KR eCampusCourses coming soon

KR ECAMPUS

Choose your country

Select your country to switch to the right eCampus site.

My eCampus

Back to courses

Advanced · Self-paced learning

Measuring and Scaling AI Business Value

Inquiry This course is not yet bookable.

Assess whether an AI initiative delivers its promised value and whether it should be scaled, adapted or stopped. Define a baseline and suitable indicators, separating available measurements from estimates and assumptions.

In the course case, monthly use rises from 100 to 300. Processing time and rework were not measured consistently, however, and the process changed at the same time. Explain what the figures do and do not show, what further measurement is needed and whether limited continuation or a stop is appropriate. New comparable data prompt a fresh assessment.

Take away the measurement and decision process you practised in the case and an editable calculation with explained formulas. Use them to assess later measurements and costs for your next portfolio review.

What you will work on

  • You establish a verifiable baseline for an AI initiative and separate measured starting values from assumptions.
  • You select indicators and data sources that make value, cost and risk traceable.
  • You assess increasing usage without demonstrated value.

Who it is for

  • Transformation leaders
  • Finance and controlling managers
  • Product and portfolio managers responsible for AI initiatives

Topics

  • From value proposition to decision question: baseline, comparison reference and observation period
  • Indicators for effect, effort, risk and adoption; leading and lagging measures
  • Data availability, measurement limits and the treatment of estimates
  • Business-case scenarios: lower value, data loss and cost overrun
  • Portfolio control: scale, adapt, stop and escalation criteria
  • Decision document and its use in the participant's own area

Your result

Your workbook connects baseline values, indicators and review costs with analysed example data. Use it to compare later results for a portfolio decision.

Prerequisites

Experience with project or portfolio decisions and a working understanding of indicators. You need access to the objectives, costs and available data of an AI initiative, or to a specific part of one. If that access is unavailable, you work on an example case with a comparable decision structure.

Format and approach

A planned course of 4–5 hours of self-study with short inputs, templates and self-checks. Criteria and a worked example help you review your work. Tasks can be completed in a replacement case without access to live organisational data.

Planned learning time: 4–5 hours

Enquire about this course

Advanced · Self-paced learning

AI Use-Case Portfolio Management

A service agent promises high value but needs data approval that is not yet available and the same scarce specialist as other initiatives. An internal search tool can be tried in a limited way with approved documents. Decide which AI initiative should start first.

Read the description

Expert · Classroom / In-house

AI Portfolio Decision Workshop

Several AI initiatives compete for budget and specialist staff. Your leadership team compares them, allocates resources and reorders the portfolio when conditions change. Keep an editable board with follow-up assignments, owners and dates for further work.

Read the description

Advanced · Self-paced learning

Accountable AI-Assisted Decision-Making

Review an AI-supported analysis before it informs a management decision. Check its claims against source data, examine assumptions and counterarguments, and prepare your own recommendation with outstanding questions.

Read the description