Knowledge that helps you choose, implement, and improve AI with confidence
AI only creates value when it fits your context, workflows, and people. Here you’ll find the core topics AISS supports—plus practical resources such as checklists, templates, and “one-pagers” to align stakeholders.
Looking for something specific? I support organizations across commercial, healthcare, public sector and tech—covering AI selection and evaluation, implementation and adoption, monitoring, AI literacy, and governance.
Knowledge in 5 focus areas
These are the areas where organizations most often get stuck in practice: selecting the right AI, implementing it well, evaluating & monitoring performance, building AI literacy, and setting up strong AI governance.
Selecting the right AI
Make grounded choices: what fits your goals, risks, and users? We translate your problem into selection criteria (quality, safety, transparency, integration, data requirements, cost) and apply them consistently.
Implementing AI into workflows
Move from “pilot” to real adoption. We define the prerequisites (process readiness, roles, data, training), and implement in close collaboration with users so AI actually gets used.
Evaluation & monitoring
Stay in control: define KPIs, evaluate holistically (quality, safety, bias, usability, workflow impact), and monitor continuously so you can intervene early when performance drifts.
Building AI literacy
Increase knowledge and skills across the organization to use AI responsibly and remain future-ready. Think role-based training, baseline assessments, and maintaining AI literacy over time.
AI governance & compliance
Embed AI in policy and decision-making. We define ownership, transparency, privacy (GDPR), and where relevant EU AI Act requirements—making AI manageable and audit-ready.
Resources & practical outputs
Practical deliverables are central. Depending on your needs, AISS provides checklists, templates, and clear “one-pagers” that help teams make decisions, track progress, and become more self-reliant.
What you can expect
- Checklists for AI selection, readiness, and go/no-go decisions
- Templates for evaluation & monitoring (KPIs, cadence, ownership)
- Infographics / stakeholder “talking sheets” to align quickly
- Examples of governance agreements (roles, risks, documentation)
AI literacy (in practice)
AI literacy starts with insight (a baseline) and becomes effective through role-specific training. Where relevant, AISS can also support with:
- A tailored AI literacy self-assessment (evaluation & monitoring)
- Slides and training materials for workshops
- An AI curriculum: program design, rationale, and learning strategy
Want a downloadable checklist or template for your audience?
Tell me your target group (e.g., healthcare, commercial, IT/management) and the topic. I can turn the content into a clear, practical checklist/template that matches your reality—perfect for sprints/workshops or as part of a longer trajectory.
Frequently asked questions (Q&A)
Many organizations run into the same core questions. These short answers are meant as a starting point. For your specific sector, systems, or risk profile, a quick intake is usually the fastest route.
How do I choose the right AI (what should I look for)?
Start with the core problem and the end user. Translate that into selection criteria (quality, safety, transparency, integration requirements, data, cost) and assess vendors/options consistently—so you avoid “tool-first” decisions.
Why does AI underperform after implementation?
Often the issue is workflow fit, ownership, or adoption. Success requires preparation (processes, roles, data, training), clear agreements, and implementation with users—not for users.
How do I evaluate and monitor AI holistically?
Don’t focus only on technical performance. Include usage, workflow impact, risk, bias, safety, and compliance. Use KPIs and a fixed evaluation cadence so you can detect drift and act early.
What is AI literacy and how do I build it?
AI literacy is the ability to understand AI well enough to use it responsibly and recognize limitations. Start with a baseline assessment, make training role-specific, and maintain it through repetition and updates.
How do I embed GDPR and (where relevant) EU AI Act requirements in practice?
Clarify up front what data is processed, why, who owns the risks, and how transparency and safety are ensured. Combine this with governance agreements, documentation, and monitoring—so compliance becomes part of the process, not an afterthought.
Didn’t find what you were looking for?
Share your question and I’ll point you to the best next step (e.g., a workshop/sprint, a guided trajectory, or a review session). You’ll quickly get clarity on what’s needed.