Corporate AI Training & Literacy

Executive AI Literacy

Executive AI literacy has to be practiced against live portfolio, risk, governance, and investment choices, with enough technical depth to challenge claims without outsourcing leadership judgment.

Leaders don't need a model-building class. They need enough technical grounding to challenge claims and work through portfolio, risk, governance, operating-model, and investment choices already on the agenda. The session follows those decisions and records who takes each question forward. Leaders leave with a shared question set, worked decision scenarios, observed confidence gaps, and named sponsors for the actions tied to their agenda.

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Illustration of Executive AI Literacy: a team practicing AI skills together in a workshop setting

Some of the 500+ brands we've worked with

See all references
  • Sabancı Üniversitesi
  • Defacto
  • Logo Yazılım
  • Babylon
  • S Sport Plus
  • Tatilsepeti
  • Amazon
  • BMW
  • Shell
  • Hyundai
  • PepsiCo
  • Red Bull
  • Decathlon
  • MediaMarkt
  • Bayer
  • Sanofi
  • EY
  • KPMG
  • GE
  • 3M
  • Domino’s
  • Lexus
  • Trendyol
  • Hepsiburada

The run order, and who signs each step off

How we work

Every concept earns its place by helping leaders test a claim, expose a missing dependency, or work through a decision already on the agenda.

  1. The agenda starts with decisions

    We interview leaders about the portfolio, risk, governance, operating-model, and investment questions already on their agenda. Your executive sponsor confirms which decisions the session should target.

  2. Enough technical depth for the room

    We explain the capabilities, limits, dependencies, and failure patterns needed to reason about those scenarios without turning the session into a technical lecture. Our facilitator decides how much technical depth serves the group.

  3. Trade-offs move into the room

    The group weighs options, asks for evidence, identifies missing controls, and practices deciding under uncertainty. Leaders in the room decide what evidence would change each decision.

  4. Follow-up leaves with owners

    We compare decisions with the agreed rubric, discuss confidence gaps, and assign follow-up actions to specific owners. The sponsors in the room accept ownership of each follow-up action.

Named artifacts you keep

What you get

The session leaves a compact decision record behind. Leaders can return to the same scenarios, trade-offs, and action owners when the next proposal or portfolio review reaches the table.

  • Report

    Executive capability-and-limit briefing note

    A concise foundation on the capabilities, limitations, dependencies, and questions relevant to your leadership agenda.

  • Workshop record

    Portfolio and governance scenario discussion pack

    Portfolio, risk, governance, operating-model, and investment cases for structured discussion.

  • Playbook

    Vendor-claim challenge and evidence-question brief

    A practical guide for reviewing proposals, plans, and vendor claims after the program.

  • Decision record

    Confidence-gap findings and sponsor action list

    The observed decision patterns, confidence gaps, and follow-up actions agreed by the group.

Scope and honest limits

When to bring us in

Your leaders are being asked to judge AI choices before they have a shared way to separate evidence, capability, risk, and hype.

A good fit when

  • Opportunity, capability, risk, and hype share one discussion, but leaders have no common frame for deciding which claims deserve evidence.
  • Executives are reviewing internal proposals and vendor claims, yet the questions they ask do not separate model capability from operating assumptions.
  • Portfolio, governance, and investment choices arrive quickly, while the group lacks enough technical grounding to judge their trade-offs.
  • Live leadership decisions are already on the agenda, but a generic case study would miss the evidence and constraints that make those choices hard.
  • Leaders hear capability and limitation claims together, yet they cannot tell which dependency belongs to the model, system, or operating team.
  • The group discusses portfolio and risk, but missing controls and untested assumptions leave the trade-off undecidable.
  • A session produces confident opinions, while action commitments, sponsors, and the decision each one supports can disappear after the room empties.

Better handled as other work when

  • You want a generic tool demonstration. This session uses live leadership decisions and evidence, while product training belongs in a different program.
  • You want someone else to make portfolio, risk, or investment choices. The scenarios sharpen judgment, but authority stays with the leadership team.
  • You expect one session to create lasting competence. It builds a shared decision frame, while named sponsors still have to carry the follow-through.

If one of these is closer to your situation, start here instead: See corporate AI training

Your trainers build AI for a living

The people who run our training build and operate AI systems the rest of the week, so the material comes from work we've shipped. Zeo has been around since 2011 and runs the Digitalzone conference community, which keeps us close to how teams across the industry are picking these tools up.

Content we've produced on this topic

Videoby Burak PehlivanEffective Use of Artificial Intelligence for E-Commerce Companies | Burak Pehlivan, icerik.comVideoby Ozan KetenciA Hands-On Guide to Elevating Efficiency with OpenAI's Custom GPT - Ozan Ketenci, ZeoArticleby Samet ÖzsüleymanPredictions and Survey Results on the Future of Generative AIVideoby Yiğit KonurAI's Future: Mastering Prompt Engineering & AI Integration - @Yiğit Konur , Wope

Tools we use

Tools behind this work

  • Anthropic

    grounds portfolio and vendor discussion in one provider's real current capability

  • OpenAI

    the second reference provider in the same real vendor comparison

  • Microsoft Azure AI

    represents the enterprise cloud-platform path in the governance and vendor discussion

  • Mistral AI

    adds a European provider option relevant to data-residency and regulatory discussion

  • Hugging Face

    represents the open-model, self-hosting alternative in the platform tradeoff discussion

Next step

Build the session around a live leadership choice

Which portfolio, risk, governance, operating-model, or investment choice is already on the agenda? Your executive sponsor sets that context, and its evidence and trade-offs shape the briefing and scenarios.
Plan the executive session

Before you decide

Questions we hear a lot

Is this a technical training session?

It provides the technical grounding needed for the decisions in scope, but it is not a model-building class. The group focuses on what leaders need to ask about capability, data, controls, operating responsibility, cost, and evidence.

What should we share in advance?

Share the leadership decision context, current portfolio, risk and governance model, strategic questions, relevant scenarios, approved terminology, and action sponsors while limiting sensitive material to what the discussion genuinely needs.

How do you assess executive AI literacy?

We use decision scenarios rather than recall questions alone. Depending on the program, we may review decision quality, confidence calibration, action closure, and whether the group retains a shared vocabulary for later portfolio discussions.

What outcome should we not expect?

One program cannot make every leader an AI expert or guarantee better investments. It gives the group a shared decision frame, practice with uncertainty, and actions that still require accountable follow-through. Sponsors accept those actions before the session ends, but the people responsible for each decision must carry them out. The assessment summary records where confidence held and where it didn't, giving a later review something concrete to examine.