Productised Consulting

Introduction

Consulting has been trying to productise itself for decades.

Frameworks, maturity models, diagnostics, standardised propositions, workflows, and early “expert systems” were all attempts to answer the same question:

How do we take expertise and deliver it consistently, repeatedly, and at scale?

But despite these efforts, most firms still face the same constraint:

They cannot consistently turn decisions into measurable outcomes at scale.

This is not a knowledge problem.

It is a decision-to-outcome problem.

In this article, we explore:

• Why productisation has always existed in consulting
• Why traditional approaches failed to scale
• What has fundamentally changed with AI
• And why productisation is now the foundation for systemised, scalable consulting

The Constraint Inside Consulting

Most consulting firms still scale in a predictable way:

• Hire more consultants
• Deliver more projects
• Grow revenue

This creates a structural limitation.

• Expertise is tied to individuals
• Delivery varies by consultant
• Growth is constrained by headcount

Commercially, this shows up as:

• Margins under pressure as delivery scales
• Increasing cost to grow
• Limited ability to create recurring revenue

AI does not fix this. It amplifies it.

Faster consultants simply produce more output - without improving how decisions translate into outcomes. This does not solve the underlying problem; it simply accelerates the structural limitations already present in the consulting model.

Productisation Has Always Existed

Let’s be clear, Productisation is not new, consulting has never been purely bespoke.

Behind most engagements sit structured assets that are productised to varying degrees and, importantly, adopted inconsistently by consultants despite firm-wide intent to standardise delivery:

• Standardised propositions
• Documented methodologies
• Defined workflows
• Maturity models and diagnostics
• Benchmarking and scoring systems

In some cases, these evolved into early expert systems:

• Questionnaires feeding scoring models
• Excel tools with macros
• Bespoke platforms generating standard outputs

Outside consulting, similar systems existed in more self-serve forms such as:

• Personality profiling
• Skills Assessment platforms

These demonstrated that automated assessment and standardised outputs were possible - but they were costly to build, rigid to change, and not flexible enough for most consulting use cases.

The direction was clear. Consulting was already moving toward Productisation long before LLMs became easily accessible, with early forms of automated assessment and advisory.

Why Traditional Productisation Fell Short

The issue was never the idea. It was the execution model.

Traditional approaches were:

• Slow to build (months or years)
• Expensive to maintain
• Inflexible once created
• One-dimensional in how they operated

And critically, They still mostly depended on consultants.

• Running questionnaires
• Interpreting outputs
• Translating results into recommendations

Which meant:

• Scale was limited
• Consistency was variable
• Cost remained high

Productisation improved efficiency. But it did not fundamentally change the model.

What Has Changed

AI does not introduce Productisation, it changes what is possible with it.

1. Speed and Cost

What once took months to build and create can now be done in hours.

Areas of expertise can be rapidly turned into:

• Diagnostics
• Maturity models
• Decision frameworks
• Advisory tools

At a fraction of historical cost.

This removes the primary barrier that held Productisation back.

2. From Static Assets to Intelligent Systems

Traditional Productisation created static outputs. AI enables dynamic, context-aware systems.

Instead of:

• Fixed scoring
• Standard outputs

We now have:

• Adaptive recommendations
• Context-driven decision support
• Continuous learning loops

Productisation is no longer about outputs. It is about how expertise is applied in real time.

3. The Critical Shift: Structured Knowledge Systems

This is the most important change, and the most commonly missed. Modern Productisation is not just about capturing assets. It is about structuring knowledge itself.

Through:

• Data models
• Ontologies
• Defined relationships between concepts

These determine:

• How decisions are made
• How actions link to outcomes
• How context is interpreted

This is what enables:

• Outcome-Led Consulting (defining success)
• Outcome Pathways (sequencing decisions)
• Consulting Operating Systems (scaling delivery)

Without this structure AI produces answers - But not outcomes.

This is one of the primary reasons AI transformation in consulting fails to deliver real impact.

From Productisation to Systemisation

This is where the role of Productisation changes.

Historically:

• It improved efficiency
• It supported delivery

Now it defines how consulting can be enabled with AI to operate at cale. Productisation becomes the foundation layer.

Without it:

• Expertise cannot be structured
• Decision-making cannot be systemised
• Outcomes cannot be delivered consistently

What This Enables for Consultancies

On its own, Productisation delivers:

• Faster delivery
• Greater consistency
• Higher quality repeatability
• Preservation of intellectual property

Knowledge moves from people… to systems.

But its real impact emerges when combined with systems.

The Dependency Chain (What Comes Next)

Productisation was once seen as the end state for consulting - but in an AI-enabled world, it is only the bridge to what is now possible.

It is the first step in a structured evolution:

• Productised Consulting → structures expertise
• Outcome Pathways → structure decisions
• Consulting Operating Systems → scale delivery
• Consultancy-as-a-System → enables continuous, scalable consulting

Each layer depends on the one before it. But without Productisation, nothing else functions.

The Commercial Impact

When expertise is productised and systemised, the economics change.

From:

• Headcount-driven growth
• Variable delivery
• Project-based revenue

To:

• System-enabled scalability
• Consistent delivery
• Recurring revenue models

This enables:

• Higher margins
• Greater leverage
• Increased client lifetime value

And critically, it aligns value with outcomes - not activity.

The Risk of Not Adapting

Firms that do not evolve face predictable outcomes:

• Increased price competition
• Commoditised outputs
• Growth constrained by hiring
• AI accelerating cost pressure, not value creation

They will move faster. But not deliver the better outcomes clients value.

Closing

Productisation is not new. But what it enables now is fundamentally different than the job it was doing before. It is no longer about making consulting more efficient. It is about making consulting scalable, repeatable, and outcome-driven.

Because in an AI-enabled world the advantage is not having expertise - It is being able to:

• Structure it
• Systemise it
• Apply it consistently
• And improve it over time

This is how consultancies move from delivering work… to delivering outcomes - at scale. And Productisation is the first building block to start with on that journey.