Data has become an organization’s most powerful currency, yet its potential remains largely untapped by many. Why? Because legacy data systems were built for a different era, one focused on control and compliance, not agility. These systems often limit cross-functional visibility, making it difficult for teams to respond to change with speed and precision. To truly leverage business intelligence and stay competitive, organizations must adopt a faster, more inclusive approach. Augmented analytics is making that possible.
What is Augmented Analytics and Why it Matters
Augmented analytics uses artificial intelligence and machine learning to automate data preparation, insight generation and explanation within business intelligence platforms. It acts as a virtual data analyst, silently working behind the scenes to identify patterns, surface trends and suggest next steps.
This shift redistributes analytical responsibility, reducing reliance on centralized teams. As a result, employees across roles—not just specialists—can explore data, test assumptions, and make evidence-based decisions in real time. The market response is significant: The global augmented analytics sector reached $11.66 billion in 2024 and is projected to grow to over $87 billion by 2032, reflecting widespread adoption.
Empowering Teams Through Automation and AI
As automation takes over routine analytics tasks, data fluency is becoming a core expectation across roles, not only among specialists. AI-enabled tools lower barriers to exploration, enabling employees to validate assumptions, assess risk, and adapt decisions swiftly. This shift is redefining business readiness, where decision-making is grounded in timely, evidence-based insight rather than delayed by technical constraints.
Organizations are increasingly adopting smarter ways to manage and leverage data, with automation tools playing a critical role in streamlining processes such as migration, integration and quality checks. This simplifies complex workflows and empowers teams to focus on generating actionable insights. Solutions like Persistent’s iAURA, a suite of AI-powered data products, are helping enterprises foster a self-service culture by giving business users real-time access to curated insights, lightening the load on centralized teams and accelerating responsiveness across functions.
In one such engagement, we partnered with a global technology leader to embed this philosophy into their engineering operations. The company faced a growing challenge of scaling decision support across geographically distributed teams managing complex systems. By consolidating years of support documentation, service logs and configuration intelligence into a unified knowledge layer, we helped build an AI-powered assistant that now guides engineers through diagnostics, resolution steps and feedback loops. The result: A 12–15% reduction in delivery timelines and a 20% improvement in defect triage efficiency — turning tribal knowledge into scalable operational intelligence.
From Predictions to Prescriptions to the Rise of Explainable AI
AI is no longer just predicting what might happen; it’s recommending what to do next. In supply chain management, for example, prescriptive analytics can optimize inventory levels by analyzing demand patterns and potential disruptions. By automating decision-making and recommending actions in real time, prescriptive analytics help businesses act more decisively and strategically.
As organizations begin to rely more heavily on AI-driven recommendations, trust becomes as important as accuracy. That’s why Explainable AI (XAI) is so important. It tells your team: Here’s what to do — and here’s why. This transparency is particularly vital in sectors like healthcare, where AI may suggest life-impacting treatments. Understanding the rationale behind these recommendations allows users to make informed decisions and ensures the ethical use of AI.
With the increased adoption of AI technologies, integrating explainability features is becoming a standard practice to enhance trust and compliance. This trend underscores the growing emphasis on transparency and accountability in AI systems, ensuring that AI-driven decisions are fair, understandable and aligned with organizational values.
Augmented analytics will only truly be pervasive if we can talk business with it. That’s where natural language processing (NLP) comes in. It enables teams to ask simple questions — “What were my Q1 sales figures?” — and gives straightforward, actionable answers. This freeform language enables employees to interact with data without having to be experts in query languages or fiddle with technical tools.
One major healthcare provider is using this capability to transform how members navigate complex insurance plans. AI agents now summarize plan options, match them to eligibility rules, and assist members in narrowing down choices—all via conversational interfaces. This has reduced call center traffic by over 40%, while enhancing agent productivity with real-time access to claims, eligibility and coverage data — showcasing how NLP bridges experience and efficiency.
The Foundation and Future of Augmented Analytics
A critical but often overlooked factor is that AI’s effectiveness depends on the quality of data and the people behind it. Without strong data literacy, robust governance, and high-quality integrated data, even the most advanced tools fall short. Organizations must invest equally in their talent and data infrastructure to fully realize augmented analytics’ promise.
Looking ahead, these tools will become increasingly adaptive, learning from user behavior, context and intent to deliver more relevant, personalized insights. As this evolution continues, we’re seeing the rise of a new organizational culture — one where decisions are faster, grounded in evidence and broadly distributed across teams.
Augmented analytics is more than a technological shift. When done right, it doesn’t just democratize data — it turns it into a shared, strategic asset that empowers every decision-maker across the enterprise.
