Case Study: Flooring System Modernisation

Most "we can't automate this" conversations in a UK SME are not really about automation. They are about a legacy system that quietly makes every modern integration impossible, and a team that has built workarounds for so long that nobody sees the underlying problem any more. Until you fix the foundation, no automation roadmap survives contact with reality.

This case study walks through what happened when a UK commercial flooring business confronted that exact gap. The team wanted to adopt AI automation. The legacy project management system they depended on had no API, no integrations, and no path to it. The brief was to replace the foundation first, then everything else became possible. The result was a complete Airtable workspace designed for AI from day one, and a team that went from 50%+ of staff time on admin to a platform ready for the RFQ, quoting, and invoicing automations that followed.

What was the legacy system actually costing the business?

Designs4You is a family-run UK commercial flooring business, managing 350+ jobs per month across maintenance and new projects. The team of 8 to 9 office staff relied on JobTrackerPro, a legacy project management tool with no API, no integrations, and no path to automation.

The system was no longer fit for purpose. Staff were printing emails, manually inputting every RFQ, updating project statuses by hand, and spending more than 50% of their time on administrative data entry rather than revenue-generating activity. Management wanted to spend more time growing the business, but the operational bottleneck was consuming everyone.

The core problem was not that the team was slow. It was that the tools they depended on made automation impossible. Without API access or modern integrations, every process improvement hit a wall. To unlock any automation, the foundation had to change first.

The legacy reality	What it looked like day to day
No API access	Every external integration was a manual copy-paste
Manual data entry	Every RFQ, every status update, every customer change typed by hand
50%+ staff time on admin	Office hours absorbed by the system, not the work
£565/month for 9 users	Recurring spend on a closed platform
13k contacts in a closed system	Customer data effectively trapped
Zero automation capability	No path to AI without the foundation changing

For wider context, Gartner has documented how legacy operational systems hold back modernisation across the small and mid-market, and the pattern is consistent: the longer a closed system sits at the centre of operations, the more expensive every workaround becomes, until the only sensible move is to replace the foundation. The UK government's productivity research has reached a similar conclusion at the macro level for UK SMEs specifically, and the Federation of Small Businesses tracks the same tech-adoption gap in its Small Business Index, where closed legacy systems consistently surface as a blocker to growth.

What did we build to make the team automation-ready?

We designed and implemented a complete Airtable workspace to replace JobTrackerPro, purpose-built for Designs4You's specific workflows and ready for AI automation from day one.

The Airtable setup included:

• 4 core tables: Projects, Customers, Fitters, and Activity Log, with interconnected relationships mirroring how the business actually operates
• 7 custom interfaces: Purpose-built dashboards for different team roles, including project detail views, quote pipelines by sales rep, weekly schedules, and ready-to-invoice tracking
• Multiple views per table: Filtered views for specific workflows (projects by sales rep, by fitter, needs attention, active customers, by region)
• Granular permissions: Role-based access so field engineers, office staff, and management each see what they need
• Team training: Hands-on training sessions with office staff and management to ensure confident adoption

Critically, every design decision was made with future automation in mind. The data structure, field types, and relationships were built so that AI automation could read from, write to, and manage projects programmatically, something that was impossible with the legacy system.

What changed once the foundation was in place?

Before (JobTrackerPro)	After (Airtable workspace)
No API access	Full API access for automation
Manual data entry for every project	AI-automated project creation possible
50%+ staff time on admin	Significant time freed for growth
£565/month for 9 users	Modern, scalable platform
13k contacts in a closed system	Clean, structured data ready for AI
Zero automation capability	Foundation for every subsequent phase

The headline change is the foundation. The more important change is the option value it created. Within weeks of the workspace going live, the team's first AI automation, an RFQ processing pipeline, cut inbound RFQ handling from 20 minutes to under 90 seconds. That automation only existed because the foundation existed. Without API access and structured data, the build would have been impossible to scope, let alone ship.

Why this matters before any AI conversation

Many businesses want to adopt AI automation but find themselves stuck on legacy systems that cannot integrate with modern tools. The instinct is to bolt automation onto what already exists. When the existing system has no API, no structured data, and no integration capability, the smartest investment is building the right foundation first.

For Designs4You, this transformation was not just a tool swap. It was the single step that unlocked every automation that follows. The RFQ processing automation, now live, and the future phases covering quoting, project management, and invoicing are only possible because the operational backbone was modernised first. Our roadmap guide for UK SMEs walks through how to sequence these decisions so the foundation work earns its keep, rather than feeling like a delay before the AI work.

Client testimonial

"We had been using the same system for years and honestly did not think there was a realistic alternative. Agenticise showed us what was possible, built the whole thing around how we actually work, and trained the team so everyone was comfortable from day one. It has completely changed how we think about our operations."

Ricky Stoltzman, CEO, Designs4You

Technical stack

For teams interested in how this works:

Airtable Team (custom tables, views, interfaces, and permissions), n8n workflow automation (for data migration and future automation phases), Microsoft 365 integration (email and document management), and Cloudinary (attachment hosting for automation compatibility).

What comes next

With the foundation in place, Designs4You's automation roadmap includes:

• Phase 2: Automated quoting, using project data in Airtable to generate quotes from a standardised pricing engine
• Phase 3: Project management and sign-off automation, streamlining the end-to-end job lifecycle
• Phase 4: Automated invoicing, triggered by project completion in Airtable

Each phase builds on the structured data and integration capability that Phase 1 established. Without this foundation, none of the above would be feasible.

Frequently asked questions

When should you replace your legacy system before automating?

Replace first when the legacy system has no API, no structured data export, and no integration path. You cannot automate what you cannot read or write to. If the team is spending 50%+ of their time on manual data entry because the legacy system makes any other approach impossible, that ratio is not an automation problem, it is a foundation problem. Fix the foundation, then automation becomes possible.

Can Airtable replace a legacy project management system?

Yes, for most UK SME operational workflows. Airtable handles structured data, relationships between tables, role-based permissions, custom interfaces per team role, and full API access for automation. For Designs4You, an 8-9 person team managing 350+ jobs a month, it replaced a closed legacy tool entirely and became the foundation for every subsequent automation. The fit depends on workflow shape, not industry.

How long does a system replacement take?

Plan on 6 to 12 weeks for a workspace replacement covering data migration, custom interfaces per role, permissions, and team training, depending on how much process documentation already exists. The data structure work takes longer than people expect because every later automation will inherit it. Skipping the structure work to ship faster is the most expensive shortcut available.

What is automation-ready data architecture?

A data structure designed so that AI automation can read from, write to, and reason about the data programmatically. That means structured field types (not free text where structure matters), explicit relationships between tables, consistent naming, and a clear contract for what each field means. Build the architecture for the automation you do not have yet, and the automation becomes a configuration problem, not a rebuild.

How do you train a team on a new operational system?

Hands-on sessions with the people who actually do the work, structured around their real day-to-day tasks rather than a generic feature tour. Build the custom interfaces and views the team needs first, so the first session is on a system that already looks like theirs. Run a follow-up session after a week of real use to catch the friction points. Adoption fails when the training is generic, not when the tool is wrong.