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Forbes
07-08-2025
- Business
- Forbes
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. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
13-05-2025
- Business
- Forbes
Anonymizing Data For AI: Protect Privacy, Preserve Value
Sean Nathaniel is CEO of DryvIQ, the Unstructured Data Management Company trusted by over 1,100 organizations worldwide. Artificial intelligence (AI) is hungry for data—the more real-world data we can give it, the better. AI applications can deliver immense value when provided with a continuous stream of robust, high-quality data. But a critical challenge looms for organizations increasing their adoption of AI: how to satiate these models without feeding them sensitive information. Enterprise data often contains personally identifiable information (PII), information about internal processes, customer data or intellectual property (IP). Removing this data from training sets may seem like the safest route, but it can weaken the models' ability to generate quality outputs. For enterprises managing large volumes of unstructured data, leveraging AI without exposing sensitive information is a concern shared by many CSOs and CISOs. The key to finding the sweet spot between data privacy and business impact is to incorporate data anonymization into AI data readiness strategies. Data anonymization strikes a smart middle ground: It enables organizations to retain the value of their data without compromising their responsibility to protect confidential customer, employee or non-public company data. The true value of AI comes from its ability to uncover actionable insights from high-volume, context-rich data. Never before have organizations been able to feed their knowledge worker content into a machine, such as meeting notes, call summaries, project outcomes and customer feedback assessments, and quickly identify trends, predict outcomes and use that information to drive business impact or unlock new revenue streams. But much of this information is often as sensitive as it is valuable. Consider a consulting firm using AI to enhance its services. By analyzing past client engagements, including statements of work, customer success metrics and proprietary strategies, consultants can refine their methods to repeat successes, providing more targeted recommendations and driving greater value for future clients. Ensuring that client identifiers, PII, IPand other sensitive data remain protected during this process is essential, but can feel impossible to do at scale. But without the right safeguards, these employees risk exposing not only confidential customer information but also their own company's private data. According to Cisco's 2025 Data Privacy Benchmark Study, more than half of respondents admitted to entering personal employee data or non-public information into GenAI tools. When this data is fed into AI systems without protections, businesses expose themselves to risk, including data breaches, regulatory penalties and serious damage to their brand and reputation. Anonymized data plays a crucial role in advancing responsible AI, enabling businesses to mitigate risks while harnessing the full value of real-world information. By classifying, encrypting, redacting or replacing sensitive identifiers within the data, companies can strike a balance between their objective of driving business value with AI and their obligation to protect private information. • Enhancing Customer Trust: Protecting customer data builds brand trust and confidence. Companies ensuring secure and responsible AI usage position themselves as leaders in an increasingly privacy-conscious landscape. • Ensuring Regulatory Preparedness: Anonymized, encrypted and redacted datasets mitigate the risk of data leaks and ensure compliance with data protection and privacy laws. • Protecting Intellectual Property: By using anonymized data, enterprises can safely train AI models without exposing their IP or trade secrets, thereby maintaining their competitive edge. To effectively anonymize and prepare data for AI, data owners must go beyond basic data curation and adopt a robust framework for data readiness. We've developed a recommended framework to help our customers prepare their data for the AI era, centered on data being relevant, organized, cleansed and secure. In practice, organizations that use this framework guarantee their data is: • Relevant: Has the data been curated to include only the information necessary to achieve the desired outcomes (as determined by the business objective of the AI initiative), while excluding outdated, trivial or redundant content? • Organized: Is the data categorized, labeled and structured to accelerate AI model training? Proper organization helps surface high-value insights while enabling more precise control over sensitive information. • Cleansed: Have customer identifiers, IP or other sensitive information been anonymized, redacted or encrypted to protect privacy without diminishing the quality of insights? • Secure: Are robust governance policies and access controls in place to ensure data is only accessible to authorized users and used strictly within the scope of the AI initiative? Building on the framework of relevant, organized, cleansed and secure data ensures that information is appropriately managed and protected when being used to train AI models. This requires a structured approach from the outset of any AI initiative. Here are four recommended steps to ensure your data is ready for responsible AI use: 1. Audit existing data repositories. Analyze all of your unstructured data repositories to understand where your most sensitive information resides and how it's currently being secured. This helps you assess your current risk level and determine the required steps to prepare your data for specific AI projects. 2. Build privacy-first AI initiatives. Commit to using anonymized datasets to power your AI initiatives. This will maintain the quality of AI outputs without the risk of leaking confidential customer, employee or company information. 3. Adopt a modern data readiness framework. Leverage intelligent data management platforms to continually ensure your data stays relevant, organized, cleansed and secure for your AI initiatives, including the automation of data anonymization techniques. 4. Reinforce governance policies. Establish robust protocols and conduct regular audits to ensure compliance with relevant privacy laws and regulations. Using high-value knowledge worker data in the AI era doesn't have to come at the cost of privacy. By implementing data anonymization as a core component of their data readiness strategy, organizations can fuel innovation while safeguarding sensitive information and intellectual property. Adopting a robust framework for data readiness enables organizations to prepare their data responsibly for an AI-driven future. Enterprises that prioritize privacy alongside innovation position themselves as leaders in this space, building strong foundations for sustainable success. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
