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Measuring AI ROI: Building the Business Case and Scaling AI Value

Evaluating AI Investments, Proving Outcomes and Prioritising Growth

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Why Choose Us

The Measuring AI ROI: Building the Business Case and Scaling AI Value training course equips participants to assess AI investments, develop credible business cases and determine whether value can scale responsibly. Organisations pursue AI to increase productivity, strengthen decision-making, improve customer experience and create new services. However, a promising demonstration alone does not establish business value. Leaders must understand each initiative’s costs, affected outcomes and whether improvements can be sustained across a team or organisation.

Assessing AI return on investment involves more than comparing software fees with projected time savings. A robust business case covers implementation, integration, data preparation, training, human review and ongoing operations, supported by a credible baseline. This baseline captures current time, cost and performance, alongside what would occur without the proposed AI solution. Participants will define measures for financial and non-financial benefits, including faster service, greater consistency and improved decisions, and build a framework for a selected use case. They will also calculate alternative ROI scenarios and specify the evidence required to stop, improve or scale an initiative. The course concludes with portfolio-level prioritisation and a roadmap for responsibly expanding AI value.

This Measuring AI ROI: Building the Business Case and Scaling AI Value training course will highlight:

  • Selecting suitable AI opportunities and developing use-case measurement frameworks
  • Establishing baselines for current time, cost, performance and no-AI outcomes
  • Accounting for implementation, integration, data, training, review and operational costs
  • Measuring financial and non-financial benefits using clear evidence
  • Calculating alternative ROI scenarios to inform stop, improve or scale decisions
  • Prioritising AI initiatives at portfolio level and planning responsible expansion
Why Choose Measuring AI ROI: Building the Business Case and Scaling AI Value
Course Goals

What are the Goals?

On completion of this course, participants will be able to:

  • Recognise AI use cases aligned with particular business objectives.
  • Set baselines and specify measurable outcomes prior to implementation.
  • Calculate the total costs involved in developing, deploying and operating AI solutions.
  • Measure financial benefits and evaluate significant non-financial outcomes.
  • Create ROI, payback and scenario analyses for AI investments.
  • Plan pilots that produce credible evidence of business impact.
  • Track adoption, quality, risk and realised benefits following deployment.
  • Rank AI initiatives within a portfolio.
  • Prepare a business case and scaling recommendation for leadership.

Who is this Training Course for?

Who Should Attend?

This Measuring AI ROI: Building the Business Case and Scaling AI Value training course is designed for:

  • Chief Information Officers
  • Chief Technology Officers
  • Chief Data Officers
  • Chief AI Officers
  • AI Programme Directors
  • Digital Transformation Directors
  • Technology Portfolio Managers
  • AI Product Managers
  • Data and Analytics Leaders
  • Business Case Analysts
  • Strategy and Innovation Managers
  • Enterprise Architects
Learning Approach

How will this Training Course be Presented?

This course uses trainer-facilitated discussions, worked finance examples, case studies and collaborative exercises. Participants choose an AI use case and gradually build its baseline, cost model, benefits framework, pilot measurement plan and business case for scaling. Activities include sensitivity analysis and presenting recommendations to a simulated investment committee.

The Course Content

Defining AI Value and Selecting the Right Use Cases

  • Connecting AI initiatives to organisational strategy
  • Distinguishing activity measures from business outcomes
  • Identifying productivity, quality, revenue and service opportunities
  • Mapping the process and its current performance
  • Establishing a baseline and a credible comparison point
  • Assessing feasibility, data readiness and operational dependencies
  • Identifying stakeholders, benefit owners and decision makers
  • Prioritising use cases for further evaluation

Building the AI Business Case

  • Defining the scope and assumptions of an AI investment
  • Estimating technology, data, integration and implementation costs
  • Accounting for training, change management and human review
  • Forecasting ongoing support, monitoring and maintenance costs
  • Quantifying time savings without overstating cash savings
  • Estimating revenue, quality and customer experience benefits
  • Calculating ROI, payback period and net present value
  • Testing optimistic, expected and conservative scenarios

Designing Pilots That Demonstrate Value

  • Setting measurable pilot objectives and success criteria
  • Choosing appropriate control groups or comparison periods
  • Selecting financial, operational, adoption and quality indicators
  • Measuring task completion time, accuracy and rework
  • Capturing user feedback and customer outcomes
  • Tracking exceptions, errors and human intervention
  • Identifying the effects of process changes beyond the AI tool
  • Defining stop, improve and scale decision points

Measuring Realised Benefits and Managing Performance

  • Comparing pilot results with the original baseline and forecast
  • Separating projected benefits from benefits actually realised
  • Tracking adoption, usage and workflow changes
  • Monitoring performance as volumes and conditions change
  • Accounting for risk, compliance and oversight costs
  • Assigning ownership for benefits measurement and reporting
  • Building an AI value dashboard for leadership
  • Updating the business case using operational evidence

Scaling AI Value Across the Organisation

  • Assessing whether a successful pilot is ready to scale
  • Identifying integration, capacity and support requirements
  • Reassessing unit costs and benefits at higher volumes
  • Managing training, process redesign and organisational adoption
  • Comparing and prioritising initiatives within an AI portfolio
  • Establishing funding stages and investment review gates
  • Presenting an evidence-based scaling recommendation
  • Developing an AI value realisation roadmap
Recognition

Certificate

  • • Wallstreet Development Academy Certificate of Completion for delegates who attend and complete the training course.

Frequently Asked Questions

You will learn about evaluating AI investments, building a business case, proving outcomes and scaling AI value. The course also addresses how to prioritise growth opportunities associated with AI. More detailed learning objectives cannot be verified from the course information provided.
Yes, building the business case for AI is a central focus of the training. It links the evaluation of AI investments with the need to demonstrate outcomes and support decisions about future growth. The precise structure, templates or tools used to develop the business case are not specified in the available material.
Yes, prioritising growth is one of the stated themes of the course. The training connects prioritisation with investment evaluation, demonstrated outcomes and the wider goal of scaling AI value. Specific prioritisation criteria or models are not detailed in the available course content.
In this course, measuring AI ROI is treated as part of evaluating investments, proving outcomes and deciding where to pursue growth. These areas help connect AI activity with business-case development and value-scaling decisions. The available course information does not specify particular industries, organisational functions or participant roles.
This course focuses on assessing AI investments in relation to their expected value and outcomes. It connects investment evaluation with the development of a clear business case. The specific evaluation methods, metrics or financial models used in the training cannot be verified from the available course information.
The course addresses how organisations can evaluate AI investments and prove their outcomes. This supports a more evidence-focused approach to discussing whether AI initiatives are delivering value. The exact measures, benchmarks or reporting techniques covered cannot be confirmed from the information provided.
The course supports workplace decision-making by linking proven AI outcomes to the scaling of value. Its focus suggests that participants will consider how investment evidence and business cases can inform growth priorities. Specific workplace exercises, implementation steps or case studies cannot be verified from the supplied information.
If you would like further information about this training course, our team is available to provide professional guidance and support: Email: [email protected] In-house training: https://www.wallstreetda.com/in-house-training

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