AI and automation are here to settle ROI questions of analytics

Today’s data and business analysts have a wealth of tools at their fingertips to do their jobs effectively. Behind every analyst team, however, is an IT leader being asked to communicate how such tools offer a Return on Investment (ROI).

Without being able to answer these questions resolutely, IT budgets will be scrutinized and, at worst, put at risk. However, the arrival of AI and IT automation may be about to flip the switch and make it much easier to prove ROI. Let’s explore…

The reporting gap

A lack of answers boils down to reporting gaps. To date, companies that have invested in advanced data analytics and data visualization platforms have fallen into the trap of neglecting the reporting that shows impact. 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 a retail business as an example. Deploying predictive analytics to optimize inventory management counts for nothing without structured reporting on stock reductions, cost savings or sales improvements as outcomes.

Analysts themselves feel the drag when they aren’t given the space to communicate outcomes. A third believe reporting should be a core part of their role but is currently overlooked.

With the introduction of AI into analytics workflows, as well as increased automation, it’s high time for enterprises to turn a page. The improvements that these changes offer (both to working with data and its outcomes) make possible a more systematic approach to reporting.

New possibilities

Our recent research found that 97% of analysts are integrating AI into their workflows, with 87% using analytics automation to streamline routine tasks.

With the right platform, it's possible for analyst teams today to automate data exploration, insight generation and the way that workflow’s function. This leads to faster time-to-value, improved decision-making and, crucially, analysts can report on progress against set KPIs in a reliable manner that doesn’t rely on manual input. Automated collection of performance-based data can track things like time savings in data preparation, cost per project and even the tracing of revenue back to insights generated via analytics.

Automation also makes the output of analytics more accessible. No-code platforms allow users to visualize key findings and insights without technical knowledge. This makes it easier for any business end users to come to, and communicate, data-driven conclusions.

Finally, sophisticated analytics platforms that come with integrated generative AI functionalities allow analysts to spin up presentations, reports and workflow summaries simply through a natural language prompt. The significant relief this provides analysts in terms of time and resources saved is obvious.

A focus on results communication

While AI and automation in analytics offer immense advantages, IT leaders need to shepherd a strategy to streamline and optimize the communication of results.

First and foremost, IT leaders should define success metrics that directly measure the enterprise impact of analytics tools, such as cost savings, revenue growth or operational efficiencies. Aligning these metrics with broader organizational objectives makes reporting coming out of data initiatives much more likely to resonate in ways that other teams care about – rather than heralding technical achievements without context.

Regular and proactive communication of insights is also crucial. Data analysts should go beyond ad-hoc reporting and establish a cadence for sharing comprehensive updates with leadership teams. These reports can highlight key metrics, emerging trends and measurable outcomes, ensuring that executives remain engaged with the impact of AI and analytics automation.

By demonstrating clear ROI through ongoing reporting, organizations can secure buy-in for further investment and scale their analytics capabilities effectively. There’s also no excuse not to do it, given how frictionless automation makes the collation and reporting of such insights.

Finally, fostering a culture of data literacy is an important step toward realizing the ROI of analytics and the tools that enable it. When more business users are working with data, IT teams have greater scope to gather day-to-day feedback from their analytics investments. A wider range or team are empowered to make smarter decisions and create tangible examples of ROI that make the case for continued investment.

Workforces in which the foundations of working with data are understood are more likely to apply new AI technologies in impactful ways. It also stands to improve their own productivity. Put in these terms, the strategic, long-term case for investment in analytics and data stacks becomes easier to articulate to any internal stakeholder.

Settling a debate

Proving the ROI of data analytics tools has long been a tough challenge for IT leaders. Without tracking the impact of their investments, many organizations will have been sitting on high ROI without even knowing it. That’s changing. Advances in AI tools and automation make it easier to track and show the value of analytics with clarity. This is in addition to the democratization of analytics that can unlock all-new levels of operational efficiency.

This shift is helping IT leaders make the case to stay the course with nascent or mature analytics programs, rather than dismantling efforts that were highly viable but difficult to link to business value until now. Enterprises mastering data and analytics will readily demonstrate measurable returns, giving them the most to gain in the upcoming intelligence era of AI disruption.

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