By Jadd Elliot Dib, Founder and CEO, PangaeaX

The AI boom of the 2020s has dominated news headlines, and businesses in virtually every industry have sought to harness AI to strengthen productivity and profitability. In many cases, the transformation promised by AI often stalled at experiments and prototypes, resulting in AI being perceived as a buzzword.

This will begin to change in 2026. AI will become less of a buzzword and more of the backbone of business operations. Processes that previously took hours or days will be reduced to minutes or even seconds. Success will not be brought by flashy tools, but by embedding AI into the everyday fabric of work to drive real revenue, cut costs, and outpace competitors. Additionally, at the core of any successful AI implementation is high-quality data. The companies that thrive will be those that clean up their data and embrace AI as a true business partner, not just a side project or marketing slogan.

What industries will feel this change first?

While the impact will be far-reaching, several industries are expected to benefit from the transformative shift earlier. For example, AI will enable healthcare providers to diagnose disorders faster and more accurately. Predictive analytics uses historical and real-time data to forecast future health outcomes, identify at-risk patients, and optimize operations. Shifting care from reactive to proactive will improve public health and increase the effectiveness of treatments.

The logistics industry will receive a huge boost from data analytics and AI. Businesses can use AI to aid in smarter route planning, resulting in fewer delivery delays. AI transforms raw supply chain information into actionable insights, and this data-driven approach moves logistics from guesswork to precise, real-time management for better resource use and competitive advantage. 

Likewise, banks and financial companies are increasingly using AI-powered tools to improve fraud detection and risk management decisions. Retailers, especially in e-commerce, will be able to give customers better product suggestions and automatically adjust prices in response to fast-changing market conditions.

Predictive analytics will take back the spotlight from generative AI

Generative AI is the most discussed form of AI, accompanied by various discussions on its ethics and some degree of controversy. However, in the coming year, predictive analytics will become more widely known to the public, as its effects become more visible. AI-powered predictive analytics tools help businesses plan further ahead than previously possible, giving a more accurate picture of future demand, sales, and risks. Additionally, predictive analytics programs are more reliable, cheaper, and easier to explain to a layperson. Businesses want results and that puts predictive analytics back on top. Despite that, generative AI should not be totally discounted. Businesses that can successfully use both are more likely to succeed, with predictive analytics providing guidance and generative AI enabling automatic action.

Data security to become a higher priority

One of the most important topics in the new frontier of AI is data security. While AI offers powerful benefits, it also introduces new vulnerabilities and is increasingly weaponized by threat actors. As a result, in 2026, AI companies will lean more toward tighter control of data. In the past decade, there have been many high-profile data breaches, demonstrating the huge risk posed by poor data security. Moving forward, data and AI companies will adopt a need-to-know approach, ensuring that individuals will only get access to specific data based on their role.

AI companies will build safer systems that allow people to use data without exposing all sensitive information. This will be very important in highly regulated sectors, such as banking, healthcare, and government. While other sectors such as tech or retail may be more flexible, security is still crucial as violations can result in major penalties and reputation loss. In 2026, companies will have a goal of widening access to data but with more safeguards in place.

Automation will change how organizations work with data

In past years, the novelty of AI and misunderstanding of its capabilities have caused many organizations and individuals to misuse it, often outsourcing too much of the thinking to AI. In 2026, many businesses will correct their course, using AI to do repetitive tasks while having humans think using their superior capacity for creativity, emotional intelligence, and contextual understanding.

The development of agentic AI will introduce AI helpers that will take over routine and ‘boring’ tasks such as cleaning data or fixing errors. Data teams will spend less time on coding pipelines, with AI freeing up their time to solve business problems. AI will also make various digital tools easier to use, which means even people without advanced degrees and credentials can work with data analytics and AI. However, companies will still need specialists for complex AI, security, and architecture. These experts’ roles are not disappearing but shifting towards high-value strategic oversight.

Businesses will see through the hype and focus on results

With more businesses gaining a better understanding of data analytics and AI, 2026 won’t be about chasing the next shiny AI trend. Instead, it will be about delivering measurable business impact. Companies that integrate AI into their core operations, clean up their data, and strike the right balance between predictive and generative capabilities will lead the pack. On the other hand, those that cling to old models or treat AI as window dressing will fall behind. The future belongs to businesses that see AI not as a tool, but as a strategic partner that accelerates decisions, safeguards data, and frees humans to focus on what matters most: thinking big and solving complex problems.

n boardrooms across the globe, something big is happening, quietly but decisively. Sustainability has evolved far beyond being a “nice-to-have” addition to an ESG report. It’s now front and centre in business strategy, especially in tech. From green computing and circular data centers to AI that optimizes energy use, companies are reshaping their technology roadmaps with sustainability as a core driver and not as an afterthought.

Not long ago, tech strategy was all about speed, uptime, and keeping costs per computation low. That mindset has evolved. Today, leaders are also asking tougher questions: How carbon-intensive is this system? How energy-efficient is it over time? What’s its full lifecycle impact? With climate pressure mounting and energy prices climbing, organisations that tie digital transformation to their institutional sustainability goals.

At its heart, green computing seeks to maximize computing performance while minimising environmental impact. This includes optimising hardware efficiency, reducing waste, and using smarter algorithms that require less energy.

A wave of recent research shows just how impactful this can be. Studies indicate that emerging green computing technologies can reduce energy consumption by 40–60% compared to traditional approaches. That’s not a marginal improvement, that’s transformational. It means smaller operating costs, longer hardware life, and a lower carbon footprint without sacrificing performance.

Part of this comes from smarter software. Techniques like green coding, which optimise algorithms to minimise redundant operations, have been shown to cut energy use by up to 20% in data processing tasks.

Organisations that adopt green computing strategies aren’t just doing good; they’re driving tangible results. Informed by sustainability principles, energy-efficient hardware and

optimisation frameworks can reduce energy bills and maintenance costs at the same time, often with payback periods of three to five years.

Data centres are the backbone of the digital economy. They power software, store vast troves of data, and support the artificial intelligence systems driving innovation. But this backbone comes with a heavy environmental load. Collectively, global data centres consume hundreds of terawatt-hours of electricity each year, which is about 2% of total global electricity.

As AI workloads surge and data storage demand explodes, energy consumption is rising sharply. Looking ahead to 2030, the numbers are hard to ignore. Global data

centre electricity demand is expected to almost double, reaching levels you’d normally associate with an entire industrialised country. That kind of energy appetite isn’t just a technical issue, it’s a strategic wake-up call for the entire industry.

This surge has forced a fundamental rethink of how data centres are built and run. Enter the idea of the circular data centre. It’s not just about better cooling or switching to renewables. Instead, it looks at the full lifecycle of infrastructure, from construction and daily operations to decommissioning, recycling, and reuse, so waste and inefficiency are designed out from the start.

The most forward-thinking operators are already implementing this approach. Advanced cooling methods, such as liquid cooling and AI-driven thermal management, are revolutionising the industry, reducing cooling energy consumption by up to 40% compared to traditional air-based systems. That’s a big win not only for energy bills, but also for long- term sustainability.

Beyond cooling, operators are turning heat waste into a resource. In Scandinavia, data centres are already repurposing excess thermal output to heat residential buildings, a real- world example of how technology can feed back into the community in a circular way. These strategies are already showing results, with approximately 60% of data centre energy now coming from renewable sources, and many operators are targeting 100% clean power by 2030.

Circular thinking extends to hardware too. Companies are designing servers and components for easier recycling, refurbishing retired equipment, and integrating modularity so that parts can be upgraded without replacing entire systems.

For businesses, circular data centres represent more than environmental responsibility. They can significantly lower capital expenditures over time and reduce regulatory risk as governments tighten emissions requirements. While AI itself has been criticised for energy use, the technology also offers some of the most effective tools for reducing overall consumption across tech infrastructure.

AI algorithms excel at predictive optimisation, they can analyse real-time sensor data to adjust cooling systems, balance computing loads, and shut down idle resources. Across case studies, such systems have reliably achieved 15–30% energy savings in energy management tasks in cloud environments, dynamic server allocation and AI-assisted workload management have contributed to energy savings of around 25% when compared with conventional operations.

By Srijith KN, Senior Editor, Technology Integrator

Celebrating more than two decades in the region—and backed by over 800 enterprise customers—Ramco Systems is not merely expanding; it is doubling down on its presence. With a 50-member local team and a roadmap anchored in deep product localization, the company’s strategy is clear: build for the region, in the region. Local language support, government-portal integrations, and strict alignment with regional data privacy laws form the foundation of Ramco’s next chapter.

At the media roundtable held in Dubai as part of Ramco@20, Integrator had a front-row view into the company’s transformation; one that is not just incremental but architectural. From pioneering client-server systems to shaping modern SaaS platforms, Ramco has long played in the innovation lane. But what they are now setting their sights on could reshape the enterprise software landscape once again: AI-native enterprise systems.

From System of Record to System of Intelligence

Ramco’s next strategic leap is a shift from traditional enterprise software—rigid, transactional, and complex—to a fluid “system of intelligence.” Imagine an enterprise app that doesn’t wait for instructions but proactively surfaces insights, flags anomalies, and allows employees to manage operations through natural conversation. That is the future Ramco is building toward.

One of their strongest verticals—HR and payroll—illustrates this ambition. They already support organizations with massive workforce structures, including companies with over 100,000 employees and more than 1,000 pay components. Under an AI-powered interface, many of these complicated workflows will compress into simple prompts, removing friction from one of the most complex business domains.

A ChatGPT-Like Canvas for Enterprise Work

The company demonstrated an early preview of its conversational interface; a clean, unified canvas where users can query pending purchase requests, generate reports, or even create purchase orders using a single natural language prompt. The UX remains consistent for all, but the underlying workflows, context, and AI-generated outputs adapt to individual users and company-specific processes.

But the most compelling use cases emerged when the discussion shifted to aviation; a sector where Ramco already has deep domain expertise.

AI on the Hangar Floor: A Glimpse into Aviation’s Future

Picture a technician standing beside a massive aircraft engine, disassembling components, identifying faults, replacing parts, and logging every detail meticulously. Aviation is unforgiving—every part must meet airworthiness standards, track flying hours, and comply with stringent regulations. Only certified personnel can work on the engine, and even the tools they use must be OEM-mandated.

Now layer AI into that setting.

As a technician opens an engine and reports an issue—say, a damaged blade—the AI instantly scans 15–20 years of historical maintenance data. It recognizes patterns and alerts the technician:

“John, you’re replacing this blade on an A380. Historically, whenever this part is replaced, another related fault tends to appear within eight months. Would you like to inspect that area as well?”

This is not a textbook recommendation. It is institutional memory—decades of real-world faults and fixes—surfacing as real-time intelligence. The system becomes a second expert on the floor, conversing with technicians, guiding actions, and ensuring nothing slips through the cracks. This simple conversational canvas, Ramco argues, has the potential to reshape ground-level operations in one of the world’s most complex industries.

The Critical Question: What About Data Privacy?

As enterprise AI evolves, the most pressing concerns are no longer about innovation; they’re about protection. So, we asked the question that matters most: How does Ramco secure customer data in an AI-driven future?

Their answer was reassuringly clear:

1. All AI workloads are hosted locally within the customer’s private environment.
2. Data never leaves the region. Workloads are deployed in the customer’s local data center.
3. Every customer gets an isolated AI instance. No shared environments, no cross-pollination of data.
4. No external web calls, ensuring full containment and compliance.

In an era where enterprises fear the opacity of AI, Ramco is betting on transparency and regional sovereignty.

The Road Ahead

Ramco’s mission is ambitious: to redefine enterprise apps through AI and shift the industry from systems that store data to systems that think. And based on what we witnessed at Ramco@20, this is not a distant vision; it is already taking shape on factory floors, in payroll departments, and inside aircraft hangars.

The next era of enterprise software won’t just automate processes. It will understand them. And Ramco is positioning itself to become one of the first global players to make that leap—from record-keeping to intelligence-building—right here in the region.

About Ramco Systems

Ramco Systems is a world-class enterprise software product/platform provider disrupting the market with its multi-tenant cloud and mobile-based enterprise software, successfully driving innovation for over 25 years. Over the years, Ramco has maintained a consistent track record of serving 800+ customers globally with 2 million+ users and delivering tangible business value in global payroll, aviation aerospace & defense, and ERP. Ramco’s key differentiator is its innovative approach to develop products through its revolutionary enterprise application assembly and delivery platform. On the innovation front, Ramco is leveraging cutting-edge technologies around artificial intelligence, machine learning, RPA, and blockchain, amongst others, to help organizations embrace digital transformation.