There is a concept that appears across project delivery, design, and software development (or any agency service, really) that neatly captures a trade-off most organisations eventually encounter.
It is often summarised as Good, Cheap, Quick.
In the Venn diagram that represents this idea, the three qualities overlap in different combinations. You can achieve good and cheap, or good and quick, or cheap and quick. But at the very centre of the diagram sits a the overlapping section labelled with a simple note:
This does not exist.
The principle is not meant to be cynical. It is simply a reminder that every project involves trade-offs, and attempting to optimise for all three variables at once usually creates problems somewhere else.
Yet when organisations begin conversations about CRM implementations or HubSpot onboarding projects, the discussion almost always starts with two familiar questions:
How fast can we do it?
And how much will it cost?
Those questions are understandable, but they rarely get to the heart of the issue. A more useful way to frame the conversation is to step back and ask a different set of questions entirely.
What is the cost of not doing this properly?
What happens if we push a system live too quickly?
Do we have a definition of what good is for our project?
Before timelines and budgets become the focus, the real challenge is defining what “good” actually means for your organisation. Without that clarity, comparisons between proposals, partners, and approaches become little more than a race to the lowest price or shortest delivery window.
And when it comes to CRM systems that underpin marketing, sales, and customer operations, those trade-offs have consequences that tend to surface long after the project has gone live.
From the outside, HubSpot onboarding can look deceptively simple. Import the contacts, build a few pipelines, connect some integrations, and start sending campaigns.
In reality, the work that determines whether the system succeeds or fails sits beneath the surface.
A proper implementation often includes:
It’s not all glamorous work and snazzy automations. In fact, it is the not-so-sexy operational layer that modern systems rely on - clean data, clear documentation and definitions, reliable frameworks for automation.
These are also the foundations that AI tools rely on. AI cannot fix poor data structures or inconsistent processes. If anything, it accelerates the consequences of them.
When organisations skip this work to move faster, things can appear fine in the short term. Campaigns go out. Deals are created. Reports look busy.
But over time the cracks begin to show, and this is something we’ve seen from experience. Clients come to us for portal audits and rehab projects - they have a big, messy problem and they’re often a result of a project being implemented without the right foundations, people or systems in place.
Fixing these environments often requires rebuilding workflows, restructuring data models, cleaning data, and retraining teams. And the uncomfortable truth is that these projects often cost more than doing the implementation correctly the first time.
When organisations are selecting an implementation partner, it is very easy for the conversation to collapse into a comparison of price and timelines. One proposal promises a faster delivery. Another appears cheaper. On paper the deliverables may look similar.
But what is often missing from that comparison is the depth of discovery and planning that sits behind the work. Lower-cost projects frequently involve reduced upfront analysis and limited system design. That can mean:
The system may technically go live faster, but it is rarely aligned with how the business actually operates. Over time this leads to the same pattern → Teams lose confidence in the data, automation becomes fragile and leadership struggles to rely on reporting. Eventually someone says the words no one team wants to hear:
“Maybe we should rebuild this.”
At that point the organisation is effectively paying for the project twice.
Before worrying about speed or cost, the most valuable exercise any organisation can undertake is defining what success actually looks like.
For some businesses, success might mean centralising fragmented systems into a single CRM. For others it might be reliable forecasting dashboards, better marketing attribution, or improved customer lifecycle visibility.
In more mature organisations, the goal may be enabling advanced automation, integrated data environments, or AI-assisted workflows.
These outcomes require deliberate design. They require an understanding of how teams operate today, and how the system should support them in the future.
Without that clarity, implementation conversations naturally drift toward timelines and budgets because they are the only measurable variables left. But once the organisation has defined what “good” means, the trade-offs become clearer.
HubSpot is an incredibly powerful platform. When implemented well, it becomes far more than a marketing tool. It becomes the operational centre for customer data, revenue reporting, automation, and increasingly AI-driven workflows.
But the value of that platform is determined far less by the speed of implementation than by the strength of the foundation beneath it.
The Good, Cheap, Quick diagram is not meant to discourage efficiency or careful budgeting. Every organisation has constraints, and every project involves trade-offs.
What the model highlights is a simple reality:
You cannot optimise for everything at once.
For most organisations undertaking a CRM transformation, the most sustainable path usually sits somewhere between good and deliberate. The upfront thinking, data preparation, and system design may take longer, but they dramatically reduce the technical debt that slows organisations down later.
Because when the foundations are right, everything that follows becomes easier.
Automation works.
Reporting can be trusted.
AI tools have clean data to learn from.
And the system that once looked like a project becomes what it was always meant to be: infrastructure for long-term growth.