The AI Times Issue 01

HONORING THE BEST

The power and promise of AI

AI is proving to be a game changer for industries, and I can’t think of any other technology that has evolved as quickly. Use cases are emerging across virtually all verticals, with many businesses already moving these from PoCs into production, and tangible benefits are beginning to surface. The global economic impact of AI is also immense—PwC estimates that AI could contribute up to $15.7 trillion by 2030, with the Middle East expected to accrue 2 percent of that, equivalent to $320 billion.

Although there is much FUD (fear, uncertainty, and doubt) surrounding AI—primarily due to concerns about it being unpredictabile and uncontrollable—no one can deny that the technology now available to businesses is a powerful tool capable of transforming industries and functions. This is why IBM has recently emphasized that companies that fail to explore and adopt the most beneficial AI use cases will soon find themselves at a severe disadvantage.

Nonetheless, this might be easier said than done when you think about the price-tag of AI tech available in the market today; it ain’t cheap and can break your bank. For large enterprises, deploying new AI applications requires access to adequate GPUs and hardware, and finding ways to integrate it into the existing infrastructure. For small businesses, the best strategy Would be to partner with AI vendors or leveraging off-the-shelf tools.

While the AI revolution is truly upon us, perhaps the most important consideration for enterprises is how to adopt AI in a responsible manner. This is why we have chosen Responsible AI as the cover story for this inaugural issue of The AI Times. Regardless of company size, the responsibility now falls on all of us to manage the risks associated with AI while ensuring its ethical development and deployment. Industry experts we consulted for this story emphasize the need to establish governance frameworks and implement strict compliance measures for AI systems.

As they say, “With great power comes great responsibility.” We need to establish guardrails and continuously monitor AI’s outcomes. Business and technology leaders must define the vision now and adopt a nuanced approach that balances innovation with safety.

Against this backdrop, we are incredibly proud to launch the region’s first magazine dedicated to AI, where we will bring you all the latest insights and developments.

Thanks for reading. JeevanThankappan

07

Accenture teams up with NVIDIA. Accenture and NVIDIA announced an expanded partnership to help the world’s enterprises rapidly scale their AI adoption.

12 Leading the AI revolution. Venkatesh Mahadevan, Founding Board Member of CaaS Global, discusses the importance of embracing adaptive leadership in a transforming world.

22

14

Small is the new big. Cherian Varghese, Senior VP of Oracle, discusses how SMBs can navigate the AI frontier.

The “AI Foresight 2024” report by CaaS Research provides a comprehensive analysis of the current state and future trajectory of Artificial Intelligence.

30

Building a trusted ecosystem. du is deploying Oracle Alloy to offer cloud and sovereign AI services to business, government, and public sector organizations in the UAE.

32

Top Large Language Models. We explore the top LLMs in the market today.

34

Transforming productivity. Exclusive interview with Haidi Nossair, Sr.Director – Client Solutions Group- META at Dell Technologies.

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 artificial intelligence innovation.

The Purpose

OF THE NORTH STAR COUNCIL

The North Star Council exists to ensure that all articles published in The AI Times maintain alignment with the magazine’s core values, editorial standards, and vision for responsible and cutting-edge AI journalism. It serves as a guiding body to uphold the magazine’s integrity, accuracy, and thought leadership in the fast-evolving field of artificial intelligence.

Council Membership

The council shall consist of no fewer than 5 members, though its size is flexible to adapt to the changing needs of The AI Times. This flexibility allows the Managing Editor to adjust the composition as necessary over time, ensuring that the council remains responsive and effective.

Selection and Appointment

Members of the council will be chosen and appointed by the Managing Editor of The AI Times. This ensures that the council members are individuals who align with the editorial vision and possess expertise relevant to the magazine’s AI-focused content.

If you would like to be a part of our North Star Council, please reach out to us at jeevan@gecmediagroup.com

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RONAK SAMANTARAY ronak@gecmediagroup.com

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Salesforce and NVIDIA Forge Strategic Collaboration

Salesforce and NVIDIA are teaming up to enhance AI capabilities for enterprises, focusing on autonomous agents and interactive avatars. Their collaboration will integrate NVIDIA’s AI platform with Salesforce’s tools, particularly Agentforce, to optimize predictive and generative AI workflows. This partnership aims to boost insights and productivity for sales, service, marketing, and IT teams using Salesforce CRM for customer data management.

“Together with NVIDIA, we’re leading the third wave of the AI revolution — moving beyond copilots to humans and intelligent agents working seamlessly to drive customer success,” said Marc Benioff, Chair and CEO, Salesforce. “This is what AI is meant to be — powered by the Salesforce Platform and Agentforce, every Trailblazer can harness AI to its fullest potential. By combining NVIDIA’s AI platform with Agentforce, we’re supercharging AI performance and creating dynamic digital avatars, delivering more engaging, intelligent, and immersive customer experiences than ever before.”

With Salesforce and NVIDIA, organizations will have access to nextgeneration AI and data capabilities as part of the Salesforce Platform and Agentforce, a groundbreaking set of tools to create and customize agents that come with a collection of pre-built agents. The new collaboration will provide Salesforce customers with accelerated value from AI investments while differentiating and maintaining a competitive edge. Using Salesforce’s AI-powered predictive and generative technologies with NVIDIA NIM microservices and NVIDIA NeMo for model customization, all as part of the NVIDIA AI Enterprise platform, businesses are expected to see advances in AI platform performance and model throughput to improve productivity and effectiveness of interactions across industries.

ADQ parnters with EQTY Lab for ClimateGPT

ADQ, an Abu Dhabi-based investment and holding company, has partnered with EQTY Lab, an AI solutions provider headquartered in Switzerland, to accelerate the responsible adoption of AI technologies throughout its portfolio. This partnership aligns with ADQ’s portfolio management approach, which focuses on consistently identifying and harnessing opportunities for sustainable value creation, transformation and growth to build future-proof sector leaders and national champions.

Through its collaboration with

EQTY Lab, teams will deploy a new AI Integrity suite that ensures ADQ’s AI models are reliable, secure, transparent, and aligned to the highest standards of AI governance, to promote trust and accountability in AI implementations. This effort is part of ADQ’s commitment to encouraging the adoption of best practices and drive transformation across its portfolio.

The partnership between ADQ and EQTY Lab began in 2023 with the launch of ClimateGPT powered by Erasmus and trained by Apptek. It is the first open-source AI model specifically built to address climate

change and sustainability. It allows researchers, policymakers and business leaders to make informed decisions and drive resilient climate action by providing access to a multilanguage model that incorporates over 300 billion pieces of climatespecific information, drawn from 10 billion web pages and millions of open-access academic articles.

The initial node of ClimateGPT is hosted at Abu Dhabi’s Al Dhafra Solar PV, the world’s largest singlesite solar plant, ensuring that the platform is wholly powered by renewable energy.

ECONOMY

GenAI to unlock $133.6bn for Saudi Arabia

As part of a mission to examine how generative AI can shape the future of work in Saudi Arabia, Access Partnership has launched a new report at the Global AI Summit (GAIN) in Riyadh.

Commissioned by Microsoft, the report, titled “The Economic Impact of Generative AI: The Future of Work in the Kingdom of Saudi Arabia”, explores the transformative potential of generative AI on the Kingdom’s economy, workforce, and public sector, showing how this technology can drive growth, innovation, and efficiency.

The findings highlight that generative AI has the potential to unlock an additional USD 133.6 billion in productive capacity within Saudi Arabia – equivalent to the nation’s entire manufacturing industry. By reprioritising tasks and enhancing creativity, the research estimates that 70% of the Kingdom’s workforce could use generative AI in 5-20% of their daily activities, emphasising the broad impact of AIintegration into everyday work life.

To fully realise these opportunities, the report argues that Saudi Arabia should equip its workforce with future-ready skills. This focuses on both foundational abilities, such as reading, writing, and critical thinking, and specialised skills in AI development, management, and ethical governance. The study outlines a strategic framework for the Kingdom, recommending policies that will increase AI access and adoption, mitigate associated risks, and inspire innovation.

Greg Francis, CEO of Access Partnership, said: “This report demonstrates the importance of establishing clear policies that ensure equitable access to AI, as well as investing in robust digital infrastructure to support widespread implementation. Collaboration between government, private companies, and academic institutions will play a vital role in this process, facilitating knowledge exchange, driving innovation, and ensuring that AI advancements are aligned with national priorities.”

Accenture and NVIDIA Lead Enterprises into Era of AI

Accenture and NVIDIA announced an expanded partnership, including Accenture’s formation of a new NVIDIA Business Group, to help the world’s enterprises rapidly scale their AI adoption.

With generative AI demand driving $3 billion in Accenture bookings in its recently-closed fiscal year, the new group will help clients lay the foundation for agentic AI functionality using Accenture’s AIRefinery, which uses the full NVIDIA AI stack—including NVIDIA AI Foundry, NVIDIA AIEnterprise and NVIDIA Omniverse—to advance areas such as process reinvention, AI-powered simulation and sovereign AI.

Accenture AI Refinery will be available on all public and private cloud platforms and will integrate seamlessly with other Accenture Business Groups to accelerate AI across the SaaS and Cloud AIecosystem.

“We are breaking significant new ground with our partnership with NVIDIA and enabling our clients to be at the forefront of using generative AI as a catalyst for reinvention,” said Julie Sweet, Chair and CEO, Accenture. “Accenture AI Refinery will create opportunities for companies to reimagine their processes and operations, discover new ways of working, and scale AI solutions across the enterprise to help drive continuous change and create value.”

The new Accenture NVIDIA Business Group will accelerate momentum with generative AI and help clients scale agentic AI systems — the next frontier of gen AI — to drive new levels of productivity and growth. This significant investment will be supported by over 30,000 professionals receiving training globally to help clients reinvent processes and scale enterprise AI adoption.

INDUSTRY UPDATES

Oracle, Google Cloud join forces

Oracle and Google Cloud today announced the general availability of Oracle Database@Google Cloud in four Google Cloud regions across the United States and Europe. Customers will now be able to run Oracle Exadata Database Service, Oracle Autonomous Database, and Oracle Database Zero Data Loss Autonomous Recovery Service on Oracle Cloud Infrastructure (OCI) in Google Cloud datacenters across U.S. East (Ashburn), U.S. West (Salt Lake City), U.K. South (London), and Germany Central (Frankfurt), expanding to many more regions in the coming months across North America, Europe, the Middle East, Africa, Asia Pacific, and Latin America.

With Oracle Database@Google Cloud, customers will for the first time get direct access to Oracle Database services running on OCI and deployed in Google Cloud datacenters. They can now take advantage of Oracle’s industry-leading database and Exadata technology to accelerate innovation and develop new applications. In addition, customers can run applications on Oracle Linux, which is now supported by Oracle on Google Cloud.

Oracle Linux images can be imported using Google Cloud’s virtual disk image import process. Within the next 12 months, customers are also expected to be able to streamline Oracle Linux image provisioning in Google Compute Engine with ready-touse images. Finally, by combining industryleading generative AI capabilities from offerings like Google Cloud’s Vertex AI, Gemini foundation models, and Oracle Database 23ai, customers can bring enterprise truth to their data and gain faster insights by operating two clouds as one while maintaining feature and pricing parity with OCI.

Oracle Linux images can be imported using Google Cloud’s virtual disk image import process

Microsoft to set up Global Engineering Development Center in Abu Dhabi

Microsoft has announced it is expanding its Global Engineering Development Center footprint to the UAE. A new development center, which will be established in Abu Dhabi, one of Microsoft’s first engineering centers to be launched in the Arab world, joining the company’s global portfolio of development centers across key strategic locations around the world. Microsoft’s Engineering

Development Center in Abu Dhabi will be part of a global ecosystem of centers dedicated to the creation of AI innovations, cloud technologies and advanced cybersecurity solutions. The engineering teams at the center will create cuttingedge solutions that will be part of Microsoft solutions globally.

Microsoft’s strategic partnership with G42 has been instrumental in establishing a thriving local

technological ecosystem, and the new Microsoft Engineering Development Center will build on these efforts by not only creating cutting-edge technologies in the region but also attracting top tech talent from around the world to develop tailored solutions that tackle pressing challenges in critical industries globally.

G42 to launch NANDA

G42 has announced that it will soon launch NANDA – a cutting-edge Hindi Large Language Model. NANDA is a 13-billion parameter model trained on approximately 2.13 trillion tokens of language datasets, including Hindi. With a name inspired by one of India’s highest peaks, NANDA is the result of a collaboration between Inception (a G42 company), Mohamed bin Zayed University of Artificial Intelligence - the world’s first graduate research university dedicated to AI - and Cerebras Systems. The model was trained on Condor Galaxy, one of the world’s most powerful AI supercomputers for training and inferencing built by G42 and Cerebras.

OpenAI valued at $157B

OpenAI has secured $6.6 billion in its latest funding round, bringing its valuation to $157 billion — nearly double its previous worth. “Our goal is to make advanced intelligence a widely accessible resource,” OpenAI stated in a blog post. “This new funding will enable us to deepen our leadership in frontier AI research, expand our compute capacity, and continue creating tools that empower people to tackle complex challenges.”

Meta announces Movie Gen

Meta, the parent company of Facebook, has launched new AI model called MovieGen, which can generate realistic video and audio clips based on user prompts. Meta claims the tool rivals those from top media generation companies like OpenAI and ElevenLabs. Meta provided samples of MovieGen’s capabilities, showcasing videos of animals swimming and surfing, as well as clips featuring real photos of individuals, depicting them performing actions such as painting on a canvas.

Alibaba Cloud unveils new AI models

Alibaba Cloud has released over 100 of its newly-launched large language models, Qwen 2.5, to the global opensource community.

VAST Data launches Cosmos to accelerate AI adoption

VAST Data, the AI data platform company, announced the launch of Cosmos, an initiative designed to transform how organizations build and advance AI, by creating a comprehensive and supportive environment to nurture innovation, collaboration, growth, and economic prosperity. This community is built of AI practitioners and for AI practitioners – as researchers, technology partners, service providers, and solutions integrators all join together to help democratize the benefits of AI.

Cosmos aims to streamline AI adoption for its members by offering a comprehensive, interconnected ecosystem that facilitates conversation, shares use cases, and provides learning opportunities through labs, vendor showcases, and general AI research news. Cosmos helps community members navigate available options and implement the most effective solutions for their unique needs.

Artificial intelligence has the ability to revolutionize business productivity. However, today it can be difficult to build an AI-enabled workforce. As new technologies and methodologies are developed, participating in Cosmos is an ideal way to stay informed and be supported on this journey. Cosmos is unlocking innovation by helping to revolutionize the way organizations develop, deploy, and utilize AI technologies from this point forward.

Composed of early participants including VAST Data, NVIDIA, xAI, Supermicro, Deloitte, WWT, Cisco, CoreWeave, Core42, NEA, Impetus, Run:AI, Dremio, and more, Cosmos is an AI community that serves to further AI progress by simplifying adoption and pioneering the next frontier of data-driven innovation.

“Today marks a monumental step forward in our journey to redefine the future of data and AI,” said Renen Hallak, Founder and CEO of VAST Data. “Since our inception, VAST Data has been dedicated to breaking down the barriers that have long confined the potential of data. With the VAST Data Platform at the center of this comprehensive, interconnected AI ecosystem of technology leaders and AI practitioners, Cosmos will help accelerate discovery, empowering innovation, and enabling the transformation of entire industries.”

Gartner: 80% of engineering workforce must upskill for Generative AI

Through 2027, generative AI (GenAI) will spawn new roles in software engineering and operations, requiring 80% of the engineering workforce to upskill, according to Gartner, Inc.

“Bold claims on the ability of AI have led to speculation that AI could reduce demand for human engineers or even supplant them entirely,” said Philip Walsh, Sr Principal Analyst at Gartner. “While AI will transform the future role of software engineers, human expertise and creativity will always be essential to delivering complex, innovative software.”

Gartner analysts expect AI will impact the software engineering role in three ways:

In the short term, AI will operate within boundaries

• AI tools will generate modest productivity increases by augmenting

existing developer work patterns and tasks. The productivity benefits of AI will be most significant for senior developers in organizations with mature engineering practices.

In the medium term, the emergence of AI agents will push boundaries

• AI agents will transform developer work patterns by enabling developers to fully automate and offload more tasks. This will mark the emergence of AI-native software engineering when most code will be AIgenerated rather than human-authored.

“In the AI-native era, software engineers will adopt an ‘AI-first’ mindset, where they primarily focus on steering AI agents toward the most relevant context and constraints for a given task,” said Walsh. This will make natural-language prompt engineering and retrieval-augmented generation (RAG) skills essential for software engineers.

In the long term, advances in AI will break boundaries and will mark the rise of AI engineering

• While AI will make engineering more efficient, organizations will need even more skilled software engineers to meet the rapidly increasing demand for AIempowered software.

“Building AI-empowered software will demand a new breed of software professional, the AI engineer,” said Walsh. “The AI engineer possesses a unique combination of skills in software engineering, data science and AI/machine learning (ML), skills that are sought after.”

Salesforce expands Dubai presence

Salesforce has officially inaugurated its new office in the United Arab Emirates (UAE) in Dubai Internet City. The company’s expansion in Dubai is part of its commitment to support AI innovation, customer success and growth in the UAE. The launch of Salesforce’s new

office at Dubai Internet City, a leading technology hub, builds on the company’s ongoing investment in the UAE. Last year, Salesforce made Hyperforce, the company’s trusted cloud platform, generally available in the UAE in partnership with Amazon Web Services (AWS).

Salesforce is experiencing

rapid growth in the UAE as more companies invest in AI-driven digital transformation and look to partner with Salesforce as a trusted AI partner. The new office, with its collaboration and innovation spaces, will enable Salesforce to showcase its latest AI technology innovations and better serve local customers.

AI’s potential highlighted at Intersec Saudi Arabia

Saudi Arabia is making significant strides in positioning the Kingdom as a global leader in AI as part of Vision 2030, with the government and private sector’s efforts in utilising AI for innovation and economic progress receiving international praise –Tortoise Intelligence ranked Saudi Arabia first in its Government Strategy Index for Artificial Intelligence last year.

The importance of the country’s commitment to AI was underscored at the Future Security and Safety Summit by Mazyad Al Utaibi, Partner Consultant, Security Technologies, Rawand Security Surveillance, during the AI on Security & Safety Industry: Impact and Concerns session.

During the presentation, Al Utaibi highlighted AI’s many positive impacts on the security sector, relating to surveillance and situational awareness, optimal resource allocation, predictive analytics, and improved communication, among others. He also reiterated how AI has positively impacted safety, referencing the workplace, emergency response, decision-making processes and traffic safety measures.

As part of the session, insights were also outlined as the potential concerns around using AI relating to privacy, decisions, ethics, AI security, jobs, and reliability.

“Privacy issues from using AI in surveillance and data analysis raise significant privacy concerns. Bias, discrimination, and fairness are where AI systems can inherit biases from their training data, leading to unfair decision-making by targeting or profiling certain individuals or groups,” said Al Utaibi.

“In addition, job displacement is a concern as automation of security and safety monitoring and management tasks could lead to job losses. Ethical and legal concerns are always there when engaging with AI in decision-making processes, such as predictive policing, which raises ethical questions about accountability and transparency, as well as long-term cultural perception,” he added.

BCG unveils comprehensive evaluation of Artificial General Intelligence

The new report, “Artificial General Intelligence: Demystifying AI’s Next Frontier,” explores AGI’s key concepts and future potential and examines how it differs from current AI technologies. The report provides an overview of AGI’s applications across industries, its technological and ethical challenges in development, and the governance frameworks necessary for its responsible implementation. It also highlights the need for stakeholder collaboration to guide AGI’s progress and ensure its benefits are realized safely and equitably.

Dr. Lars Littig, Managing Director & Partner at BCG and EMESA Leader of BCG’s Center for Digital Government unveiled the report’s key findings during the Global AI Summit. Commenting on their significance, he stated: “AGI represents the next critical leap in artificial intelligence, with the potential to reshape our world fundamentally. While generative AI is an entry point, AGI unveils a significant breakthrough where AI will reach or surpass human-level intelligence and cognitive capabilities across a wide range of tasks. We must guide this technological advancement with ethical frameworks and the right governance structures as we progress toward this future. Today’s decisions are crucial to actively shaping the pathway to AGI. The next few years will be critical for developing and implementing concrete action plans for AI regulations that balance fostering innovation and ensuring responsible and beneficial AI development.”

LEADING THE AI REVOLUTION

Venkatesh

Mahadevan, Founding Board Member of CaaS Global, discusses the importance of embracing adaptive leadership in a transforming world.

Having worked as a CIO and board member with global Fortune 100 companies, extensively in the Indian, Middle East and North Africa (MENA), & Southeast Asia, markets over the course of my career, I have witnessed firsthand, the tremendous growth potential and unique challenges facing these regions. I have witnessed the transformative power of technology throughout my career. Now, as AI reshapes the business landscape, leaders must embrace this change with a strategic and human-centred approach. There have been significant developments in the field of artificial intelligence with the creation of large language models (LLMs). Generative AI such as OpenAI’s ChatGPT, which is widely known and available to the general public worldwide, and Google’s Gemini are versatile tools in the AI space. The market for artificial intelligence grew beyond $184 billion in 2024, a considerable jump of nearly $50 billion compared to 2023. This staggering growth is expected to continue, with the market racing past $826 billion in

2030.

EMBRACE CONTINUOUS LEARNING

To lead effectively in an AIdriven world, continuous learning is paramount. Leaders must stay abreast of AI developments, understanding both the technology and its implications for their organizations. This requires a commitment to education—whether through formal training, attending conferences, or engaging with thought leaders in AI.

As Sundar Pichai, CEO of Alphabet Inc., aptly stated, “AI is one of the most important things humanity is working on. It is more profound than electricity or fire.”

Leaders must harness this profound shift by continually enhancing their knowledge and skills.

LEAD BY EXAMPLE

Leadership by example is essential in fostering a culture that embraces AI. As leaders adopt AI tools and practices, they set a precedent for their teams. For instance, in one of the solutions we developed for business,

I have personally integrated AIdriven analytics into decision-making processes, demonstrating their value in enhancing efficiency and insight. This hands-on approach not only builds trust but also encourages teams to experiment with AI in their workflows.

CULTIVATE AN AI-FIRST CULTURE

Creating an AI-first culture involves more than just implementing technology; It requires a mindset shift throughout the organization. In cultivating an AI-first culture, it is essential to recognize the delicate balance between an organization’s ability to embrace change and the leader’s vision for that change. Effective leaders must not only articulate a clear and compelling vision for integrating AI but also foster an environment that encourages adaptability and innovation. Leaders should encourage experimentation and innovation, allowing teams to explore AI applications that can enhance their work. By aligning their vision with the organization’s capacity for

transformation, leaders can inspire teams to embrace AI as a vital tool for growth and success, ultimately driving the organization forward in an increasingly competitive landscape.

MASTER CHANGE MANAGEMENT

The Single most ingredient for success….I am sure some of you have grappled with this challenge just as I have in different situations that an organization throws up!

Effective change management is critical as organizations transition to AI-enabled operations. As a Leader, you must communicate the vision and benefits of AI clearly, addressing any fears or misconceptions. Do not leave the room without all the questions being answered and clarifications provided. Empathy and transparency will be key in guiding your team through the Transformation.

DEVELOP AGILE DECISION-MAKING

In an AI-enhanced environment, agility in decision-making becomes a competitive advantage. Leaders should leverage AI’s data-processing capabilities to inform their strategies while remaining flexible to adapt as new insights emerge.

In my view, in order for our organizations to navigate complexities with confidence, we have to adopt the dual approach of utilizing AI for analysis while ensuring to maintain human intuition

DEMOCRATIZING TECHNOLOGY WITH AI

One of the most exciting aspects of AI is its potential to democratize technology, making it accessible to a wider audience. As leaders, we have a responsibility to ensure that the benefits of AI are not limited to large enterprises but extend to small and medium-sized businesses as well. By developing user-friendly AI tools and platforms, we can empower entrepreneurs and innovators to harness the power of AI in their ventures.

I believe that democratizing AI will foster greater innovation, create

new job opportunities, and drive economic growth across various sectors.

AI IN OUR DAILY LIVES

AI is not just a futuristic concept; it is already woven into the fabric of our daily lives. One instance that resonates with me is the use of AI in personal virtual assistants, such as Siri or Google Assistant. I recall a moment when I was juggling multiple projects and deadlines. I asked my virtual assistant to schedule a meeting, and it not only found a suitable time but also sent out calendar invites to all participants. This small but significant interaction saved me precious time and allowed me to focus on more strategic tasks. It’s a reminder that AI can enhance our productivity and streamline our routines, making our lives easier.

PREDICTIONS FOR THE FUTURE

As we move forward, I foresee a transformative shift in leadership dynamics for organizations that successfully integrate AI into their operations. The most effective leaders will be those who can harmoniously blend essential human qualities—such as empathy, intuition, and emotional intelligence—with the advanced capabilities of AI. This integration will not only enhance decision-making but also foster a more inclusive and innovative workplace culture.

Furthermore, as AI technology continues to evolve, we are likely to witness the emergence of new leadership roles specifically focused on AI strategy and ethics. These roles will be crucial in ensuring that AI technologies are developed and implemented responsibly, aligning with the organization’s values while serving humanity’s best interests. Leaders in these positions will advocate for ethical AI practices, mitigate biases, and promote transparency, thereby building trust with stakeholders.

The future of leadership will not be about being replaced by AI; rather, it will center on augmenting our capabilities with this powerful

technology. As we embrace this transformation, we must remember that the essence of leadership lies in our ability to connect, inspire, and guide our teams through uncharted waters.

The leaders who can navigate this new landscape will not only drive their organizations toward success but also redefine what it means to lead in the 21st century.

In the age of AI, there will be two types of leaders who emerge:

1. Those who allow AI to control and manipulate them, falling victim to the allure of automation without understanding its implications. These leaders will be distracted by the shiny promises of AI, neglecting to develop the skills and mindset necessary to harness its true potential.

2. Those who proudly call themselves “AI-empowered” leaders. By embracing this mindset, you will take the first step toward mastering AI and using it to your advantage. These leaders will be the ones who deeply understand AI’s capabilities and limitations, integrating it seamlessly into their decision-making processes while maintaining a strong grasp on their own intuition and values.

The AI-empowered leaders will be the ones who shape the future of their organizations and the world. They will be the ones who can navigate the complexities of AI, separating hype from reality and using this technology to drive innovation, efficiency, and humancentric progress. By committing to this path, you are not only investing in your own growth as a leader but also positioning your organization for success in the AI-driven future.

is a Fellow at British Computer Society and a thought leader.

VENKI MAHADEVAN

Can you tell us about your role at Oracle?

CHERIAN VARGHESE

SENIOR VICE PRESIDENT.

ORACLE, DISCUSSES HOW SMBS CAN NAVIGATE THE AI FRONTIER.

“Small is the new big”

I started a new role at the beginning of the year, heading our Oracle AI business for the whole of EMEA. While this is a new job for me, I’ve been with Oracle for about 18 years. In addition to this role, I also manage a few others, including running our OCI business for EMEA, focusing on growth markets and SMB, as well as managing the ISV business for Oracle. So, I wear three hats, but my primary focus is around the AI business. My 12-hour workdays are dedicated to ensuring that our AI business embarks on a new trajectory.

How does AI for small businesses differ from enterprise AI in terms of your strategies?

Actually, it’s quite a revelation in the industry. Traditionally, we would have expected

large enterprise customers to be the first to jump on the AI bandwagon. Surprisingly, it’s not the case. It’s the small and medium businesses, new startups, and LLM builders who are leading the charge in adopting AI more rapidly. Larger enterprises are primarily focused on generative AI, using a few prebuilt models, but the smaller businesses are at the forefront of training and building large models, making AI a monetization engine for them as they move forward.

I often joke that ‘small is the new big,’ and that’s where the real momentum is. If you look at the U.S. market compared to EMEA, the trajectory is entirely different. In the U.S., the largest billion-dollar deals are coming from LLM builders, mostly from small and medium businesses and startups. We believe that this trend will eventually follow in EMEA. While enterprises will enter the big

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AI space, it’s unlikely that traditional banks or telcos will be the first to fully embrace AI. Instead, it will be the smaller businesses and a select few larger companies that will be the major spenders on AI.

Can SMBs afford to invest in AI?

As SMBs, you may feel that you cannot afford it, but timing is critical. You need to stay ahead of the curve. AI is still in its introductory phase, and while there is an investment required, the real question is: Can SMBs fund it? I believe they can, thanks to the maturity of the venture capital ecosystem today. Look at platforms like Shark Tank—everyone is pitching. Even the smallest entrepreneurs, though they may not be flush with cash, are supported by a strong network of VCs and funders, which is much stronger than in the past.

For SMBs, the key isn’t just how you solve a problem, but what problem you’re solving. If you’re addressing a critical issue, people are willing to invest. There’s a lot of money available in the market, and investors are eager to support ventures that have the potential to make a big impact. Every good idea is being heard by the market, and once someone bites, funding is not an issue. Investors are willing to put money on the table if they see the potential for a big return.

For SMBs, the mantra is ‘think big, start small, but start right.’ You need to have a clear vision of how you’ll become a unicorn quickly. Small companies today are aiming to be the next unicorn, and they want to grow fast. The goal is to have a great idea, secure initial funding, and build momentum from there.

How does this work for cash-strapped SMBs?

SMBs are unlikely to train their own models; instead, they’ll opt for smaller, off-the-shelf Gen AI solutions that cost around $50k to $100k. For that price, you can get a good Gen AI use case or a pre-built model. Yes, it might seem expensive for an SMB, but consider the cost of training a model, which can reach up to $10 million. The difference between training and inferencing on your own versus purchasing a pre-built LLM from one of the available systems is significant. Buying pre-built models that are easily integrated is much faster.

Do you recommend open source LLMs for SMBs?

We are strong proponents of open source.

For example, our platform HeatWave is unique in that it comes with an inbuilt LLM, specifically designed for the SMB segment. HeatWave, which was formerly known as MySQL, now allows you to have your own database with an inbuilt LLM. It’s open source and can integrate APIs into any existing sources, allowing you to build, pull, and draft from various data sources at a very affordable price.

Can you provide more insights into Oracle’s AI offerings and how they cater to different business needs?

I would say the whole AI offering is split into four layers. The primary, or bottom-most layer, is the infrastructure layer. This consists of GPUs, CPUs, and object storage, which are managed in your data center. Whether you’re an SMB or an enterprise, you still need an infrastructure layer to operate. It runs across Oracle’s hundreds of data centers. GPUs are essential today, and it depends on the models you’re running. For a small SMB, you might start with Nvidia’s 8-inch chip. For larger models, you may need H100 or even H200. Availability can vary between zones, so it’s an evolving process. This foundational layer is what we call the AI infrastructure layer.

The next layer is the data layer. In this layer, the most well-known product is Oracle’s 23ai, which uses vector search to correlate data from databases, object storage, and more. The data layer is also an open architecture, so we work with tools like Coherence, Mistral, and LLaMA. This allows different large language model teams to operate within the data layer, providing both Oracle’s tools and a complete open platform.

It’s the small and medium businesses, new startups, and LLM builders who are leading the charge in adopting AI more rapidly.

Next is the SaaS layer. In this, we address a key question: how can SMBs afford AI? Through NetSuite and Fusion applications, we offer AI built into ERP systems. We have around 50 models integrated into ERP, HCM (Human Capital Management), and CX (Customer Experience). For example, Oracle uses AI within our own HCM system to source candidates. Large companies are starting to adopt these AI-driven tools within HCM and CX layers. Now, couple these with the packaging of partners who build their own models to support the entire AI ecosystem, and that’s how we approach the AI journey.

The path to Responsible AI

HOW ENTERPRISES CAN IMPLEMENT RESPONSIBLE AI PRACTICES

As AI makes deep inroads into enterprises with new use cases emerging almost every week, it is imperative to understand the implications of integrating this transformative technology into business processes and decision-making. This is why businesses worldwide are acknowledging the need for Responsible AI – an approach to managing the risks associated with AI.

Accenture defines Responsible AI as the practice of designing, developing, and deploying AI with good intentions to empower employees and businesses, and to fairly impact customers and society— allowing companies to engender trust and scale AI with confidence.

Industry experts emphasize that when implemented correctly, Responsible AI not only mitigates risks but also enhances AI system performance, builds trust, encourages adoption, and drives value creation.

Thomas Pramotedham, CEO of Presigh, says, “The power of AI and big data is clear. Enterprises can uncover deep insights from their data and turn them into opportunities. This capability, however, comes with great responsibility to ensure fairness, transparency, and privacy.”

To achieve this, the leadership of an enterprise needs to align themselves

“The power of AI and big data is clear. Enterprises can uncover deep insights from their data and turn them into opportunities”

THOMAS PRAMOTEDHAM, CEO OF PRESIGH

“Responsible adoption of AI sets the foundation for improved ROI as it builds trust among users, customers, and stakeholders.”

RAMPRAKASH

RAMAMOORTHY,

DIRECTOR OF AI RESEARCH AT MANAGEENGINE

around a vision and commitment to accountability. Senior teams should discuss the issues surrounding Responsible AI, identify opportunities, establish governance frameworks, develop threat response strategies, and ensure accountability for any improper actions.

Thomas notes that another aspect to consider is the creation of a governance, risk, and compliance framework to standardize best practices. This framework should cover the entire AI system, from training data to AI models and security considerations. Enterprises must remember that employees are one of their most important stakeholder groups, and so they must be regularly educated on Responsible AI, the organization’s vision, and governance processes. Sustaining Responsible AI practices requires a combined and collective effort from everyone in the company to understand the technology’s capabilities, limitations, and risks.

Many businesses rely on processing personal data, and the protection of sensitive information, like health records, financial accounts, and biometric data, is paramount. The rapid growth of enterprise AI has only increased the need for appropriate encryption, access controls, data anonymization techniques, and, of course, robust cybersecurity measures.

According to Ramesh Parthasarathy, SVP of Engineering at Freshworks, the approach to responsibly using AI in business operations should be two-pronged. First, the AI tools used should be built responsibly, with AI ethics in place to ensure transparency, fairness, and bias mitigation. Once implemented in this manner, clear guidelines for usage should be defined to ensure these tools are used as intended.

“Product and engineering teams need to stay up-to-date with the latest industry regulations to remain aligned with evolving standards.”

RAMESH PARTHASARATHY, SVP OF ENGINEERING AT FRESHWORKS

Additionally, providing ongoing training for employees on AI ethics and responsible practices is key. Product and engineering teams need to stay up-to-date with the latest industry regulations to remain aligned with evolving standards.

Ramprakash Ramamoorthy, Director of AI Research at ManageEngine, says

responsible adoption of AI sets the foundation for improved ROI as it builds trust among users, customers, and stakeholders. Transparent and explainable AI enhances trust as it enables users to comprehend how their decisions were made and challenge them if necessary.

“An ethical framework serves as a guiding compass for AI development, ensuring that systems avoid harmful biases. Continuous learning is essential for maintaining responsible AI practices. By regularly evaluating and updating systems, organizations can address emerging challenges and ensure that their AI remains responsible and trustworthy,” he adds.

THE FOUNDATION OF RESPONSIBLE AI

The first task is to find the best-fitting AI model for the use case. It was relatively easy in the past as there weren’t many choices available, but the situation has changed significantly in the last 18 months.

Sascha Giese, Global Technical Evangelist, SolarWinds, says that once a model has been found, the training starts, which is the most critical part. “Think of sending your kid to university—the longer the education, the better the outcome. Finally, there’s the element of limiting AI’s theoretically endless abilities. I’m again using a kid as an example; sometimes, you have to show them limitations and say ‘no,’ and they learn to differentiate between good and bad or ethical and unethical.”

Ramprakash from ManageEngine points out that the foundation for Responsible AI combines an ethical framework and enterprise efficiency. At its core are ethics, fairness, transparency, privacy, safety, and accountability. However, implementing these principles effectively often depends

on an enterprise’s digital maturity. A digitally mature organization creates an environment conducive to responsible AI adoption.

To achieve this, enterprises should streamline processes, data, and automation. Streamlined processes improve efficiency and transparency, while streamlined data ensures accuracy and accessibility, which are crucial for training unbiased AI systems. Automation of tasks reduces errors and frees employees to focus on other important tasks. Overall, these steps are the foundation for Responsible AI, where AI adoption is both technologically advanced and ethically grounded.

WHAT STEPS SHOULD ENTERPRISES TAKE TO ENSURE RESPONSIBLE AI USE ACROSS BUSINESS OPERATIONS?

Simon Morris, VP Solution Consulting, ServiceNow, says that although Generative AI (GenAI) is still a new business and technology trend, the methods to protect businesses do not need to be reinvented. Chief Risk Officers should establish a governance framework that matches the regulatory obligations and the appetite for the kinds of risks that AI can introduce. Enterprises should outline policies, procedures, and best practices for AI development and deployment that cover ethical guidelines and compliance requirements.

“An observation I have made from my conversations with customers on this journey is that businesses are struggling with the decision to ‘buy or build’. The costs of building bespoke AI applications are fearsome, and many projects that are in-flight today are unfortunately going to result in an expensive white elephant that will cause concern at the C-suite and board level,” he adds.

SHOULD COMPANIES PLACE POLICIES FOR AI AUDITING OR THIRD-PARTY EVALUATIONS TO CHECK FOR ETHICAL AI DEPLOYMENT?

Absolutely, says Thomas from Presight. “There are a number of benefits from AI auditing and third-party evaluations. Companies can secure an objective assessment of their AI systems, ensure compliance with regulatory requirements, and enhance transparency and trust with their various stakeholders.”

Regular audits are useful to identify potential risks and vulnerabilities, enabling

companies to take action before it is too late.

Sascha from SolarWinds says that for AI, this is crucial for both the vendor and the customer. While regular software might need occasional bug fixing, AI is a little more “alive” and needs constant supervision and optimization. “This means that the decision-making process of an AI should be transparent and checked from time to time. Or, in a more straightforward example, we check in with our employees occasionally, and we should behave the same with a

“It involves not only considering the algorithmic aspects but also the ethical implications of AI deployment.” It involves not only considering the algorithmic aspects but also the ethical”

SIMON MORRIS, VP SOLUTION CONSULTING, SERVICENOW

machine. Even if it sounds a little weird today, it will become a reality tomorrow.”

ARE THERE ANY SPECIFIC FRAMEWORKS OR GUIDELINES AN ORGANIZATION SHOULD FOLLOW TO ENSURE AI TRANSPARENCY?

“Responsible adoption of AI sets the foundation for improved ROI as it builds trust among users, customers, and stakeholders.”

SASCHA GIESE, GLOBAL TECHNICAL EVANGELIST, SOLARWINDS

There are several ethical frameworks and guidelines that an organization can follow, from the EU’s landmark AI Act to PAI (Partnership on AI), which was founded by Google, Meta, Microsoft, Amazon, and IBM.

“I would advise organizations to establish an AI Assurance or Responsible AI Committee. This dedicated body would serve to prioritize safeguarding the organization’s core principles, encompassing legal compliance, human rights, equity, and more. The committee’s role would be to pinpoint both existing and forthcoming risks and prospects associated with AI that the organization could anticipate and strategically address,” says Ramesh from Freshworks.

It would need to develop a structured approach for cybersecurity, data science, and engineering divisions to adhere to, incorporating specific milestones within the AI development lifecycle, as well as the necessary authorizations for the deployment of novel AI technologies. Furthermore, it is imperative to carry out systematic bias assessments and evaluations—on a monthly or quarterly basis—of the AI-driven outputs generated by the marketing department, utilizing clear and precise benchmarks.

RESEARCH

AI IN ACTION

AI ADOPTION RATES

Around 70% of organizations are integrating AI into their operations, with 31.6% in the early stages and 19.4% fully implemented.

The “AI Foresight 2024” report by CaaS Research provides a comprehensive analysis of the current state and future trajectory of Artificial Intelligence (AI).

The report explores AI adoption trends, challenges, opportunities, and strategic implementations by governments and industries across various sectors, including healthcare, finance, urban development, and government services.

AI APPLICATION FIELDS

Predictive analytics leads adoption at 27.4%, followed by natural language processing at 18.5% and computer vision at 15.2%.

AI USE CASES ACROSS INDUSTRIES

1. Healthcare: AI is transforming patient diagnostics and management, improving accuracy by 30% and projected to create a multi-billion-dollar market by 2025.

2. Finance: AI is being used for fraud detection, risk assessment, and customer service via chatbots, enhancing operational accuracy by 34.4%.

3. Retail: AI aids in inventory management and personalized customer experiences, improving the shopping experience and operational efficiency.

4. Manufacturing: AI is used for predictive maintenance and optimizing production, resulting in increased efficiency and cost reductions.

GOVERNMENT INITIATIVES TO PROMOTE AI IN THE MIDDLE EAST

• Saudi Arabia: AI is being integrated into public services such as healthcare, Hajj management, and smart cities like Neom,

AI ADOPTION MOTIVATIONS

Key drivers include efficiency improvements (22.2%), improving decision-making (23.6%), and customer experience enhancement (15.2%).

CHALLENGES IN AI ADOPTION

• Lack of Expertise: 41.9% of organizations cite a skills gap as a major hurdle in AI adoption.

• High Implementation Costs: 15.2% of organizations struggle with the financial costs of implementing AI systems.

• Data Privacy and Security: Concerns over data integrity and privacy regulations present obstacles to AI deployment.

with a $500 billion investment in AIdriven urban development.

• United Arab Emirates: AI Strategy 2031 focuses on enhancing government performance and establishing AI hubs. The UAE is also leading AI education partnerships with institutions like Oxford University.

• Kuwait: AI is being applied to urban planning through GeoAI technologies, addressing challenges in integrating AI for smart city development.

AI INVESTMENTS IN THE MIDDLE EAST

• Saudi Arabia: Vision 2030 includes significant investments in AI, with $500 billion earmarked for Neom, positioning the country as a technology leader.

• UAE: AI Strategy 2031 is a cornerstone for future economic development, with heavy investments in AI research and partnerships with global universities.

• Egypt: Focused on AI integration across education and public services, with the establishment of the National Council for AI and innovation hubs.

AI UTILIZATION ACROSS ORGANIZATIONAL FUNCTIONS

• Operations: AI is used to streamline logistics and supply chain management, with 66.6% of organizations reporting increased operational efficiency.

• Customer Service: 44.2% of organizations employ AI chatbots and virtual assistants to enhance customer satisfaction.

• Marketing: AI tools analyze consumer behavior, improving marketing ROI and optimizing ad placements for 36.1% of companies.

• Human Resources: AI helps with resume screening, employee onboarding, and predictive analytics, supporting 22.3% of organizations in talent management.

FUTURE FORECAST

1. Smart Cities: AI will play a pivotal role in urban development projects such as Neom, integrating AI-driven systems across all facets of city life, from transportation to public utilities.

2. Workforce Dynamics: AI will create new job roles in data science and machine learning while augmenting existing jobs. The report predicts that 63.2% of organizations will need to upskill their workforce to meet future demands.

3. AI in Public Services: Governments in the Middle East are expected to increase AI adoption in sectors like healthcare and transportation, improving service delivery and operational efficiency.

“Our findings reveal how AI is not just transforming industries like healthcare, finance, and retail but also revolutionizing sustainability, smart cities, and national security. The Middle East is not merely following global trends; it is setting them, leading in technological innovation, enhancing customer experiences, and fostering data-driven decision-making. With significant investments in research, development, and talent, the region stands at the forefront of AI evolution in 2024,” says Jayakumar Mohanachandran, CRO of CaaS Research.

The AI + Industry Cloud Advantage

TVIBHU

KAPOOR

Regional Vice President - Middle East, Africa & India, Epicor, says it’s a potent mix for GCC manufacturing transformation

he GCC manufacturing sector is a crucial cog in the regional economy. Some 95,000 companies employ more than 1.17 million people adding about US$140 billion in value. And governments are keen to encourage growth. Saudi Arabia’s National Industry Strategy aims to triple the manufacturing sector’s GDP contribution and double non-oil industrial exports by 2030. The United Arab Emirates’ Operation 300bn has a similar aim, targeting a AED300-billion (US$82 billion) annual value-add by 2031.

Driven by government visions, market trends, and regulatory obligations, the region’s manufacturers are deep in the throes of change. They are stretching themselves beyond their comfort zones to embrace emerging technologies such as the Internet of Things (IoT) and artificial intelligence (AI). In the realm of generative AI alone, business leaders from various industries see a transformative technology with the potential to shake up their own corner of the sky. PwC’s Strategy& estimates the GCC could see US$9.90 of economic growth for every dollar invested in GenAI. And this is just one niche area. Predictive analytics, advanced robotics, and a slew of other tools are directly relevant to GCC manufacturers’ ambitions.

Another pivotal development in the tech world, that will be an extraordinary accelerant for ambitious manufacturers, is the emergence of cloud-native toolboxes that are purpose-built for industry use cases. Vertical cloud platforms, more commonly known as industry cloud platforms (ICPs), are clouds built with a single purpose. Just as a doctor’s tools of the trade differ wildly from those of a firefighter, an industry cloud reflects the specific needs of a single sector — from accounting-

ledger design to IoT support to even the nooks and crannies of compliance. The cloud has always offered flexibility to scale operations without the need for exorbitant budgets and the maintenance of new infrastructure. Now, with ICPs, enterprises can find enviable levels of requirements fit without the need for an extensive procurement process.

PLUGGING GAPS

The challenges ahead call for “doing more with less” — a mantra that has been around since 2008. This concept does not arise solely from budget constraints. Regional skills gaps also play a part. So, if a manufacturer were to acquire the means to automate its more mundane tasks, it could increase accuracy and efficiency in these areas while positioning itself as a more attractive employer. Manufacturing workers are always keen to spend their days on safer, more challenging activities that increase their personal marketability, and automation can make this a reality.

Change — especially technological change — is not easy for manufacturers. Deployment of new equipment can lead to expensive downtime and other disruptions like productivity dips during training. And yet, to remain competitive in a digital economy, manufacturers must boldy embrace digitalization. This is where industry cloud platforms (ICPs) have been gaining ground. Tailored tools, robust security, and streamlined compliance are very attractive alongside other benefits such as outsourced maintenance and automatic updates. However, tech leaders have been concerned about the ROI of ICPs. That is, until AI revealed itself as a potential missing ingredient that could propel industry clouds into the mainstream. Together, ICP and AI form a compelling combination.

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Unlocking the future

GUEST ARTICLE

TERENCE

SATHYANARAYAN

Writes about how AI is driving value in oil & gas and marine offshore support industries.

The oil and gas industry in 2024 faces a transformation that reshapes its traditional value chain. Historically linear, from exploration to distribution, the sector now embraces Artificial Intelligence (AI) to create a dynamic, data-driven chain. AI promises increased efficiency, safety, and cost savings at every stage.

Modern AI-ready value chains are built on data, enabling predictive maintenance, optimized drilling, and real-time decision-making. For example, AI can monitor offshore equipment, predict failures, and adjust production flows in real time. However, significant challenges—particularly in regions like the Middle East—remain, including data silos, outdated infrastructure, and gaps in AI literacy.

The Middle East, a global leader in oil production, faces structural hurdles in adopting AI. Many companies struggle with legacy systems that hinder the efficient use of data. The key lies in developing cohesive data management strategies that integrate operational and information technology (OT and IT).

Data hygiene is a critical issue. Without clean and contextualized data, AI applications lose their effectiveness. Additionally, companies face challenges in digital sovereignty and the creation of ethical governance frameworks. Overcoming these obstacles is essential to realizing AI’s full potential.

AI is also transforming the adjacent marine offshore support industry, which manages vital logistics for oil and gas operations. Through hyperautomation, marine logistics firms can streamline fleet management, optimize fuel consumption, and reduce carbon emissions—supporting sustainability goals like GreenOps.

AI-driven logistics, powered by real-time IoT sensors, significantly improves efficiency. Autonomous vessels equipped with AI reduce human error and enhance offshore supply chain reliability.

Integrating AI in marine logistics will improve the entire ecosystem’s efficiency, allowing companies to respond better to volatile market demands.

INSIGHTS FROM INDUSTRY LEADERS: PREPARING FOR THE AI TRIFECTA

Aditya Kaushik, CIO of a leading maritime entity serving the Oil and Gas industry envisions an organization centered around the “AI Trifecta”—Hyper Automation, GenAI, and the Metaverse. However, success will depend on implementing a strong Data Management Strategy.

• Hyper Automation: Aditya’s team is laying the groundwork for AI-driven automation across fleet operations. Once live, it will reduce manual intervention and boost safety, but requires a clean, integrated data pipeline.

• GenAI: He anticipates that Generative AI will accelerate seismic data analysis and unlock hidden reservoir value. To succeed, data must be well-managed and contextualized.

• Metaverse: Simulating high-risk tasks in virtual environments can enhance offshore preparedness, but accurate data governance is key to success.

Aditya emphasizes, “Our data strategy is the linchpin that will determine whether we thrive in this AIdriven future.”

CONCLUSION: THE AI-READY FUTURE OF OIL AND GAS

The oil and gas industry is on the verge of an AI-driven revolution that promises to enhance safety, efficiency, and profitability. However, success depends on addressing fundamental challenges in data management and digital infrastructure. Those who successfully integrate AI will shape the future of energy.

NEXT-GEN OBSERVABILITY

Go beyond traditional IT monitoring.

Actionable, AI-powered insights across any environment.

INTERVIEW

Can you describe your role at Zebra Technologies?

I lead all of our employee-facing technology and, in some areas, also our customerfacing technology. This includes enterprise systems such as ERP, CRM, and other core enterprise tools, as well as end-user services like laptops, help desks, infrastructure, networking, and cloud servers. In addition to these, my team also manages customerfacing portals, such as our repair and partner portals for ordering, interacting, and transacting, as well as all of our B2B connectivity for customers to place EDI-type orders. My role spans the breadth of Zebra’s operations.

How are you leveraging AI within Zebra?

Over the past few years, we’ve focused on AI, particularly within our service help desk. Our service help desk at Zebra supports customers globally, and we’ve been using AI for sentiment analysis as well as making proactive recommendations when customers call in about repairs. Depending on the persona, we may ask, ‘Did you realize the end of your contract is coming up? Can I give you more information on that?’ This kind of AI-driven service has been around for a

“Blending

human and machine”

while, but with the rise of generative AI, it’s been enhanced.

MATT AUSMAN

, CIO OF ZEBRA TECHNOLOGIES, EXPLAINS HOW AIDRIVEN INNOVATION IS TRANSFORMING INDUSTRIES.

Being a technology company at heart, we leverage our engineering team’s expertise in AI. I have the advantage of being able to call our engineers, who are working on AI predictive models, and asking for their help. Many IT organizations don’t have that inhouse capability, but since we’re also selling technology to our customers, I can tap into that potential to help accelerate our internal efforts.

We’ve also started incorporating AI

into our supply chain, using it for predictive analysis in supply chain planning and how we more efficiently operate our distribution centers. These are the areas where Zebra has implemented AI internally to optimize operations.

How do you envision AI transforming the industries that Zebra serves?

I will give you my perspective: I think there’s a short-term transformation, but I also like to consider how things will be a decade from now. What will the world look like? In the longer term, I believe it will fundamentally transform the way we operate. But when I think about the short term, I see more of a merging of humans and machines, working together more symbiotically.

For example, today, I have teams that leverage analytics to help them predict what parts we should order. I expect our customers to leverage some of that in our software solutions. We acquired a company called Antuit, which is now part of our Workcloud Suite, and it’s helping customers with demand forecasting. It uses predictive analytics for ordering, stocking shelves, and tracking how much inventory is going out. While that might not feel transformative from a customer’s perspective, they should see better-stocked shelves. None of us like walking into a grocery store and seeing empty shelves, right? If we can do a better job of predicting what our customers need, it will improve the retail experience.

Looking at other use cases, we’re exploring task management automation. Will humans interact with wearables that tell them where they need to be and when? About 25 years ago, I worked in a grocery store as a cashier, bagger, and stocker. Back then, someone had to call us on a speaker broadcasting to the entire store, to tell us to collect trolleys from the parking lot or clean up a spill. You hoped someone responded to the call. In today’s world, that should be automated: I should get a notification on my wearable, letting me know it’s my responsibility to clean up the spill or handle the task.

In the short term, automation should be flexible and accessible while helping augment the capabilities of workers to take on more value-added roles. And as a result, we’re going to see an improved customer experience, though it will still resemble how we operate today. When I think about the long term, say 10 years from now, do I think we’ll see more autonomous robots in

the world? Absolutely. I think we will move beyond human augmentation to instances where machines handle more interactions than we currently see.

Look at fast food in the United States. Now, you can place your order yourself, without interacting with a human. In the past, you would speak to a person who manually processed your order. Can machines do it faster and better, not yet, but at some point they will? Now, you walk up and order through a machine interface, which has replaced the human interface. As generative AI improves, the line between speaking to a machine and speaking to a human will blur even more.

We’ve started to explore and plan for this this in our call centers. If you call in and who you’re talking to sounds human, but it’s really a machine, is that a bad thing if your issue still gets resolved? Think about chatbots. We’ve all used a chatbot and gotten frustrated when it doesn’t answer our question. But if it does answer the question and solves the issue, we’re satisfied. As AI becomes more capable of replicating human-like interactions, that’s when I believe we’ll see even more transformative changes.

Zebra focuses on enterprise asset intelligence supported by hardware such as scanners and other hand-held devices. What role does AI play in these products? One example where AI can help is in the healthcare industry. Imagine a bag of blood which has many barcodes and labels. It can be a challenge for how a nurse determines which one to scan or how they ensure the wrong one isn’t scanned? They hold the bag, cover parts of it with their hand, and then try to scan around it, trying to select the correct barcode. There could be four barcodes on the bag, so the nurse has to decide which one to scan.

A type of AI could be used in combination with optical character recognition (OCR) where a nurse could simply scan the bag, and the system knows which barcode to pick. If it would take 30 seconds to scan a barcode before, with AI it would take two seconds, that’s half a minute they get back to spend with a patient. That is a nice incremental improvement for nurses. Just think about the efficiency—those seconds they save add up. This is a great example of how image processing, combined with human logic, could transform the way our customers operate in the future.

“Our AI efforts have mostly been datadriven, with a focus on marketing, supply chain, and services. ”

INTERVIEW

“Building a trusted ecosystem”

du is deploying Oracle Alloy to offer cloud and sovereign AI services to business, government, and public sector organizations in the UAE.

Let’s talk about the Alloy deal, sovereign AI, and everything related to that. So, what does this really mean in the context of the UAE, especially for public sector and government entities?

Frst of all, I think for us, Alloy is a product that fits the purpose perfectly when it comes to providing sovereign infrastructure and positioning ourselves as a national cloud provider. This was the main intention when we started the Alloy engagement. Later on, a lot of advancements happened in AI, and we realized that AI requires a high level of sovereignty, particularly in the public sector. There will come a point where you won’t be able to fully leverage AI’s capabilities in the public and government sectors due to regulatory requirements.

Alloy offers the perfect solution by providing secure and sovereign infrastructure, along with a suite of GPUs

JASIM AL AWADI

CHIEF ICT OFFICER, DU

that can be offered as a service to clients. This enables them to build their LLMs and meet all their requirements in a sovereign environment. That’s why we don’t regret moving forward with Alloy. I believe it was the right choice, and today, we can clearly see how Alloy has differentiated us in the market against our competitors.

So is the strategy now to transform from a telecom service provider into a hyperscale cloud service provider? Is that going to be a new business line for you?

Just to shed some light, I think du has been in the ICT business since 2016. As an organization, we have long recognized the need to grow beyond our core offerings. That’s why, a few months ago, we launched duPay, our FinTech service that goes beyond our traditional ICT department. As part of our strategy to expand beyond our core services, du now offers a broad service portfolio, including data center services, cloud security, advanced technology such as robotics and machine learning, as well as Blockchain services.

These are the services that go beyond the traditional ones we used to offer. And today, with Alloy, we are positioning ourselves as a national cloud provider. The traditional way of providing cloud services—focusing only on infrastructure and hardware—is no longer sustainable. You need to offer an entire ecosystem, including applications, on top of it. Alloy provides us with the capability to position ourselves as a hyperscale provider in the market

When do you plan to start offering this sovereign AI services?

Our region will be live by Q2 next year. Once the region is live, we’ll begin offering these services to our clients. Currently, we are working closely with clients, alongside the Oracle team, and the plan is to first move them to the public cloud and then transition them to the sovereign cloud.

Are you leveraging Alloy to modernize your own internal IT systems and engineering systems?

Of course, today you cannot ask your clients to migrate to the cloud without doing it yourself. This is why part of our plan is to migrate our internal workloads as well. Currently, our CIO is personally working with me on this exercise, and we are planning to move all applicable workloads to the cloud.

Are there any specific verticals, other than the public sector, that will benefit from the combination of AI and cloud?

The banking and financial sector is one of the industries that will benefit the most from this, as they are heavily regulated and require strong sovereign capabilities. The healthcare sector as well, since it deals with highly sensitive client details and requires sovereignty. I believe these are the two main sectors that will benefit significantly from Alloy, in addition to the security sector, including organizations like Dubai Police, the armed forces, and other security entities.

Are you now able to offer customized services? How is it different from standard public cloud offerings?

What sets it apart from standard public cloud offerings is the ability to provide niche applications tailored specifically for our clients, particularly those that are unique to the region and country. These customized services cater to the specific needs of our customers, in addition to everything available in the public cloud offering. This is how we differentiate ourselves and our solutions from standard public cloud services.

How do you see the adoption of AI within the public sector in the UAE? Is it really picking up now?

“The banking and financial sector is one of the industries that will benefit the most from this”

The great thing about the UAE is that we are blessed with the vision of our government. Yes, of course, the UAE has always been at the forefront of adopting new technologies. When AI emerged in the market, many countries were hesitant or even resistant to it, but the UAE embraced it from day one. Today, we have 22 chief AI officers and AI ministers announced across various sectors, including banking and government ministries. The UAE is one of the few countries that is actively pushing AI adoption.

As for the demand, it is extremely high, but the challenge lies in the availability of infrastructure, especially GPUs. The demand is through the roof, but we still need to scale up the infrastructure to fully support this growing need, particularly in terms of GPU resources.

INFRASTRUCTURE

TOP LARGE LANGUAGE MODELS

WE EXPLORE THE TOP LLMS SHAPING THE FUTURE OF AI, HIGHLIGHTING THEIR UNIQUE FEATURES, AND CAPABILITIES.

JAIS

JAIS, created by the G42 company Inception, is a 70 billion parameter model built for developers of Arabicbased natural-language processing (NLP) solutions and promises to accelerate the integration of Generative AI services across various industries, enhancing capabilities in areas such as customer service, content creation, and data analysis.

JAIS 70B delivers Arabic-English bilingual capabilities at an unprecedented size and scale for the open-source community. As a 70 billion parameter model, it has increased ability to handle complicated and nuanced tasks, as well as better capability to process complex datasets. JAIS 70B was developed using continuous training, a process of finetuning a pre-trained model, on 370 billion tokens of which 330 billion were Arabic tokens, the largest Arabic dataset ever used to train an open-source foundational model.

The model is the result of a collaboration between Inception, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) — the world’s first graduate research university dedicated to AI — and Cerebras Systems. It was trained on Condor Galaxy, the recently announced multi-exaFLOP AI supercomputer built by G42 and Cerebras.

FALCON

Falcon 180B, from Abu Dhabi Government’s Technology Innovation Insititute (TII) is a super-powerful language model with 180 billion parameters, trained on 3.5 trillion tokens. It’s currently at the top of the Hugging Face Leaderboard for pre-trained Open Large Language Models and is available for both research and commercial use.. This model performs exceptionally well in various tasks like reasoning, coding, proficiency, and knowledge tests, even beating competitors like Meta’s LLaMA 2. Among closed source models, it ranks just behind OpenAI’s GPT 4, and performs on par with Google’s PaLM 2 Large, which powers Bard, despite being half the size of the model.

GPT-4

OpenAI’s GPT-4 is a large multimodal model that, while less capable than humans in many real-world situations, demonstrates human-level performance on several professional and academic benchmarks. For example, it passes a simulated bar exam with scores ranking in the top 10% of test takers, whereas GPT-3.5 scored around the bottom 10%.

GPT-4 accepts prompts that combine both text and images, allowing users to specify tasks that involve both vision and language. It generates text outputs (such as

natural language or code) based on inputs of mixed text and images. Despite its advanced capabilities, OpenAI cautions that GPT-4 shares similar limitations with earlier models, most notably its unreliability (it can “hallucinate” facts and make reasoning errors). When using language model outputs, especially in high-stakes scenarios, careful attention is required.

GEMINI

Google Gemini is a next-generation large language model (LLM) developed by Google DeepMind, designed to compete with models like GPT-4. Gemini integrates advanced natural language understanding and generation with multimodal capabilities, allowing it to process and generate text, images, and other types of data. Its architecture emphasizes not only text-based tasks but also a wide range of AI applications, such as code generation, content creation, and interactive dialogue. With a strong focus on accuracy and contextual understanding, Gemini aims to reduce common issues like hallucination while delivering more reliable and context-aware outputs across various professional and academic fields.

LLAMA

Meta’s LLaMA 3 is the latest release in their open-source large language models,

Gemini aims to reduce common issues like hallucination while delivering more reliable and contextaware outputs.

available for broad use. It includes pretrained and instruction-fine-tuned models with 8B and 70B parameters, supporting a wide range of applications. LLaMA 3 delivers state-of-the-art performance across industry benchmarks, with enhanced reasoning capabilities. Meta emphasizes its commitment to open access, encouraging the community to drive innovation across applications, developer tools, evaluations, and optimization. This release aims to foster the next wave of AI advancements, and Meta eagerly anticipates community feedback and contributions.

CLAUDE

Claude is a large language model developed by Anthropic, designed to prioritize safety and alignment with human values. Known for its focus on generating helpful, honest, and harmless responses, Claude aims to address common concerns in AI systems, such as hallucinations and harmful outputs. It is built to assist with a wide range of tasks, from natural language understanding to code generation, while maintaining a strong emphasis on ethical use. Claude’s development reflects Anthropic’s commitment to creating AI that is both powerful and aligned with human intentions and safety.

HAIDI NOSSAIR

SR. DIRECTOR – CLIENT SOLUTIONS GROUP – META AT DELL TECHNOLOGIES,

Sheds light on how the tech giant is powering tomorrow’s workplace with its lineup of AI PCs

“Transforming productivity with AIpowered PCs”

How do AI PCs fit into Dell’s overall AI factory strategy?

The Dell AI Factory with NVIDIA provides organizations with the building blocks for seamless integration of AI models and frameworks into their operations, enabling them to turn their ideas into practical applications. From digital assistants to new code generation and natural language search applications, the AI Factory framework allows organizations greater control over their proprietary data and scales efficiently.

With this, Dell’s AI PCs play a crucial role in the strategy by providing the necessary computing power and capabilities for various AI applications. These PCs, equipped with advanced hardware and software, can handle tasks such as machine learning, data analysis, and natural language processing. They serve as essential components within the AI factory ecosystem, working in conjunction with other hardware and software solutions to enable organizations to develop and deploy AI models efficiently.

What are the most significant features that differentiate Dell’s AI PCs from traditional ones?

Dell’s AI PCs stand out from traditional ones with several key elements. A central feature is the Neural Processing Unit (NPU), which acts as an AI acceleration engine. The NPU handles dedicated AI tasks, offloading them from the CPU and GPU, resulting in enhanced performance, better security, and increased productivity. This optimization allows for a more responsive system and longer battery life, ideal for users who rely on heavy computational workloads.

Additionally, Dell’s AI PCs come preinstalled with specialized AI software and frameworks, streamlining the development and deployment of AI models. The integration of Intel Core Ultra processors and Intel vPro® technology ensures that these PCs can handle demanding AI workloads, offering significant productivity gains. AI tasks are distributed across the CPU, GPU, and NPU, enabling users to perform tasks like generating AI-based images up to five times faster.

Security is also a major focus for Dell’s AI PCs. Security is enhanced by offloading threat detection to the NPU. This provides faster, more comprehensive detection of malicious sites and vulnerabilities without relying solely on cloud-based solutions.

The built-in features also improve remote collaboration, enabling enhancements

like auto-framing, background blur, and eye-tracking, which are crucial for video conferencing. Paired with Intel Core Ultra processors, these features help users enjoy up to 38% more battery life, making the devices perfect for a full day of remote work and video calls. The portfolio also features Windows 11 and a Copilot key to make it even easier to get things done and stay in the flow of work.

Furthermore, our brand-new class of Copilot+ AI PCs powered by Snapdragon X Series offer extraordinary performance, upleveled battery life and next-generation AI capabilities.

How do you see the demand for AIpowered PCs evolving in the near future? What are the key industries or user segments driving this demand?

The demand for AI-powered PCs is anticipated to see continued growth in the coming years. It is estimated that the NPU equipped AI PC will grow from nearly 50 million units in 2024 to more than 167 million in 2027, representing nearly 60% of all PC shipments worldwide.

This will be driven by several key factors. Industries such as healthcare, finance, manufacturing, research and the public sector, are increasingly adopting AI to improve efficiency, make data-driven decisions, and gain a competitive edge. As more organizations recognize the benefits of AI, the demand for the underlying hardware, including AI PCs, will also increase.

From running complex AI workloads on workstations to using day-to-day AIpowered applications on laptops, the AI PC will be an important investment that pays dividends in productivity and paves the way to a smarter, more efficient future.

How is AI being utilized to enhance the security of Dell AI PCs, especially against evolving cyber threats?

At Dell, we build security into every layer of the AI ecosystem. We make it a priority to design, develop and deliver secure IT products and solutions. With the AI PCs, we deploy advanced AI to enhance security by continuously monitoring and learning from user behavior, identifying potential threats in real-time. For example, Dell is working with CrowdStrike and Intel to offload security functions onto the device via the NPU. This provides more comprehensive threat detection, helping customers swiftly detect malicious sites and security vulnerabilities.

167 MN UNITS IN 2027

50 MN UNITS IN 2024

IT IS ESTIMATED THAT THE NPU EQUIPPED AI PC WILL GROW FROM NEARLY 50 MILLION UNITS IN 2024 TO MORE THAN 167 MILLION IN 2027.

“At Dell, we build security into every layer of the AI ecosystem.”

FOUNDER & CEO OF HYPERFUSION. INTERVIEW

QUENTIN REYES

“Changing the game”

Hyperfusion is offering a secure and scalable platform to deploy AI workloads, hosted in a Tier 3 data center in the UAE. We discuss how the company is making AI computing accessible to both established enterprises and emerging companies alike.

Can you provide an overview of your company?

Hyperfusion is a high-performance computing company specializing in AIrelated applications and projects. We manage one of the largest computing clusters in the Gulf region. What we refer to as high-performance computing involves highly data-intensive work, utilizing specific servers and data centers that are not typically used for standard app computing.

What are you showcasing here at the event?

We are showcasing our expertise in AI, particularly through the products developed by our AI team. One initiative we’re proud of is offering UAE businesses a free Arabictrained chatbot that can be hosted on their own servers. This provides them with immediate, easy wins in AI without requiring a significant leap forward. Additionally, we’re demonstrating our ability to scale such a large computing cluster here, something no other company in the country can do.

How has the response been from the attendees regarding your AI-related products?

The response has been very positive. Many people were initially apprehensive about AI, mainly because they perceive it as expensive. By offering easy wins and accessible solutions, we’ve been able to ease their concerns. Another area of interest has been how we can help customers optimize their computing costs by understanding their tasks and ensuring they’re as efficient as possible, ensuring cost-effectiveness.

Do you have any specific use cases you are showcasing?

Yes, we have one of our clients demonstrating a great use case involving video streaming. Despite the poor internet connection at this event, they have managed to offer a full livestream gaming experience using our remote

Our operations are housed in several segregated data centers, physically separated from other activities.

GPUs. This is a fantastic example of how our hardware solutions can make a difference. You can even interview them later if you’d like.

Could you elaborate on more use cases?

Another use case involves our AI team demonstrating chatbots, which are similar to ChatGPT, but hosted entirely on our GPUs and systems. This allows companies to maintain control over security and privacy while running AI applications. Essentially, everything is managed in-house, giving our clients control over their user experience.

What about the cybersecurity concerns related to AI? How is Hyperfusion addressing those?

Cybersecurity is our number one concern, especially regarding data protection. That’s one of the reasons we started Hyperfusion. We have earned an IR certification from the Federal Government in the UAE, and we maintain a “security by default” approach. This includes monthly productivity reviews, best practices on every employee’s laptop, and a strong emphasis on education. Typically, data leakage stems from user actions, so it’s crucial to educate our clients and ensure best practices are followed.

What cybersecurity measures do you have in place?

Our operations are housed in several segregated data centers, physically separated from other activities. We have proactive security measures in place, including separation of employee laptops, strict data management policies, and dedicated security teams. We regularly run red team and blue team exercises to test our security systems. Of course, cybersecurity is always a proactive approach, but we are well aware of the risks due to the sensitive data we manage.

The AI maturity blueprint

GUEST ARTICLE

FABIO SPOLETINI

Group Vice President – South EMEA, ServiceNow, shares learnings from those that are setting the pace.

To understand what makes an AI Pacesetter and sets them apart, ServiceNow collaborated with Oxford Economics on a global survey of 4,470 executives at organizations that have tried to put AI to good use. We identified five maturity pillars for AI in the enterprise:

1. AI STRATEGY AND LEADERSHIP

Of all of them, the strategy and leadership pillar emerged as the best predictor of a high overall index score. It should be no surprise that strong leadership is the foundation of success. Among organizations identified as Pacesetters by the index, two thirds of respondents (67%) said they had “good visibility [into] AI deployment and utilization” compared with 33% of Everyone Else. Some 65% of Pacesetters reported that “we are operating with a clear, shared AI vision toward business transformation across the wider organization”, while only 31% of others said the same.

2. WORKFLOW INTEGRATION

Pacesetters tend to be more open to allowing AI to change their organization rather than wrestling to change AI so it fits an already existing process. This is most starkly apparent when it comes to breaking down siloes — something many senior executives resist. Innovation and growth can be the result when enterprises follow the lead of Pacesetters that use AI in areas such as data cleaning, management, integration, visualization, and transformation (76%), where only 42% of non-Pacesetters do so, or in performance management (68%), where only 36% of others do so. AI that is not allowed to operate at full capacity is often a wasted investment.

3. TALENT AND WORKFORCE

AI talent in the GCC is rare. It has become the norm in the region to mix external recruitment with internal upskilling, sometimes even prioritizing the latter.

When asked about their AI recruitment priorities for the next fiscal year, 76% of Pacesetters said they would be hiring AI configurators (versus 49% of others who mentioned this role) and 72% (as opposed to 48% of others) said data scientists were top of their list. As a general rule, Pacesetters seemed more attuned to the ongoing global race to build the best AI talent pool, whether this meant attracting new employees or investing in existing ones.

4. AI GOVERNANCE

Just as critical as the employees who will deliver the output are the rules that will guide them as they innovate. Governance mitigates risk, pure and simple. Pacesetters understand, for instance, that the quality of data is reflected in the outputs of the models fed by that data. A greater number of Pacesetters have made significant progress on acquiring technologies to integrate and optimize data (63% vs. 43% others), formalizing data governance and privacy compliance (62% vs. 44%), and addressing evolving data governance and creating AI-specific policies to protect sensitive data and maintain regulatory compliance (59% vs. 42%)

5. REALIZING VALUE

In general, we found that large budgets were being directed towards technology. AI on its own accounted for an average of 15% of total IT budget, which in turn was an average of 9% of total revenue. But organizations that lack clear KPIs for their AI investments will struggle to keep them funding, which is why those in enterprises with high AI maturity ratings invariably stay laser focused on business value. We asked about the impact of AI investments on business outcomes. Limiting responses to those that showed a 1% ROI or greater, we found that 84% of Pacesetters had enjoyed increased efficiency or productivity against just 65% of non-Pacesetters.

The rise of the chief AI officer

Across the GCC, progress in AI adoption continues at breakneck speed. Prior to the ascendancy of generative AI (GenAI), McKinsey predicted that the combined GDP of the Arab Gulf region could rise by 9% because of artificial intelligence (extra “value” of US$150 billion, according to analysts). But even in May 2023 when the estimate was published, McKinsey saw the potential of GenAI and stated in its report that its projections “could be quickly surpassed” by the emergence of large-language models (LLMs) and other autonomous-creation technologies.

As knuckles whiten and pulses race, the AI rollercoaster seems out of control. But history shows that GCC governments are never content to wait and see. They strategize; they act. In June this year in the United Arab Emirates (UAE), home to the world’s first minister of state for AI, HH Sheikh Hamdan bin Mohammed bin Rashid Al Maktoum, Crown Prince of Dubai and Chairman of The Executive Council, approved the appointment of Chief AI Officers (CAIOs) in 22 government departments. The CAIO is a role that has garnered much attention since AI became a wild stallion in need of a whisperer. While the idea may seem natural and timely, the complexities behind real-world appointments is worth exploring.

SID

BHATIA

Regional VP & General Manager for the Middle East, Turkey, and Africa at Dataiku, questions whether the chief AI officer is a much-needed wrangler for a wild stallion or simply an unnecessary stable hand. GUEST ARTICLE

Let’s start with the basics. The CAIO’s job description shows much overlap with other tech execs, such as the Chief Information Officer (CIO) and Chief Data Officer (CDO). Without absolute, unquestioned ownership of AI, the ability of the CAIO to effect real purpose in the journey to Everyday AI (an all-embracing cultural shift where an organization’s entire workforce operates every second with AI in mind) is limited. For the CAIO to be worth the resources expended in their headhunting, recruitment, onboarding, and ongoing cost of retention, the enterprise must use the resource properly. The CAIO must be allowed to take the reins of strategy, implementation, and governance so they may guide the organization towards fulfilling business objectives and, in the case of private companies, gaining competitive advantage.

USURPIN’ TURF

The CAIO will develop and execute AI programs. They will align outputs with business goals and drive an Everyday AI culture that embeds ethics, banishes bias, exudes transparency, and delivers data privacy. In short, the CAIO is an amalgam of business strategist and risk manager. As part of this hybrid role, the officer will lay the groundwork for AI success by devising ways of acquiring and retaining top AI talent. But

pushback from CIOs and CDOs is inevitable, given the enormous stake they may already have in AI-related areas.

The CIO may have established the organization’s overall technology strategy, IT infrastructure, cybersecurity posture, and digital transformation programs from the ground up. AI is probably an integral part of their portfolio. They may see the CAIO less as a horse whisperer and more as an unnecessary stable hand. The CDO may have a similar view, given that their role may be considered to extend naturally into AI, including its governance. From the data expert’s perspective, a CAIO could threaten non-AI data initiatives such as self-service analytics.

The first and most obvious countermeasure to potential power struggles is to define each role to eliminate overlaps. Acknowledge the tight interdependencies between AI, data, and IT, and mandate close collaboration between the CAIO, CIO, and CDO to ensure AI initiatives are supported by all departments and aligned with business goals. Over time, it is to be hoped that trust and efficiency will emerge from this collaboration. But the CAIO must lead in AI governance. In any enterprise, a risk to one is a risk to all. Other stakeholders must be encouraged to see the difference between AI and data governance and to recognize the

need to support the CAIO as they take the lead in the former.

BY COMMITTEE

But just because the role of AI leader is clearly defined, it does not necessarily follow that responsibilities for Everyday AI should be on the CAIO’s shoulders alone. Collaborative approaches that include multiple team members from across the organization could be used to turbocharge AI maturity. Committees may emerge that either replace the CAIO or are chaired by the CAIO but include the CIO, CDO, and many other business stakeholders. Committee members would share ownership of AI initiatives, allowing leaders to align strategy and governance on IT, data, and AI before a single project is launched.

Where a CAIO is present, the CIO should be restricted to focusing on broader IT infras tructure, and the CDO to looking after data assets.

This first-things-first approach has proved a winner in general digital transformation programs. It would likely be a similar boon to the AI journey, with or without a CAIO. We have seen so many digitalization efforts collapse under the weight of departmental silos. Lessons cannot be salvaged from failures while finger-pointing persists. When siloes are eliminated and everyone takes responsibility for every project, we have achieved joint accountability — a key component of Everyday AI and a strong countermeasure to turf wars.

So, is the wild-stallion wrangler just visiting, or here to stay? While the CAIO role may be fulfilled by a cross-functional committee in some organizations, others may see a dedicated executive as a musthave. Either way, the CDO and CIO will be critical to the campaign for an AI future — not as lieutenants to the CAIO, but as fellow generals working together to overcome the complexities of AI integration in a world hungry for its leverage.

EVERYWHERE, EVERY DAY

As with everything, attitude matters. If there is resistance to the CAIO, job descriptions will matter little, and a committee approach may better serve the AI program. But where collaboration can blossom and the spirit of joint accountability is embraced, the rewards of an independent arbiter and strategist could be significant. The GCC seems set to lead in the AI space. It could be that the Everywhere CAIO could be the ticket to Everyday AI.

GENERATIVE

DRIVING THE FUTURE OF GENERATIVE AI

CHRIS WIGGETT

Head-Data and Analytics, NTT DATA in Middle East and Africa, writes about how new AI modelling is boosting the power of next-gen AI systems.

Imagine a scenario where a customer needs to contact a call center regarding a complex billing issue. First, an AI agent immediately analyzes the customer’s query, finds relevant information in the company’s database, and suggests potential solutions to the human agent. Now, the human agent can respond more efficiently and accurately. The customer spends less time on the call and enjoys an improved overall experience.

The AI agent can also automate routine tasks such as sending follow-up emails to check whether the customer is satisfied with the resolution of their issue, freeing the human agents to focus on more complex problems.

IT’S A WIN-WIN SCENARIO FOR THE CUSTOMER AND THE CALL CENTRE ALIKE, BUT HOW DID WE GET HERE?

The advent of generative AI (GenAI) has been a milestone in the evolution of

AI-driven technologies. Unlike narrow AI, which is tailored to specific tasks, it can perform an array of intellectual tasks similar to those a human can undertake – like analysing data, writing and illustrating. Within this broad spectrum, agentic modelling has emerged as a specialised area focused on developing autonomous agents that can act on behalf of humans.

It involves creating systems that process data and perform predefined operations, with some level of autonomy in decision-making. These systems learn from interactions with their environment, adapt to new situations and make choices that align with their programmed goals or objectives.

GENAI AS A CORPORATE PRIORITY

Organisations across industries are now incorporating GenAI into their strategic planning. A common business goal is to use these tools to simplify complex processes and enhance digital capabilities to improve the customer experience (CX) – and boost customer retention. According to Forbes Advisor survey, 64% of organisations believe that AI will boost their overall productivity, This illustrates rising confidence in AI’s potential to transform business operations.

The GenAI market is responding to the need for platforms that meet these requirements by developing models tailored to industry-specific requirements. This is contributing to exponential growth in the market. According to an IDC forecast, spending on GenAI – including software, hardware, and IT and business services – is expected to reach US$151.1 billion in 2027, with a compound annual growth rate of 86.1% between 2023 and 2027.

AGENTIC MODELLING AND ARTIFICIAL GENERAL INTELLIGENCE

In this context, the interactive agent foundation model is a framework for AI systems that can interact effectively with humans or other virtual agents in a dynamic environment. These models provide a base for more complex functions that let AI agents understand and respond to inputs, make decisions and take contextually appropriate actions.

Agentic models gather data from multiple sources and provide real-time assistance to both human agents and customers during customer interactions, thereby helping the call-centre operators make more informed decisions and

Agentic models gather data from multiple sources and provide realtime assistance to both human agents and customers during customer interactions, thereby helping the call-centre operators make more informed decisions and improving the quality of service.

improving the quality of service.

Process-aware agentic models in business operations more generally can employ GenAI capabilities to improve the employee experience, facilitate task completion and streamline workflows.

APPLYING AGENTIC MODELLING

The interactive agent foundation model represents a move towards artificial general intelligence, a sophisticated form of AI capable of reasoning, planning, problemsolving, abstract thinking, understanding complex concepts and rapid learning from experiences.

This model’s flexibility and broad applicability show potential for use in various fields including robotics, gaming and healthcare. As these agents learn to understand text-based commands and operate within simulated settings, the model facilitates the creation of smart robots and virtual assistants that can interpret and execute intricate instructions across industries.

As part of Microsoft’s AI for Good initiative, agentic models are being used to autonomously analyse data, predict outcomes and make informed decisions to advance goals in sustainability, health, humanitarian aid and social justice.

However, these developments also raise questions about the ethics and implications of AI systems making decisions that can affect humans and their environment.

These concerns underscore the importance of working with expert service providers to implement smart, AI-driven solutions safely and securely.

THE START OF A NEW CYCLE OF INNOVATION

Advancements in GenAI and agentic modelling are set to revolutionise the technological landscape, offering unprecedented levels of autonomy and adaptability.

These technologies are not only enhancing organisations’ current capabilities. They’re also paving the way for innovations that will redefine AI’s potential to drive progress and create value – if it’s used responsibly and ethically, with a deep understanding of the risks involved and how to manage them.

Beyond the hype

YASSER HASSAN

MANAGING DIRECTOR, MENAT AT AWS, EXPLORES PRACTICAL USE CASES FOR GENERATIVE AI IN SMALL BUSINESSES.

The conversation around generative artificial intelligence has sparked both excitement and skepticism. The potential of AI is undeniable, with applications like chatbots improving customer service and machine learning algorithms detecting fraud or predicting equipment failures. But there’s often a gap between the hype and practical applications, leaving small or medium businesses (SMBs) wondering whether they can also join in on AI applications.

According to this US Statista survey, 44 percent of small business owners and marketing decision-makers stated that their biggest concern regarding using artificial intelligence and/or automation technology for marketing was their data security. Concerns about the price of implementation of the technology followed with 41 percent. This highlights the importance of efficient operations for SMBs, as they strive to balance productivity with budget constraints in order to remain competitive. With the right strategy, companies of your size can find affordable ways to use AI and make datadriven decisions that boost business.

HOW TO GET YOUR SMB STARTED IN GENERATIVE AI

You do not need an in-house data science team and high compute power to get started. Cost is often perceived as a barrier, however, the democratization of AI—along with the advent of many tools and services offering lowcode to no-code solutions and pay-as-you-go models—has changed this landscape. In the cloud, you can benefit from AI capabilities without requiring deep technical skills.

That said, one prerequisite remains: having digitized data in the cloud. Before getting started, you should evaluate your existing data or abilities to gather such information. This data could include text files, spreadsheets, videos, images, and more. If not already in the cloud, it will need to be migrated, where it can be used for training and fine-tuning models.

Once you have completed an assessment of your data, the next step is to thoroughly evaluate potential use cases to meet business needs. With those defined, you can then explore available options. One option, if you have in-house IT staff, would be to make use of your data to train your own model or take an existing model and making small tweaks to it (what we call “fine tuning”). Another option would be to make use of an existing foundation model that can address the use case and leverage that in your applications.

If you’re like many SMBs without a dedicated IT staff member, we suggest working with skilled AWS Partner Network consultants who specialize in companies of your size. Many offer free consultations or assessments before you decide to commit.

SIX USE CASES FOR SMBS

At Amazon Web Services, we understand the importance of getting ahead of these

conversations to dispel the “hype cycle” mentality that AI is just another short-lived trend. Let’s explore some easy, entry-level ways you can use generative AI to solve realworld challenges for your SMB.

1. CONTENT CREATION AND OPTIMIZATION

Generative AI tools can assist small businesses in creating and optimizing content for websites, blogs, social media platforms, and education. From creating compelling product descriptions to crafting engaging courses, AI-driven content generation streamlines the process, enabling you to save time and effort.

AWS SMB customer, Dende.ai, transforms the way students from all over the world learn new content and review key concepts. The company has built a platform for students to upload their study material and receive a summarized version—as well as flash cards—in a matter of seconds. The company leverages several innovations brought by generative AI to achieve this ultra fast and reliable way of optimizing an approach to studies. Alec Conti, founder of dende.ai, shared, “With Amazon Bedrock we have reduced information processing time by 40 percent compared to previous solutions. We have been able to generate more new content, while maintaining a high level of quality and reliability.”

2. CUSTOMER ENGAGEMENT THROUGH PERSONALIZED MARKETING CAMPAIGNS

With AI-powered solutions, small businesses can deliver personalized marketing campaigns that resonate with their target audience. By analyzing customer data and behavior, generative AI can enable you to tailor email marketing content, social media ads, and website recommendations, which can enhance customer engagement and conversion rates. Check out a sample solution on how you can offer a personalized customer experience with machine learning.

3. CUSTOMER SUPPORT CHATBOT INTEGRATION

Enable your support staff to work on issues that truly require 1:1 involvement. Building custom chatbots using AWS services allows small businesses to provide efficient, 24/7 customer support. These AI-powered chatbots can handle routine inquiries, provide product recommendations, and even assist in completing purchases. This can improve customer satisfaction and

reduce agent costs. Check out a sample solution on how to build a Q&A chatbot using AWS services.

4. FIND ANSWERS AND TRENDS HIDDEN IN UNSTRUCTURED DATA

One exciting use case for generative AI is searching through unstructured data—such as videos, text files, email, and images— to produce accurate and relevant search results quickly. Unlike traditional keywordbased search, it can interpret the intent behind vague or abstract searches and return results that match the implied meaning. This allows users to search naturally using conversational language rather than having to come up with the exact keywords needed. By using the power of generative AI, search engines can deliver more accurate, complete, and human-friendly results when reviewing massive troves of data. For example, if you often need to process and review dozens of documents, check out this sample search solution with AWS.

5. DATA ANALYSIS AND PREDICTIVE INSIGHTS

AWS offers a range of AI and machine learning services that enable small businesses to extract valuable insights from their data. By using a tool such as Amazon SageMaker, businesses can analyze trends, forecast demand, and optimize operations, driving informed decision-making and competitive advantage. Check out this sample guide on predicting loan defaults for financial institutions.

6. IMAGE AND VIDEO PROCESSING

Generative AI capabilities extend to image and video processing, opening up opportunities for small businesses in industries like e-commerce, retail, and entertainment. With services like Amazon Rekognition, businesses can automate tasks such as image tagging, content moderation, and video analysis, enhancing productivity and user experience.

We know our AWS SMB customers have big dreams when it comes to expanding their businesses and the promise of AI excites them, but might not know where to get started. Once data is in the cloud, it can help SMBs improve content creation, optimize marketing efforts, or enhance customer support, to name a few. Generative AI offers practical solutions that were once out of reach for many small businesses but can become a reality.

41% SMBs concerned about the price of AI implementation.

RESHAPING THE FUTURE OF WORK

Artificial Intelligence (AI) automation is rapidly transforming workplaces across various industries. By automating tasks that were once exclusive to human intelligence, AI is revolutionizing efficiency, productivity, and decision-making processes.

AI automation’s ability to analyze vast amounts of data quickly and accurately is a game-changer. For instance, in manufacturing, AI-powered robots can perform repetitive tasks with precision and speed, reducing human error and increasing output. Additionally, AI can optimize supply chains by predicting demand, identifying bottlenecks, and improving inventory management. For example, AI algorithms can analyze historical sales data to forecast future demand, enabling businesses to optimize production schedules and avoid stockouts or excess inventory.

In customer service, AI-powered chatbots can handle routine inquiries, freeing up human agents to focus on more complex issues. These chatbots can provide instant responses to frequently asked questions, resolve simple problems, and even assist with tasks like booking appointments or placing orders. This not only improves customer satisfaction but also reduces the workload on human agents, allowing them to provide more personalized and effective support.

While the potential for job displacement is a concern, AI automation also creates new opportunities. As machines take over routine tasks, human workers can focus on more strategic and creative endeavors. For example, in marketing, AI can automate tasks like social media management and email campaigns, allowing marketers to focus on developing creative content and building relationships with customers.

Additionally, the development and maintenance of AI systems themselves require specialized skills, leading to the creation of new jobs in fields like data science, machine learning, and AI engineering. As AI technology continues to advance, there will be a growing demand for professionals who can develop, implement, and maintain AI

ALAA BAWAB

General Manager, Lenovo Infrastructure Solutions Group (ISG), Middle East and Africa systems.

AI can enhance decision-making by providing data-driven insights. By analyzing vast amounts of information, AI can identify patterns and trends that may not be apparent to humans. For example, in healthcare, AI can analyze medical records to identify patients at risk of certain diseases, allowing for early intervention and improved outcomes. AI-powered algorithms can also analyze medical images to detect anomalies that may be missed by human doctors, leading to more accurate diagnoses and treatments.

In finance, AI can be used for fraud detection, risk assessment, and algorithmic trading. AI-powered algorithms can analyze financial data to identify suspicious activity and prevent fraud. Additionally, AI can be used to assess investment risk and develop trading strategies.

While the benefits of AI automation are significant, ethical considerations must be addressed. Issues such as data privacy, bias, and accountability are crucial to ensure that AI is used responsibly. For example, AI algorithms can perpetuate biases present in the data they are trained on, leading to discriminatory outcomes. It is important to ensure that AI systems are developed and deployed in a way that is fair and equitable.

As AI technology continues to advance, we can expect to see even more innovative applications and transformative impacts on the workplace. For instance, the development of more advanced natural language processing capabilities will enable AI systems to understand and respond to human language more effectively, leading to more sophisticated AI-powered chatbots and virtual assistants. Additionally, advancements in robotics and automation will enable AI systems to perform a wider range of physical tasks, such as driving vehicles and performing surgeries.