AI productivity
Most companies have the AI tools. Very few have changed how they work.
Buying licences is not the same as adopting AI. Teams end up with ChatGPT, Copilot and a handful of assistants, and a year later the actual work runs exactly as it did before — the same copy-paste, the same re-keying, the same reports assembled by hand. The gap is not the technology. It is that nobody redesigned the work around it.
Published 2026 research puts numbers on this: around 71% of organisations use generative AI regularly, while more than 80% report no measurable impact at the company level. In one study, 87% of leaders said their internal AI power-users were at least five times more productive than peers in the same role — but only 29% of those same companies reported significant return. The capability is clearly reachable. Spreading it is what fails.
Five levels of AI adoption
Most organisations we talk to are at Level 1 and assume they are at Level 3. Finding your real level is usually the first useful conversation.
- 1
Individual tools
People use AI assistants on their own. Real gains, entirely personal, invisible to the business. Nothing is repeatable and nothing is measured.
- 2
Shared workflows
Specific recurring tasks have an agreed AI-assisted way of being done. Prompts and steps are written down, so quality stops depending on who happens to be doing it.
- 3
Connected to your systems
AI reads from and writes to the tools you actually run — CRM, ticketing, documents, database. This is where most projects stall, because it is the first step that requires real engineering.
- 4
Agents with boundaries
Defined tasks run start to finish with tool access, inside explicit limits, with a human approving anything consequential.
- 5
Embedded operations
AI is part of how core processes run, monitored and measured like any other production system — including knowing when it is wrong.
What this looks like in practice
Concrete examples, not categories. Each of these is a recurring process where information currently moves by hand.
Sales
- Lead arrives
- Company researched automatically
- Qualified against your criteria
- CRM updated
- Draft response prepared
- Salesperson notified with context
Customer support
- Enquiry received
- Intent identified
- Knowledge base searched
- Draft reply with cited sources
- Complex cases escalated to a person
- Resolution recorded
Operations
- Document arrives
- Fields extracted
- Values validated against your rules
- Categorised and filed
- System updated
- Exceptions flagged for review
Internal knowledge
- Someone asks a question
- Approved company sources searched
- Answer returned with citations
- Uncertainty acknowledged rather than guessed
- Gaps logged for the owner
Reporting
- Scheduled trigger
- Data pulled from each system
- Figures reconciled
- Report assembled
- Anomalies highlighted
- Distributed to the right people
How we approach it
Audit
We map how your team actually works — not how the process document says it works. Which tasks recur, how long they take, and where information is moved by hand.
Prioritise
Every candidate is ranked by time it consumes, how well-defined it is, and what it would cost to change. Some are worth doing now, some never.
Design
We decide, per workflow, whether the answer is AI, conventional automation, a better integration, or simply a different process. Often it is not AI.
Implement
Built against your real systems and real data, with error handling, fallbacks and human review where the stakes justify it. Not a prototype on sample data.
Enable
The people doing the work are shown how it runs and what to do when it gets something wrong. Adoption fails more often than technology does.
Measure
We agree beforehand what success means — usually manual hours removed or turnaround time — and check it afterwards against the baseline taken in step 01.
When AI is the wrong answer
We would rather talk you out of a build than sell you one that will not hold up.
The process isn't defined
If two people do the task differently and neither is documented, automation will encode the confusion. Fix the process first.
The volume is low
Automating something that happens twice a month rarely repays the build and the maintenance that follows.
A rule would do
If the logic is deterministic, a scheduled job or a plain integration is cheaper, faster and far more predictable than a model. We will say so.
Being wrong is expensive
Where an error carries legal, financial or clinical consequences, the right design keeps a person in the loop — or does not use AI at all.
An existing tool covers it
Your CRM or helpdesk may already do this in a settings page nobody opened. That is a better answer than anything we could build.
How we handle your data
Putting AI near business data raises fair questions. These are the defaults we work to. They are commitments about how we build — not certifications, and we do not claim any.
Least access that works
A system gets the narrowest permissions that let it do its job. Read-only where writing is not required, and scoped to the specific records involved rather than the whole database.
You decide what leaves your systems
Which data is sent to a third-party model is an explicit decision we make together, documented, not an implementation detail you discover later.
Model choice is a requirement, not a preference
Where data cannot go to a third party, that constrains the options — including self-hosted or open models, or not using a model at all. We design to the constraint rather than around it.
Approval before consequence
Anything with financial, legal or contractual weight is prepared by the system and committed by a person.
Auditability
Actions taken automatically are logged so you can reconstruct what happened and why — which matters most on the day something goes wrong.
Retention is configured, not assumed
What gets stored, where, and for how long is set deliberately per system, including turning off provider-side retention where that option exists.
Start with what is actually costing you time
Tell us which recurring task eats the most hours in your week. We will tell you honestly whether it is worth automating, what it would take, and when the answer is to leave it alone.