Businesses today sit atop a goldmine of data, yet operate as if they’re digging in the dark. According to research firm IDC, global data creation will exceed 221 zettabytes by 2026 – the staggering equivalent of 221 billion 1TB drives packed with potential insights. And yet, 72 percent of organizations suffer from “decision paralysis,” unable to translate that data into meaningful action.
In an era where speed and agility are crucial for success, leveraging data in real-time and at scale is essential. To thrive, businesses must be able to harness data insights at market speed.
Where’s the data coming from?
Modern businesses are generating more data than ever, from more sources than ever. Much of it originates from increasingly connected ecosystems. The proliferation of Internet of Things (IoT), sensors, and smart devices is one major driver. Market research firm IoT Analytics estimates that 130 new devices come online every second, generating real-time data across industries, from the factory floor to home appliances.
At the same time, digital and social platforms generate a relentless stream of unstructured information: hundreds of thousands of tweets, millions of Google searches, and billions of emails churn through the Web every single minute. And within businesses themselves, enterprise systems such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and supply chain software are continually generating operational data that often remains trapped in silos.
Even artificial intelligence is now a source of this growth. Generative AI and Machine Learning models require massive training datasets, and increasingly, they’re producing synthetic data that further expands the volume of content in the digital universe.
Why traditional systems fail
Under this deluge, traditional systems are faltering. Most enterprise data is unstructured – think emails, images, videos, documents, and sensor logs. Traditional business intelligence tools were designed for structured databases, not for interpreting this chaotic and messy sprawl.
The result is a rising cost of inaction. When customer support emails go unread, potential churn signals are missed. When machine sensors are left unmonitored, preventable breakdowns can halt operations. And when competitors harness AI to make sense of all their data while others rely on partial views, the gap between the data-rich and the insight-poor widens dangerously.
The modern solution: AI on cloud platforms
How can businesses turn the tide? AI delivered through cloud platforms offers a powerful way forward. Unlike legacy tools, AI models in the cloud can process and analyse vast quantities of data in real time. Companies like Netflix are already using such capabilities. Its recommendation engine, for instance, refreshes itself in milliseconds, providing highly personalised experiences at scale.
Cloud platforms also offer unprecedented scalability. Businesses can move from gigabytes to petabytes of data without worrying about infrastructure failures. More importantly, AI is no longer the exclusive domain of data scientists. With the rise of low-code and no-code platforms, business users across functions can experiment with AI, build simple models, and drive outcomes without requiring deep technical expertise.
Democratizing AI for SMEs
In other words, what was once considered cutting-edge has become cost-effective. Cloud-based AI services typically operate on a pay-as-you-go model, removing the barrier of upfront investment and making advanced capabilities accessible to smaller enterprises.
This shift is levelling the playing field. Small and medium-sized enterprises (SMEs) can now access the same technology stack as global incumbents and apply it in agile, customer-centric ways. From hyper-personalised marketing to AI-assisted inventory management, these tools are enabling SMEs to punch well above their weight. A local retailer, for instance, can now deploy AI-driven inventory systems that were once only feasible for global giants.
Roadmap for cloud AI adoption
Successfully adopting AI, however, requires more than just plugging into a platform. Businesses must start with clear objectives and use cases. Where can AI provide the most immediate and measurable value? It could be improving customer insights, increasing operational efficiency, or automating repetitive workflows.
Once the objective is clear, the next step is assessing data readiness. Businesses need to ensure their data is clean, structured, and accessible for AI analysis. Proper data management lays the foundation for effective AI implementation.
Next comes selecting the right cloud AI platform. Companies need to choose a solution that aligns with their specific business needs, offers the tools and capabilities to support their AI projects, and can scale with their growth.
Once the technology is in place, it’s critical to embed AI into the daily rhythm of decision-making. AI shouldn’t sit on the sidelines – it should inform, enhance, and automate critical decisions. But implementation isn’t a one-and-done exercise. AI models need constant tuning, monitoring, and iteration to remain aligned with business goals, market conditions, and customer expectations.
At the same time, building an AI-first culture is essential. Everyone across the organization – from front-line staff to executives – must understand how AI supports the company’s mission and how to work with it effectively. This cultural shift supports innovation and keeps the business competitive.
The bottom line
We’re now at a tipping point. The question is no longer whether businesses can afford to invest in AI; it’s whether they can afford not to. Those who delay risk being buried under their own data, trapped in a cycle of inefficiency and missed opportunities. Meanwhile, their AI-enabled competitors will anticipate trends, personalise services at scale, and adapt to market shifts in real time.
In 2025, agility equals AI maturity. Those who act now will turn data into competitive advantage. Those who don’t will be outpaced – not from lack of data, but from inaction. It’s time to harness your data – and your future – with AI.
Stuart Pearce is Vice President of Solution Sales in Asia Pacific (APAC) for ServiceNow. Before ServiceNow, he founded and expanded tdglobal, a data solutions firm, until its sale to an investment consortium in 2022. He was also the founder of BigTBox, a provider of ecommerce platforms. Stuart has also spent 10 years at IBM, where he managed Big Data and Analytics channels across Eastern Europe, Middle East, and Africa.
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