AI opportunity discovery
Structured workshops and process reviews that surface use cases where AI changes a business outcome, not just a workflow step.
AI Adoption
From AI experiments to enterprise adoption that holds up.
We help executive teams decide where AI belongs in the business, prove it with credible business cases, and put the data, architecture and governance in place to run it at scale.
Typical challenges
What we are usually asked to fix.
-
Pilot fatigue
Many proofs of concept, few in production. Nobody can say which ones deserve the next euro.
-
Value without a baseline
Benefits are asserted rather than measured, so investment cases are hard to defend or revisit.
-
Data and architecture gaps
Use cases stall on data access, quality or integration long after the model works.
-
Unclear accountability
Risk, legal, IT and business teams each hold part of the decision, and nobody owns the whole.
Curiosbot capabilities
Eight capabilities, one connected adoption path.
Use-case prioritization
A transparent scoring model across value, feasibility, data readiness and risk, agreed with the business before anything is built.
Business-case development
Cost, benefit and risk models with explicit assumptions and a measurement plan your finance team can audit.
Responsible AI
Practical policies, review steps and controls aligned with the EU AI Act’s risk-based approach and your own risk appetite.
Data and architecture readiness
Assessment of data sources, quality, security, integration patterns and platform options for each shortlisted use case.
Prototyping
Fast, bounded prototypes that test the hardest assumption first, with clear criteria for stop, adjust or proceed.
Implementation
Delivery support and assurance: integrating with core systems, designing the human workflow and preparing users.
Scaling and governance
Operating model, monitoring, ownership and portfolio reviews that keep AI value visible after go-live.
Engagement model
Start small, go as deep as the value justifies.
Durations are indicative and depend on scope, access and organizational complexity. We confirm them after a first conversation.
Typically 3–6 weeks
AI opportunity assessment
Discovery, scoring and a prioritized portfolio with a recommended first wave.
Typically 4–8 weeks
Business-case and readiness sprint
Deep dives on selected use cases: business case, data readiness, architecture and risk.
Ongoing, scoped to need
Adoption programme support
Senior guidance and assurance while your teams and partners build and scale.
Deliverables
What you can expect to hold in your hands.
- Prioritized AI use-case portfolio with scoring rationale
- Business cases with assumptions, baselines and benefit-tracking approach
- Data and architecture readiness assessment
- Responsible-AI policy set and review workflow
- Prototype findings and go / no-go recommendations
- AI governance model and adoption roadmap
Transformation roadmap
A sequence you can plan and fund.
- Weeks 1–2
Frame
Objectives, constraints and stakeholders. Baseline of current initiatives and spend.
- Weeks 3–5
Prioritize
Use-case scoring, readiness checks and an agreed first wave.
- Weeks 6–12
Prove
Prototypes and business cases against the assumptions that matter most.
- Quarter 2 onward
Scale
Implementation, governance and a rhythm of portfolio review.
FAQ
Frequently asked questions
Do you build AI solutions or only advise?
Our core role is advisory and assurance, with hands-on prototyping where it reduces uncertainty. For larger builds we work alongside your teams or preferred implementation partners so the recommendation stays independent.
How do you approach responsible AI and the EU AI Act?
We translate regulatory categories and your own risk appetite into review steps, documentation and controls that teams can follow. This supports your compliance work; it does not replace legal advice.
We already have several pilots. Where do we start?
Usually with an inventory and a scoring exercise. That tells you which pilots to scale, which to stop and which gaps (data, ownership, integration) block the rest.
Can you work with our existing cloud and AI platforms?
Yes. We are platform-agnostic in our advice and assess options against your architecture, security and cost constraints.
How do you measure success?
We agree baselines and measures during the business case, before build. Benefits are tracked against those definitions rather than reconstructed afterwards.
Related insights
Further reading.
Moving from AI experimentation to enterprise adoption
Why promising pilots stall, and the five decisions that move AI from proof of concept into everyday operations.
Creating measurable AI investment portfolios
A portfolio approach to AI funding: consistent scoring, staged commitments and benefits you can actually verify.
Bring us the AI decision you are weighing.
A first conversation is a working session, not a sales pitch. Bring the ambition and the constraints; we will help you shape the sequence.