The AI Times Issue 04

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GCC nations leading the AI revolution

This month, I had the opportunity to hear the UAE’s AI Minister, HE Omar Sultan Al Olama, discuss the country’s vision for leveraging AI to accelerate economic and social growth. The UAE’s bold vision, spearheaded by initiatives like the National AI Strategy 2031 and the Dubai AI Roadmap, aims to contribute $96 billion to the national GDP by 2030. Through AI-powered public services and seamless digital solutions, the UAE exemplifies how technology can elevate quality of life and societal progress.

Equally inspiring is the UAE’s focus on human capital. Programs such as training 30% of Dubai’s population to become expert AI users demonstrate a commitment to empowering individuals. This strategy underscores a collective effort to make AI accessible and impactful.

Following the UAE’s lead, other GCC nations are aggressively investing in AI. A case in point is Saudi Arabia’s $100 billion Project Transcendence, which aspires to establish the Kingdom as a global AI hub, rivalling leading tech centers. The program’s ambitious scope encompasses expanding data centers, fostering tech startups, developing the workforce, and forming partnerships with top technology firms.

Qatar, aligning with its National Vision 2030, has allocated $2.5 billion for AI-focused education and research, aiming for a highly skilled workforce.

Kuwait’s Vision 2035 integrates digital transformation with investments in infrastructure and AI leadership. Bahrain’s focus on ethical AI practices and partnerships, such as with Amazon Web Services, is equipping local talent for a tech-driven future.

Oman’s National AI Program and Fourth Industrial Revolution Center are projected to increase the digital economy’s contribution to GDP, reinforcing its commitment to innovation.

These collective efforts highlight the GCC’s determination to lead in the AI era. By investing in infrastructure, human capital, and strategic partnerships, the region is not just preparing for the future—it is shaping it. As these nations embrace AI, the world will witness a transformative wave of technological leadership and progress emanating from the Gulf.

JEEVAN THANKAPPAN

JeevanThankappan

32 The legal revolution.

Kellie Blythe, Partner, Commercial (Technology and Data), at Addleshaw Goddard, on the impact of AI on the legal field.

34

Harnessing Generative AI.

James Hendergart Senior Director –Business Operations, F5, analyses the impact of GenAI on operational data practices.

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36 Future of finance.

Sid Bhatia Area VP & General Manager, Middle East, Turkey & Africa, Dataiku, on how finance teams that modernize with GenAI can leader business to market dominance.

Bridging learning gaps. Mahmoud Mousa Assistant Professor at School of Mathematical and Computer Sciences, Heriot-Watt University Dubai, on AI-driven education in the Middle East.

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AI’s Make or Break Year Ahead. Gabie Boko, Chief Marketing Officer, NetApp

42 Smarter Play.

Sehrish Tariq, Deciphers how Artificial Intelligence is elevating athletic performance.

NORTH STAR COUNCIL

Our North Star Council serves as the editorial guiding light of the AI Times, providing strategic direction and ensuring our content remains on the cutting edge of AI innovation.

Our Members

Dr. Jassim Haji President of the International Group of Artificial Intelligence

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Dataiku unveils 2025 GenAI trends report

Dataiku, the Universal AI Platform, has released its annual trends report, “5 GenAI Trends for 2025: Beware the AI Agent Reckoning,” highlighting five transformative trends that will shape the enterprise AI landscape. The report focuses on the

next frontier of generative AI (GenAI): the game-changing rise of AI agents and their potential to deliver a transformative impact on enterprises worldwide.

The Dataiku trends report indicates a pivotal market shift. As GenAI becomes increasingly commoditized, AI agents are emerging as a critical differentiator. According to Gartner®, “By 2028, at least 15% of day-to-day decisions will be made autonomously through agentic AI, up from zero percent in 2024.” These agents promise to move beyond analysis, executing tasks autonomously and enabling enterprises to avoid bottlenecks, reduce time to action, and drive unprecedented gains in business value — from discovering entirely new revenue opportunities, to breakthroughs in R&D, to achieving exponential gains in operational efficiency, and more.

“We’ve witnessed the rapid evolution of GenAI in an extremely tight timeframe, which has raised the stakes for companies to sharpen their AI strategies,” said Florian Douetteau, Dataiku co-founder and CEO. “AI agents are not on their way, they are already here redefining the rules of business — and that is only going to accelerate. Company leaders have no choice but to move decisively to avoid falling into the commodity AI trap, as competitors are poised to turn their AI advantage into meaningful differentiation, business transformation, and market domination.”

Samsung Galaxy S25 Series Sets the Standard of AI Phone

Samsung Electronics has launched the Galaxy S25 Ultra, Galaxy S25+ and Galaxy S25, setting a new standard as a true AI companion with our most natural and contextaware mobile experiences ever created. Introducing multimodal AI agents, the Galaxy S25 series is the first step in Samsung’s vision to change the way users interact with their phone – and with their world. A first-of-its-kind customized Snapdragon 8 Elite Mobile Platform for Galaxy chipset delivers greater on-

device processing power for Galaxy AI and superior camera range and control with Galaxy’s next-gen ProVisual Engine. With One UI 7 , the Galaxy S25 series is a true AI companion that understands the context of your needs and preferences and provides personalized AI experiences with privacy assured at every turn. It’s the starting point of a shared vision with Google to imagine Android with AI at the core, bringing together developers and partners from around the world.

Florian Douetteau, Dataiku co-founder and CEO

Deloitte ME enhances Tax AI solution, boosts adoption

Deloitte Middle East’s Tax and Legal practice has announced the launch of the second generation of its in-house pioneering AI-powered solution, Tax Genie 2.0, designed to drive innovation in an increasingly complex tax landscape. Deloitte is at the forefront of AI adoption to reshape and transform industries, with cutting edge solutions that set new standards for progress.

Developed by the Middle East chapter of the Deloitte AI Institute, Tax Genie 2.0 encompasses all areas of the Tax & Legal business, including tax, legal, finance, human resources, risk management, and beyond for Tax professionals. Tax Genie 2.0 is based on GPT4o with RAG architecture. Muhammad Bahemia, Deloitte Middle East Tax and Legal Leader, highlighted the transformative potential of Tax Genie 2.0, stating, “The launch of the second iteration of Tax Genie exemplifies our unwavering commitment to innovation in tax and legal services across the Middle East. Our vision is to ensure our clients are well positioned on Gen AI to lead and succeed in the future. Our clients can benefit from Deloitte’s innovation and deep technical capabilities in Gen AI in the Tax & Legal space and this has consistently positioned us as global Leaders.”

Although being an in-house platform, Tax Genie 2.0 is a flagship example of Deloitte’s GenAI capabilities. The platform features over 1,000 specialized workflows for a wide spectrum of tax, legal and operational matters. With an intuitive interface and workflow-based architecture, the platform is designed for ease of use, enabling tax and legal professionals to leverage its capabilities without the need for specialized technical skills.

SAP Survey reveals AI aspirations and strategic investment priorities for Saudi businesses

AlFaifi, Senior Vice, President and Managing Director, SAP, Middle East Africa – North

Saudi enterprises are ramping up their AI investments and targeting key business functions for AI transformation, according to a recent YouGov survey commissioned by SAP. The results will be used to drive SAP’s efforts to expand collaboration with customers and partners in the Kingdom to create industry-specific AI solutions and use cases at its Experience Innovation Center in Khobar.

The survey gathered insights from IT decision-makers across a variety of Saudi industries, uncovering key trends and investment areas that illustrate the growing role of AI in the economy. Notably, 40% of surveyed Saudi companies plan to dedicate over 50% of their technology budgets to AI by 2025, with strategic AI applications being prioritized in customer service (45%), finance (41%), and sales (41%).

The survey also considered how companies would approach selecting and investing in solutions. The top priority was seen as investment in an enterprise resource planning (ERP) solution with embedded AI capabilities by 39% of companies, followed by AI solutions custom-built by a third party (33%), in-house developed AI systems (17%), and a series of AI solutions from multiple vendors (7%).

“Saudi enterprises are harnessing the transformative power of AI to boost operational efficiencies and enrich customer experiences,” says Ahmed AlFaifi, Senior Vice President and Managing Director, SAP, Middle East Africa –North. “We are encouraged that the top investment priority is an AI-infused ERP system as this is the approach we promote through solutions such as SAP S/4HANA.

Ahmed

NVIDIA advances AI frontiers with CES 2025 announce ments

NVIDIA CEO and founder Jensen Huang took the stage for a keynote at CES 2025 to outline the company’s vision for the future of AI in gaming, autonomous vehicles (AVs), robotics, and more.

“AI has been advancing at an incredible pace,” Huang said. “It started with perception AI — understanding images, words, and sounds. Then generative AI — creating text, images, and sound. Now, we’re entering the era of ‘physical AI,’ AI that can perceive, reason, plan, and act.”

With NVIDIA’s platforms and GPUs at the core, Huang explained how the company continues to fuel breakthroughs across multiple industries while unveiling innovations such as the Cosmos platform, next-gen GeForce RTX 50 Series GPUs, and compact AI supercomputer Project DIGITS.

RTX 50 series: “The GPU is a beast”

One of the most significant announcements during CES 2025 was the introduction of the GeForce RTX 50 Series, powered by NVIDIA Blackwell architecture. Huang debuted the flagship RTX 5090 GPU, boasting 92 billion transistors and achieving an impressive 3,352 trillion AI operations per second (TOPS).

“GeForce enabled AI to reach the masses, and now AI is coming home to GeForce,” said Huang.

Holding the blacked-out GPU, Huang called it “a beast,” highlighting its advanced features, including dual cooling fans and its ability to leverage AI for revolutionary realtime graphics.

OpenAI funds $1 million study on AI and morality at Duke University

OpenAI is awarding a $1 million grant to a Duke University research team to look at how AI could predict human moral judgments.

The initiative highlights the growing focus on the intersection of technology and ethics, and raises critical questions: Can AI handle the complexities of morality, or should ethical decisions remain the domain of humans?

Duke University’s Moral Attitudes and Decisions Lab (MADLAB), led

by ethics professor Walter SinnottArmstrong and co-investigator Jana Schaich Borg, is in charge of the “Making Moral AI” project. The team envisions a “moral GPS,” a tool that could guide ethical decision-making. Its research spans diverse fields, including computer science, philosophy, psychology, and neuroscience, to understand how moral attitudes and decisions are formed and how AI can contribute to the process.

MIT Sloan, Astra Tech share insights on GenAI’s potential in Middle East

MIT Sloan Management Review Middle East, in collaboration with Astra Tech, has released a white paper on ‘Leveraging Actionable Gen AI in the Middle East’ to kickstart 2025 amid rapid technological evolution. This comprehensive report explores the transformative opportunities presented by Large Language Models/ Small Language Models (LLMs/SLMs) and Large Action Models (LAMs), focusing on how businesses in the UAE and the wider Middle East harness these AI models to gain a competitive edge.

Notably, the survey reveals that 53.25% of respondents were from the UAE, underscoring the country’s leading role in AI adoption, particularly in leveraging LLMs/SLMs for customer service and product development. Companies in the UAE are integrating AI deeply in customer service and product development, with nearly half (44.74%) using Large Language Models (LLMs) or Small Language Models (SLMs). This highlights the nation’s focus on enhancing customer interactions and personalizing product offerings.

AI-driven data creation to spur next-wave cloud storage growth

According to a recent, global Recon Analytics survey commissioned by Seagate Technology, business leaders from across 15 industry sectors and 10 countries expect that adoption of artificial intelligence (AI) applications will generate unprecedented volumes of data, driving a boom in demand for data storage, in particular cloud-based storage. With hard drives delivering scalability relative to terabyte-per-dollar cost efficiencies, cloud service providers rely on hard drives to store mass quantities of data. Recently, analyst firm IDC estimated that 89% of data stored by leading cloud service providers is stored on hard drives[2]. Now, according to this Recon Analytics study, nearly two-thirds of respondents (61%) from companies that use cloud as their leading storage medium expect their cloud-based storage to grow by more than 100% over the next 3 years.

Saudi Arabia’s First Generative AI-Powered Automotive AI Agent

Petromin has partnered with Gupshu to introduce PETROMINit!, an advanced Customer Service AI Agent in the Kingdom of Saudi Arabia. The AI Agent leverages Generative AI capabilities on Gupshup Conversation Cloud to offer seamless and efficient vehicle support in both Arabic and English.

PETROMINit! is available on WhatsApp, offering customers seamless and instant access to support through a familiar and convenient channel. By simply messaging on +966 9200 22776 in natural language, customers can instantly engage with the Generative AI-powered AI Agent. PETROMINit! leverages advanced large language models (LLMs) and natural language understanding to deliver remarkably human-like support interactions akin to speaking with a live automobile support specialist.

“At Petromin Corp, we’re reimagining automotive customer service through the power of intelligent AI Agents. PETROMINit! isn’t just a technological upgrade—it’s a strategic leap forward that demonstrates how autonomous AI can fundamentally transform customer engagement. Through our collaboration with Gupshup, we are proud to introduce a solution that redefines convenience for our customers and sets a new standard for automotive customer support in Saudi Arabia. said Hussein M Dajani, Group Chief Marketing and Customer Centricity Officer, Petromin Corporation.

Customers can access support for a comprehensive range of services including vehicle quick servicing from Petromin Express, Petromin Autocare, Petromin Autocare EV solutions, Petromin Autocare premium repair options, Petromin body & paint, Petromin Tristar, and loyalty points assistance. The PETROMINit! AI Agent makes it easier for customers to get support during urgent situations, such as a car breakdown. Instead of searching for the nearest service station, they can now conveniently get assistance by chatting on WhatsApp.

Hussein M Dajani, Group Chief Marketing and Customer Centricity Officer, Petromin Corporation. & Beerud Sheth, Founder & CEO of Gupshup.

“Hunter“ Supercomputer goes into service in Stuttgart

Recently, the University of Stuttgart’s High Performance Computing Centre (HLRS) launched “Hunter”, a new 15 million Euros

supercomputer powered by AMD technology. This system, built by HPE and utilising AMD Instinct MI300A APUs, represents a significant step forward for European HPC and artificial intelligence (AI) capabilities.

By leveraging the leadership performance and energy efficiency of AMD Instinct APUs, which integrate CPUs and GPUs on a single chip, Hunter will deliver a world-class infrastructure for large-scale simulation, artificial intelligence, and data analytics applications to tackle complex challenges in diverse areas such as engineering, weather and climate modelling, biomedical research, and materials science, among others.

Users of Hunter will address complex problems in engineering, weather and climate modeling, biomedical research, and materials science, among other fields. Hunter will also provide secure access to powerful, secure high-performance computing (HPC) and AI resources for industry and for public sector agencies.

“Hunter offers scientists at the University of Stuttgart and across Germany a futureproof infrastructure for AI-based simulations and high-performance computing of a new quality,” said Prof. Peter Middendorf, Rector of the University of Stuttgart. “Hunter also benefits the entire ecosystem of our university with its global players, its strong medium-sized companies, and its growing start-up scene.”

Prediction: 2 AI Stocks Will Be Worth More Than Palantir Technologies by Year-End in 2025

Palantir Technologies led the S&P 500 (SNPINDEX: ^GSPC) higher last year. Shares advanced 340% as the company reported increasingly strong demand for its artificial intelligence (AI) platform. Palantir is worth $163 billion as of Jan. 20, but certain Wall Street analyst expect

Shopify and Arista Networks (to surpass that figure in 2025:

• Shopify is worth $134 billion. The stock must return at least 22% for its market value to top $163 billion in 2025. Anthony Chukumba at Loop Capital has set Shopify with a target price of $140 per share. That

forecast implies 36% upside from its current share price of $103.

• Arista Networks is currently worth $150 billion. The stock must return at least 9% for its market value to top $163 billion in 2025.

Oracle to train 350,000 in AI, technologies

Oracle plans to train and certify 350,000 people across key countries in the Middle East, including the UAE, Saudi Arabia, Egypt, Qatar, Morocco, and Jordan, in the most in-demand technologies to help meet the strong demand for Oracle Cloud in the Middle East.

As part of this skills development initiative, Oracle will offer learning programs on several technologies including Oracle Cloud Infrastructure (OCI), Oracle AI services, OCI Generative AI, Low Code, Oracle APEX, DevOps, Cloud Data Management, and Security & Cloud Applications Business Process.

“Oracle is driving one of the fastest cloud expansions in the Middle East to help our customers innovate and explore new growth avenues with Oracle’s full suite of 150+ AI and cloud services across dedicated, public, and hybrid cloud environments,” said Gary Miller, executive vice president, Customer Success Services, Oracle. “This strong demand for Oracle Cloud is creating exciting new job opportunities for IT professionals that can help our customers implement advanced digital technologies and succeed. Oracle’s latest skills development initiative in the Middle East will help create a readily available local pool of Oracle certified professionals that will play a key role in the success of the Middle East’s AI economy, which is expected to reach $320 billion by 2030.”

Oracle will deliver this multi-year initiative in collaboration with key local public sector partners. The program will be delivered as a digital learning experience through Oracle MyLearn, Oracle’s comprehensive training and enablement platform from Oracle University, which is used by millions of technology trainees around the world. The program will offer rigorous foundational training in cloud technologies that will then channel students to professionallevel training and certifications—including in areas such as DevOps, AI, applications business processes, machine learning, and data science—as well as additional training curated individually based on learning levels and educational goals.

PLATMA joins Alchemist Accelerator’s B2B program

PLATMA, an AI Business Automation Platform based in the MENA region, is joining Alchemist Accelerator, one of Silicon Valley’s leading B2B accelerators for technology startups.

PLATMA was selected as one of 20 companies for the Alchemist Accelerator’s 2025 program, representing the top 3% of all applicants, highlighting both the company’s growing potential and the MENA technology sector’s increasing prominence in the global market. The platform, which transforms natural language into functional work and business processes using artificial intelligence, will receive investment from Alchemist Accelerator, access to its educational program, and strategic support from leading technology corporations and venture funds.

“It’s a great honor and responsibility to be part of such an influential program as Alchemist Accelerator. We plan to continue PLATMA’s development in the MENA region and use the knowledge and resources gained to provide better solutions to local users,” said Yaroslav Kologryvov, Cofounder and Chief Business Development Officer (CBDO) of PLATMA.

The acceleration program, starting January 13, 2025, includes intensive mentorship from industry leaders, including former executives from Oracle, Yammer, and other technology companies. Special attention will be given to product development, sales, and usability through consultation with leading experts, including Timothy Chou, Ph.D., Former Chief Executive Officer of Oracle On-Demand and Stanford Lecturer; Adam Pisoni, Co-founder of Yammer (acquired by Microsoft); and Elaine Wherry, Chief Experience Officer and Co-founder of Meebo (acquired by Google).

How AI will redefine key industries by 2025

How do you see AI evolving by 2025, and what key breakthroughs should we expect?

AI in the UAE is soaring, propelled by visionary strategies and concrete expectations. The UAE’s National AI Strategy 2031 stands as a testament to this dedication with His Highness Sheikh Mohammed bin Rashid Al Maktoum stating, “We want the UAE to become the world’s most prepared country for Artificial Intelligence,” which not only reflects an ambitious stance but also a strategic one, deliberately aligning with the broader UAE Centennial 2071 vision for sustained progress.

During the collaborative platforms such as du’s Dubai AI Retreat Roundtables, the recurrent theme was clear—AI is powering the evolution of various industries. The financial health of the AI market in the UAE corroborates this narrative, with the forecasted compound annual growth rate (CAGR) of 28.54% leading up to the year 2030.

As for the key breakthroughs on the horizon, we should anticipate AI’s integration to deepen across sectors, with financial services poised at the forefront of this wave. Considering the anticipated boon to the UAE’s GDP—projected to near 14% by 2030—it’s clear that AI is geared up to be an economic game-changer. Specifically, in the financial sector, the predictions are even more eye-opening, with benefits potentially reaching as high as $37 billion by the year 2035.

What industries will benefit the most from AI advancements in the next few years?

A report by PwC underscores the transformative potential of AI in laborintensive sectors such as retail and health. These sectors, characterized by their

“In retail, AI can revolutionize inventory manage ment, customer experience, and even predict consumer trends with uncanny accuracy.”

high volume of repetitive and processdriven activities, have a broader scope for automation, making them ripe for AI integration. In retail, AI can revolutionize inventory management, customer experience, and even predict consumer trends with uncanny accuracy. Meanwhile, in healthcare, AI’s impact could be lifechanging, with improved diagnostic accuracy, personalized treatment plans, and operational efficiencies that can lead to better patient outcomes.

This transformation will contribute significantly to the UAE’s economic landscape, with the IMF World Economic Outlook (WEO) projecting a substantial rise in the UAE’s real GDP to 5.1% by 2025. This surge is indicative of the widespread impact AI is expected to have across multiple sectors, driving efficiencies, spurring growth, and fostering innovations.

How do you predict AI adoption rates will change globally by 2025?

The global landscape of AI adoption is poised to grow exponentially. The UAE provides a compelling case study, with already 42% of businesses integrating AI into their operations, showcasing the proactive stance businesses are taking to embrace this transformative technology. Furthermore, the sentiments of IT professionals in the UAE echo this robust adoption trend, with 65% reporting that there’s been a considerable acceleration in AI deployment within the past two years.

This trend is reflective of a larger global movement towards AI integration. Studies, such as the one from Exploding Topics, project a notable 38% growth in the global AI market by 2025. This forecast not only highlights the rapid rate at which AI is being adopted worldwide but also signals the

JASIM AL AWADI

CHIEF ICT OFFICER, DU

broader shift in recognition of AI’s potential to drive business transformation.

Do you think governments will establish more stringent regulations on AI by 2025?

The precedence set by the European Union’s GDPR and the United States’ CCPA for data privacy and protection points towards an inevitable global shift towards tighter oversight on AI, particularly in

areas concerning the ethical usage of AI, data protection, and consumer trust. Governments are likely to introduce rigorous regulations to confront the challenges posed by AI, including data breaches, bias, and transparency in AI algorithms. These regulations aim not only to protect consumers but also to encourage responsible innovation within the AI industry.

The evolving pace of AI further necessitates adaptive regulatory frameworks that can accommodate future advancements, with collaborative efforts from policymakers, industry stakeholders, and technologists playing a crucial role in shaping these policies. In the future, we can anticipate a regulatory landscape that not only fosters innovation and maintains ethical standards in AI development and deployment but also ensures that AI technologies are leveraged responsibly and ethically, with a central focus on safeguarding consumer rights and trust.

How can organizations ensure transparency and fairness in AI decisionmaking processes?

Initially, this involves the establishment of a unified AI vision that aligns all stakeholders on the strategic objectives and ethical considerations surrounding AI usage. This vision should prioritize transparency and fairness as core values, guiding the organization’s AI initiatives. It’s important to identify and address potential challenges and opportunities related to AI, such as biases and ethical dilemmas, while also exploring avenues for leveraging AI to enhance organizational efficiency and fairness.

Organizations must formulate and implement actionable initiatives aimed at promoting ethical AI practices. These initiatives should include the development of interpretable AI models, regular audits for fairness and transparency, and integration of ethical considerations into project plans. Additionally, ensuring that the workforce is not only technically proficient but also well-versed in the ethical implications of AI technologies. Emphasizing cross-sector collaboration further enriches this approach, drawing on a diverse pool of expertise and resources to foster an environment of shared best practices and industry-wide standards for ethical AI.

Balancing innovation and regulation

What advancements in AI are expected to be the most transformative by 2025?

By 2025, AI is poised to revolutionize the banking sector, bringing enhanced personalization, advanced risk management, and greater operational efficiency. Predictive analytics will play a pivotal role, enabling banks to harness vast amounts of customer data to anticipate individual needs and preferences. This will lead to financial products and services that are not only tailored but also intuitive in meeting customer expectations.

Conversational AI is set to transform customer service by becoming an integral part of digital interactions. Chatbots and virtual assistants will provide instant, personalized support across various platforms, making banking experiences more seamless and engaging than ever before.

At the same time, AI-driven decisionmaking frameworks will enhance processes such as credit scoring and loan approvals. These innovations promise not only faster and more objective outcomes but also ensure compliance with evolving regulatory requirements.

The integration of AI into banking is an exciting evolution, one that is set to redefine how financial institutions connect with their customers and deliver value.

How will AI adoption differ across industries in 2025 compared to today?

In the year ahead, we can expect AI to become far more sophisticated and widely adopted across industries, with each sector tailoring its use to meet specific priorities. In banking, the emphasis will likely shift toward hyper-personalization and risk management, while other industries may focus more on improving operational efficiency and cutting

“The integration of AI into banking is an exciting evolution, one that is set to redefine how financial institutions connect with their customers and deliver value.”

costs.

One of the key differences will lie in how deeply AI is integrated into core functions. In banking, it’s anticipated that AI will play a central role in areas such as risk assessment, compliance, and customer relationship management. This contrasts with other sectors, where its applications might remain more surface-level.

The regulatory landscape will also shape how AI evolves in banking. With stricter compliance requirements, banks may face slower adoption rates compared to industries with fewer regulatory hurdles. However, these challenges will likely spur innovation in ensuring AI’s ethical and responsible deployment.

Another interesting trend will be the crossover of AI solutions between industries. For example, banking is expected to draw inspiration from sectors like healthcare, adopting predictive analytics and other advanced capabilities that have proven effective elsewhere. This interdisciplinary exchange could accelerate the development of more robust and innovative AI applications, enhancing outcomes across the board.

What emerging AI technologies could disrupt the status quo in the next two years?

As banks continue to adopt AI for critical decision-making, transparency will become a cornerstone of building trust. Explainable AI (XAI) will play a pivotal role by enabling institutions to clearly articulate the reasoning behind AI-driven decisions. This not only enhances customer confidence, but also ensures compliance with regulatory requirements, making AI applications both effective and accountable.

AI’s role in cybersecurity is set to grow as threats become more sophisticated.

CHRIS SHAYAN

HEAD OF ARTIFICIAL INTELLIGENCE, BACKBASE

By proactively identifying vulnerabilities, detecting unusual activity, and responding to threats in real-time, AI-driven solutions will become essential for fortifying banks against cyberattacks. These technologies will help financial institutions stay one step ahead in the ever-evolving landscape of cybersecurity.

The emergence of federated learning is another development that could redefine how banks handle sensitive data. By allowing AI models to be trained on decentralized data sources without transferring information,

this technology safeguards privacy while facilitating collaboration across institutions. It’s an innovative way to balance data protection with the need for advanced AI capabilities.

Natural language processing (NLP) is also poised to make a significant impact. With its ability to analyze unstructured data from sources like customer feedback and social media, NLP can help banks gain a deeper understanding of customer sentiment and preferences. This, in turn, allows for more personalized and responsive services, strengthening customer relationships.

What new regulations or standards for AI are expected by 2025?

The EU AI Act will come into force in 2025. This landmark legislation will establish a riskbased framework for AI systems. High-risk applications will face stricter regulations, with a likely focus on Transparency and Explainability, Data Governance, Bias and Discrimination mitigation, and Human Oversight.

In the US, an increased regulatory scrutiny of AI is expected, particularly in areas such as facial recognition and algorithmic bias. We could also anticipate guidance and regulations from agencies like the FTC and NIST. Meanwhile in the United Kingdom, a national AI regulatory framework is under development, which may significantly impact businesses operating in the region.

There are also International Standards and Guidelines being developed. For example, the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) are developing international standards for AI, focusing on ethical development and deployment. And we also see that the Organization for Economic Co-operation and Development (OECD) is working on AI principles and guidelines, which are expected to influence AI policy development globally.

These evolving regulatory efforts highlight a global commitment to ensuring AI is developed and deployed responsibly, with transparency, fairness, and ethical principles at the forefront. And of course, these frameworks will have signficant impact on businesses and the AI roadmaps they continue to execute on through 2025.

The game-changing advancements by 2025

What advancements in AI are expected to be the most transformative by 2025? 2025 will be a transformative year for AI as it will carry on redefining its use cases and applications across industries. We will see generative AI taking the lead with text and image generation to hyper-personalized video generation. We will see marketing, education, retail, and entertainment, among others, taking advantage of generative AI to market their products and services on a huge scale.

Autonomous systems are gaining popularity be it self-driving cars to selfgoverning machines. We see some of the insane applications in logistics and manufacturing that includes moving objects from one place to another and oftentimes, these are repetitive tasks where humans might feel fatigue but machines can operate continually with regular maintenance.

Another advancement in AI would be explainable AI (XAI) where organizations will be able to trust decisions made by AI unlike today where the conclusions can be based on multiple deductions and may change based on the models used.

How will AI adoption differ across industries in 2025 compared to today? Over the years, the tech landscape has come a long way from being skeptic around AI adoption to becoming a pro-AI adopter with integrations in almost every aspect of a business. We can now see AI applications be it in leisure, planning, deployment, manufacturing, transport, and almost every other space that exists out there.

The tech community is embracing AI

“We can now see AI applications be it in leisure, planning, deployment, manu facturing, transport, and almost every other space that exists out there.”

at a lightning fast speed as the technology has shown trustworthiness. Today, AI applications are widespread, yet industries like healthcare, education, and finance are still grappling with complexities around implementation, regulation, and trust. In sectors like manufacturing and logistics, we’ll see advanced automation and predictive analytics becoming ubiquitous.

One of the key differences in 2025 will be the increased reliance on autonomous AI systems that require minimal human intervention, further reducing costs and errors across sectors. Additionally, AI will become more ethical and transparent, as the integration of AI ethics frameworks, enhanced security features, and privacy safeguards will imbibe greater trust among businesses and consumers. As a result, industries will move from experimenting with AI to fully leveraging it, shaping their core strategies and enhancing operational outcomes at an unprecedented scale.

What emerging AI technologies could disrupt the status quo in the next two years?

Over the next two years, several emerging AI technologies are poised to disrupt industries. Generative AI is one of the most exciting developments, particularly in content creation, design, and coding. Tools like OpenAI’s GPT models and DALL-E are already revolutionizing how creative tasks are handled, allowing businesses to automate everything from marketing copy to graphic design and even software development. Additionally, the growth of AIpowered autonomous systems is expected

RANJITH KAIPPADA

MANAGING DIRECTOR, CLOUD BOX TECHNOLOGIES

to disrupt sectors such as transportation, logistics, and manufacturing. Self-driving vehicles and drones, guided by AI, will begin to change the logistics landscape and drive efficiency across supply chains. AI-powered cybersecurity is also on the horizon, with advancements in threat detection, anomaly detection, and predictive analysis enabling a more proactive approach to combating cyber threats.

Quantum computing, while still in its early stages, may make significant strides in the next two years, allowing AI models to solve problems too complex for current systems to tackle, especially in fields like pharmaceuticals and material science. Finally, AI in healthcare will evolve, with personalized medicine and AI-driven diagnostic tools becoming more accurate and accessible, possibly revolutionizing disease detection and treatment plans.

What new regulations or standards for AI are expected by 2025?

By 2025, it is highly likely that governments and international bodies will introduce more stringent regulations and standards around AI to address ethical concerns, data privacy, and transparency. One of the key areas of regulation will be AI accountability, requiring companies to ensure that their AI systems are explainable, fair, and non-discriminatory. This will include frameworks that mandate businesses to disclose AI decision-making processes, especially in sensitive areas like finance, healthcare, and criminal justice.

The European Union is already leading the way with its Artificial Intelligence Act that aims to create a clear regulatory framework to ensure AI development is safe and trustworthy. Similar efforts in other regions, including North America and Asia, are expected to gain momentum. Furthermore, data privacy regulations will evolve to address the unique challenges posed by AI technologies, particularly in how they handle personal data. Standards for AI explainability and bias mitigation will become crucial, and companies will be required to regularly audit their AI systems to ensure compliance. As AI continues to evolve, the global community will likely work toward aligning these regulations to establish universal standards that ensure responsible innovation and protect consumers from harmful outcomes.

JIM CHAPPELL

GLOBAL HEAD OF AI AND ADVANCED ANALYTICS, AVEVA

The next frontier of industrial innovation

What advancements in AI are expected to be the most transformative by 2025?

2025 is one of the most exciting moments for industrial technology, as artificial intelligence (AI) continues to evolve, and technologies such as Generative AI and LLMs become more mainstream. Industrial innovation in 2025 will be marked by software humanization as well as the ability to easily turn vast amounts of data into actionable, real-time insights. We’ll see GenAI becoming the basis of a simplified user experience which will begin to lower the barrier of using advanced industrial solutions. And we’ll see applied AI becoming an industrial workhorse, with a move toward Industry 5.0.

How will AI adoption differ across industries in 2025 compared to today?

AI adoption will continue to accelerate in 2025 as it becomes more accepted and, in some cases, a requirement. For many industries, predictive and prescriptive analytics are now a standard, and companies are often considered behind the curve if they haven’t adopted it. Over 20 years of proven success drives the continued adoption of these and other types of AI. This growth is further propelled by an ever-increasing amount of data being generated and stored. And with the ease of use of GenAI and LLMs, mainstream AI adoption will continue to accelerate into the foreseeable future.

What emerging AI technologies could disrupt the status quo in the next two years?

We’ll see new use cases for AI continue to emerge. AI tools will fill labor gaps and pick up more specialized tasks. Autonomous systems will soon be able to suggest the best course of action in real time. Generative AI’s human-like interfaces, in particular, will augment and democratize decision making, enabling even non-technical workers to access complex datasets quickly and improve business outcomes. Workers will increasingly use data-driven technologies to access better training and preparation, share ideas and lessons learned with colleagues, and leverage subject matter expertise. AIinfused Generative Design will allow users to explore and optimize engineering design processes.

What new regulations or standards for AI are expected by 2025?

New rules and regulations will continue to be created to safeguard critical systems, protect humans, and provide overall transparency and traceability of AI. As currently available technologies evolve towards Industry 5.0, attention will focus on Responsible AI, where AI applications are built according to safe, trustworthy and ethical principles. These frameworks will use practices like capturing citations to document inputs and outputs while protecting data and maintaining user privacy. AI will increasingly assist in complex tasks, such as process optimization and forecasting, but it will always work in collaboration with humans, who will have the final say.

GLOBAL CIO EXPERTISE, DRIVING INNOVATION FOR PEOPLE AND PLANET

CONSULTING | RESEARCH | ON DEMAND

RESEARCH

INSIGHT & BENCHMARKING

EMERGING TECHNOLOGIES

GOVERNANCE

RISK & COMPLIANCE

CYBER SECURITY

DIGITAL TRANSFORMATION

DEOPS & DIGITAL INFRASTRUCTURE

ERP & CRM

FEDERICO PIENOVI

CHIEF BUSINESS OFFICER & CEO FOR APAC & MENA AT GLOBANT

Building trust in AI

How do you see AI evolving by 2025, and what key breakthroughs should we expect?

Globant’s perspective on AI evolution is rooted in transformative innovation. By 2025, we foresee Agentic AI Systems leading the way. Unlike traditional AI models that rely heavily on human intervention, these systems introduce a collaborative, autonomous approach, emulating the dynamic of a multi-specialist team. At Globant, we are pioneering solutions that harness this shift to drive meaningful outcomes across industries.

This progression toward systems with “agency”—autonomy and adaptability— represents a seismic shift in how we design AI. Such systems are not only efficient but intuitive, laying the foundation for a future where technology works more seamlessly, empathetically, and collaboratively than ever before.

What industries will benefit the most from AI advancements in the next few years?

While AI will touch every sector, media & entertainment, retail, manufacturing, and healthcare stand out as early adopters of Agentic AI Systems. Globant is already implementing solutions where AI agents collaboratively process complex workflows in real time. For instance, in retail, AI agents validate refund claims, enforce policy checks, and process credit notes—all within minutes.

These systems exemplify how industries can benefit from a new era of AI that prioritizes adaptability and problemsolving, significantly enhancing operational efficiency and customer experience.

How do you predict AI adoption rates will change globally by 2025?

AI adoption will grow exponentially as it transitions from being a visible tool to an invisible, omnipresent force. At Globant, we believe this evolution is about embedding AI into the fabric of daily life—creating systems that are not just smart but emotionally intelligent and empathetic.

Picture an AI that doesn’t just respond to a query but anticipates your needs, understands unspoken contexts, and seamlessly integrates into your life. Globally, this shift will democratize AI, making it accessible across diverse economies and industries, further accelerating adoption.

Do you think governments will establish more stringent regulations on AI by 2025?

As AI becomes more widespread, stricter regulations are inevitable. Governments are increasingly focused on ethical concerns, data privacy, and security. At Globant, we support the development of clear, ethical frameworks that foster innovation while ensuring accountability and fairness.

Globant believes regulatory alignment is critical for sensitive sectors like healthcare, law enforcement, and finance, where the stakes are highest.

How can organizations ensure transparency and fairness in AI decisionmaking processes?

Organizations can ensure transparency and fairness in AI by implementing robust governance, ethical guidelines, diverse training, continuous audits, explainable AI tools, and fostering open communication.

2024

HILEL BAROUD

CEO OF PROVEN CONSULT

AI reshaping industry strategies

What advancements in AI are expected to be the most transformative by 2025?

I believe that the most transformative AI advancements in 2025 will be related to decision-making tasks. Today, most AI solutions are utilized to analyze, structure and present data to humans allowing humans to take decisions. As we progress through 2025, Agentic AI will start taking over more decision-making tasks by utilizing (machine) learnings from historical and predictive data analysis. For compliance and regulatory reasons, some tasks will continue to require humans. For those tasks, Agentic AI will act as a collaborator by analyzing the data and offering recommendations to humans. This cohesive approach significantly improves efficiency of task execution without sacrificing accountability.

How will AI adoption differ across industries in 2025 compared to today? AI adoption will continue to expand through 2025 as organizations are much more open to AI adoption and are embracing its

application. Top management are allocating budgets and rewards for AI initiatives creating a competitive environment where departments are racing to implement AI solutions. I believe that the pace and scope of adoption of AI will differ across industries, driven by the strategic visions and priorities outlined by GCC countries. The urgency of adoption of AI solutions varies significantly across industries. For example, in industries like healthcare and finance, AI adoption is essential to remain competitive and ensure survival. While in industries like retail and hospitality the integration of AI is more of a value-added tool to enhance operations and improve customer experience.

What emerging AI technologies could disrupt the status quo in the next two years?

The AI technology that I expect to disrupt the status quo in the next two years is “Generative AI”. These AI engines developed the ability to generate humanlike text and produce realistic and creative images disrupting industries like marketing, entertainment and education. As generative AI continues to evolve, it will play an important role in advancing productivity and creativity in entirely new ways. The rapid growth of generative AI will undoubtedly raise concerns around intellectual property, deepfakes and misinformation. This will require new legal frameworks and ethical guidelines to ensure responsible utilization of these AI tools.

What new regulations or standards for AI are expected by 2025?

As AI continue to take on more decisionmaking roles, regulatory oversight will expand to ensure these AI solutions operate in compliance with the country’s rules and regulations. In the GCC, most countries have established a “Ministry of AI” for this purpose. New rules related to Data Protection and Data Governance are continuously emerging to protect user’s privacy. Although the majority of the rules being issued are related to privacy and governance, we are also seeing a shift in focus towards rules and regulations related to ethical framework and AI standards. Having these regulations will introduce the ability for AI solutions to seamlessly connect and communicate with each other creating extraordinary solutions through an intelligent ecosystem of interoperability.

FUN & WEEKENDS THRILL JOIN

STEFAN LEICHENAUER

VP ENGINEERING, SANDBOXAQ

A look at the shifting industry landscape

What advancements in AI are expected to be the most transformative by 2025?

In 2025 we should expect to see advancements in three areas: Agentic models, Reasoning models, and Quantitative models.

The common denominator is that we are approaching the limits of a pure LLM approach. Agentic and Reasoning approaches focus on giving models access to additional tools, or allowing multiple models to collaborate on a problem. For example, rather than just accepting the answer that an LLM provides to a question, we can allow a second model to critique the answer and ask a follow-up question for more detail. But many problems are fundamentally not language problems. Especially when dealing with problems connected to real world processes, such as those we encounter

in drug discovery, materials design, or climate science, a different kind of model is needed. This is where Quantitative AI is used, and instead of an LLM we need an LQM (Large Quantitative Model). The hallmark of Quantitative AI is a grounding in numerical data coming from real-world measurements or high-fidelity simulations.

How will AI adoption differ across industries in 2025 compared to today?

We expect AI adoption to expand into industries where LLMs alone are not sufficient to solve the fundamental problems. Industries like agriculture, construction, financial services, and manufacturing will integrate AI models, not just for basic automation, but for agentic problem-solving, such as optimizing supply chains or designing sustainable infrastructure materials.

We will continue to see more complex AI models deployed in areas that have already seen initial adoption. For instance, we can expect more substantial AI being integrated into software development workflows, where we will move beyond simple code completion and into planning and program architecture. All of these capabilities have the aim of increasing human productivity, and it will be very important to adopt continuous learning practices for the workforce to keep up with new developments.

What emerging AI technologies could disrupt the status quo in the next two years?

The most disruptive AI technology in the next two years will be agentic AI that combines Large Quantitative Models (LQMs) with Large Language Models (LLMs)

These hybrid systems will transform how AI operates by enabling it to plan and execute multi-step projects with hallucination-free, reliable results. For example, a deep quantitative assistant could take a complex task, like predicting the price of gold, and break it down into a set of assumptions to be validated and mini-tasks to be completed, like assessing the markets of related precious metals and examining the implications of geopolitical events on prices. Then LQMs can simulate these scenarios numerically while LLMs synthesize all of the available literature. The sheer number of variables that are taken into account will be greater than ever before possible, and as a result we will have actionable insights that are more reliable and complete.

ANTONIO RIZZI

AREA VP SOLUTION CONSULTING, SOUTH EMEA AT SERVICENOW

The varied pace of AI adoption across industries

What advancements in AI are expected to be the most transformative by 2025?

In 2025, AI will be less about eye-catching demonstrations of “intelligence” and more about deeply integrated, specialized, and trusted systems that quietly enhance a wide range of activities.

So far, I have seen that businesses have struggled to keep up with the pace of innovation in the AI space because their enterprise data is locked into silos. 2025 will be the year where businesses connect AI to their data by investing in enterprise data fabric solutions.

How will AI adoption differ across industries in 2025 compared to today?

We will see the adoption of AI Agents with the ability to work deeply integrated in business process and leverage enterprise aggregated data to automate actions with human supervision. We will progressively shift from a Human-in-the-loop paradigm (where humans essentially act as orchestrators of different specialized AIs) to Human-on-theloop, where humans supervise the work of multiple AIs that self-coordinate to solve complex tasks.

What emerging AI technologies could disrupt the status quo in the next two years?

Multimodality will continue to progress with capabilities like full vision, allowing AI to learn from humans by simply looking at a shared screen session. We can also expect a significant advancement in specialized neuromorphic hardware that will allow AI to be more powerful and energy efficient moving away from today’s power intensive GPU infrastructures.

What new regulations or standards for AI are expected by 2025?

By 2025, it is anticipated that the EU AI Act, and related liability directives, will be in effect, setting a strict precedent. The U.S. will likely strengthen enforcement through existing agencies and possibly enact dedicated AI legislation, while the UK refines its flexible, principles-led approach. International standards bodies and global organizations will produce guidelines that many countries integrate into their legal frameworks, resulting in a more cohesive yet still evolving regulatory environment for AI worldwide.

4 FEB 2025

5 – 6 FEB

THE RISE OF COPILOTS

SEHRISH TARIQ

The concept of copilots has emerged as a transformative force, redefining how we approach collaboration and productivity. From AI-powered tools that streamline complex tasks to virtual assistants that anticipate our needs, copilots are no longer confined to the cockpit— they are now integral to our daily workflows. These digital copilots leverage artificial intelligence, machine learning, and advanced analytics to enhance decisionmaking, improve efficiency, and empower individuals and teams across industries. Sehrish Tariq dives into the top copilots shaping the future of work and unlocking unprecedented potential in human-machine partnerships.

• Purpose: Integrated into Microsoft 365 applications, Microsoft Copilot assists with tasks across Microsoft applications, such as Word, Excel, PowerPoint and Outlook.

• Capabilities: Allows users to interact with Microsoft applications using simple, conversational prompts instead of complex commands. Seamlessly works across Word, Excel, PowerPoint, Outlook, Teams, and other Microsoft 365 apps. Operates within Microsoft’s robust security infrastructure, ensuring data privacy and compliance with regulations.

• Purpose: Designed to simplify and enhance process automation in Appian’s lowcode platform, it acts as an AI assistant to help users manage complex workflows.

• Capabilities: Enables natural conversations using NLP for intuitive interactions.

Uses predictive analytics to forecast process outcomes and recommend actions. Facilitates integration with external AI models for enhanced functionality, such as decision support or content generation.

• Purpose: Built to empower cybersecurity teams, it serves as a real-time assistant for managing and mitigating threats.

• Capabilities: Identifies emerging security risks through AI-driven analysis. Provides actionable insights to prevent cyberattacks and reduce vulnerabilities. Automates routine security tasks, allowing teams to focus on critical incidents.

• Purpose: Aimed at improving customer engagement and workflow efficiency within Creatio’s CRM and BPM platforms.

• Capabilities:

Automates repetitive tasks like lead scoring and email follow-ups.

Provides actionable recommendations based on customer data for personalized service. Enhances team collaboration by intelligently routing tasks and insights.

• Purpose: A digital assistant for IT service management, streamlining support and operational workflows.

• Capabilities: Automates ticket categorization, prioritization, and resolution.

Interacts with users through conversational AI for quicker support.

Provides predictive insights to improve IT service delivery.

• Purpose: Helps IT administrators optimize network performance and reliability in cloud environments.

• Capabilities: Detects and predicts network anomalies before they affect users.

Recommends configurations for improved network efficiency.

Simplifies network management through AIdriven insights and automation.

• Purpose: Enhances customer service by providing realtime support and feedback to agents.

• Capabilities: Conducts real-time sentiment analysis to guide agent responses.

Offers coaching tips during live interactions to improve service quality.

Analyzes trends to refine overall customer engagement strategies.

• Purpose: Supports IT and DevOps teams in incident management, ensuring faster resolution and fewer disruptions.

• Capabilities: Uses AI to prioritize incidents based on severity and impact.

Automates routine incident response tasks like notifications and escalations.

Provides actionable recommendations to prevent recurring issues.

• Purpose: Assists software development teams in managing application lifecycle processes with greater efficiency.

• Capabilities: Offers predictive insights into project risks and outcomes. Recommends ways to streamline development pipelines.

Enhances collaboration by providing a unified view of project progress and dependencies.

• Purpose: Helps project managers optimize resource allocation and task prioritization within Planview’s PPM platform.

• Capabilities: Uses AI to forecast project timelines and resource needs.

Automates routine tasks like progress tracking and reporting.

Provides insights to align project outcomes with organizational goals.

• Purpose: Enhances Salesforce CRM with conversational AI to simplify customer relationship management tasks.

• Capabilities: Interacts via natural language to provide realtime customer insights. Automates data entry, lead management, and report generation. Personalizes customer engagement strategies based on AI-driven predictions.

• Purpose: Jasper is an AIpowered content creation platform designed to help users craft marketing materials, blog posts, social media content, and more.

• Capabilities: Offers templates for various writing styles and purposes. AI-driven suggestions for improving tone, style, and engagement. Multilingual support to cater to global audiences. Integrates with tools like Grammarly and Surfer SEO for enhanced content optimization.

• Purpose: Embedded into SAP’s ERP solutions, Joule is a comprehensive AI assistant designed to optimize business processes across departments.

• Capabilities: Provides proactive insights and recommendations for decision-making.

Automates complex workflows in areas like finance, supply chain, and human resources. Enhances user experience through intuitive natural language interactions.

• Purpose: A privacy-focused AI-powered search engine with personalized search results and integrated AI tools.

• Capabilities: Features AI writing and summarization tools, including YouWrite.

Allows users to customize search preferences for a tailored experience. Offers instant answers and quick results for queries. Incorporates AI for code snippets, math problem solving, and content generation.

• Purpose: An advanced conversational AI assistant built for creative and professional tasks.

• Capabilities: Real-time data access to provide up-to-date answers. Ideal for generating articles, essays, and reports. Handles natural language queries and offers voice-totext interactions.

Integrates with other tools for seamless workflow automation.

• Purpose: A conversational AI assistant from Google designed to provide contextual answers, generate creative content, and assist with research tasks.

• Capabilities: Delivers real-time information from Google Search. Can generate text, summarize long documents, and answer complex questions. Supports multimodal inputs, including text, images, and code.

Built with privacy and transparency in mind, leveraging Google’s AI infrastructure.

• Purpose: An AI-powered coding assistant designed to help developers write and complete code efficiently.

• Capabilities: Offers context-aware code suggestions within IDEs.

Supports multiple programming languages like Python, JavaScript, and Ruby. Helps with boilerplate code, debugging, and learning new syntax.

Seamlessly integrates with Visual Studio Code and JetBrains.

• Purpose: An AI assistant for coding, focused on boosting developer productivity with smart code completions.

• Capabilities: Provides whole-line or full-function code completions. Learns from team-specific coding styles for tailored suggestions. Works across major IDEs and supports most programming languages. Privacy-focused, with options for local deployment.

• Purpose: A code search and intelligence platform designed for teams working on large, complex codebases.

• Capabilities: Enables developers to search across repositories with precision. Offers insights into code dependencies, changes, and usage. Provides support for cross-repository and multilanguage searches.

Enhances collaboration through in-depth code reviews and analysis.

• Purpose: An AI-powered tool for understanding and documenting code. Ideal for developers who want insights into complex codebases.

• Capabilities: Explains code snippets in plain language. Automatically generates documentation for functions and classes.

Integrates seamlessly with existing workflows. Helps with onboarding new developers to understand codebases faster.

• Purpose: A collaborative AI coding assistant designed to automate repetitive tasks and boost coding efficiency.

• Capabilities: Automatically refactors and optimizes code. Provides code suggestions based on context and project-specific needs.

Supports multiple IDEs and integrates with Git workflows.

Focused on improving code quality and team productivity.

COMPARISON AND KEY DIFFERENTIATORS

Content Creation & Writing: Jasper, Chatsonic, and You. com excel in natural language processing for creative and professional writing tasks.

Coding Assistance: GitHub Copilot, Tabnine, Figstack, SourceGraph, and Mutable.ai target developers, each offering unique capabilities like contextual code completion (GitHub Copilot, Tabnine), code understanding (Figstack), and repository-wide code intelligence (SourceGraph).

Conversational AI: Gemini and Chatsonic cater to broader AI interaction needs, supporting content generation and real-time data access.

INTERVIEW

The legal revolution

Kellie Blythe, Partner, Commercial (Technology and Data), at Addleshaw Goddard, on the impact of AI on the legal field.

How do you see generative AI reshaping the legal profession in the next 5–10 years?

As trust in AI models increases, and more firms licence their own in-house instances of Large Language Models (LLMs), I can envisage the following immediate changes:

o Understanding how algorithms work, what data they have been trained on, and any associated limitations on their output, will be essential when determining how to effectively mitigate risk. Lawyers will need to be confident in tackling the unique issues they present.

o Clients will increasingly distinguish between strategic, high-value and complex legal services, which will be outsourced to traditional law firms, and low risk or routine work, which will be commoditised and delivered for a low cost, by companies, or by in-house legal teams, who will be able to do more with less, in each case by leveraging AI.

o With “big data” offering better insights

PARTNER, COMMERCIAL (TECHNOLOGY AND DATA), AT ADDLESHAW GODDARD,

and improved outcomes, we will continue to see consolidation in the legal market. Larger pools of data, coupled with leading AI tools, will offer businesses powerful insights and become a point of difference when evaluating advisors.

o Law firms will make increasing use of data scientists, engineers and other non-legal professionals to help them compete in the market.

o Law firm boards will require their CTOs to consider the role of AI in connection with all strategic investment decisions.

What specific challenges do law firms face when integrating AI into their practices?

The business of law is predicated on trust. In light of this, most firms are adopting a cautious approach, deploying their own “closed loop” AI models, to ensure that no data leaves their servers. Adopting comprehensive policies and delivering training plays a key role in ensuring that employees understand the risks, and the potential consequences, of breaking the rules.

The cost of licensing a third party LLM is likely to be prohibitive for smaller firms, which may remain a barrier to adoption in parts of the industry.

There is also the challenge of managing the inflated expectations in the performance of AI solutions in delivering legal work. The more hype around these solutions is driven by investment in the market the harder it gets to have sensible conversations around performance. Legal work is complex and very much still a people business, the push from clients for law firms to use AI is great, but the challenge comes on being able to use it for the right work and at the right time within a legal matter.

Downward pressure on legal bills from clients due to slight misunderstandings in the impact that AI will have on law firm efficiencies is a difficult problem to solve. Over time the integration of AI into legal service delivery will mean that cost savings and return on investment becomes clearer. But we are very much at the beginning of this change and will need to focus on delivering over the next few years in order to match the expectations in the market.

In your opinion, are there any ethical or regulatory concerns surrounding the use of AI in legal processes? Absolutely. Despite the lack of binding

regulation, many countries, like the UAE and the KSA, have adopted non-binding guidance and ethical frameworks to help guide stakeholders and set expectations. At a very basic level, these rules inform what permissions you should obtain to input information when making a request and what you need to tell users about how the underlying system generates its results. In addition, firms should conduct a risk vs benefit analysis when deploying AI tools. Fundamental concepts like transparency, fairness (and eliminating the potential for bias), accountability, explainability and privacy by design, should be proactively addressed. These principles are embedded in ethical AI frameworks, as well as other pillars of law, like privacy and consumer protection. Firms will be increasingly reliant on their professional advisors and IT colleagues to help them quantify the risks.

Can you tell us more about AGPT and how it stands out compared to other AI tools in the legal market?

Having used other machine learning and AI solutions over the past 8 years, our Innovation Group were able to immediately leverage their experience to engage with Generative AI in late 2022. The development of our internal AI solutions “AGPT” began in the spring of 2023. Over a period of 6 months our Innovation Group, alongside a working group of 100 lawyers, studied how the LLMs performed on a range of different legal tasks. As early adopters, we had time to test a range of solutions, as well as develop our own internal approaches. We then spent time refining use cases, testing the outputs, refining prompts and training our lawyers.

“The cost of licensing a third party LLM is likely to be prohibitive for smaller firms, which may remain a barrier to adoption in parts of the industry.”

AGPT uses the latest models from Microsoft Azure, currently running GPT-4o, and is based within AGs infrastructure as part of our Azure environment. This is important for the security and confidentiality of the information we use as a firm. We have spent countless hours building out our internal prompt library and use cases, allowing our lawyers to run a detailed prompt at the click of a button. We are constantly updating AGPT, with features such as translation, a prompt library, suggested prompting for document types rolled out, with future plans for largescale multi-document reviews.

GUEST ARTICLE

Harnessing Generative AI

Generative AI (GenAI) chatter is coming from everywhere. The question is what’s catching on? How is it making the world a better place? Where is the business value? These questions are equally relevant when you consider the challenges facing organizations that are figuring out if, and when, to implement GenAI into their operations (AIOps).

Based on my past year of experiments with generative AI and broad exposure to industry trends during my daily research at F5, I offer the following five takeaways to help guide organizations that are seeking to understand the impact of GenAI on operational data practices. As a result, these organizations will be better positioned to align GenAI technology adoption timelines with their existing budgets, practices, and cultures.

1. GENAI MODELS LOVE SEMI-STRUCTURED AND UNSTRUCTURED DATA

Operational data is a hodgepodge of semistructured data (objects) and unstructured data sets. Large language models (LLMs) are quite flexible and effective with this range of data formats. This makes LLMs a perfect technology to employ for analyzing operational data sets. Organizations can conduct a range of experiments and

JAMES HENDERGART

Senior Director –Business Operations, F5, analyses the impact of GenAI on operational data practices.

evaluations in-house to verify the efficacy, ease of use, and cost of various GenAI enabled solutions. Using LLM inference to detect interesting data patterns with fewer false positives puts the speed and scale of machines in line with the goals of teams consuming operational data flows.

2. ORGANIZATIONS DON’T NEED TO BUILD MODELS

Organizations that focus on knowing which techniques are used by which models for their specific tasks at hand don’t have to build their own models. For example, named entity recognition (NER) is a branch of natural language processing (NLP) that is proving to be an effective technique for establishing key elements within semi-structured data. An example of NER could be a list that comprises a category such as days of the week or a description such as whole numbers greater than 1 and less than 5. The result is greater accuracy during inference than rule-based pattern-matching techniques that are not GenAI-enabled. As research and the practice of using techniques like NER continue to advance, operations teams can focus their attention on leveraging the techniques that have proved successful, rather than building models.

3. DATA GRAVITY IS REAL

Data gravity is an underlying force that influences decisions about whether to place compute nearer to where data is created or to move data closer to where compute is already deployed. The greater the volume of data, the stronger the gravitational force, resulting in increased compute capacity placed nearer to it. For training (creating and tuning models), data is aggregated and moved closer to compute. For inferencing (using models), the model is moved closer to where the prompt is issued.

If a model is accessed by bringing a copy in-house—versus calling the API of an instance hosted by a third party—it makes sense to move the model closer to the prompt, and/or any additional private data set vectorized as part of the prompt. On the other hand, if the model is hosted by a third party exposing their API over the internet, then the model and the inference operations are not moving at all. In these cases, inference and private data vectors may be moved to a “network near” location using a data center colocation interconnect or by attempting to match hosting locations with the model provider, if possible.

Awareness of the forces that pull data and compute together as well as those that force them apart help lead to informed choices in the pursuit of finding the right balance between cost and performance.

4. DON’T IGNORE DATA SILOS, DEAL WITH THEM

With GenAI processing, it’s more important than ever to break down data silos to simplify and speed up operational data analysis. However, for the foreseeable future, it appears that data silos will remain, if not proliferate.

With GenAI processing, it’s more important than ever to break down data silos to simplify and speed up operational data analysis. However, for the foreseeable future, it appears that data silos will remain, if not proliferate.

The question is more about how to deal with data silos and what technology choices to make. In terms of accessing data stored in multiple locations, the choices are to copy and move the data or to implement a logical data layer that uses federated queries without moving the data. Regardless of which choice is made, recognizing the streaming data sources that exist and evaluating operational use cases for time/ data freshness constraints will help you select the necessary elements of your data technology stack, such as streaming engines, query engines, data formats, and catalogs. Technology choice gives data teams the power to choose the most effective and easy-to-use technologies while balancing performance and cost. Ideally, an organization’s data practice matures with time while always giving the organization the flexibility to choose what works best for it at a given stage of maturity.

5. AUTOMATION IS A FRIEND— DON’T FEAR IT

When solutions add automation, they scale by turning tacit knowledge of experts in data privacy and SecOps into a repeatable AIOpsenabled practice that can be executed by machines. Only then are data, security, and privacy teams freed up to add intelligence. Intelligence enhances the efficacy of policies by more finely defining how specific data can be used by whom, for how long, and for what purpose—all while tracking where the data lives, which copies are being made, and with whom it is being shared. This frees up time for strategic planning, new technology evaluation, and communicating with the business to refine data access policies and approve exceptions.

Speed, scale, and automation are characteristics of a mature AIOps practice, leading to better output, faster decisions, and optimized human capital. GenAI is opening doors that technology has been unable to open…until now. The five learnings above provide some trail markers for IT operations, security operations, and privacy operations to consider as these teams implement GenAI into their AIOps.

Future of finance

According to PwC’s analysis unit, Strategy&, the GCC region is set to reap US$23.6 billion in economic impact from generative artificial intelligence (GenAI) by 2030. In what analysts called a “conservative top-down estimate”, Saudi Arabia will see US$12.2 billion in value, and the UAE US$5.3 billion. The rest will be shared among Qatar (US$2.6 billion), Kuwait (US$1.6 billion), Oman (US$1.3 billion), and Bahrain (US$600 million).

The second-highest ranked industry in Strategy&’s projection was banking and financial services, which is predicted to see a US$3.5-billion bump. The Gulf’s BFSI sector has long been a trendsetter in technology adoption. With a savvy eye on consumers’ shifting preferences, legacy banks have been quick to cater. There are many examples, from the rise of digital banks — Emirates NBD’s Liv, Mashreq Neo, Gulf International Bank’s meem, and Bank ABC’s ila — to the roaring trade in FinTechs, such as Dubai’s PayTabs, Optasia, and Sarwa, Abu Dhabi’s NymCard, and Kuwait’s One Global.

As the BFSI-tech union continues to deepen, it is clear that GenAI has a part to play. But it also has a broader role in the finance function across industries. In many ways, large-language models (LLMs) were made for financial professionals. When it comes to arduous, cyclic tasks like preparing quarterly reports, reconciling ledgers, and aligning with regulatory standards, GenAI models can do the work

SID BHATIA

Area VP & General Manager, Middle East, Turkey & Africa, Dataiku, on how finance teams that modernize with GenAI can leader business to market dominance.

of dozens of people in a fraction of the time. A reduction in sweat and tears means happier employees. Greater accuracy means happier regulators. And enhanced efficiency means happier board members.

What GenAI brings is a means to plug gaps and overhaul juddering processes. Legacy systems and manual workflows are routinely accompanied by silos — a sinister word that in the business world denotes inefficiency. GenAI can essentially become a field marshal for change in five main areas.

1. MARKET INTELLIGENCE

Market intelligence is a labor-intensive process at the best of times, but where data silos are present, it becomes maddening; and if information is not current, the task is impractical. With GenAI, finance departments can automate gathering and collation of data from sources as diverse as market reports, social media, and news pages. AI is a natural pattern-matcher, so weeding out emerging trends, where challenging for a human, is a trivial exercise for GenAI, which can quickly provide a buffet of actionable insights that lead to shrewder decision-making.

2. COMPLIANCE

The Gulf region is exceptionally businessfriendly, but governments are pragmatic in their attitudes to regulation. To protect consumers and economic security, mandates are periodically introduced that change enterprises’ risk profiles. In

seconds, GenAI can consume and commit to memory gargantuan chunks of information — a task that would take weeks for a human professional to complete. GenAI can then automate the extraction of information from a range of sources and classify it in line with regulatory requirements. From employees’ payroll information to customers’ PII, automation increases accuracy and speed and takes a lot of the headache out of compliance management.

3. CONTRACT INTERPRETATION

What GenAI can do for compliance extends to contract management. It can extract key clauses and present advice to decisionmakers with considerably less risk of missing the fine-print than if a human had performed the task. GenAI can speedily discover and arrange salient information such as payment terms. It is also ideally placed to retrieve information that human agents would have difficulty in committing to memory, like supplier-specific clauses. GenAI allows employees to focus on negotiation and compliance, thereby (once again) improving risk management.

4. SOCIAL LISTENING

Customers live digitally and share their experiences with everyone. For commercial entities like banks, this sharing may be with the brand in the form of direct feedback, or it may be with other consumers on social media. For a bank with millions of customers, it would be impractical to have employees

trawl even the company’s own feedback data in search of insights. LLMs, however, are very much up to the challenge. They can sift through surveys, reviews, and social pages rapidly. They can find connections and recurring themes, recognize sentiments, and tie comments back to specific products or product lines. And they do so without bias, ensuring more accurate insights. This is the kind of information businesses can use to improve offerings and delight customers.

5. FINANCIAL SUMMARIES

The enterprise must take care during procurement to ensure the chosen AI partner can integrate GenAI with full security, governance, and operational controls, so customers and regulators can both be satisfied with the result.

Documentation, when done manually, is one of the most time-consuming tasks in an employee’s daily workload. Financial commentaries are orders of magnitude lengthier and more complex. But when GenAI takes over data synthesis and analysis of trends, performance and risk, it can rapidly produce initial drafts of deliverables such as quarterly performance summaries or market-outlook reports. This alleviates the burden on finance professionals who need only fine-tune the commentary. The labor saved by GenAI puts time back into the hands of innovative humans who can use it to strategize and create.

GENAI NEXT

AI’s power lies partly in its ability to cover a wide range of use cases. GenAI amplifies this power because of its ability to take on such a wide range of previously humancentric activities. The benefits of GenAI for finance teams are plentiful and obvious. The main challenge for organizations eager to avail themselves of these advantages will lie in how to securely integrate GenAI into their workflows. Every finance manager wants more accurate forecasts and faster processing. The enterprise must take care during procurement to ensure the chosen AI partner can integrate GenAI with full security, governance, and operational controls, so customers and regulators can both be satisfied with the result.

A finance team equipped with GenAI will be a strategic leader within any organization, whether in the FSI sector or elsewhere. The capability to lubricate the machinery of finance, and therefore of business, comes as standard. And when team members become comfortable with their AI colleague, the business will be setting standards of excellence for others to follow — in compliance, customer experience, employee experience, and product innovation.

Bridging learning gaps

The rapid integration of artificial intelligence (AI) in Middle East’s education landscape has led to a seismic shift in the region. Across the region, nations are embracing AI to address entrenched learning gaps, develop customized courses that fulfil the students’ needs, improve accessibility, and foster skill development, all while aligning with transformative national initiatives like the UAE National Strategy For Artificial Intelligence 2031 and Saudi Arabia’s Vision 2030. By personalising education and enabling inclusive learning environments, AI is not only reshaping how students learn but also ensuring that education systems are equipped to meet the demands of the future workforce.

PERSONALISING LEARNING: A TAILORED APPROACH

Teaching has traditionally followed a onesize-fits-all approach. This traditional method often leads to significant challenges in classrooms such as losing interest, or, even worse, failing to fully grasp the material. The effective usage of AI in various educational systems overcomes these challenges, as it enables a wider audience to be effectively reached. Adaptive systems like Century Tech can provide students with what they specifically need at any given time, whether it’s answering a question, providing instant feedback, or delivering a custom lesson to fill in gaps in their learning profiles.

In the Middle East, Remote Student

MAHMOUD MOUSA

Assistant Professor at School of Mathematical and Computer Sciences, Heriot-Watt University Dubai, on AI-driven education in the Middle East.

Monitoring technologies, powered by AI, have been introduced in UAE’s education sector, which facilitates a more personalised way of learning. The UAE schools are using AI-powered learning platform called Alef Education that personalizes learning experiences and provides real-time analytics to teachers about the students’ strengths and weaknesses. According to Global Market Insights report, AI in the education sector will continue its upward growth trend and reach $20 billion by 2027, while Middle East will witness one of the fastest rates of adoption. The growth will be fueled by government funding in smart education systems and digital transformation. A PWC report forecasts that the region’s AI contribution will increase by 20% to 34% annually, with the UAE and Saudi Arabia seeing the fastest growth rates.

LEVERAGING AI TO ENHANCE EDUCATIONAL ACCESSIBILITY AND INCLUSIVITY

AI in education also faces accessibility and inclusivity challenges; however, it has the potential to mitigate them as well—widening access to education for underprivileged students and those with disabilities. Voiceto-text applications, speech recognition, and real-time translation services, for instance, enable students to overcome language and communication barriers. Aligned to the Vision 2030 goal of building an inclusive society, AI-based solutions like Madrasa and Qalam are being used in Saudi Arabia and the UAE to ensure students with special

needs have access to inclusive classrooms.

Additionally, virtual classrooms enabled by AI are extending to unprivileged areas around the world that help bridge geographical divides. The growing edtech industry in the region is contributing to this transformation. Start-ups like Nafham, Almentor, Lamsa, and Noon Academy have developed tools that allow students located in remote locations to receive the same level of teaching as a qualified tutor, enjoy engaging videos and content, take part in businesses, and have a chance to participate in social learning. Such initiatives are as, according to UNESCO, the global number of out-of-school children has risen by 6 million since 2021 and now totals 250 million.

ENHANCING SKILL DEVELOPMENT FOR THE FUTURE WORKFORCE

Universities in the region have started their transformation to fundamental knowledgebased economies. For instance, HeriotWatt University Dubai runs several highly successful programmes to train the next generation of AI experts and prepares future leaders and professionals for the AI-driven workforce. Such initiatives are expected to yield significant benefits. In our commitment to remain future-focused and technologically advanced, we don’t merely impart knowledge in AI; we’ve also harnessed the power of AI in conjunction with other state-of-the-art future technologies like the Internet of Things (IoT) to elevate our student experience. For instance, we’ve

deployed sensors within our library and in a few classrooms, providing invaluable insights into classroom demographics, temperature fluctuations based on capacity, and students’ preferred spaces within the campus based on footfall and traffic. Additionally, UAE’s AI strategy plans to educate more than 100,000 students in AI technologies by 2031, thus preparing the workforce for a technology-driven future. Recent estimates by PwC suggest that in 2030, AI could add up to $320 billion to the economy of the Middle East, highlighting the need for embedding skill-based AI education in the learning systems.

CHALLENGES AND ETHICAL CONSIDERATIONS

AI in education also faces accessibility and inclusivity challenges; however, it has the potential to mitigate them as well—widening access to education for underprivileged students and those with disabilities.

The prospects of improving the education system through AI are enormous, but realising these prospects is also fraught with challenges. Concerns around data privacy, algorithmic bias, misinformation, academic integrity, and digital gaps, however, still persist. Ensuring that AI systems are fair and unbiased is vital to prevent entrenching existing social injustices. Also, when AI applications become more common, protecting students’ information and other aspects of cybersecurity becomes more critical. Governments and institutions must develop strict policies guiding AI usage in education to enhance creativity and, at the same time, be ethical.

ALIGNING WITH UAE’S VISION 2031

AI-orientated education fits perfectly within the vision goals of countries like the UAE and Saudi Arabia that seek to construct sustainable and diversified education primarily based on innovation and creativity. Through AI, these countries are improving educational results and preparing the next generation of experts equipped with the latest technologies and tools to fuel the economy.

Consider, for example, Saudi Arabia’s Smart Initiative on Education, which uses AI alongside other digital tools to boost learning and focus on women and other marginalised communities as part of Vision 2030, which also focuses on equal education opportunities. Similarly, the UAE’s Centennial 2071 Plan imagines an education system enabling future generations to succeed in technology.

AI’S MAKE OR BREAK YEAR AHEAD

NETAPP’S 2024 DATA COMPLEXITY REPORT REVEALS THAT ORGANIZATIONS WORLDWIDE ARE BRACING FOR A YEAR OF AI TRANSFORMATION, SECURITY CHALLENGES, AND SUSTAINABILITY.

GABIE

BOKO

Chief Marketing Officer, NetApp

Imperatives.NetApp the intelligent data infrastructure company, today released its second annual Data Complexity Report, which examines how global organizations are navigating the increasing complexity of managing their data for AI. This year’s report provides a global view into how AI will impact organizations in 2025 and beyond, offering insights to help businesses leverage AI’s potential while navigating the complexities and risks that accompany this transformative technology.

“2025 is shaping up to be a defining year for AI, as organizations transition from experimentation to scaling their AI capabilities,” said Gabie Boko, Chief Marketing Officer, NetApp. “This year’s Data Complexity Report shows that businesses are making significant investments to drive innovation and efficiency, but these efforts will succeed only if global tech executives can address the mounting challenges of data complexity, security, and sustainability. Intelligent data infrastructure, with unified data storage at its core, will be key to unlocking AI’s potential.”

AI INVESTMENT: WILL AI BREAK THE BANK?

Two-thirds of companies worldwide report that their data is either fully or mostly

optimized for AI—meaning their data is accessible, accurate, and well-documented for AI-use cases. However, despite this progress, 2025 will still demand investment in AI and data management. In fact, 40% of global technology executives believe that unprecedented investment in AI and data management will be required for their companies in 2025. While companies have made strides in optimizing data for AI, achieving future breakthroughs will demand even greater commitment and resources.

DATA SILOS: WILL YOUR DATA IMPEDE AI SUCCESS?

Data unification is emerging as a critical driver of AI success, with 79% of global tech executives recognizing the importance of unifying data to achieve optimal AI outcomes. Companies that have unified data storage have been able to remove data silos by connecting data regardless of type or location across hybrid multicloud environments so it is always accessible. Companies that prioritize unifying data are more likely to reach their AI goals in 2025, with only 23% of companies that prioritize unifying data saying they won’t reach their goals, versus 30% of companies that don’t prioritize unifying data. Investing in data management and infrastructure has become the top priority for organizations, with executives emphasizing it twice as much as other AI-related initiatives – a trend set to grow.

Looking to the future, organizations that embrace data unification will be better positioned to fully harness the transformative power of AI, ensuring they stay ahead in an increasingly competitive landscape.

DATA SECURITY: WILL CYBER THREATS SCALE ALONG WITH AI?

Global tech executives are bracing for a significant rise in security threats alongside AI adoption, with 41% predicting a sharp increase in 2025. Data privacy and security concerns have remained top challenges globally year-over-year, with AI-leading countries like India, Japan and the United States (whom are farther ahead in their AI

AI’S MAKE OR BREAK MOMENT OVERVIEW

journey) nearly twice as likely to report an increase in the number of security issues compared to AI-lagging countries like Germany, France, and Spain.

The rise of AI has increased the attack surface of many organizations, creating new challenges such as protecting AI-models themselves, defending data sets that are more exposed to attacks, and ensuring data is available and secure for use in AI applications.

AI-driven security challenges are weighing heavily on the minds of global tech leaders, with 59% identifying these threats as a leading global stressor. This heightened focus reflects the growing complexity of cyber risks. Executives at the board and C-suite levels continue to prioritize cybersecurity and ransomware protection, with 38% ranking it as their top priority. However, there’s a silver lining: the strategic measures organizations have implemented appear to be paying off. The focus on cybersecurity as a top priority has decreased by 17% since 2023 — a promising sign that progress is being made in combating these ever-evolving threats.

DATA SUSTAINABILITY: IS AI PUTTING THE PLANET AT RISK?

As AI adoption accelerates, 34% of global tech executives anticipate major shifts in corporate sustainability processes, and 33% expect new government energy policies and

Data unification is emerging as a critical driver of AI success, with 79% of global tech executives recognizing the importance of unifying data to achieve optimal AI outcomes.

investments. AI-driven data growth and the infrastructure needed to turn data into business value uses a lot of energy, which runs counter to sustainability goals, with AI-leading countries experiencing a greater impact than AI-lagging countries. Carbon footprint reduction remains extremely or very important, especially in regions with high AI adoption, though its focus has declined year over year, from 84% of companies in 2023 to 72% in 2024. The challenge moving forward will be managing the environmental costs of AI while maximizing its potential for innovation.

This year’s Data Complexity Report highlights a pivotal shift: businesses that invest in intelligent data infrastructure, prioritize security, and factor in sustainability are not only future-proofing their operations but also gaining a significant competitive advantage in the AI-driven landscape.

“AI’s transformative potential hinges on secure, scalable, and sustainable data strategies,” said Krish Vitaldevara, Senior Vice President and General Manager, NetApp. “The organizations leading in advanced analytics and AI are those that have unified and well-cataloged data, robust security and compliance for sensitive information, and a clear understanding of how data evolves. By tackling these challenges, they can drive innovation while ensuring resilience, responsibility, and timely insights in the new AI era.”

during practice and games, helping coaches identify risk factors before they become serious issues.

Smarter Play

Artificial intelligence isn’t exactly the first thing that comes to mind when we think about sports. Sure, we’ve all seen technology in action with things like instant replays or tracking data during games, but AI’s real potential in sports goes much deeper than that. It’s revolutionizing how we understand player performance, prevent injuries, and even manage entire teams.

The numbers are hard to ignore: According to Business Research Company, the market for AI in sports was valued at $2.2 billion in 2022. By 2032, that figure is expected to grow to nearly $30 billion, with an annual growth rate of over 30%. The industry is starting to see that AI is not just a trend—it’s here to stay, and it’s changing the way sports are played, coached, and experienced.

AI IN PERFORMANCE ANALYSIS

Traditionally, performance analysis has been a mix of intuition, stats, and video breakdowns. Now, AI is stepping in with far more granular insights. It’s one thing to look at a player’s shooting percentage or sprint speed; it’s another to track every movement on the field, analyze it, and provide actionable insights for improvement.

Take basketball, for example. AI can now track a player’s every step, turn, and jump during a game. It identifies which areas of the court they perform best on and where they need to improve. For coaches, this data is invaluable. Not only does it help refine individual skills, but it can shape team strategy as well. AI provides real-time analysis that was once only possible in a post-game review—if at all. Now it’s available during the game.

INJURY PREVENTION

Preventing injuries has always been a major focus in sports, but AI is taking it to the next level. Wearable tech powered by AI is monitoring players’ vitals and movements

SEHRISH TARIQ

Deciphers how Artificial Intelligence is elevating athletic performance

In football, for example, AI is tracking player movements to analyze joint stress and muscle fatigue. The system can warn if a player is overexerting themselves or using improper technique, potentially preventing injuries before they happen. This type of realtime data can help adjust training programs, catch muscle strain early, and ultimately keep athletes healthy. It’s no longer just about avoiding injury—it’s about proactively preventing it using data-driven insights.

With AI collecting more and more data on athletes, it’s also important to talk about the ethical side of this technology. Athletes’ personal data—things like biometric readings, movement patterns, and physical strain—can be incredibly sensitive. How this data is handled and who has access to it is a major concern.

Cricket, for example, is increasingly using AI to track players’ physical performance during training. But with so much private data being collected, teams need to ensure it’s protected. There’s also the issue of AI in sports betting. If AI can predict a player’s performance down to the smallest detail, could that information be used to manipulate betting odds? Transparency, security, and accountability are critical here.

There’s no question that AI has the potential to change sports in profound ways. But like any new technology, it comes with its set of challenges. Here’s what we’ll need to navigate as the industry embraces AI:

1. Data Privacy: As AI’s role grows, so does the amount of sensitive data being collected. Protecting that data and ensuring it’s used responsibly is a top priority.

2. Ethical Considerations: From performance analysis to injury prevention, AI raises ethical questions about data ownership, transparency, and fairness. The industry will need to balance innovation with ethical responsibility.

Ultimately, the road ahead for AI in sports looks bright. Whether it’s through enhancing performance, preventing injuries, or improving team management, AI is becoming an integral part of the sports world. But like any innovation, it will only be effective if implemented responsibly.

AI is here to stay, and it’s making sports smarter, safer, and more exciting than ever before.

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