A project manager trained for AI implementation.
Most assistants can talk about project management. Alex is trained on PMBOK® 8 plus a dedicated enterprise AI implementation module: data governance, ethics gates, bias audits, MLOps and change management for AI.
AI projects fail differently.
Managing them takes a different skill.
An AI implementation combines the unpredictability of research, the compliance demands of a regulated industry and the change-management load of a business transformation — all at once. Generic project advice doesn't cover it.
Data is the project plan
Most of the technical effort in an AI project goes into data. Alex helps you build a data management plan before any model work begins.
Ethics is a PM responsibility
Bias audits, fairness metrics and explainability requirements are not the data scientist's job alone. Alex treats ethical delivery as part of the plan.
Change management is harder
AI affects how people feel about their jobs more than most technology change. Alex helps you manage the fear, close the culture gap and build adoption.
Models degrade after go-live
Data drift, bias drift and concept drift erode performance over time. Alex plans monitoring, retraining and ownership before launch, not after.
Success metrics come first
What does good look like, in numbers? Alex insists on accuracy thresholds, business KPIs and measurement timeframes before technical work starts.
Pilot first, always
Alex recommends the smallest safe pilot that proves or disproves the idea. Going straight to full deployment is the most expensive AI mistake there is.
Six gates before any model reaches production.
Alex knows the enterprise approval process and will flag it when a project is trying to skip a gate.
Technical gate
Model performance meets every agreed threshold — accuracy, precision, recall and F1 — against the production data set.
Ethics gate
Bias audit passed. Fairness metrics acceptable across demographic groups. Explainability documented and reviewed.
Legal and compliance gate
Data provenance clean. Personal-data handling compliant. Regulatory filings complete for your jurisdiction and sector.
Security gate
Penetration tested. Adversarial inputs handled. Access controls validated. Audit logging confirmed.
Change-readiness gate
Users trained, support model in place, rollback rehearsed, champions active, communications sent.
Business gate
Pilot results validated against success criteria. KPI baseline established. Measurement plan agreed for 6, 12 and 18 months.
Every AI project document, built in.
Alex produces these on demand, tailored to your project and organisation.
Twelve AI-specific risks Alex watches for you.
Pre-loaded in the risk register with the most common failure modes — and what to do about each.
| Risk | Probability | Impact | Alex's guidance |
|---|---|---|---|
| Poor data quality invalidates the model | High | Critical | Data audit before commitment; quality gates before training. |
| Model bias creates regulatory exposure | Medium | Critical | Ethics review at every milestone; bias testing in the delivery pipeline. |
| Training data contains personal-data violations | Medium | Critical | Data provenance audit and legal review before any training. |
| Stakeholder expectation gap | High | High | Realistic KPIs in the charter; early demonstrations to calibrate expectations. |
| Model drift after deployment | High | High | Monitoring pipeline, retraining schedule and service levels in place at go-live. |
| Change resistance blocks adoption | High | High | Change plan from day one; champions programme; adoption measured, not assumed. |
| Key ML talent leaves mid-project | Medium | High | Knowledge documentation, cross-training and a vendor back-up plan. |
| Vendor or model lock-in | Medium | High | Portable data formats, an exit plan and a second model evaluated before contract. |
| Inference cost exceeds the business case | Medium | High | Cost per transaction modelled in the pilot; budget alerts and usage caps. |
| Regulation changes mid-project | Medium | Medium | Regulatory watch on the RAID log; design to the stricter standard where uncertain. |
| Security of the model and its data | Medium | High | Threat model, access controls and adversarial testing in the security gate. |
| Return on investment not realised in time | High | Medium | KPI checkpoints at 6, 12 and 18 months; a benefits realisation plan owned by the business. |
Running an AI implementation?
Let Alex manage it.
Data governance, ethics gates, team dynamics, rollout strategy — ask Alex about your project, then run it inside your own network.