Why now?
We look for a real trigger: growth, cost, capacity, customer pressure, compliance or recurring process friction.
Fresh Kiwi · AI consultancy
Start small. Prove value fast. Scale what works.
This is what an end-to-end Fresh Kiwi engagement could look like for an organisation of around 80 employees.
Example output
Fictional example · TechStart NL · 80 employees
| Attribute | Example |
|---|---|
| Employees | 80 |
| Departments | Sales, Operations, Finance, HR, Customer Service, IT |
| Current AI | Copilot + disconnected public AI tools |
| Goal | Grow without proportional headcount growth |
| Status | Fictional example — not a real client reference |
Capabilities
0. Qualification — before the audit starts
Is there a sponsor?
Someone must be able to organise access, set priorities and make decisions.
Is there scale?
A task performed by one person for ten minutes a month is rarely interesting. We look for repetition, volume and impact.
Is there budget willingness?
No purchase commitment is required, but there should be willingness to invest if the business case is strong.
Is the environment workable?
We test systems, data, access, security and feasibility at a high level without designing solutions yet.
What does success mean?
We agree what should be measurably better within six to twelve months.
Capabilities
1. Discovery — understand before automating
Business goals
Which three goals matter this year and where does operational pressure get in the way?
Headcount pressure
Which teams are asking for more people and what would those new employees actually spend time doing?
Process friction
Where do we see waiting, duplicate work, correction loops, copy-paste and recurring reporting?
System landscape
We inventory Microsoft 365, CRM, ERP, HR, support, storage and existing integrations.
AI maturity
Which tools and pilots already exist and where could Shadow AI be developing?
Security & governance
Which data is sensitive, who may access what and where must approvals remain mandatory?
Method
2. From 80 employees to an opportunity portfolio
Not everybody needs a long interview. We combine surveys, workshops and targeted validation.
01Discover80 employees receive task survey · ~400 task observations collected · Duplicate observations clustered
02Prioritise~63 unique tasks validated · Value and time quantified
03PilotAI suitability tested · Technical feasibility assessed
04OperateSecurity/privacy/compliance scored · Top opportunities ranked
05Scale5 business cases developed
- 0180 employees receive task survey
- 02~400 task observations collected
- 03Duplicate observations clustered
- 04~63 unique tasks validated
- 05Value and time quantified
- 06AI suitability tested
- 07Technical feasibility assessed
- 08Security/privacy/compliance scored
- 09Top opportunities ranked
- 105 business cases developed
Opportunity audit
3. Illustrative portfolio
Example data showing what a decision-ready outcome can look like.
63unique tasks
8Do Now
17Next
21Explore
17Don’t Automate
AI Opportunity Portfolio63 opportunities
VALUEFEASIBILITY
8DO NOW
17NEXT
21EXPLORE
17DON'T AUTOMATE
Example output
4. Select the first pilot
| Opportunity | Before | After | Estimated value |
|---|---|---|---|
| Weekly reporting | 8 hrs/week | <30 min/week | ~€32k/year |
| Lead research | 5 hrs/week | ~1 hr/week | ~€28k/year |
| Invoice processing | Heavy manual work | Automated extraction + review | ~€41k/year |
Perspective
5. Prove value fast
We implement one low-complexity, highly measurable opportunity first. Baseline, KPIs, errors, time and user experience are agreed before implementation so the value review is evidence-based.
Perspective
6. Scale what works
After value is proven, we move to the next priority in the same portfolio. Existing Fresh Kiwi capabilities are configured where possible instead of being rebuilt.
Perspective
7. Managed AI & reassessment
After go-live we monitor usage, cost, quality and risk. We periodically reassess opportunities as processes, data and model capabilities change.
Next step