Workload Forecasting & Diagnostics: The Discipline Behind High‑Performance In‑House Legal Teams

Executive Premise: You must own your data analytics agenda

Workload forecasting and diagnostics is not complicated. What makes it rare is not technical difficulty, advanced analytics, or expensive systems – it is ownership.

Most in‑house legal teams already have everything they need to understand their workload: the work itself, the people doing it, the points where matters slow down, the requests that should never have reached a lawyer, and the spend that feels too high but is hard to explain.

What they usually lack is a deliberate decision to treat workload understanding as a core leadership responsibility rather than something that might emerge incidentally from a CLMS, a dashboard, or a future “analytics phase”.

This paper makes four propositions.

First, most in‑house legal teams are not doing workload forecasting and diagnostics in any formal or sustained way.

Second, you do not need sophisticated systems to start – and buying them early often delays progress rather than accelerating it.

Third, there is a proven glide path to mastery that delivers immediate value, compounds over time, and creates the discipline required for more advanced technology to work when you eventually adopt it.

Fourth, too many teams (especially larger teams), think they will buy a software solution (eg a CLMS) that will instantly portal them through to data analytical utopia. This is wrong - it almost never happens. And, it is not needed. 

So, this whitepaper is going to share with in-house leaders everything they need to know in order to get their head around workload forecasting and diagnostics - you can also call it legal analytics. 

Why This Matters More Than Almost Anything Else Legal Can Do

In‑house legal teams rarely struggle because their lawyers are not capable. They frequently struggle because demand is unmanaged and poorly understood.

Pressure builds quietly. Volumes increase year on year. Turnaround expectations shorten. Exceptions multiply. Senior lawyers find themselves dragged into routine work. External spend grows without a clear narrative as to why.

By the time the issue is acknowledged, the discussion is already framed in the least productive way possible – emotional, reactive, and defensive.

“We’re overloaded.”

“We need more people.”

“We need better systems.”

Without evidence, those statements are hard to defend. They may be true, but they are not persuasive - and any requests by the in-house leader for resources to deal with the problem - fuels the "legal as an out of control cost centre narrative". 

Workload forecasting and diagnostics changes the nature of these conversations. Let that sink in!

It allows legal leaders to explain pressure rather than simply feel it. It enables them to articulate where demand is coming from, what type of work is consuming capacity, where it slows down, and what will happen if demand increases by a further ten percent next year.

That shift – from instinct to evidence – is the difference between reacting to workload and managing it. And when you combine that with the discipline of evidencing how your legal team investments deliver verifiable ROI (another data analytics discipline) - then legal is no longer a cost centre - its a value creator.  

Workload forecasting and diagnostics is as a discipline sits upstream of almost every other legal operations initiative. Process improvement, self‑service, automation, headcount decisions, technology investments, KPIs, benchmarking, and transformation planning all depend on a clear understanding of workload. If that understanding is weak, everything downstream is compromised.

And as a  low cost initiative for most legal teams - it is a game changing self help strategy that in-house leaders will increasingly loose credibility if they choose to ignore it.  

Why We Use the Term “Workload Forecasting & Diagnostics”

The legal industry has not helped itself with language. “Legal analytics” sounds abstract, technical, and vendor‑led. For many senior lawyers it triggers an immediate – and understandable – scepticism that what follows will be complex, expensive, and disconnected from day‑to‑day reality.

We use the term workload forecasting and diagnostics deliberately. It describes what the discipline actually involves: understanding the flow of work through the legal team, identifying patterns, and using that insight to make better decisions. It is practical, accessible, and grounded in the realities of in‑house practice.

Technically, this work does sit within the broader category of legal analytics. More specifically, it is process‑level and workload‑level analytics. But labels are less important than outcomes.

This paper is not about turning lawyers into data scientists or building elaborate dashboards. It is about helping legal leaders run their function deliberately rather than by feel.

Metrics, KPIs, and Why Order Matters

Most legal teams jump too quickly to KPIs. That impulse is understandable – KPIs feel tangible and reassuring – but it is usually premature.

Metrics are simply signals: volumes, cycle times, costs, frequencies, distributions. They are plentiful, noisy, and often messy. KPIs, by contrast, are a choice. They are the small number of metrics selected because they meaningfully influence behaviour and decision‑making.

The mistake many teams make is selecting KPIs before they understand their workload. When that happens, they often measure what is easy rather than what matters, and optimise behaviours that do not address the real constraints on performance.

The correct sequence is straightforward: 

1. Diagnose the work first.
2. Understand the patterns and pressure points. Decide what genuinely matters.
3. Then – and only then – formalise KPIs. In practice, well‑chosen KPIs tend to emerge naturally once diagnostic insight exists.

What Workload Forecasting & Diagnostics Actually Involves

At its simplest, workload forecasting and diagnostics is the disciplined examination of a small set of fundamental questions.

Who is sending work to legal?

What are they asking for?

How does that work flow through the team?

How long does it take at each stage? Who actually does the work?

When and why does it go to external counsel?

What does it cost?

How does the business experience the service it receives?

What is the scope for efficiency improvements?

Most in‑house teams can answer these questions anecdotally. Very few can answer them systematically. That distinction is critical. Anecdote lives in individual experience and is hard to share, defend, or scale. Systematic insight, by contrast, can be embedded into the operating model and used repeatedly to inform decisions.

The objective is not reporting for its own sake. The objective is decision‑grade insight – insight that directly informs how the legal function is structured, resourced, and prioritised.

A Market Reality Check

It is important to state this plainly, because it reassures rather than criticises.

The vast majority of in‑house legal teams do not conduct formal workload forecasting or diagnostics. Even sophisticated teams tend to rely on the instincts of experienced leaders, informal feedback from the business, and visible pain points such as backlog or external spend spikes.

Those instincts are often directionally accurate, particularly where leaders have deep organisational knowledge. But they have limits. Instinct is difficult to transfer, impossible to audit, and weak when challenged by finance or the executive team.

The moment a legal leader seeks additional investment – whether headcount, technology, or automation – instinct loses persuasive power. Data does not replace judgement, but it gives judgement credibility. That is the real value of workload diagnostics.

The Workload Diagnostic Framework (Explained in Practice)

Up to this point, the case for workload forecasting and diagnostics has been conceptual. This section is where it becomes practical.

When teams hear the word “framework”, they often expect something abstract or academic. That is not what follows. The diagnostic framework below is simply a structured way of looking at the work legal already does every day. It does not introduce new complexity; it introduces clarity.

The core idea is straightforward: if you want to understand, forecast, and ultimately optimise legal workload, you must look at it from several complementary angles at the same time. Looking at only one dimension – volume, or cost, or turnaround – will always give you a distorted picture. The power comes from seeing how those dimensions interact.

Who Is Sending Work to Legal

Every workload problem starts with demand. Yet most legal teams have only a vague sense of where that demand is actually coming from.

In practice, demand is rarely evenly distributed. A small number of individuals, teams, or business units often generate a disproportionate share of legal requests. Sometimes that concentration is appropriate – a high‑growth business unit, a regulated function, or a deal‑heavy region. Often, however, it reflects something else: a lack of capability in the business, poor upstream processes, or an over‑reliance on legal for low‑risk decisions.

Understanding who is sending work to legal allows leaders to move beyond generalised statements like “the business is busy” and into much more useful conversations. It enables questions such as: which roles consume the most legal capacity, whether that demand is increasing or stabilising, and whether it is driven by genuine risk or by habit. Over time, this insight becomes critical for forecasting, training, and stakeholder management.

What Work Is Being Requested

Not all legal work has the same value, risk profile, or optimisation potential. Treating it as if it does is one of the fastest ways to exhaust a team.

A disciplined workload diagnostic requires work to be categorised in a consistent way. This does not need to be overly granular, but it does need to be agreed. Contract work, advisory support, disputes, compliance activities, corporate governance, and regulatory matters behave very differently in terms of predictability, cycle time, and resourcing requirements.

Once work is categorised, additional layers of insight become possible. Some work is genuinely strategic and high‑risk; some is routine and repeatable. Some lends itself naturally to automation or self‑service; some does not. Without making these distinctions explicit, legal teams end up applying senior attention to low‑value work while under‑investing in areas that genuinely matter.

This is also where the seeds of future efficiency are planted. If you cannot clearly describe the work coming in, you will never be able to redesign how it is handled.

How Work Enters and Flows Through Legal

This is the point at which many analytics initiatives quietly fail.

Most legal teams would like to have an “official” legal instruction intake protocol - but most dont. Instructions are received via email, messaging apps, corridor conversations, and forwarded threads  - and sometime not at all.  Work is picked up opportunistically - clients approach their favourite lawyers.  Approvals happen offline. Does this sound like your legal team?

From an analytics perspective, this matters enormously. If work is not flowing through a consistent intake and handling mechanism, system‑generated data will always be partial and often misleading.

From a management perspective, it matters even more. Understanding how work actually flows – where it waits, where it loops back, where it gets stuck – is essential for identifying bottlenecks that have nothing to do with legal capability and everything to do with process design.

How Long the Work Takes – and Why

Cycle time is one of the most frequently cited indicators of legal performance, yet it is also one of the most misunderstood.

When turnaround times are long, the default assumption is often that legal is slow. In reality, delays are far more commonly caused by poor intake information, waiting for business approvals, unclear prioritisation, or negotiation dynamics that sit outside legal’s control.

Breaking cycle time down into stages is therefore far more useful than looking at averages. It allows legal leaders to see where time is actually spent and to distinguish between delay that is inherent to the work and delay that is avoidable. This is critical for both process improvement and for setting realistic expectations with stakeholders.

How the Work Is Resourced

Workload diagnostics inevitably raises uncomfortable questions about who is doing what work.

In many teams, senior lawyers spend a surprising amount of time on low‑risk, repeatable tasks. Paralegals and junior lawyers may be under‑utilised. External counsel may be engaged not because work is complex, but because internal capacity is poorly aligned.

Looking at workload through a resourcing lens allows these issues to be discussed as design problems rather than personal critiques. It enables legal leaders to ask whether the current mix of roles is appropriate, whether work is being routed to the right level, and whether capacity constraints are structural or temporary.

Financial and Spend Signals

Connecting workload to cost is where diagnostics becomes particularly powerful.

External spend rarely appears at random. It tends to cluster around particular work types, business units, or moments of internal pressure. Without workload data, these patterns are hard to see and even harder to explain.

By linking demand, handling, and cycle time to spend, legal leaders can move beyond blunt cost‑cutting conversations and into much more sophisticated analysis. They can distinguish between spend that reflects genuine complexity and spend that is compensating for internal design flaws.

Client Experience and Quality Signals

Finally, workload must be viewed through the lens of outcome and experience.

Speed alone is not success. A fast answer that creates rework or undermines trust is not a win. Diagnostics should therefore include signals relating to quality, consistency, and stakeholder satisfaction.

This does not require elaborate surveys. Even basic indicators – repeat queries, template adherence, escalation rates – can provide valuable insight when viewed in the context of workload and flow.

Taken together, these dimensions form a complete picture of how legal work enters the function, how it is handled, and where value is created or lost. Importantly, none of this requires sophisticated systems. It requires a structured way of looking at what is already happening.

Scope for Productivity and Efficiency Improvement

One of the most under‑appreciated benefits of workload forecasting and diagnostics is the sheer breadth of productivity and efficiency improvement it unlocks. We will show you in detail in later sections at least 30 insight scenarios that will be provide a logical and efficient pathway to legal department performance improvement. 

Without diagnostic insight, improvement efforts tend to be blunt. Teams default to generic solutions – more headcount, new technology, tighter SLAs – without being able to articulate precisely where efficiency is being lost or why productivity is constrained. With diagnostics in place, that ambiguity disappears.

By examining demand patterns, work types, flow characteristics, resourcing mix, and cycle‑time behaviour together, legal leaders gain a clear line of sight into where meaningful performance improvement is actually possible. In many cases, the opportunity is not marginal. It is structural.

Common productivity levers that emerge at this stage include reducing unnecessary legal touchpoints on low‑risk work, redirecting repeatable requests into self‑help or automated pathways, rebalancing work away from senior lawyers, smoothing demand spikes through better forecasting, and addressing approval or intake bottlenecks that add no legal value but consume disproportionate time.

Importantly, these opportunities are not hypothetical. They are evidenced directly by the team’s own data. That evidence allows leaders to distinguish between efficiency gains that require investment and those that can be achieved through redesign, clarification, or behavioural change alone – often at little or no cost.

This part of the framework therefore acts as a bridge. It connects diagnostic observation to performance improvement. The insights surfaced here form the raw material for the much richer set of conclusions explored later in this paper, where we step through the wide range of operational, resourcing, and strategic decisions that workload diagnostics enables.

From Framework to Insight: What the Data Actually Tells You

Once the diagnostic framework is in place, something important happens very quickly. The conversation inside the legal team changes.

Instead of debating symptoms, the team starts to see causes. Instead of arguing about whether legal is overloaded, leaders can point to where demand is coming from, what kind of work it represents, and how it behaves once it enters the system. Patterns that were previously felt intuitively become visible and, crucially, discussable.

This is where workload diagnostics stops being descriptive and starts to become transformational.

Teams begin to see recurring work types that absorb disproportionate amounts of time and attention. They see bottlenecks that have nothing to do with legal judgment and everything to do with how work is initiated or approved. They see senior capacity being consumed by work that could be handled differently, and external spend that is compensating for internal design issues rather than genuine complexity.

Perhaps most importantly, they begin to see predictability. Certain types of work arrive in waves. Certain business units spike demand at predictable points in the year. Certain matters follow similar trajectories every time. This predictability is the foundation of forecasting – and without it, legal teams are condemned to permanent reaction.

The Decisions This Data Enables (Why This Is Worth Doing)

This is where workload forecasting and diagnostics earns its keep.

Up to this point, the discussion has focused on understanding workload. This section is about using that understanding to make materially better decisions. Without diagnostic insight, these decisions are usually made on instinct, anecdote, or political pressure. With it, they become evidence‑based, explainable, and repeatable.

Set out below are 30 performance‑critical questions that mature workload forecasting and diagnostics allows an in‑house legal leader to answer with confidence. These are not abstract curiosities. Each one directly informs productivity, cost, risk management, or credibility with the business.

■ Recurring Work Types: Are there recurring categories of work that consume disproportionate time and should be handled differently (standardisation, playbooks, delegation, or self‑help) rather than repeatedly reinvented?

■ Self‑Help Eligibility: Which requests can be resolved through guidance, FAQs, templates, or automated tools without any lawyer intervention — and how much capacity does that immediately release?

■ External Spend Drivers: Which work types are driving external legal spend, and is that spend caused by genuine complexity, timing pressure, poor intake, or internal capacity misalignment?

■ Senior Lawyer Leverage: Are senior lawyers spending their time on work that genuinely requires senior judgment, or is high‑value capability being diluted by routine or low‑risk tasks?

■ Demand Concentration: Which business units, teams, or individuals consume the most legal capacity, and is that consumption proportionate to risk, revenue, or strategic importance?

■ SLA Calibration: Are service levels aligned to actual risk and business value, or are low‑risk requests being treated with the same urgency as mission‑critical matters?

■ Over‑Engineering Risk: Where is legal over‑engineering responses to low‑risk work, adding time and friction without commensurate risk reduction?

■ Under‑Investment Hotspots: Where is legal under‑investing time or expertise in high‑impact areas because capacity is being absorbed elsewhere?

■ Automation Readiness: Which work types are predictable and stable enough to be automated with confidence, rather than being poor candidates that would fail in production?

■ Seasonal Demand Patterns: What seasonal or cyclical demand patterns exist, and how can resourcing be planned proactively rather than reactively around them?

■ KPI Selection: Which metrics are genuinely meaningful enough to be elevated into KPIs, and which are useful only as background diagnostic signals?

■ ROI Prioritisation: Where will incremental investment (people, tools, automation, training) deliver the highest return in productivity or risk reduction?

■ Bottleneck Ownership: Where do delays actually occur in the end‑to‑end workflow, and which of those delays are within legal’s control versus owned by the business?

■ Intake Quality Impact: How much rework, delay, or clarification is caused by poor‑quality instructions, and what would improving intake discipline be worth in capacity terms?

■ Make‑vs‑Buy Decisions: Which work types should be retained in‑house, which should be externalised, and which should be redesigned so neither option is necessary?

■ Paralegal & Junior Leverage: Are lower‑cost resources being used to their full potential, or is work being escalated unnecessarily to higher‑cost roles?

■ Workload Distribution: How evenly is work distributed across the team, and where does imbalance create burnout risk or under‑utilisation?

■ Predictability Ratio: What proportion of the team’s workload is genuinely unpredictable versus highly forecastable — and are those two categories being managed differently?

■ Approval Friction: Which approval steps add meaningful risk protection, and which simply add delay without changing outcomes?

■ Training Priorities: Which recurring work types or error patterns indicate that targeted training would deliver immediate productivity gains?

■ Playbook Coverage: Which high‑volume work types lack clear playbooks or guidance, forcing lawyers to make the same judgment calls repeatedly?

■ Risk Appetite Alignment: Is legal applying a consistent risk appetite across similar matters, or are outcomes varying depending on who handles the work?

■ Business Capability Gaps: Which requests signal capability gaps in the business that legal is quietly compensating for — and should that continue?

■ Escalation Triggers: Which matters escalate unnecessarily, and what diagnostic signals predict escalation before it happens?

■ Cost per Matter Signals: What does cost per matter (true or proxy) reveal about inefficiency, misallocation, or poor workflow design?

■ Forecast Accuracy: How accurate are legal’s own workload forecasts over time, and where does variance suggest structural instability?

■ Panel Optimisation: Which work types are best suited to which external firms, and where panel design is misaligned with actual demand?

■ Interruption Load: How much capacity is lost to ad‑hoc interruptions, and what structural changes would protect focus time?

■ Change Readiness: Which parts of the workload are stable enough to absorb change initiatives without operational risk?

■ Credibility with Finance: Can legal clearly explain how workload, cost, and productivity relate — in language finance understands?

Taken together, these questions form the decision backbone of a high‑performing legal function. Without workload diagnostics, they are answered inconsistently or not at all. With it, legal leaders gain the ability to intervene deliberately rather than reactively.

The CLMS Reality Check: Why Solution Bought “Analytics” So Often Disappoints

This is the point at which many legal teams are tempted to look for a technological shortcut. This is where MNC-itis kicks in - and big legal teams start looking for shiny boxes! 

Contract Lifecycle Management Systems and Matter Lifecycle Management Systems are often sold as the gateway to analytics maturity. The promise is seductive: implement the platform, enforce a bit of discipline, and rich insights will flow automatically from dashboards and reports.

There is an element of truth in that promise. Most leading CLMS platforms are genuinely capable of producing useful analytics. They can report on cycle times across drafting, negotiation, and approval. They can show workload volumes, backlogs, and ageing. They can surface clause deviations, risk exceptions, renewal dates, and obligation data. Some can even extract portfolio‑level insights using AI.

But capability is not the same as outcome.

In practice, most in‑house teams use only a very thin slice of what these platforms can theoretically deliver. Volume counts. Status reports. Rough turnaround averages. Perhaps a handful of operational SLAs. The more sophisticated analytics – risk trending, clause deviation heatmaps, obligation performance, portfolio optimisation – quietly wither from lack of inputs, governance, and sustained use.

This is not because the tools are weak. It is because analytics is not a product feature. It is an organisational behaviour.

If templates are not standardised, clause comparison is meaningless. If metadata is optional or inconsistently applied, dashboards lie – or go blank. If negotiation happens in email and Word outside the system, workflow analytics become fiction. If obligations are not clearly owned post‑signature, compliance reporting becomes theatre.

The uncomfortable reality is that many CLMS implementations fall short of expectations not because the software cannot do what was promised, but because the operating conditions required for analytics to be true were never put in place.

This leads to a simple but important rule.

If your contracting process is not already structured, a CLMS will not magically generate insight. It will simply generate prettier uncertainty.

The correct sequence is therefore the opposite of what many teams attempt.

+ Build workload discipline first. 

+ Establish intake, classification, and basic diagnostic insight. 

+ Then allow technology to amplify what is already working. 

When teams follow that sequence, CLMS analytics suddenly become valuable rather than disappointing.

 

So, if No CLMS, Who Actually Does This Work?

Another reason workload forecasting and diagnostics is neglected is that responsibility for it is often unclear.

In practice, there are several viable resourcing models, and none of them is inherently wrong.

In smaller teams, this work is often initiated by a senior lawyer or the general counsel. That approach can work as a starting point, but it is fragile. Insight lives with an individual and disappears when attention shifts elsewhere.

In more mature teams, paralegals or legal operations professionals often take ownership of diagnostic reporting. With the right framework and leadership support, this model can be highly effective. It brings continuity, objectivity, and repeatability.

Where a dedicated legal operations function exists, workload diagnostics should sit squarely within its remit. This is core operating intelligence, not an occasional project.

There is also a strong case for external support, particularly where speed, independence, and benchmark context matter. Many teams simply do not have the time or internal bandwidth to design a diagnostic program from scratch while running the day‑to‑day legal function. In those cases, using a specialist provider can dramatically shorten the path to value.

GLS exists because we see this pattern repeatedly. Teams know they need this insight, but struggle to prioritise and structure it internally. External support is not a crutch; it is often the most efficient way to establish the discipline and then embed it sustainably.

The only genuinely bad option is having no one responsible at all.

A Practical Glide Path to Mastery

One of the most persistent myths in this area is that workload analytics must be done all at once, with full systems and comprehensive data. As we have seen above, that is absolutely not the case - in fact - it should be avoided at all costs!

In reality, the most successful teams take a staged approach. 

They start with disciplined intake. A simple legal service request protocol – even a basic online form – immediately improves data quality and visibility. 

From there, they track a small number of apex metrics: volumes, broad work types, and high‑level cycle times. They review those signals regularly and ask better questions each time.

As insight grows, they add depth selectively. They look more closely at bottlenecks, resourcing mix, and spend drivers. Only once the discipline exists do they formalise KPIs or invest in more sophisticated tooling.

This glide path delivers immediate value while building the capability required for more advanced analytics to work later. It avoids the common trap of over‑investing early and under‑delivering on insight.

GLS has more than done its bit for the in-house community to help you get going. Our world class Legal Service Request Form options, ane extensive know-how are all available to your team for free. Visit the Legal Service Request Form Station to access everything you need for free. 

The Role of AI – Helpful, Not Magical

Artificial intelligence has a role to play in workload diagnostics, particularly in pattern recognition, anomaly detection, and forecasting. We use it when we perform Workflow Forecast and Diagnostics works - because this is what we do - all day everyday.

Used well, AI can accelerate insight and surface correlations that would be difficult to spot manually. Used poorly, it simply adds another layer of opacity.

AI does not remove the need for clear definitions, disciplined intake, or leadership judgment. It amplifies whatever operating model exists beneath it. 

Teams that have done the groundwork will find AI genuinely helpful. Teams that have not will find it confusing and underwhelming.

However, the key takeaway is that the average in-house lawyer if more than qualified and capable view raw data and identify the patterns that emerge and where to plant a flag to mark and area where improvements could be made. 

Bringing It All Together: An Authoritative Conclusion

Most in‑house legal teams are not doing workload forecasting and diagnostics in any formal or sustained way. Not because it is difficult, and not because they lack technology, but because no one has taken clear responsibility for it.

This discipline does not require sophisticated platforms, data science teams, or large budgets. It requires intent, structure, and follow‑through. Teams that start small see immediate benefit. Teams that stay disciplined build capability. Teams that later adopt systems do so from a position of strength rather than hope.

Workload forecasting and diagnostics is one of the rare areas of legal operations where the upside is enormous, the cost is modest, and the path is proven. It changes the quality of internal conversations, the credibility of investment requests, and the ability of legal to operate as a confident, value‑creating function.

It is not complicated. But it does require you to own it.