Why a Structured but Agile Discovery Approach Is More Necessary Than Ever

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Today’s in-house legal teams face an increasingly difficult mandate: manage rising discovery complexity while operating under tighter budgets and higher expectations for consistency, speed, and defensibility.

The challenge is no longer just volume. Data types shift in real time; collaboration platforms introduce new features without warning; and enterprise information changes faster than static discovery programs can absorb.

The result is strain — operational, financial, and strategic. Due to the changes mentioned, many organizations now face inconsistent workflows, unpredictable costs, and growing risk exposure. At the same time, boards and senior leadership expect not only predictable spend, measurable KPIs, and repeatable processes, but also greater innovation and efficiency — delivering more impact with fewer resources while demonstrating business-aligned value.

Against this backdrop, discovery programs must evolve. But evolution doesn’t mean abandoning structure; it means building a disciplined foundation with controlled agility.

Establishing the foundation

A case-by-case, ad hoc approach to discovery cannot keep pace with today’s demands. Structured, repeatable workflows emerged for good reason: they create clarity around roles, improve quality, and reduce risk. But to be effective now, that structure must rest on an explicit, well-maintained foundation.

Every program should define:

• Why the process exists (business purpose and risk posture).
• Where it applies (matter types, jurisdictions, data categories).
• Who owns which steps (legal, IT, privacy, outside partners).
• How success is measured (KPIs, cycle time, cost targets, quality benchmarks).

If a program already exists, modernization doesn’t require starting over. Instead, redefine the framework to be adaptable to change.

From static to iterative

Today’s greatest risk is not lack of structure; it’s rigidity. Static workflows cannot absorb new data types, incorporate platform updates, or capitalize on emerging technologies. Modern programs must intentionally build mechanisms for iteration.

This requires three parallel tracks:

1. Monitoring changing enterprise data. A cross-functional group, typically including legal, IT, and privacy, should regularly review new or updated enterprise systems, identify discoverability implications, and issue guidance before new data types surface in a matter. This keeps teams proactive rather than reactive.
2. Experimenting with discovery technology. A separate legal operations-driven effort should evaluate and pilot new discovery tools and workflows. This can be lightweight: each team member brings updates to a monthly meeting, or the group sets a goal to pilot one or two solutions annually. Some organizations brand this a “lab” to reinforce that experimentation has a defined place.
3. Implementing controls to minimize risk. When testing the new technologies above, add controls to validate the results along the way. Engage your partners as you go to implement both automated and human-driven QC steps. Extend your timelines to provide additional room for process refinement.

Clear expectations with service providers are key. Build pilot flexibility into contracts and request proactive updates on innovations relevant to your workflows. This avoids budget surprises and leverages partners’ expertise more effectively.

Safely turning innovation into value

Innovation should occur in controlled, low-risk environments where the potential value is clear.

For example, piloting a natural language processing tool to identify personally identifiable information (PII) or protected health information (PHI) on regulatory materials rich in personal data can reduce manual review time significantly. By combining technology-assisted workflows with human validation, teams can assess value, refine the process, and confirm defensibility before broader use.

A simple test and learn framework works well:

1. Identify the potential business value.
2. Select a low-risk matter or sample dataset.
3. Run the dataset through current and proposed workflows.
4. Measure quality, speed, user experience, and cost.
5. Decide whether to scale, refine, or reject the change.

Small, controlled pilots allow teams to innovate without disrupting active matters.

Embedding controls and continuous monitoring

Controls and metrics are essential to disciplined agility. Quality checks throughout the process help ensure consistency and surface anomalies early. Over time, metrics reveal trends indicating whether workflows are functioning as designed or drifting.

Partners can play a strategic role here as well. Aligning with providers on which steps they support — and what reporting and insights they should deliver — creates a unified view across matters. Regular joint reviews, such as QBRs, help institutionalize continuous improvement and maintain alignment with enterprise goals.

A future-ready discovery program

Discovery is evolving quickly, shaped by new technologies, shifting data behaviors, and heightened expectations for operational excellence. Programs that rely solely on rigid structure or ad hoc adaptation will struggle. The most resilient models are those built on a strong foundation paired with deliberate mechanisms for controlled innovation.

By grounding discovery in clear principles, creating space to experiment, partnering closely with providers, and embedding strong controls and metrics, legal teams can build programs that are consistent, defensible, cost-efficient, and capable of evolving as rapidly as the data they manage.