products and services by increasing the speed and efficiency of data-driven quality management and minimizing mistakes or defects in the development and production processes. — Agur Jõgi, CTO, Pipedrive

Generative AI Will Transform Every Web and Mobile Application

The use of GenAI to power new customer and employee experiences will move mainstream. Enterprises will start embedding GenAI to generate new modalities of interactions across industries worldwide. — Kevin Cochrane, CMO, Vultr

Companies Will Prioritize Minding the Gap Between Data Foundations and AI Innovation

There is no AI strategy without a data strategy, and companies will need to prioritize closing gaps in their data strategy — specifically, the foundational elements of more efficiently accessing more accurate data securely. — Justin Borgman, co-founder and CEO, Starburst

For Attackers, AI Is a Trusty Sidekick; For Defenders, It's a Game-Changer

For all the FUD (fear, uncertainty, and doubt) about an AI arms race between attackers and defenders in cybersecurity, AI is proving to be far more of an asset for security teams than hackers. Generative AI is helping bad actors write malware and phishing emails, but there was no shortage of malware before AI and people were already happy to click on phishing attempts. For defenders, on the other hand, AI has been a game-=changer. The powerful technology is tailor-made for solving security teams' most-pressing challenges: too much data, too many tedious tasks, and not enough time, budget, or people. AI is democratizing cyber defense by quickly summarizing vast swaths of data, normalizing query languages across different tools, and removing the need for security practitioners to be coding experts. In 2024, we'll see AI's impact in automation as defenders use AI to make incident response more efficient. AI is a once-in-a-decade leap forward, and it's carrying cyber defenders farther than hackers. — Eoin Hinchy, co-founder and CEO, Tines

Beware of AI Marketing Hype

There are some legitimate and very exciting uses of generative AI, but some marketers would lead you to believe its powers are far more ubiquitous than it really is — at least for the moment. As a result, in 2024 you'll need to review your vendors that claim to use AI to understand what type of AI they are using, what data is being fed into their systems, and how that data is being handled. Third-party vendor assessments should require an understanding of what generative AI models (open vs. closed) and what iterations of the model (GPT-3.5, GPT4) are being used. — Alex Hoff, Chief Strategy Officer and co-founder, Auvik

AI Inflection Point for Manufacturers Will Arrive in 2024

Those with modern tech stacks will find it easier to leverage AI to drive true impacts to both top and bottom lines. — Dario Ambrosini, CMO, Propel Software

Advancement of AI for Good and Bad Purposes

For good purposes: In 2024, we'll see an ongoing race for better and more useful generative AI models, likely including OpenAI's GPT-5 and a growing number of capable open-source alternatives; growth and maturation of agent-based AI solutions, which train multiple AI models and tools to accomplish sophisticated tasks; increased incorporation of AI into security products; a corresponding increase in focus on proper security guardrails to enable smart business decisions about AI; and a progression toward automating higher-order tasks.

For bad purposes: Instead of pie-in-the-sky assertions about generative models building malware, automation will reduce the amount of manual pre-work that attackers have to do; we'll see ongoing incidents of data breaches and security concerns related to AI adoption; LLMs will become a more commonly exploited surface for cyberattacks; lower skilled attackers will utilize sophisticated AI to penetrate information systems; there will be increased usage of generative AI by malicious actors to create attacks, both social engineering and technical; and deep fakes for impersonations, generation of more realistic phishing emails, and generation of code for exploits will be utilized with this innovative technology. — Randy Lariar, AI Security Leader, Optiv

Ethical Frameworks and Regulation for AI

Ethical frameworks and regulation are necessary for AI and not just a distraction for organizations as they pursue their bottom line. We cannot avoid AI, as it's the only way we can scale our operations in the asymmetrical cyber battlefield. Ethical frameworks and regulatory governance will become critically important to help AI function efficiently and equitably. Every new piece of software or service will have an AI or ML element to it.  Establishing best practices for ethics in AI is a challenge because of how quickly the technology is developing, but several public- and private-sector organizations have taken it upon themselves to deploy frameworks and information hubs for ethical question. All of this activity is likely to spark increasing amounts of regulation in the major economies and trading blocks, which could lead to an increasingly piecemeal regulatory landscape, at least for now. It's safe to predict that the current "Wild West" era of AI and ML will fade quickly, leaving organizations with a sizable compliance burden when they want to take advantage of the technology. — Nick Savvide, Director of Strategic Accounts, Asia Pacific, Forcepoint

The AI Cyber Threat: Fine-Tuning LLMs for Good & Bad

It's easy to look at the cybersecurity implications of bad actors fine-tuning LLMs for nefarious purposes through an extremely negative lens. And while it is true that AI will enable hackers to scale the work that they do, the same holds true for security professionals. The good news is national governments aren't sitting still. Building custom LLMs represents a viable path forward for other security-focused government agencies and business organizations. While only the largest well-funded big tech companies have the resources to build an LLM from scratch, many have the expertise and the resources to fine-tune open source LLMs in the fight to mitigate the threats bad actors — from tech-savvy teenagers to sophisticated nation-state operations — are in the process of building. It's incumbent upon us to ensure that whatever is created for malicious purposes, an equal and opposite force is applied to create the equivalent toolsets for good. — Aaron Mulgrew, Solutions Architect, Forcepoint

An Army of Smaller, Specialized LLMs Will Triumph Over Giant General Ones

As we saw during the era of "big data" — bigger is rarely better. Models will "win" based not on how many parameters they have, but based on their effectiveness on domain-specific tasks and their efficiency. Rather than having one or two mega-models to rule them all, companies will have their own portfolio of focused models, each fine-tuned for a specific task and minimally sized to reduce compute costs and boost performance. — Nick Elprin, co-founder and CEO, Domino Data Lab  

GenAI Will Unlock the Value and Risks Hidden in Unstructured Enterprise Data

Unstructured data — primarily internal document repositories — will become an urgent focus for enterprise IT and data governance teams. These repositories of content have barely been used in operational systems and traditional predictive models to date, so they've been off the radar of data and governance teams. GenAI-based chatbots and fine-tuned foundation models will unlock a host of new applications of this data, but will also make governance critical. Companies who have rushed to develop GenAI use cases without having implemented the necessary processes and platforms for governing the data and GenAI models will find their projects trapped in PoC purgatory, or worse. These new requirements will give rise to specialized tools and technology for governing unstructured data sources. — Nick Elprin, co-founder and CEO, Domino Data Lab  

Regulatory Efforts Will Be Ineffective in Preventing the Misuse of AI, While Still Hindering the Adoption of AI

The EU will push for impractically restrictive, and occasionally contradictory, regulation. The U.S. will put toothless policies forward — like the recent executive order — that aren't effective at mitigating risks from bad actors (who will disregard and circumvent the regulations anyway), and that do little, if anything, to require organizations to implement processes and capabilities necessary for the safe, secure, and trustworthy use of AI. — Nick Elprin, co-founder and CEO, Domino Data Lab  

2024 Will Be the Year of Adaptability and Useability of AI Tools

2023 was the year of cautious experimentation of AI tools, but in 2024 organizations will shift their focus toward responsible deployment. While much remains that companies don't fully understand about AI, along with its associated risks, there are many opportunities to take advantage of moving forward in business and life. Falling behind in the AI adoption race can pose significant challenges for organizations. However, there is no one-size-fits-all model for organizations to follow. Technology leaders will need to assess which use cases benefit from the integration of new AI tools and which tools are better left untouched. They will also need to ensure that GenAI tools are used in a safe and responsible way governed and controlled by organizational governance processes. This strategic approach ensures that AI adoption aligns with an organization's unique goals and needs. — Barry Shurkey, CIO, NTT DATA

Using AI to Scale Up Business

While 2023 was a breakout year for AI as we became acquainted with tools like ChatGPT, 2024 will be about using the technology more broadly to shape organizations' digital agendas and create more impactful business outcomes. Organizations should be exploring new ways AI can unlock efficiency, such as an internal AI chatbot for helpdesk ticket deflection, customer-facing AI chat and search, and AI-powered tools to support HR processes, to name a few. AI boasts significant potential and stands to revolutionize the way we operate — we'd be remiss to not include it in our 2024 IT plans. Also, as AI use becomes more widespread, a key priority for IT leaders in 2024 will be training and educating employees on using the technology in their day-to-day roles. — Jeremy Rafuse, VP and Head of Digital Workplace and IT, GoTo

AI Will Accelerate Storage and Security Requirements   

By nature, generative AI models produce a vast amount of data. Because of this, in the upcoming year organizations can expect to see a surge in their data storage and security needs, leading to investments in scalable storage solutions, whether on-premises, cloud-based, or hybrid. The dynamic and continuous production of data generated by AI will necessitate more frequent backup cycles, and enterprises will need to implement more robust data lifecycle management solutions to determine data retention, archival, or deletion policies, ensuring that only valuable data is stored long-term. Ensuring the integrity of backups will also be paramount given the business-critical nature of AI-generated insights. Given that AI-generated data can be sensitive and critical, heightened security measures will be the last piece to the accelerated storage puzzle, meaning data security will need to be weaved into the fabric of all generative AI projects, including prevention, detection, and data recoverability. — Tony Liau, VP of Product, Object First

GenAI Will Quickly Move From the Peak of Inflated Expectations to the Trough of Disillusionment

There's a lot of hype right now around generative AI, to put it mildly. However, all of this hype means that for some organizations, adoption of this technology is more of a matter of "keeping up with the Joneses" rather than because it is truly the best solution for a specific problem they are trying to solve. As a result, we're likely to see a lot of money invested in failed generative AI projects — hence, the falling into the trough of disillusionment. It's the shiny new object, and many CIOs and other senior leaders may feel pressured to be able to say they have a generative AI program in place. The key to limiting these failed projects will lie in ensuring that your organization understands the specific reason for using generative AI, that it's tied to a defined business outcome, and there's a method established for measuring the success of the investment. — Rex Ahlstrom, CTO and VP of Innovation and Growth, Syniti

GenAI Will Change the Nature of Work for Programmers

GenAI will change the nature of work for programmers and how future programmers learn. Writing source code will become easier and faster, but programming is less about grinding out lines of code than it is about solving problems. GenAI will allow programmers to spend more time understanding the problems they need to solve, managing complexity, and testing the results, resulting in better software: software that's more reliable and easier to use. — Mike Loukides, Vice President of Emerging Tech Content, O'Reilly Media

A New Generation of AI-Assisted Programming Tools

Copilot is just the start. We'll see a new generation of AI-assisted programming tools. We are already seeing tools for managing prompts; we will soon have libraries of prompts designed to direct GenAI to accomplish specific tasks. And, while Copilot is primarily useful for low-level coding, we will soon see generative AI tools for high-level tasks like software architecture and design. — Mike Loukides, Vice President of Emerging Tech Content, O'Reilly Media

AI Will Drive Adoption of Proactive Security Models

AI will drive the adoption of proactive security models. There will be a greater focus on proactive approaches and tools including firewalls, zero trust, malware, and hardening. The top GenAI threat issues are growing privacy concerns, undetectable phishing attacks, and an increase in the volume/velocity of attacks. Addressing the complex security challenges AI poses requires strategic planning and proactive measures. On O'Reilly's learning platform, we have seen a huge increase in interest in most security topics. Governance, network security, general application security, and incident response have shown the largest increases. Security is on the map in a way that it hasn't been in many recent years. — Mike Loukides, Vice President of Emerging Tech Content, O'Reilly Media

Significant Attacks Against AI Applications

We will see significant attacks against AI applications in the wild. AI provides cybercriminals with new attack vectors, such as prompt injection, that we don't yet know how to defend against. These attacks will include subverting AI to generate hate speech and misinformation, along with sending users to sites that install malware. Companies deploying AI in real-world applications will need to understand these new attack vectors and monitor their AI systems with these attacks in mind. — Mike Loukides, Vice President of Emerging Tech Content, O'Reilly Media

A Push for Greater AI Explainability

The business community has witnessed significant advances in artificial intelligence over the last two years. Yet a defining characteristic of sophisticated AI systems, including neural networks, is that they do not always behave as we might expect. Indeed, the path an AI system chooses to arrive at a destination may vary significantly from how a human expert would respond to the same challenge. Studying these choices — and building in tools for AI explainability — will become increasingly important as AI systems grow more sophisticated. Organizations must have the ability to analyze the decision-making of AI systems to put adequate safeguards in place. Additionally, the outputs that AI systems provide to explain their thinking will be critical toward making further improvements over time. — Paul Barrett, CTO, NETSCOUT

What the Tech Industry Expects From Automation in 2024

Artificial Intelligence and Automation Will Drive Productivity, Prosperity

Despite lingering security concerns, 2024 will see a tighter embrace of artificial intelligence and automation. Financial pressures will lead to additional AI investment, and executives will be expected to know how to use data to drive automation and ensure productivity. What might be challenging for the workforce is that business leaders will base their productivity standards on what AI can do quickly. — Richard Ricks, founder and CEO, Silver Tree

Traditional MSPs and IT Departments Will Start to Fade

In 2024, the IT departments that many of us grew up with will continue to fade away. A digital-first agenda will replace the corner IT office. Companies will still rely on people when face time and complicated issues are essential, but automation will become an even greater force. — Richard Ricks, founder and CEO, Silver Tree

Natural Language Will Pave the Way for the Next Evolution of No-Code

Automation is only effective when implemented by teams on the frontline. Five years ago, the best way to place powerful automation in the hands of non-technical teams was via low- or no-code interfaces. Now, with AI chatbots that let people use natural language, every single team member — from sales to security — is technical enough to put automation to work solving their own unique problems. The breakthrough in AI was the new ability to iterate in natural language, simply asking an LLM to do something a bit differently, then slightly differently again. Generative AI and LLMs are obliterating barriers to entry, like no-code tools once did for the need to know how to code, and no-code will be the next barrier to fall. We've already moved from programming languages like Python to Microsoft Excel or drag-and-drop interfaces. In 2024, we will see more and more AI chat functions replace no-code interfaces. We can expect non-technical teams throughout organizations embracing automation in ways they never thought possible. Natural language is the future on the frontline. — Eoin Hinchy, co-founder and CEO, Tines

The Automated Enterprise

We'll see organizations truly doubling down on automation to transform their entire company. Automation from the helpdesk and back office to the go-to-market and engineering teams will be done in a much more integrated way. — Eric Johnson, Chief Information Officer, PagerDuty

Do you agree or disagree with these AI and automation predictions, or do you have some of your own that didn't make this list? Let us know in the Comments section below!

About the Author(s)

Rick Dagley

Rick Dagley is senior editor at ITPro Today, covering IT operations and management, cloud computing, edge computing, software development and IT careers. Previously, he was a longtime editor at PCWeek/eWEEK, with stints at Computer Design and Telecommunications magazines before that.

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