AI transition consulting for EU mid-caps
Move AI from pilots to production with one operating model.
TokenShift aligns architecture, workforce transition, and governance in one sequence for France-first and EU-regulated enterprises: Diagnose, Build, Transition, Assure.
Commercial baseline
Proof point
Break-even can be reached with three enterprise clients under the current operating model.
That discipline matters when buyer committees are evaluating six and seven-figure programs and need confidence that delivery, governance, and workforce adoption are owned together.
Why enterprise AI pilots stall
Programs slow down when architecture decisions, workforce readiness, and governance ownership are split across teams. The result is slower production rollout, unclear ROI, and rising sponsor fatigue.
- Architecture moves faster than the operating model around it.
- Managers and front-line teams are asked to adopt new workflows too late.
- Governance arrives after the pilot instead of shaping production readiness up front.
- Executive sponsors hear progress updates, but not the owner map required to scale.
One partner across four execution phases
Diagnose
Assess AI readiness, sponsor alignment, workforce impact, and governance risk. Deliver a board-ready roadmap with explicit owners.
Build
Implement target architecture and redesign workflows so AI becomes part of day-to-day delivery, not a side experiment.
Transition
Redesign roles, equip managers, and secure adoption accountability so production capability survives beyond the launch team.
Assure
Run executive governance loops, optimization cycles, and value-tracking so the program keeps producing measurable outcomes.
Offer portfolio
Diagnose
EUR 75K-150K
4-6 weeks
For executive teams that need a credible owner map, risk view, and readiness decision before committing a larger program.
Transform
EUR 250K-750K
6-12 months
For teams moving from isolated pilots to a working operating model with architecture, workflow redesign, and transition execution.
Accelerate
EUR 750K-2M+
12-24 months
For enterprise-scale rollouts that need coordinated execution, governance cadence, and sustained adoption across multiple teams.
Illustrative scenarios
Pre-revenue does not mean pre-method. These scenarios show how the framework works in buyer situations that already exist today.
Manufacturing
Pilot cell to plant rollout
A 2,000-employee manufacturer has promising copilots in engineering and quality, but no shared owner map for shift supervisors, plant leaders, and data governance.
- Diagnose clarifies the sponsor chain and KPI baseline.
- Build hardens the workflow and tool architecture.
- Transition equips supervisors and local managers for adoption.
BFSI
Speed with controlled governance
A financial services group wants faster AI deployment without losing control over model risk, approvals, escalation, and line-management accountability.
- Diagnose maps governance and control gaps before rollout.
- Build aligns use cases with controlled target architecture.
- Assure creates the executive reporting loop required to keep speed and trust together.
Pharma & regulated operations
Controlled adoption under scrutiny
Teams need to move faster with documentation-heavy processes, validated workflows, and subject-matter knowledge that cannot be lost in the transition.
- Transition focuses on role redesign and manager tooling.
- Assure keeps traceability and value realization visible to sponsors.
- The operating model is designed before scale, not retrofitted after the pilot.
Weekly insights for buyer committees
TokenShift publishes implementation-focused guidance on pilot-to-production execution, workforce transition, executive governance, and sector-specific rollout choices.
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Executive governance for AI at scale
Executive governance for AI must support execution speed while keeping ownership, risk, and value realization visible in the same operating rhythm.
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Workforce transition without delivery drag
AI adoption scales faster when workforce transition is planned as a production dependency, with clear role design, workflow changes, and sponsor visibility.
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Why AI pilots fail to reach production
Enterprise AI pilots fail when architecture, ownership, workforce transition, and governance are treated as separate workstreams instead of one production system.
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How CFOs should evaluate AI programs beyond pilot ROI
Pilot ROI is useful but incomplete. CFOs need to evaluate whether the operating model can actually carry the program into controlled production.
Questions leaders ask before scaling AI
Why do pilots stall after promising demos?
Because the pilot proves a tool can work, but not that the operating model around it is ready for production ownership, adoption, and governance.
When should workforce transition start?
During Diagnose and Build, not after deployment. Managers need role clarity, tooling, and accountability before AI becomes part of daily work.
What should a 90-minute readiness workshop decide?
The sponsor map, target decision sequence, current blockers, and whether the next move is Diagnose, a contained build, or a governance reset.
How do regulated sectors keep speed without losing control?
By designing traceability, escalation, and human-accountability into the rollout plan before scale, not by layering them on once the pilot is already live.
Start with a workshop, keep a lighter entry path open
The dominant CTA is the 90-minute Readiness Workshop. For prospects not ready to book, the self-assessment captures demand and starts the nurture path.