The age of AI has brought amazing progress across many industries, and data analytics is no exception. In fact, recent industry research reveals a surge in AI adoption, with 80 percent of data professionals already using AI in their daily workflows – a significant increase from 30 percent in 2024.
Today’s AI-powered tools are more accessible and user-friendly than ever – and are ready for use from day one. This shift is prompting organisations to move from thinking “AI someday” to “AI today” – where the biggest risk isn’t implementation complexity, but falling behind competitors who’ve already taken the leap.
Yet for businesses new to AI, knowing where to begin can be daunting. That’s why it’s crucial for organisations to understand both its benefits and how to take a strategic approach.
The AI revolution in data analytics
While the underlying principles of producing high-quality, trustworthy data remain the same, AI is transforming the way data is developed, managed, and consumed – not just by engineers and analysts, but by decision-makers and end users across the organisation.
Unlike previous technological shifts that rolled out over the last few years, such as the gradual migration from on-premises to cloud or from databases to data warehouses, AI is rewriting the rules with lightning speed. And it’s not just changing how data is processed; it’s redefining how people interact with it. Natural language interfaces, intelligent agents, and AI-assisted development are making it possible for analysts and engineers alike to ask more complex questions, build data models faster, and uncover insights with unprecedented velocity.
Crucially, this new era places even greater emphasis on data governance and data quality. Analytics teams are under mounting pressure to ensure their data is reliable and actionable, and AI can still make mistakes. The ability to trace, grade, and govern data is now a necessity in order to make informed decisions, maintain integrity and ensure the reliability of any AI-driven insights.
Practical tips for adopting AI
For organisations ready to harness AI’s potential, here are three best practices to adopt:
- Double down on what works. It can be tempting to succumb to the buzz LLMs, particularly, the latest and shiniest ones – but LLM tech tends to carry a higher cost that may not be justified. Traditional machine learning tools still work well for many businesses. Focus on building, refining and fine-tuning preexisting models that are already well-understood, cost-effective and valuable for many business use cases.
- Start small. Take small, manageable steps, instead of jumping into advanced AI projects. Begin with discrete tasks, such as summarising customer support tickets or generating meeting notes in tools like Notion. AI isn’t perfect, and oftentimes still requires human intervention, but the goal at this stage is to learn by doing. Gaining hands-on experience with AI will help you better understand its capabilities, limitations and whether the tool is a fit for your use case.
- Prioritise data quality. AI-generated results are only as good as the data from which they’re created. So, if the data itself is flawed, biased, or untrustworthy, the AI’s output will reflect those flaws – just like a human repeating misinformation. Consequently, knowing where data came from, how it was collected, and whether it’s been validated is key.
AI as a reinforcement, not a replacement
As data analytics and AI continue to intertwine, the question on everybody’s mind is whether these technological advancements will replace the need for human intelligence. Ultimately, whilst AI is spearheading rapid innovation and growth, it’s made possible by human ingenuity. As research has shown, LLMs exercise neither reasoning nor judgment – those remain the purview and the responsibility of those with organic neurons.
AI is a complement to human judgment, not a replacement. Rather than taking jobs, AI is enhancing roles, streamlining processes and delivering high quality results. Brilliance has always been the currency of the realm for amazing employees; now, we add adaptability. The key quality of high performing employees in the coming years is going to be how fast and well they embrace all the AI technologies at their disposal.
Recent industry research reveals that 40 percent of respondents’ headcounts increased in the last year, compared to just 14 percent in the previous year. Even with increased AI use, there is still clear demand for skilled workers that can use these tools to translate insights into action.
Looking to the future of data analytics in the AI era
In traditional database systems, data quality meant ensuring granular accuracy, such as whether a bank balance figure was correct. Analytics changed that to define quality as data that was complete, consistent, fresh, traceable, and well structured. Now, with AI being used in analytics, the stakes are higher – data shape, data drift, provenance, and governance are required quality metrics in order to not only get the right answers but to be able to explain what data was used to get them.
Executives expect to make decisions based on data that meets all these criteria. In a fast-moving business environment, poor data leads to poor decisions – with real consequences. And, if the AI systems make mistakes or introduce bias, companies may be held liable for those outcomes.
As a result, what’s emerging is a more holistic, interconnected approach to analytics. Data transformation has always offered the opportunity to turn raw data into clarified, actionable outcomes. What’s new is that AI allows this data to be actively understood, explained, and optimised by intelligent systems, accelerating time-to-decision by orders of magnitude.
As AI continues to spearhead growth and change, businesses must rethink their data strategies to ensure they maintain a competitive advantage – fuelling business growth and inspiring innovation.
