Executive session · Workshop

Your teams worked with AI in 2026. Do you know its value, risk and effort?

Learn how your AI models and agents in production can be stitched up for better value, lower risk and production readiness, and how to get ready for 2027 with your team of people and agents.

Book the 1.5-hour session Plan a 1-day workshop

Three questions for your leadership team

Before you set 2027 goals

2026

What did it deliver?

Do you know the value, risk and effort of each AI solution, and can you prove it with data?

Improve

What should change?

Which solutions are good as they are, and which should be improved or stopped?

2027

What is the plan?

What are your AI goals and plans for 2027, based on the lessons of 2026?

Why now

AI agents act, but they do not take responsibility

They change without notice

Model updates, prompt edits and new data shift behavior quietly.

Someone must own them

Every agent needs a named person accountable for its decisions.

Checks must be continuous

Every decision checked against rules, not an occasional review.

Model metrics are not enough

A model can look fine while complaints, cancellations or revenue move.

Framework

Three questions for every AI solution

Value

How much value does it create compared with the process before AI, and can you prove it?

Risk

Who can it harm, and how fast would you notice when it breaks?

Effort

How many people and how much time to build, update and monitor it?

What you learn

From production readiness to a 2027 plan

The model or LLM proposes; rules, people and logged execution set the limits.

  1. Probabilistic

    Model / LLM

    Scores, understands, proposes an action

  2. Rule-based

    Policy gate

    Written rules, limits, permissions

  3. Human decision

    Approval

    For risky or irreversible actions

  4. Logged, reversible

    Execution

    Audit trail, rollback, kill switch

  1. Monitor three layers

    The model, every agent decision, and company and department metrics with alarms.

  2. Watch what no metric shows

    Confident wrong answers, tone, loss of trust, misuse, approvals that became a formality.

  3. Design human approval

    What people approve and when, so they are never worse off than the agent alone.

  4. Get independent eyes

    Three setups for independent evaluation: outside, on your outputs, or on your inputs and model.

  5. Ready your people and agents

    Your agents, your people and the hybrid team each need different training.

  6. Plan 2027

    Keep, improve or stop each solution; projected value, how risk is reduced, effort and time.

How to start

Start with 1.5 hours, or dissect two use cases in one day

Executive session

1.5 hours

  • Value, risk and effort framework
  • Production readiness for models and agents
  • Continuous oversight and human approval
  • People and agents in 2027
  • Pre- and post-session survey
Book the session

Workshop

1 day, 2 use cases

  • Two of your own AI use cases, taken apart on value, risk and effort
  • What to keep, improve or stop
  • A draft 2027 roadmap for each
  • With the TAZI ML and AI Platform and a TAZI Forward Deployed Engineer
Plan the workshop

What your teams take away

A portfolio of templates

Value case study

Show the value of a solution, with evidence.

Handoff report

Everything an owner and a monitoring team need to take over.

Monitoring report

Value, risk and effort each period, with findings and owners.

2027 roadmap case study

Next steps, projected value, risk reduction, effort and time.

Built for

Executive and management teams, and the owners of AI use cases.

  • Banking
  • Wealth management
  • Insurance
  • Telecom
  • Automotive and manufacturing

Your training team

Prof. Dr. Zehra Çataltepe

Co-founder & CEO, TAZI AI

PhD in Computer Science from Caltech, former professor and department chair at Istanbul Technical University. She has spent more than two decades taking machine learning and AI agents from prototype to production in financial services and insurance: explainable, human-in-the-loop and continuously learning systems, monitoring and drift detection.

  • 18 patents
  • 100+ peer-reviewed publications

In addition, for the 1-day workshop

TAZI ML & AI Platform

Your two use cases, analyzed live on the platform

TAZI Forward Deployed Engineer

Works hands-on with your team on the use cases

Pick one AI solution. Write down its value, risk and effort.

If any of the three is hard to fill in, that is your first finding. Let’s talk about it.

Contact us