6 days ago
The Business Case For Data Readiness: Rethinking ROI In The Age Of AI
Sean Nathaniel is CEO of DryvIQ, the Intelligent Data Management Company trusted by over 1,100 organizations worldwide.
Rapid AI adoption has transformed data preparation from an IT initiative to an enterprise-wide priority. Historically, conversations around unstructured data have focused on risk: minimizing exposure, ensuring compliance and meeting regulatory demands. But with AI driving such monumental change—and using this data to do it—that framing is no longer enough.
Unstructured data readiness is more than just a safeguard; it's a growth strategy, and should be evaluated as such.
Businesses are now analyzing and organizing data not just to avoid risk, but to unlock value and drive measurable outcomes. By making data (and knowledge worker content in particular) relevant, organized, cleansed and secure, they can supercharge automation, unleash AI and make faster, sharper decisions. That shift in thinking requires a shift in how we frame the business case for data readiness strategies.
Data is more than just a technological byproduct of conducting business—it's now a driver of business impact. And that makes every business leader, not just IT, responsible for driving data readiness forward.
Why Risk-Based Business Cases Fall Short
Data initiatives have traditionally focused on reducing risk or cutting costs, but that narrow view misses the real opportunity. Today, enterprise content is a strategic asset, powering AI, automation and insights that drive growth. With over 90 percent of enterprise data being unstructured, organizations without a clear data readiness strategy aligned to business objectives risk missing out on significant value.
To make the case for data readiness today, IT and business leaders must move beyond minimizing downside and focus on the upside, demonstrating how these efforts enable growth and help achieve strategic goals.
Beyond ROI: The Three Returns of Data Readiness
Recently, Gartner introduced a framework for evaluating the business outcomes of AI-related investments. Rather than looking solely at return on investment (ROI), they recommend assessing two additional dimensions:
• Return On Employee (ROE): Return on employee engagement and productivity
• Return On Future (ROF): A strategic bet that the initiative will benefit the future growth of the company
A comprehensive data readiness strategy delivers across all three.
When content is organized and AI-ready, employees spend less time sifting through data and more time solving problems. Clean, well-classified content enables AI to generate accurate and meaningful insights with the push of a button. This allows teams to act faster and make decisions with greater confidence, amplifying the impact of each employee and improving productivity across the organization. Atlassian's AI Collaborators Report shows top AI users save up to 105 minutes daily—equal to an extra workday each week—and are 1.5 times more likely to invest that time in learning new skills.
According to an IDC report, generative AI delivers substantial returns, with organizations seeing an average of $3.70 for every dollar spent. For top-performing organizations, that ROI increases to $10.30.
These returns are only possible when AI has access to clean, organized and high-quality data. Without it, initiatives can stall or underperform, missing their full potential. Data readiness ensures that AI investments deliver the measurable financial outcomes that we expect.
One of the most strategic benefits of data readiness is its ability to support long-term adaptability and innovation.
AI strategies are evolving by the day. Until recently, agentic AI was hardly part of the conversation—now, Microsoft predicts that more than 80% of organizations expect agents to be moderately or extensively integrated into their AI strategy within the next 12 to 18 months.
New tools, new use cases and new regulatory demands will continue to emerge, and it's nearly impossible to predict precisely how the technology will evolve next. A strong data foundation ensures that organizations can pivot with confidence, adapt to change and act on new opportunities at scale.
Building A Business Case That Wins
To secure executive buy-in, data leaders must translate technical efforts into business outcomes. Executives do not invest in cleanup; they invest in performance, agility and growth. Here are a few ways to shift the conversation:
Lead with insight, not infrastructure. Use a targeted data scan or proof of concept to uncover risks, inefficiencies and untapped opportunities within unstructured data repositories and business applications. Let those early results shape the narrative and demonstrate impact, proving the value of developing a data readiness roadmap.
Tie efforts to active business initiatives. Position data readiness as the foundation for priorities such as AI adoption, content platform modernization or other transformation initiatives. When aligned with current business strategies, data projects are far more likely to gain traction.
Forecast return with clarity. Use pilot results or industry benchmarks to forecast impact, whether that means increased ROI from operational efficiency, higher ROE through improved productivity, or stronger ROF by enabling future initiatives.
Demonstrate scalability. Demonstrate how your approach can be tailored to various teams, systems and objectives. A one-size-fits-all solution may not work, but a framework that can scale to meet a variety of needs, including those that may arise in the future, will resonate with decision-makers.
Staying Ready For What's Next
AI may be today's driver of change, but data readiness is about more than preparing for the present moment. As enterprise data volumes continue to grow and business priorities shift, organizations need strategies that scale with them.
Data readiness is no longer a one-time project. It is an ongoing, strategic initiative that enables speed, agility and innovation. Companies that invest now in scalable, flexible data strategies will be ready not just for AI, but for whatever comes next.
The most successful enterprises will not just react to change; they will stay ahead of it. And that begins with how they ready their data today.
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