Companies need a combination of resilience and intelligence to develop in a fragmented world

Enterprises in the Middle East, Central Asia, and Africa (MECAA) are entering a new phase of digital growth. Cloud technologies are being actively implemented, Artificial Intelligence (AI) is becoming a priority at the board level, and digital services are rapidly expanding across all sectors. Simultaneously, the operating environment is becoming more complex due to geopolitical tensions, changing trade relations, and evolving data regulation norms.

This creates new expectations: organizations can no longer focus solely on innovation. They must also ensure stability, security, and control over operations even amid external uncertainty. In this context, resilience and AI merge into a single strategy: one ensures continuity, the other drives growth, and neither can exist in isolation.

The influence of geopolitics on technology has become more apparent in recent years. Requirements for data localization are tightening in several markets, control over cross-border data transfer is increasing, and dependence on global platforms and supply chains is being re-evaluated. For businesses, this means that infrastructure solutions are no longer purely technical; they have taken on strategic significance. Organizations must clearly know where their data is, how it is protected, and who controls it. They must also ensure the ability to continue operating regardless of disruptions caused by regulatory changes, geopolitical events, or supply chain constraints.

This shift is particularly relevant in regions where digital growth occurs alongside policy development. Businesses are developing rapidly, but they are also expected to comply with regulations, ensure security, and maintain accountability. As a result, resilience takes on a broader meaning—it is no longer just about recovering from failures, but about maintaining control, continuity, and confidence amidst growing unpredictability.

Disaster Recovery as a Service (DRaaS)

In this situation, the role of Disaster Recovery as a Service (DRaaS) is increasing. It is no longer viewed merely as a backup solution activated when problems arise. Instead, it is integrated into a broader approach to business continuity, data protection, and regulatory compliance. In a world of distributed multi-cloud environments, enterprises need the ability to quickly restore systems, while ensuring that recovery processes align with modern workloads, including real-time data-dependent applications, cross-border operations, and increasingly, AI.

Downtime now has broader implications: it affects not only operational activities but also compliance obligations and customer trust. In sectors such as financial services, telecommunications, and public services, these consequences are even more pronounced. This is why resilience is moving to the core of corporate strategy, enabling organizations to act confidently even when conditions around them change.

Experience and regional scale are becoming equally important components of enterprise resilience. In the MECAA region, organizations are increasingly seeking partners who understand local regulatory requirements, changing data policies, and sector-specific operating conditions. Amid growing global fragmentation, resilience is becoming a matter of sovereignty as much as it is a technological one. Businesses are assessing not only system recovery speed but also the location of critical data, who manages it, and how to continue operating during geopolitical, regulatory, or supply chain disruptions.

Concurrently, businesses across the region are accelerating the adoption of AI. Governments and businesses are investing in AI to improve efficiency, enhance services, and support economic diversification. However, scaling AI presents its own challenges. AI workloads require significant resources and depend on data. They need constant uptime, reliable data channels, and robust governance. Any infrastructure failure can directly impact outcomes, whether it is model performance, decision accuracy, or operational continuity.

There is also growing attention on how AI is deployed. Data sovereignty, privacy, and regulatory compliance are becoming critical aspects. Enterprises must ensure that AI systems are not only efficient but also trustworthy. In response to this need, cloud AI platforms are emerging, consolidating computing power, data, and AI tools in a managed environment designed to transition organizations from fragmented experiments to predictable, industrial-grade results.

It is becoming clear that resilience and AI cannot be managed in isolation. The more enterprises rely on AI, the more dependent they become on the stability and security of their underlying systems. If an AI platform fails, the consequences go beyond mere downtime—they affect analytics, decision-making, and business operations. If the data used for training or inference is compromised or lost, the reliability of the entire system can suffer.

Furthermore, traditional approaches to resilience may not fully cover the scale and complexity of AI environments. Large datasets, distributed workloads, and continuous processing require more advanced recovery and protection mechanisms. Here, alignment between DRaaS and cloud AI platforms is crucial. Together, they create a foundation where innovation can scale without increasing risk. DRaaS guarantees fast and reliable recovery of critical applications and data, while the AI cloud provides an environment for developing and deploying intelligence at scale. Integrating these capabilities allows enterprises to adopt innovations with confidence, knowing that their operations remain protected.

This integrated approach is changing how enterprises view resilience and AI. It is not about preparing for failure on one side and stimulating innovation on the other. The goal is to create an environment where both aspects can coexist. Organizations can continue to operate during outages while simultaneously scaling AI initiatives with confidence. This is especially important in regions where regulatory expectations and geopolitical dynamics continue to shift.

The integrated approach also simplifies operations. Instead of managing separate systems for recovery and AI, organizations can align their strategies, reduce complexity, and focus on achieving business outcomes. More importantly, it strengthens executive-level trust. Technical decisions are made with a clearer understanding of risks, control, and long-term value.

As enterprises in the MECAA region navigate this landscape, several points stand out. They must view resilience as a strategic opportunity that supports not only traditional applications but also modern, data-intensive workloads. It is necessary to ensure that AI platforms are designed with governance, security, and continuity in mind from the outset. Furthermore, infrastructure solutions should be aligned with broader business and regulatory priorities, including data sovereignty, compliance, and operational control. Finally, there is a need to move towards simpler, more integrated architectures. Combining resilience and AI into a single structure reduces risks, increases flexibility, and ensures greater long-term adaptability.

The global technology landscape is defined not only by stability; it is shaped by change, uncertainty, and rapid innovation. In such an environment, enterprises must balance ambition with assurance. Resilience and AI are not competing priorities, but complementary capabilities. One builds the foundation, and the other drives the future. Organizations that recognize this connection will be better prepared to overcome failures while continuing to innovate. They will move forward with greater clarity, confidence, and control, even in a fragmented world.

In the MECAA region, enterprise technology solutions are now shaped by a combination of the drive for growth and operational risks. Regulatory expectations are becoming clearer, data policies are evolving, and geopolitical factors continue to influence infrastructure design and management. In this environment, organizations are looking not only for cutting-edge technological opportunities but also for partners capable of ensuring stability, accountability, and long-term direction. Tata Communications collaborates closely with enterprises across the region, combining local engagement with global capabilities in cloud technologies, networks, and infrastructure. With over 16 years of regional presence and more than 300 clients in the MECAA region, Tata Communications combines expertise in networking, cloud technologies, security, and five broader operational structures integrated to help enterprises navigate increasingly complex technological and regulatory conditions.

Supporting enterprises in the UAE, Saudi Arabia, Qatar, Oman, and Kuwait has given Tata Communications direct insight into how resilience requirements differ across various industries. From retail and telecommunications to airlines, real estate, healthcare, and financial services, a common trend is becoming increasingly evident: the success of digital transformation initiatives now depends as much on operational resilience, governance, and continuity as it does on the implementation of technology itself. As organizations expand cloud, AI, and resource-intensive workloads, uninterrupted operation, cyber resilience, and governance are becoming board-level priorities alongside innovation and growth.

As a global cloud provider, Tata Communications integrates DRaaS, hybrid and multi-cloud platforms, managed infrastructure, security, and network capabilities into a unified operational environment. Instead of viewing resilience, connectivity, and cloud transformation as separate initiatives, organizations can align them as part of a holistic strategy supporting both operational continuity and long-term innovation. This allows organizations to advance transformation programs while maintaining clear control over data, operations, and compliance requirements.

Resilience is built into this approach at the platform level. Cloud environments unify infrastructure, managed services, and disaster recovery into a single operational model. Enterprises can deploy workloads on dedicated or hybrid architectures that provide stable performance, stronger isolation, and greater transparency across different environments. The same foundation is designed to support AI expansion. Through integrated cloud capabilities, organizations can access development tools, data platforms, and operational services via a unified structure. This accelerates the transition from experiments to production results while maintaining governance, compliance, and operational control. By combining resilience and AI capabilities, enterprises can manage infrastructure, data, and intelligence as part of one coordinated system, rather than as separate layers. This enables innovation at scale without introducing unnecessary complexity or risk. Ultimately, the conversation is no longer just about infrastructure; it is about creating the conditions for enterprises to innovate with confidence, operate under control, and remain resilient in a world where disruption has become a constant reality.

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iol.co.zaOctober 30, 09:50

Expert explains why AI productivity gains are not leading to business growth

Despite the undeniable influence of artificial intelligence (AI) on modern society, there remains a sharp debate over whether its overall impact is positive or negative. Just a year or two ago, the internet was flooded with AI-generated images that quickly entered corporate advertising. Soon after, social media platforms like YouTube were filled with AI-generated thumbnails—visuals that initially attracted viewer interest but now increasingly cause audience fatigue and skepticism.

However, outside of consumer social media feeds, artificial intelligence holds significant potential to transform corporate operations, provided it is implemented strategically. In an exclusive interview with IOL, Paul Banting, co-founder of growthguru.ai, analyzed the common narrative that AI has become an overrated tool that does not deliver expected productivity growth.

Banting stated that the problem is not that AI is overrated, but that it is being judged against the wrong promise. He emphasized that productivity gains are real and supported by research. According to a McKinsey survey on AI adoption in corporations, 80% of respondents noted improved individual productivity, yet only 37% claimed that AI directly contributed to increased profit for their organization—a metric that has remained virtually unchanged over the past year.

Therefore, in Banting's view, it is more accurate to say that AI is not overrated, but fundamentally misunderstood. Leaders mistakenly assume that saved time automatically translates into revenue growth. In reality, companies must actively direct the hours freed up by AI toward achieving tangible, high-return commercial opportunities.

While most public attention naturally focuses on optimizing outdated workflows, Banting warns that task efficiency should not become the primary goal of the enterprise. Around 79% of executives expect AI to drive substantial revenue growth by 2030, compared to 40% today. Nevertheless, only 24% have a clear idea of where this future revenue will originate. The true objective, Banting explained, is using AI to identify urgent commercial gaps and capture high-value market opportunities ahead of competitors.

The Growthguru.ai platform was developed precisely to bridge this strategic gap, focusing on business development rather than general workflow automation. Instead of offering a single universal assistant, their platform utilizes specialized AI agents targeting specific commercial levers: Perky helps partners access relevant incentives, discounts, and co-financing from suppliers; Maya develops go-to-market strategies and co-marketing initiatives; Alex guides partners through certifications and supplier tiers; and Lexi tracks important enterprise updates, such as critical Microsoft platform releases.

Nevertheless, alongside the commercial potential lies a darker side—AI-enabled crime, where issues of accountability and legal liability remain unresolved. Banting argues that criminals using AI for financial fraud must be held responsible for their actions, regardless of the tools they employ. However, if proprietary or third-party enterprise AI causes operational or financial damage, the responsibility lies directly with the business that implemented it. Legal liability cannot simply be shifted to the model provider. While software developers must create secure and compliant architectures, the choice of implementation rests entirely with the enterprise. BCG confirmed this finding in its applied AI benchmark.

olhardigital.com.brOctober 22, 12:00

The future of AI will be determined by a combination of various models, not one dominant technology

In recent years, the development of artificial intelligence has been perceived as a technological race, where after each launch, new comparisons, benchmark tables, and predictions emerged about which company was approaching leadership in the next major digital transformation. It often seemed that the entire debate boiled down to one question: which model is the best at the moment?

However, after a recent immersion in Silicon Valley, the author got the feeling that this question, while still relevant, is no longer central. Part of this understanding stems from the fact that he visited Silicon Valley almost ten years after his first visit.

There is something interesting about immersing oneself in an ecosystem that unites some of the world's most influential companies, investors, and universities. Just as when traveling to any culture, we begin to absorb local peculiarities, in the US, especially in California, you inevitably become influenced by the speed of releases, technology consumption, and the constant feeling that the future is being built there.

Something similar is happening in Silicon Valley: there is a natural tendency to focus on themes dominating regional conversations and consider them an accurate reflection of the coming years. This often has a basis, as many significant technological changes of the last few decades took place there before reaching the rest of the world.

But any highly connected environment carries a risk: the risk of confusing consensus with absolute certainty. This is what attracted the most attention during the trip.

Unlike most public discussions about AI, few conversations revolved around competition between models or the search for the next technological leap capable of changing the market. A more frequent topic was how artificial intelligence is integrating into the products, processes, and operations of organizations, rather than which laboratory owns the most advanced system at the moment.

This shift in focus manifested differently throughout the week. Discussions with executives from Snowflake and Elementum, for example, were not about which model ranks highest or which company would release the next big thing. The discussion was focused on the transformation of enterprise software. The general consensus was that a phase is beginning where intelligent agents start interacting directly with large volumes of data, performing tasks that previously required manual navigation through systems, report analysis, and making several operational decisions by a human.

For decades, most enterprise software was created to organize information and facilitate human labor. Now, there is an expectation that these systems will not just present information but also act upon it. This may be one of the most important changes brought about by AI. The impact is not only in the technology itself but also in how companies begin to function. For the first time in many years, the discussion seems less related to the tool being used and more to the ability to quickly, integratively, and efficiently turn data into action.

At the same time, some visits showed that not everyone follows the exact same path. For instance, at Liquid AI, there was a viewpoint contrasting with most popular narratives in the sector. While a large part of the industry continues to bet on ever-larger models and ever-more powerful infrastructure, this company is developing more compact systems designed for local operation on devices such as mobile phones, vehicles, and onboard equipment.

The interest in this concept is not just in the technology itself. It reminds us that innovation does not happen in one direction. There is a natural inclination to link technological progress with increased computational power, investment, and model size. However, in many cases, the task is precisely the opposite: creating more efficient, accessible, and easily integrated solutions into people's daily lives and businesses.

Perhaps this was one of the main lessons of the trip. When we look at the AI market from afar, it seems like there is one race going on. But when we look closely, we realize that many different races are happening simultaneously. There are large generative models; there are specialized systems; there are multimodal applications combining text, image, audio, and video; there are enterprise solutions designed to solve very specific problems; and there are companies betting on making all of this easier and more distributed.

Therefore, the idea that the market will move towards one dominant technology is becoming less likely. A scenario of coexistence of different approaches is emerging, each suited for specific contexts, constraints, and needs.

This shift is also changing how organizations approach technology strategy. For a long time, the logic was relatively simple: find the best available tool and implement it. With the advent of AI, the equation includes a number of other variables. Issues of integration, governance, privacy, speed of adoption, operational costs, and business compliance are becoming as important as the complexity of the chosen technology.

In practice, a company may gain much greater value from a simple, well-integrated solution in its processes than from using extremely advanced technology in isolation. Perhaps this is why the discussion of implementation is taking up more space than the discussion of capabilities.

Another aspect that caught attention was the frequent turning of technological conversations into conversations about people. This became particularly evident during the visit to Stanford. Despite the university being in one of the most advanced ecosystems in the world for AI, a significant part of the discussions remained focused on topics such as education, talent development, shaping organizational culture, and leadership development.

This does not seem coincidental. The history of Silicon Valley has never been exclusively a story about technology. It has always been deeply connected to the ability to transform knowledge into innovation. Tools change. Platforms change. Companies appear and disappear. But preparing people who can learn, adapt, and create remains a constant element.

This point becomes even more relevant as AI becomes accessible to more and more organizations. Access to the technology, naturally, tends toward democratization. Knowledge of how to use it strategically, however, remains a difficult-to-replicate advantage.

The systematic implementation of AI requires much more than just hiring a platform or providing new resources to teams. It requires rethinking processes, developing competencies, revising workflows, creating oversight mechanisms, and preparing people to work in an environment where part of the analysis, decision-making, and execution is supported by intelligent systems.

Therefore, the main takeaway the author had after this experience is not about which company will lead the AI race or which model will become the standard in the coming years. The feeling is that we are leaving the phase where the center of discussion was isolated technology and entering a stage much more focused on practical application. The next phase of AI will not be defined by one revolutionary model replacing all others. It will be built on a combination of various technologies, the ability to integrate them into existing processes, and most importantly, the ability of organizations to turn these capabilities into tangible results.

Ultimately, artificial intelligence remains a technological topic. But after so many conversations during the trip, it is hard not to conclude that it is increasingly turning into a discussion of strategy, execution, and organizational adaptation. Perhaps this is why the most important question is no longer who possesses the most advanced AI. The question that seems to be gaining more importance is another: who will be able to use it better.

olhardigital.com.brOctober 21, 22:31

OpenAI expands cloud presence and makes models available on Google Cloud

OpenAI is extending its presence in the cloud sector by integrating its artificial intelligence (AI) models into the Google Cloud infrastructure, which increases the availability of its technologies for developers and corporations.

This new agreement establishes that the GPT-6 Astra, GPT-6.1 Sol, and GPT-6 Luna models will be available in the Google Cloud Model Garden catalog. This move marks another advancement by the creator of ChatGPT beyond the existing collaboration with Microsoft.

As announced this Wednesday (21), these models are being offered in preview phase to a specific group of enterprise clients. Furthermore, the integration will enable developers to use Google Cloud APIs and link local workflows, such as Codex CLI and Desktop, to the Gemini platform to perform inferences.

This partnership strengthens OpenAI's position among the largest cloud infrastructure providers. Previously, the company had signaled plans to place its models on Amazon Web Services (AWS) after the expiration of Microsoft's exclusivity period over its intellectual property.

Google Cloud will incorporate three OpenAI models into its Model Garden, a catalog that aggregates proprietary, third-party, and open-source offerings. The intention is to give companies the freedom to select the most appropriate technology for each need, considering metrics such as cost, performance, and response speed.

The released models are listed in the announcement. According to Google Cloud, the strategy aims to allow companies to switch between different models according to the requirement of each application, avoiding dependence on a single solution for all tasks. The catalog, which already contains over 200 models, will now offer access to the three main closed-source large language models on a single enterprise AI platform.

The collaboration also includes the commercial listing of OpenAI models on the Google Cloud Marketplace. This means that consumption will be unified in a single Google Cloud invoice, allowing expenses to be allocated to pre-existing cloud commitments or the platform's Flexible Savings Plans. The purpose is to simplify acquisition and allow companies to distribute their workloads across various models without the need for multiple contracts with different vendors.

Thomas Kurian, CEO of Google Cloud, stated in a release that the company's goal is to provide a wide range of models so that customers can choose the best option for each activity, prioritizing quality and cost reduction. Greg Brockman, president and co-founder of OpenAI, emphasized the relevance of this partnership in making the company's more advanced models and Codex accessible in the environments where customers already develop their applications.

The announcement also detailed the security and governance safeguards applicable to running OpenAI models on Google Cloud infrastructure. The company guarantees that customer data, including prompts, contexts, and results, will remain within the designated data perimeter and will not be used to train foundational models. Zero Data Retention (ZDR) options are scheduled to be implemented in the future.

OpenAI models will run on dedicated Google Cloud infrastructure. Additionally, future availability of confidential computing resources is anticipated. Traffic will be managed by isolated and protected gateways, while authenticated identity mechanisms, audit logs, and network security controls will allow administrators to monitor interactions with the models and AI agents. These guarantees constitute the conditions established by Google Cloud for the enterprise use of third-party models on its infrastructure.

Although the models are already available in preview version for selected enterprise clients, other integrations are planned. In the coming weeks, OpenAI models are expected to be integrated into Google Antigravity, Google's programming tool, and Gemini for Enterprise, expanding the scope of work for developers and organizations.

OpenAI and Google already had a prior business relationship; last year, the creator of ChatGPT used Google Cloud infrastructure to operate ChatGPT and its API. This expansion of the partnership occurs at a time of growth in Google's cloud business. In the second quarter, the annualized revenue growth of the segment reached approximately 82%, compared to 63% in the first quarter. Meanwhile, AWS registered 37% growth, and Microsoft showed a 42% expansion in the same period. The growing demand for custom chips and AI models boosted Google Cloud.

Founded in 2015, OpenAI emerged partly as an alternative to the concentration of AI research in companies like Google. In 2017, researchers from the organization published an academic paper on the Transformer architecture, fundamental to the advancement of the technology. Subsequently, OpenAI popularized generative AI with the launch of ChatGPT. Currently, the company is seeking investment with a valuation of US$ 1.4 trillion (approximately R$ 7.56 trillion) and plans to conduct an Initial Public Offering (IPO).

With the arrival of the models on Google Cloud, OpenAI diversifies its technological distribution channels, reaching corporate clients through the three major cloud infrastructure platforms.