Transforming with data: how to build a digital-first enterprise in 2025 

Summary

Digital transformation in 2025 hinges on data—particularly real-time, AI-powered insights that drive automation, personalization, and smarter operations.

As nearly two-thirds of business leaders prioritize digital transformation, leveraging data across structured, unstructured, and real-time sources allows companies to anticipate customer needs, optimize spending, and fuel innovation.

Key strategies include building a strong data infrastructure, investing in data literacy, and integrating AI, IoT, edge computing, and robust cybersecurity.

The blog emphasizes using external and multi-modal data (e.g., images, text, video) to enrich enterprise insights and urges firms to treat data as a strategic asset—enabling personalized customer interactions, streamlined processes, and sustained competitive advantage.

Digital transformation is still pivotal for enterprises in 2025, and businesses around the world are embracing it as a critical driver for future success. 

61% of business leaders see digital transformation as a major priority, and as many as 94% of large organizations in the US and UK have a digital transformation strategy. Other research shows that digital transformation is a key technology initiative for 74% of organizations.  

Are you keeping pace? 

In 2025, data is more crucial than ever in driving customer-centric digital transformation.

Data is central to digital transformation because it drives AI-powered insights & automation, real-time analytics, and hyper-personalized customer experiences, while also supporting advancements in IoT, edge computing, and cybersecurity.  

These data-driven capabilities enable businesses to make better, informed decisions, optimize operations, and deliver exceptional customer experiences, ensuring they remain competitive in an ever-evolving digital landscape. 

By leveraging data in its various forms – real-time, near real-time, and historical – businesses can adapt swiftly to market changes, streamline operations, and drive innovation and growth.

For instance, by combining real-time data from customer interactions with historical data on previous usage patterns, companies can predict and promote the most relevant offerings to customers in real-time, enhancing data-driven decision-making and customer engagement.  

Data provides a strategic advantage, enabling companies to differentiate themselves in a hyper-competitive landscape.  

Further, it enhances risk management through predictive analytics and fuels AI and machine learning, improving automation and business processes.

Overall, effective data utilization is essential for achieving business goals and staying ahead in an evolving digital era. 

How can a data-focused approach help businesses?

• Understand what customers want 

• Spend better on marketing  

• Run operations more smoothly and cut costs 

• Innovate products and improve their market position 

• Keep customers coming back and boost their overall value 

In a world full of information, businesses that can use data effectively will outshine their competitors. 

Is it digital transformation or AI transformation? 

Some people believe that with the rise of AI, the term “digital transformation” is outdated in 2025 and, as such, prefer to call it “AI transformation.” Others think it should simply be called “business transformation” or just “transformation.” 

However we call it – businesses are always changing—whether it’s because of growth opportunities, a need to work remotely during a worldwide pandemic, the push for automation in tough economic times, or, as now, AI changing how we work and live. 

In future blogs, we’ll delve into the critical role data plays in modern digital transformation, providing organizations with the tools to make informed decisions, enhance customer experiences, and streamline operations.  

We’ll also explore how businesses can leverage data to gain a competitive edge by implementing AI, IoT, and personalized customer strategies, ensuring they remain agile and innovative in an ever-evolving digital landscape.  

With practical insights into building a data-driven culture and integrating emerging technologies, our insights will equip leaders to navigate the complexities of today’s data-centric world for future success. 

Enterprises must build a data and AI strategy that not only addresses business impact and ROI but also focuses on AI principles, governance, talent, operations, and activation of the appropriate use cases.  

How can you use data-driven strategies to make better decisions?

As we know, to stay competitive today, it’s essential to use data-driven strategies to make smart decisions, improve customer experiences, and streamline operations. Using tools like predictive analytics, personalization, dynamic pricing, and consumer insights can keep your business leading the way. 

Today, understanding data involves navigating the vast and complex array of structured, unstructured, and real-time information now available. The sheer volume and diversity of data require businesses to adapt and innovate continually.  

Yet, the importance of data is still often underestimated in the day-to-day reality of businesses today. Many companies have vast amounts of data, but they don’t know how to make use of it effectively. They lack either the tools, the skills, or the mindset to extract meaningful insights.  

As Gartner writes: “Enterprises face increasing challenges in successfully utilizing data and implementing new and complex analytics and AI technology available by tech providers.” 

Not utilizing data is a big mistake, as data is in actuality the lifeblood of digital transformation. Thus, investing in data infrastructure, data literacy, and data-driven decision-making is critical from the outset. 

A strong data foundation is essential for harnessing the full potential of AI, necessitating robust systems for aggregating data from all sources—both internal and external. This comprehensive approach enables the creation of a cohesive enterprise view, critical for insightful decision-making.  

To maximize AI’s capabilities, it’s crucial to enrich this data with business context and knowledge, ensuring AI applications are effectively leveraged.

Organizations must recognize that valuable data extends beyond what’s in their enterprise systems, like CRM platforms.  

Leaders should include external and multi-modal data, such as images, texts, documents, and videos, in their strategies, integrating diverse data types to drive innovation and stay competitive in an increasingly data-driven world. 

Data and digital transformation are expected to be deeply integrated across industries, with key trends including widespread adoption of AI, advanced analytics, the Internet of Things (IoT), edge computing, quantum computing, and a focus on hyper-personalized customer experiences, all underpinned by robust cybersecurity measures to protect sensitive data.  

Check out our future blogs for more on data, with a special lens on how to stay customer-focused.  

FAQ

Q: What makes data essential in digital transformation?
Data is the foundation of modern digital transformation—driving AI-powered insights, real-time analytics, automation, and hyper-personalized customer experiences. By leveraging diverse data types (real-time, historical, unstructured), organizations can optimize operations, innovate, and stay competitive in fast-evolving markets.

Q: How can businesses ensure digital initiatives align with broader goals?
Successful transformation hinges on strategic alignment—embedding digital efforts into overall business objectives. This involves defining measurable outcomes, aligning leadership vision with digital strategies, and adopting modular, agile platforms that reflect business priorities.

Q: What practical steps should companies take to harness data effectively?
Start by building a strong data foundation: invest in data infrastructure, governance, and literacy. Integrate real-time analytics to respond rapidly to customer needs. Enrich internal data with external sources and AI-ready models to generate actionable insights across operations.