Put AI to work on the processes that produce revenue.
Tenders. Account growth. Pipeline and forecasts.
We help mid-caps and large B2B companies identify, transform and put into production their high-value commercial processes, with measured results, human validation and built-in governance.
A business issue, pilots already running, or simply the need to know where AI can create value? Let us sort it out in 20 minutes.
One process at a time. Senior-led. Measured result.
An AI pilot is stalled
It works as a demo, but nobody owns taking it into production.
GenAI is already in use
Teams use it every day, but the value is neither industrialised nor measured.
A high-value process is identified
You know where the time goes, but no AI initiative exists yet.
In all three cases the first step is the same: the process, not the tool.
Talk about your priority · 20 min →The problem, then what the AI does
Three commercial processes, to qualify in your company
These are uses to qualify, not results obtained. They show the work the AI would do, the human validation it requires, and what would be measured to know whether it is worth it.
Situations we meet with executives
- RFPs consume too much senior expert time.
- Strategic accounts produce too few new opportunities.
- Pipeline and forecast quality remain unreliable.
- GenAI is already in use, but measurable business impact remains weak.
- AI pilots exist but have not become operational capabilities.
Tenders tie up experts for too long.
Find the relevant material in your previous answers and documentation, prepare a sourced response and flag what is missing before validation.
Time to a validated response, corrections needed, final quality.
Existing accounts generate too few new opportunities.
Cross commercial information with customer signals to propose documented leads to the account owner, who decides whether to pursue them.
Opportunities actually qualified and accepted by sales.
Sales forecasts are hard to make reliable.
Spot inconsistencies between CRM data, commitments made and next steps, then prepare the points to examine in pipeline review.
Forecast reliability and resolution of anomalies.
Which part of the result comes from the AI, and which part simply from a better process? The engagement has to show both. Otherwise there is no reason to buy an AI intervention rather than classic sales consulting.
See the three priority processesOne concrete workflow
Tender response: today, with AI, and what gets measured
A constructed example based on a typical case, not a client result. It illustrates the shape of the work expected from the AI, the human checks it requires and its limits. It relies on Autobidding, an internal accelerator used in engagements, which is not the main offer.
Today
- A 180-page specification lands
- Experts dig through old bids for the answers
- Manual consolidation of the document
- Round after round of review, up to the day before submission
With the AI workflow
- Specification ingested
- Requirements extracted, one by one
- Previous answers and evidence retrieved
- First draft, every statement sourced
- Uncertain areas and unsourced requirements flagged
- Expert validation, section by section
- Decision to bid, final response
What gets measured
- Response time
- Expert time consumed
- Reuse rate
- Corrections after review
- Win-rate impact, where measurable
Worked example
The same workflow, step by step
- 1
Input
A specification running to several dozen pages for a payment service. The company's previous responses, product sheets and compliance documentation are made available in a controlled space.
- 2
What the AI produces
A first structured response, requirement by requirement. Every statement is linked to its internal source: a previous response, a product sheet, a compliance document. Requirements with no source are flagged as missing, not filled with plausible wording.
- 3
What the human checks
The expert reviews the flagged points, arbitrates contractual and security commitments, and validates each section. Nothing leaves without that validation. Sales leadership decides whether to bid at all.
- 4
What is measured
Time from receipt to a validated response, the number of corrections made by the expert, and final quality as the team rates it. Without those three measures, nobody knows whether the tool helps.
- 5
Limits observed
The AI only knows what it was given: outdated documentation produces an outdated answer, sourced with the same confidence. New or ambiguous requirements remain expert work. The gain is in retrieval and assembly, not in judgement.
Two kinds of proof, not to be confused
"This is what our setup can do in this context." That is what this example shows.
"This is the gain measured in real conditions at a client." TokenShift publishes none until one is verified, with the client's agreement.
Priority sector
Where commercial performance, operational complexity and control requirements have to work together.
And more broadly across B2B
Three offers
Decide, put into service, keep the value
Prices are public. For each offer, what matters is what changes for you at the end, and what it asks of your organisation.
Who delivers? TokenShift frames, designs, organises and checks. Your teams or your technical suppliers build and integrate. Software development, licences and infrastructure are not included in the fees.
See the split of workWho you work with
A senior operator, not a team of juniors
The person you talk to in the first conversation is the person who leads the engagement.
TokenShift is led directly by Pascal Mauzé, a senior European enterprise operator with 25+ years in regulated B2B environments, payments and financial services first, across large-deal execution, commercial transformation, business process redesign, CRM adoption, and governed technology deployment.
Architecture, security, governance, adoption, CRM and sector expertise are added by a tight network of named associates, only when the process requires it. Former employers are not TokenShift client references, and are not presented as such.
Background and operating modelDelivery patterns
How each engagement runs, and what it produces
These examples describe the shape of each engagement and the artefacts it leaves behind. They are delivery patterns, not client results: no measured outcome and no named reference is claimed.
Pilot portfolio rationalization
When several AI pilots compete for budget, the first useful output is a defensible process choice, sponsor chain, and guardrail view.
First process ready to launch
When a use case is validated but not operational, the work is to redesign execution, human review, handoffs, and measurement before go-live.
Live-process control rhythm
Once one process is live, value, adoption, exceptions, vendors, and change decisions need a monthly operating cadence.
Working tools, not product theatre
Internal accelerators make the work concrete
TokenShift uses a few internal accelerators to make the work concrete. They are not the main offer. They serve as proof of capability in engagements, for diagnosis, prototyping and the design of the target process.
RegRadar
A governed regulatory-change process showing how AI outputs can be structured, reviewed, assigned, and controlled.
Autobidding
A tender response accelerator showing how a complex bid can move faster with human review, guardrails, and reusable company context. It is the setup behind the worked example above.
Feedback360
Used selectively when sales coaching, manager behavior, or adoption confidence is what blocks the launch.
AI in Production — Episode 1 of 3
Which AI pilots to stop, fund, or put into production?
Thursday 24 September 2026, 8:30–9:15 CET · 45 minutes · in French
FAQ
The questions executives ask before committing
Guides and executive notes
Take the method with you
Three PDF guides written from the executive notes — the six controls, the CFO file, the EU AI Act and DORA kit — and the newsletter that carries the next notes.
- PDFThe six controlsFrom Pilot to Governed Production: the Six Controls and the Executive Checklist
- PDFThe CFO fileThe CFO File on AI in Production: cost per outcome, ROI and the five lines to demand
- PDFEU AI Act and DORAEU AI Act and DORA: the Operating Teams' Kit
The newsletter
Executive notes on AI in governed production. Double opt-in, unsubscribe in one click.
Talk about your priority
A first 20-minute conversation to understand your issue, check whether AI is worth exploring, and settle the next useful step. No engagement commitment.