Question 1

What does AI literacy mean for boards and supervisors?

Accepted Answer

Article 4 of the EU AI Act addresses AI literacy for people working with AI on behalf of an organisation. Board members need a practical frame of reference to assess use cases, boundaries and risks. Our training supports that learning. Ask your legal adviser to determine which measures and documentation your organisation needs; participation is not a compliance certificate.

Question 2

How do we maintain human oversight of agents?

Accepted Answer

Define who reviews the result and who can approve an action for each workflow. Every output is checked in the testing phase. Record stop rules and decisions. Legal requirements differ by use case; high-risk AI in particular requires a specific legal assessment.

Question 3

How do we limit shadow AI?

Accepted Answer

Provide an approved working environment, clear data agreements and usable examples. Foundation training helps users understand settings, instructions and boundaries. A ban alone does not replace a workable approach.

Question 4

Will we work with confidential business information?

Accepted Answer

Start with low-risk material and use only accounts and data approved by your organisation. Agree access, privacy settings and data boundaries with your IT and privacy team before the session. Do not share sensitive files without that assessment.

Question 5

Is our data used to train models?

Accepted Answer

That depends on the provider, plan, settings and your agreement. Check those terms before entering business data. The first layer of our approach is configuring the chosen model properly; an enterprise account alone is not a universal guarantee.

Question 6

Who owns the instructions, personas and configurations?

Accepted Answer

The foundation paper describes instructions, configurations and knowledge files as company property, on your laptop or in your own cloud. Define ownership and handover of custom work in the engagement. A transferable foundation helps when switching providers later.

Question 7

Why train boards and supervisors together?

Accepted Answer

Shared practical experience makes discussions about opportunities and boundaries more concrete. Both groups see how a model is configured, where errors arise and what review is needed. We recommend the team format for an executive and supervisory board training together.

Question 8

Is this suitable for non-technical supervisory board members?

Accepted Answer

Yes. You work with instructions, context and your own judgement. Programming experience is not required for foundation training. You work on your own laptop, with Wendy and Lavanya guiding the session.

Question 9

How does this fit a private equity portfolio?

Accepted Answer

Use the same agent brief, baseline, roles and stop rules across companies. This makes decisions comparable. Every workflow still needs its own data, risk assessment and owner; returns do not follow automatically from the methodology.

Question 10

Why do many agent projects stop?

Accepted Answer

The foundation paper cites Gartner’s June 2025 forecast that more than 40% of agentic AI projects will be cancelled before the end of 2027 due to rising costs, unclear value or weak risk controls. This is a forecast, not a measured outcome. Start with a business case and testable stop rules.

Question 11

What is the jagged frontier?

Accepted Answer

AI can perform well on one task and be convincingly wrong on another that seems equally difficult. The cited Harvard/BCG experiment illustrates this unevenness. Assess output for each use case; general benchmarks do not replace testing your own work.

Question 12

What does the €4,000 team training include?

Accepted Answer

One full day for up to fifteen participants, covering model settings, persona and brand book, and live agent building. LinkedIn is the low-risk practice case in the proposal. You receive thirty days of support afterwards. We do not promise guaranteed EBIT gains or a payback period.

Sharp questions.
Practical answers.

Ready to bring AI into your team’s daily work?

Sharp questions.Practical answers.