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Felicia Andrews
Published on 4 August 2026
How to drive workforce productivity by improving your information health
One of the challenges many organisations face today is decision-making latency, fueled by the scattering of information across a collection of tools. Information quality, and ease of access, enables quick, confident decisions. Implemented effectively, AI can become a force multiplier, helping teams navigate this information and make faster, more informed decisions.
Your team is under pressure to close a high-value contract. The client requires a specific compliance clause to sign and close. A quick search on the intranet returns multiple documents with no clear indicator of which one is the most current and relevant. Then the chase begins. Who owns the document, are they online and available, why are there so many versions? After unanswered Slack messages, a Zoom meeting, a few emails, and several hours lost, a decision is made and a compliance document is sent to try and move the deal forward.
This isn't a failure of talent, or technology, it's a problem with information quality and connectivity. McKinsey found that knowledge workers spend 1.8 hours per day (9.3 hours per week) searching for and gathering information. That time adds up quickly in fragmented knowledge environments.
Data is now centre stage as a strategic asset, and your advantage lies in how easily your teams can find and use it. Information health is the extent to which (and speed at which) your workforce can find reliable, accurate, and actionable answers. This goes well beyond a minor IT annoyance of out of date intranets or filestores. When you optimise this environment, you unlock organisational productivity, strategic agility, and internal trust.
If productivity is the goal, is the answer more tools? More information? Or is it how we connect all of this together to reduce the friction between a problem and a solution?
A harmonised tech stack makes information easier to find
For years, many leaders assumed that expanding the digital toolkit would automatically streamline knowledge management. As a result, organisations have adopted cloud storage, internal wikis, project management hubs, and enterprise messaging platforms. While this explosion of tools has given teams unprecedented functionality, it has also distributed collective brilliance across isolated Google Drives, Confluence pages, Jira tickets, and chat threads.
Research into workplace tool sprawl suggests that, as information spreads across disconnected systems, it becomes harder for employees to find, trust, and share what they need.
The opportunity ahead lies in shifting from a culture of searching to a culture of finding. When accurate, up-to-date information flows, you remove the constant cognitive burden from your teams. Employees no longer need to jump between applications or wait on colleagues to verify if a piece of information is still accurate. By organising knowledge, you replace second-guessing with confidence, allowing your teams to move with speed and clarity.
Enterprise AI is only as strong as the information behind it
The rush to deploy generative AI and retrieval-augmented generation (RAG) systems has pushed information quality to the forefront. Many executives now view AI-powered search and knowledge assistants as a way to connect disparate data sources and make organisational knowledge more accessible. However, the effectiveness of these tools depends heavily on the quality of the information upon which they are built or to which they have access. Guidance on RAG systems consistently highlights that retrieval quality, source reliability, and data governance are among the strongest predictors of AI accuracy and trustworthiness.
Organisations with clean, governed and well-maintained knowledge repositories are more likely to get accurate, trustworthy and actionable results from AI. As a result, information health is becoming a prerequisite for successful AI adoption, not a separate knowledge-management initiative.
The time is now to shift from treating corporate data as a static archive to treating it as a living, curated asset. When internal AI agents draw from current, authoritative documentation, you can significantly reduce hallucinations, improve response consistency, and increase confidence in AI-generated outputs. I'd go so far as to say that information quality, governance, and discoverability is one of the quickest ways organisations can leverage the value of their AI investments.
The metrics that matter
Managing information health means moving beyond vanity metrics, such as the number of users logged into a portal.
Instead, monitor information health through practical metrics such as search success, content freshness, time-to-answer, and zero-result searches.
| Metric | Focus area | Strategic opportunity |
|---|---|---|
| Search-to-find ratio | The percentage of internal queries that result in a user actively utilising a document. | High ratios indicate a well-tuned search architecture that connects employees with exactly what they need on the first try. |
| Content freshness score | The ratio of actively verified documentation versus legacy files in active directories. | Keeping this score high ensures that both human teams and AI retrieval systems operate at peak accuracy. |
| Time-to-Answer (TTA) | The average duration an employee spends searching platforms before confidently taking action. | Minimising TTA is a direct boost to bottom-line productivity, letting teams spend more time on high-value, creative work. |
| The zero-result indicator | A report tracking specific keywords entered by employees that return no relevant results. | Tracking these gaps provides leadership with a real-time, user-generated roadmap for where to build new training or documentation. |
You can build a living knowledge ecosystem.
While every organisation's knowledge environment is unique, successful information health initiatives tend to share several common characteristics. A sustainable model of information health can be built around four operational pillars:
1. Refine the repository
You can systematically review your active spaces to elevate the authority of your content. By archiving redundant or outdated drafts, you ensure that both your people and your AI search crawlers interact exclusively with the gold standard of your company data.
2. Introduce knowledge lifecycles
Information thrives when it has an active caretaker. Establishing clear lifecycles for critical assets and ensuring documentation is gently flagged for a quick review every six months keeps the entire ecosystem fresh, reliable, and trustworthy.
3. Empower knowledge champions
Maintaining a brilliant information architecture is a highly strategic role. Designating accountability to data owners within each area of your business empowers them to serve as the guardians of their department's "public"-facing documentation, ensuring information remains well-organised and easy to navigate.
4. Build frictionless feedback loops
Every time an employee notes a missing answer or an outdated policy, they should have a simple, one-click way to flag it. Creating an environment where your teams can easily contribute to and uphold data accuracy transforms your entire workforce into an active community of curators, ensuring your ecosystem grows smarter with every interaction.
Better information health creates a smarter, faster enterprise
Is your information trustworthy? Can people find it? Can systems bring it together?
Information health is becoming a critical enabler of workforce productivity. When your workforce can quickly surface trusted answers, you reduce operational friction, accelerate onboarding, and free your employees to spend more time on higher-value work.
Before investing heavily in more enterprise applications, take a closer look at the foundation those systems will rely on. The quality, accessibility, and trustworthiness of your organisation's information may be among the biggest untapped opportunities to improve productivity, accelerate decision-making, and increase the impact of AI.