Proposals and concepts
Reuse what the company already knows instead of rebuilding every document from the beginning.
Practical AI adoption
Turn isolated AI experiments into dependable work.
Scaling Intelligence turns prompts, drafts and handovers into one clearly owned, human-reviewed workflow.
Personally guided. Grounded in your actual work. No tool demonstration.
What often happens
Proposal Draft 4 open points remain
Customer response needs another review knowledge sits in the inbox
Monthly report figures still missing inconsistent template
Handover collect the notes ownership unclear
Dependable
01 Input
02 AI step
03 Review by a named owner
04 Finished output
OwnershipData boundaryQuality measure
The starting point
Individual AI use does not change a workflow.
People produce stronger drafts and faster summaries. Yet proposals, reports and handovers often take just as long as before. The benefit stays with individuals while the company continues to work through the same old process.
Reports and analysis
Bring recurring inputs together and make expert review visible.
Customer responses
Respond more consistently without handing responsibility to a tool.
Handovers and onboarding
Turn information from conversations, files and inboxes into something the next person can use.
The shift
Not more activity. One workflow you can rely on.
The decisive move is not another AI licence. It is agreeing what the finished output must be, who reviews it, which data may be used and how the team will recognise a real improvement.
Useful ideas remain private.
- different approaches
- knowledge in chats and inboxes
- unclear review
- every output starts again
Good work becomes repeatable.
- one clear input
- a named owner
- a visible review step
- a reusable starting point
The method
We start with the work, not the tool.
One bounded, important output creates enough clarity for a sound first step. The result is not a large transformation programme. It is a workflow tested on real work.
- 01
Choose the work
We begin with a recurring output that currently consumes unnecessary time or coordination.
- 02
Design the workflow
Inputs, AI steps, ownership, review and data boundaries become one clear working sequence.
- 03
Test it on real work
A concrete example shows what holds up, what needs adjustment and where human judgement belongs.
- 04
Make it reusable
Templates, examples and rules make the next run easier than the first.
Ways to start
Three situations. One clearly bounded next step.
The right starting point depends on whether you first need direction, a shared team practice or a specific output delivered.
01
Direction
Choose the first workflow worth changing
For companies already using AI but still unsure which concrete workflow should change first.
Outcome: one selected workflow and a robust worked example.
02
Team practice
Establish a shared way of working
For teams that need to turn individual habits into a safe, repeatable practice with clear review and data boundaries.
Outcome: shared routines tested on the team’s own work.
03
Delivery
Complete an important piece of work
For a proposal, report or other output that is needed now and can also become a reusable pattern.
Outcome: the finished deliverable and the workflow behind it.
Dependable from the start
Responsibility is part of the workflow.
Human review, clear data boundaries and a named owner are not safeguards added later. They are what makes practical AI use dependable.
Ownership
One named person is accountable for the workflow.
Review
Every visible output receives human review.
Data boundaries
The team knows which information may enter which tool.
Impact
Time, rework and reuse are examined in concrete terms.
The first step
The guided AI adoption assessment creates clarity.
In a structured conversation, we examine how AI is used today, where recurring work gets stuck and which next step fits your situation.
personally guidedgrounded in real workan honest assessment instead of a sales script
Frequently asked questions
What to know before the conversation.
Which companies is the assessment for?
It is designed for small and mid-sized companies where proposals, reports, customer responses, research or handovers account for a significant share of the work. Headcount is less important than having a recurring output that needs to work better.
How does the guided AI adoption assessment work?
It is currently a guided, structured conversation. We look at your existing AI use, recurring work and the conditions needed for a dependable workflow. You then receive a candid assessment and a recommendation for the most sensible next step.
Does the assessment automatically lead to a project?
No. The assessment ends with a clear view of the situation. If a bounded implementation step makes sense, we explain which starting point fits and why. If a project is not useful right now, we will say that as well.