Today’s data and business analysts are equipped with a wide array of tools designed to drive smarter decisions. However, behind every analyst team is an IT leader tasked with the challenge of demonstrating the tangible value these tools deliver. Without clear evidence of return on investment (ROI), IT budgets can quickly come under pressure. Fortunately, the rise of AI and automation is shifting this narrative, offering new and measurable ways to justify impact and investment. Here’s how…
The problem of ROI blind spots
A common reason business leaders can’t answer ROI questions is due to gaps in their reporting processes. Many companies fall into the trap of spending their budget on advanced analytics and visualisation tools without focusing on reporting that can illustrate business impact. For example, if no one tracks the time saved from that platform or its influence on decision making, its impact goes unnoticed in the wider business.
Take the healthcare industry as an example. Hospitals might use clinical decision support systems (CDSS) to improve the accuracy of diagnoses and patient outcomes. But without structured reporting on error reduction, time saved or improved treatment efficiency, the impact remains invisible to everyone not directly involved.
Analysts often feel the strain when they’re not empowered to communicate the impact of their work. Our research reveals that one in three believe that reporting should be a central part of their role, but say it’s currently undervalued.
However, organisations have a clear opportunity to evolve by harnessing AI and automation. These technologies not only streamline data handling but also pave the way for more structured, consistent, and impactful reporting. Not only does this help to demonstrate ROI, but it also elevates the role of analytics across the enterprise.
The potential of AI-powered analytics
Almost all analysts (97%) now incorporate AI into their daily workflows, with 87% adopting analytics automation to handle repetitive tasks more efficiently. With the right platform in place, analyst teams can automate key processes such as data exploration, insight generation, and workflow management.
This not only accelerates time-to-value and enhances decision-making but also enables consistent, KPI-aligned reporting without relying on manual input. Automated performance tracking can capture metrics such as time saved during data preparation, project costs, and even revenue attribution linked to analytics-driven insights.
Additionally, advanced analytics platforms equipped with generative AI capabilities allow analysts to quickly generate presentations, reports, and workflow summaries using simple natural language prompts. The time and resource savings this delivers are substantial, freeing analysts to focus on more strategic initiatives.
Making the case for analytics investment
Despite the advantages of AI and automation, these tools alone are not enough. IT leaders must define a clear AI strategy to ensure results are communicated effectively and employee buy-in is achieved. IT leaders should start by defining what success looks like by establishing metrics that measure the impact of analytic tools on the organisation, whether that be reduced cost and improved operational efficiency.
When these metrics align with broader business goals, data strategies become more valuable to other teams by providing context that goes beyond showcasing technical achievements in isolation.
Proactive and continuous communication also holds significant importance. Analysts shouldn’t rely on ad-hoc reporting but must regularly communicate outcomes to senior leadership. Their reports should evaluate key metrics, emerging trends and outcomes about the impact of AI and automation. Organisations that consistently demonstrate ROI through regular reporting are more likely to secure the senior buy-in needed for greater investment in technology, while scaling their analytics capabilities. Given how effective automation is at simplifying the process of collating and reporting insights, there’s no reason not to adopt this strategy.
Building a culture of data literacy for broader business impact
The benefits of AI-powered analytics can and should reach further than the analytics team, though. Automation enhances the accessibility of analytics outputs to the wider organisation. No and low-code platforms empower users to visualise insights and key findings without needing technical expertise, making it easier for business users to interpret and communicate data-driven conclusions.
Equally important is building a culture of data literacy to realise the ROI of analytics. The more business users understand and engage with data, the greater ability for IT teams to collect consistent feedback from their analytics investments. With improved decision-making capabilities, IT teams can provide concrete use cases for ROI that encourage additional investment.
Teams with a solid understanding of data fundamentals are better positioned to harness the full potential of new AI technologies. This not only enhances the impact of AI adoption but also boosts individual and collective productivity. As a result, the long-term strategic value of investing in analytics and data infrastructure becomes much clearer and easier to communicate across the organisation.
Scaling ROI success
For IT leaders, communicating the ROI of data analytics has always been a significant obstacle. Consequently, some businesses may even have overlooked that their efforts were delivering significant ROI in the first place. But with AI and automation, we’re entering a new era of transparency where the value of analytics can be tracked, shared, and scaled – and importantly, receive the credit it deserves.