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AI transformation consulting
AI transformation, one proven workflow at a time, not a big-bang programme
Transformation fails when it is announced instead of shipped. We map where AI helps each role, train the team on their actual tasks, build the tools, and expand only after the first one has a track record everyone trusts.
- Mapped by role
- Trained on real tasks
- Expanded only after proof
Trusted by teams at
What transformation means in practice
It is three things in order. Find the opportunities role by role, so nobody is guessing. Put tools and training in people's hands on their real recurring work. Then measure use and expand where it sticks.
Training on generic AI theory does not change habits. Training on last week's actual finance close or research brief does. We build the sessions from the team's real tasks and mark what to automate, what AI assists with and what still needs sign-off.
We advise leadership directly on rollout approach and place engineers where a pattern needs proving inside a live team.
Where this wins
Leadership that wants AI adopted, not just approved
Where licences exist but usage is patchy and nobody can say what changed.
Companies unsure where AI helps
We map opportunities across the business before building anything, then narrow to the few worth deploying.
Product teams that want AI features without a rebuild
We add AI capability inside the live product and leave an architecture that keeps shipping features.
When NOT to use this
- · You want a rollout run for you by a large consultancy team. We are a small senior team and say so.
- · There is no executive sponsor. Transformation without one stalls after the first workshop.
- · You need change management for thousands of staff. That is outside our capacity.
How a transformation engagement runs
Start with one workflow and one owner. Expand only when the first has a track record.
01
Opportunity map
Week 1 to 2Interview each function and list where AI could help, with effort and risk noted.
DeliverableOpportunity map by role
02
Narrow to a first set
Week 2Pick the few opportunities worth building and name an owner for each.
DeliverablePrioritized shortlist with owners
03
Train and build
Week 3 to 6Train each team on their real tasks while we build the role-specific tools.
DeliverableTrained team and first deployed tools
04
Measure use
Week 6 to 8Track who uses what, where it stalls and what to fix.
DeliverableAdoption report
05
Expand deliberately
Roll the proven pattern to the next team or function.
DeliverableExpansion plan
Typical timeline
Two weeks to an opportunity map, then phased engagements of four to eight weeks per function
Stack we build with
Claude · OpenAI · n8n · Claude Code · TypeScript · Python · Supabase
Where transformation work starts
Finance team upskilling
Training on the recurring close and reporting tasks the team already does.
Role-specific AI skills
Deployed tools per role, brought together in one console.
AI features inside a live product
Personalization, segmentation and churn prediction added without a rebuild.
What you have after 30 days
Clarity on where AI helps, and one team already using it.
Week 1 to 2
Opportunity map
Every function's candidate use cases listed with effort, risk and an owner.
Week 2
Shortlist
The few opportunities worth building first, chosen with leadership.
Week 3
First training session
Built from the team's real recurring tasks, not generic AI theory.
Day 30
First tool in use
One role-specific tool live with a named owner and a usage measure.
Scope and exclusions
In scope
- · Opportunity mapping across roles and departments
- · Hands-on training sessions built from real tasks
- · Role-specific AI tools and a shared operating console where useful
- · Adoption measurement and a phased expansion plan
- · Direct advice to leadership on rollout approach
Not in scope
- · Large-scale change management programmes
- · HR policy or workforce planning decisions
- · Selecting and negotiating enterprise software licences on your behalf
- · Guaranteed adoption numbers
Results from comparable work
Achieved results only. Clients that have not agreed to be named are described instead.
A MedTech company
47 AI opportunities mapped before anything was built, narrowed to 16 deployed role-specific AI skills, with daily usage brought together in one operating console.
A finance team at a food-industry company
Trained on the team's real recurring tasks, covering what to automate, what AI assists with and what still needs sign-off. 12 people went from a single training session to daily use inside two weeks.
A listed enterprise software company
Leadership was advised directly on how AI adoption should roll out across a large, distributed engineering organisation. One embedded engineer proved the pattern in a live team, which became the reference other teams followed.
How this is priced
Priced per project and scoped after a short discovery call, not sold as a fixed package. Cost follows the number of systems, the access model and the security requirements.
Scoped build
Scoped after a discovery call
Most engagements start with one narrow, high-value piece so you see it running in production before anything expands.
Embedded engineer
From $60,000/year
A dedicated engineer building and maintaining the work inside your team, instead of a scoped project.
Book a free audit call
Tell us where AI should start helping
A 30 minute call. We look at your roles and current tooling and tell you which first workflow would earn the trust to expand.
Book a 30min Free Strategy Call
In this call, we'll walk through your project scope, timeline, and goals - so we can both check if we're a fit. No obligation, no slide deck, just a working session.
Don't want a call? Email walid@ayautomate.com
Opportunity Map
Implementation Path
Fast Follow-Up
“The team is super fast - sometimes we had to slow them down. We managed to scale the company without investing into hiring.”
Elie Salame
COO, Adstronaut.io
Video Call
Phone Call
In-Person
We've created products featured in
Walid Boulanouar
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This call is for teams ready to move. If that's you, pick a time.
30min
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Usually responds in 1 hour
No commitment required
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FAQ
AI transformation questions
How is this different from an AI strategy engagement?+
Strategy sets direction. Transformation work also trains the team and ships tools role by role, then measures use. If you want ongoing senior leadership instead, the fractional CAIO page covers that.
How long does it take?+
Two weeks for the opportunity map. After that, each function is a phased engagement of roughly four to eight weeks. We do not sell a multi-year programme up front.
Do you train non-technical staff?+
Yes. Sessions are built around each team's real recurring tasks, so the training becomes part of the working week.
What does it cost?+
Scoped after a discovery call, because it depends on how many functions and tools are in scope. Placement of an embedded engineer starts from $60,000/year.
Can you guarantee adoption?+
No, and nobody honest can. We measure use, fix what blocks it and expand only where it sticks.