Why Choose Measuring AI ROI: Building the Business Case and Scaling AI Value Training Course?
Organisations are investing in AI to improve productivity, strengthen decisions, enhance customer experience and create new services. Yet a promising demonstration does not, on its own, establish business value. Leaders need to know what an AI initiative costs, which outcomes it changes and whether those changes can be sustained when the solution is used across a team or organisation.
Measuring AI return on investment requires more than comparing software fees with estimated time savings. The business case must account for implementation, integration, data preparation, training, human review and ongoing operation. It also needs a credible baseline: how long the work takes today, what it costs, how well it is performed and what would happen without the proposed AI solution. Some benefits can be expressed financially, while others—such as faster service, better consistency or improved decisions—need clear measures before they can support an investment decision.
This Measuring AI ROI: Building the Business Case and Scaling AI Value training course gives participants a structured approach to selecting AI opportunities, developing a business case and testing whether expected benefits occur. Participants will build a measurement framework for a chosen use case, calculate alternative ROI scenarios and define the evidence needed to decide whether to stop, improve or scale an initiative. The course concludes with portfolio-level prioritisation and a roadmap for expanding AI value responsibly.
What are the Goals?
By the end of this course, participants will be able to:
- Identify AI use cases linked to specific business objectives.
- Establish baselines and define measurable outcomes before implementation.
- Estimate the full costs of developing, deploying and operating AI solutions.
- Quantify financial benefits and assess important non-financial outcomes.
- Build ROI, payback and scenario analyses for AI investments.
- Design pilots that provide credible evidence of business impact.
- Monitor adoption, quality, risk and realised benefits after deployment.
- Prioritise AI initiatives across a portfolio.
- Develop a business case and scaling recommendation for leadership.
Who is this Training Course for?
This training course is suitable to a wide range of professionals but will greatly benefit:
- Senior executives and business unit leaders
- Strategy and digital transformation professionals
- Finance and investment planning teams
- AI programme and product managers
- Operations and process improvement managers
- Data and analytics leaders
- Innovation, risk and governance professionals
How will this Training Course be Presented?
The course combines instructor-led discussion, worked financial examples, case studies and group exercises. Participants select an AI use case and progressively develop its baseline, cost model, benefits framework, pilot measurement plan and scaling business case. Exercises include sensitivity analysis and a presentation of recommendations to a simulated investment committee.
The Course Content
- 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
- 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
- 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
- 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
- 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
Certificate
- AZTech Certificate of Completion for delegates who attend and complete the training course
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Register now or contact our team to discuss schedules, delivery formats, and customised options.