#34 - Your Data Is Digital Debt

Or the most valuable asset you’ve been ignoring. The robots are coming—and they’re starving.

Dear colleagues, friends, and stewards of the NOW of work,

There’s something I’ve been thinking about for a long time. I talk about it in keynotes, in boardrooms, and with every leader I coach. And I want to say it out loud here:

Messy data isn’t just a technical problem, it is all our problems and it’s digital debt.

It’s the pile of outdated job descriptions, the five versions of your benefits policy, the unsearchable SharePoint graveyard, the tribal knowledge that disappears when someone quits, and the inconsistent spreadsheets you swear you'll clean up “next quarter.”

It’s not just in HR. It’s everywhere. In every function. In every organization.

And in 2025, it’s time we finally take this seriously.

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Let’s Get the Mindset Right

When most people hear "data problem," they think, "Oh yeah, the IT team is on that."

Wrong mindset. This isn’t an IT problem. It’s a leadership problem.

Every time we write a policy without organizing it for insight, shove a doc into a random folder, or leave a question undocumented because “people know how it works,” we’re adding to the debt. The decisions we don’t make about structure, ownership, and clarity? Those are the decisions that haunt us later.

“Digital debt is like any other debt: invisible until the interest rate explodes.”

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The Myth: “AI Will Just Figure It Out”

There’s a comforting belief floating around:

“Once we bring in AI, the mess won’t matter. These large language models are so smart—they’ll make sense of it.”

That’s like saying, “We’ll get a great chef, and they’ll figure out what to do with our expired ingredients, unlabeled jars, and mystery meat in the freezer.”

“AI doesn’t clean up your mess. It scales it.”

If you feed it garbage, you get beautifully worded, confidently wrong garbage right back. And with GenAI, wrong answers sound really convincing.

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I've Done a Little Research...

...and the numbers speak volumes. If you're trying to build the business case for cleaning up your data, here’s what I found:

• Gartner reports that bad data costs the average organization $12.9 million per year in rework, lost productivity, and missed opportunities.

• IBM and MIT Sloan estimate $3 trillion in annual losses in the U.S. economy due to poor data quality.

• According to IDC, 80–90% of enterprise data is unstructured—and largely invisible to your systems or search.

• McKinsey estimates that knowledge workers spend 20–27% of their time searching for information.

• And only 3% of enterprise data meets basic quality standards.

So no—this isn’t just a technical challenge. It’s one of the biggest business risks to what people are calling “AI Adoption” and we’re ignoring it.

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Use GenAI to Help Prep Before You Perform

Here’s the twist: GenAI isn’t just the thing that needs good data. It’s also the thing that can help us clean it up.

You can now:

• Scan unstructured content like intranets or drives and organize it for insight—not just for storage.

• Summarize messy documents into structured, templatized assets.

• Link documents to structured systems (e.g., connect policy docs to HRIS fields).

• Turn a junk drawer into a knowledge library that actually works.

Example:
You’ve got 200 policy documents buried in folders—different formats, unclear titles, old content.
GenAI can scan, summarize, extract topics and owners, and flag duplicates or outdated info. In hours, you’ve created a searchable, trusted, up-to-date knowledge library—without a six-month project or extra headcount.

“Let the robots help set the table before we ask them to serve the meal.”

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Voice Is Data, Too

When we talk about messy data, most people picture documents, spreadsheets, and disconnected systems. But here’s what often gets missed:

Every conversation, coaching session, voicenote, or hallway answer is data, too.

Whether you’re typing or speaking, you’re generating knowledge. And in today’s world, voice-based knowledge is as valuable as anything written down—sometimes even more.

GenAI can now:

• Transcribe and summarize meetings instantly

• Extract key decisions, commitments, or knowledge nuggets from voicenotes or manager calls

• Turn voice memos into knowledge assets—structured and reusable

“Voice is the most human form of data—and it deserves a front-row seat in how we prepare for AI.”

The Robots Are Coming—But They’re Starving

2026 will be the year of AI agents. These autonomous copilots will show up across every function—answering questions, coaching managers, automating tasks.

But if your data is unstructured, unlabeled, and outdated?

They’ll have nothing to work with.

“You don’t want to spend 2026 asking why your AI agents sound dumb. You want to spend it scaling what works.”

This is our moment. H2 2025 is the prep window. Let’s use it wisely.

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What You Can Do Right Now

✅ 1. Inventory What You Have

Don’t just “take a look.” Build a map:

• Structured: your systems, forms, data fields

• Unstructured: PDFs, docs, tribal know-how, Slack threads

• Ownership: who owns each type of data?

• Quality: Is it still useful? Still used? Still trusted?

Example:
You lead HR. Your inventory might include:

• HRIS, ATS, and LMS platforms (structured)

• Job descriptions, comp plans, benefits guides, DEI strategy decks (unstructured)

• Ownership tags: Total Rewards, Talent Acquisition, People Ops

• A status check: outdated? duplicate? searchable?

2. Score Your Maturity

A quick 1–5 scale can show where you stand:

• 1 – Ad Hoc: We don’t know what we have.

• 2 – Fragmented: Some structure, but most is siloed or outdated.

• 3 – Organized: Inventory started, but not findable or fully trusted.

• 4 – Searchable: It’s usable and feeding some insights.

• 5 – AI-Ready: Connected, contextual, and fueling automation.

3. Let AI Do the Heavy Lifting

• Try summarization tools on your docs

• Use GenAI to generate metadata, categorize by need

• Don’t overengineer—just start where you are

4. Shift the Culture

• Create a culture of data stewardship, not just storage

• Give people ownership, not just access

• Normalize cleaning as part of delivery, not a side project

“If we keep feeding messy numbers into the spreadsheet, we can’t blame the formula when the answer’s wrong.”

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From Tribal Knowledge to Enterprise Power

Tribal knowledge is gold. But it’s also fragile.

The real transformation is taking what’s in people’s heads—and turning it into something the entire business can use, scale, and trust.

“The future of business isn’t about knowing everything. It’s about knowing what you know—and being able to find it or have when it matters.”

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🔊 Final Call-to-Action

Let’s stop layering frosting on moldy cake. Let’s clean up the kitchen before we hand it over to the robots.

Let’s chant it together:

Data is sexy.

Because it is. It’s what enables trust. It’s what fuels strategy. And it’s what will separate the companies who dabble in GenAI from the ones who win with it.

Let’s do the work now—so we’re not scrambling later.

Let’s shift from now to next.

—Jason

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About Jason Averbook

Jason Averbook is a globally recognized thought leader, advisor, and keynote speaker focused on the intersection of AI, human potential, and the future of work. He is the Senior Partner and Global Leader of Digital HR Strategy at Mercer, where he helps the world’s largest organizations reimagine how work gets done — not by implementing technology, but by transforming mindsets, skillsets, and cultures to be truly digital.

Over the last two decades, Jason has advised hundreds of Fortune 1000 companies, co-founded and led Leapgen, authored two books on the evolution of HR and workforce technology, and built a reputation as one of the most forward-thinking voices in the industry. His work challenges leaders to stop seeing digital transformation as an IT project and start embracing it as a human strategy.

Through his Substack, Now to Next, Jason shares honest, provocative, and practical insights on what’s changing in the workplace — from generative AI to skills-based orgs to emotional fluency in leadership. His mission is simple: to help people and organizations move from noise to clarity, from fear to possibility, and from now… to next.