AI Use-Case & Business Case Assessment
Find the AI opportunities worth investing in.
Prioritise AI use cases before committing capital
Artificial intelligence creates thousands of possible use cases. Your organisation should not pursue thousands of them.
Namandla helps South African organisations identify, evaluate and prioritise enterprise AI use cases according to business value, feasibility, readiness, cost and risk.
The objective is to decide what should be pursued, piloted, investigated, deferred or rejected. Sometimes the most valuable AI recommendation is: Do not spend the money.
Move from AI ideas to investment decisions
Finding AI ideas is easy. Determining which opportunities justify funding requires a consistent business lens.
A meaningful AI use case
Solves a real problem, supports strategic priorities, has suitable data, is technically feasible, can be adopted, carries acceptable risk and creates measurable value.
A defensible AI investment
Explains the full cost, expected outcome, implementation path, organisational change, governance requirements and evidence that would justify scaling, or stopping.
Namandla evaluates AI use cases as business investments, not technology demonstrations.
What Is an AI Use Case?
A useful AI use case should identify:
- The ProblemWhat is inefficient, difficult or constrained today?
- The UserWho experiences the problem?
- The ProcessWhere does the activity sit?
- The Desired OutcomeWhat should improve?
- The InformationWhat data or knowledge is required?
- The AI RoleWhat should AI actually do?
- The Human RoleWhat judgement, review or approval remains with people?
- The ValueWhat measurable outcome justifies investment?
The technology mechanism comes after those questions.
Namandla AI Opportunity Model
1. Business Value
Potential outcomes include productivity, cost reduction, increased capacity, revenue, better decisions, faster analysis, customer experience, control and risk reduction.
2. Strategic Alignment
Does the use case support growth, margin improvement, Finance transformation, operational efficiency, customer service, digital transformation, employee productivity or risk reduction?
3. Feasibility
Consider technical complexity, system availability, integration, process consistency, platform fit, delivery capability and dependencies.
4. Data Readiness
Assess availability, accessibility, quality, ownership, permissions, privacy, sensitivity, structure and integration.
5. Organisational Readiness
Consider workflow change, training, sponsorship, process redesign, governance, change management and operating ownership.
6. Risk
Consider information security, privacy, financial impact, operational impact, customer impact, reliability, bias, oversight, regulatory exposure and vendor dependency.
7. Economics & Measurability
Assess expected benefit, implementation cost, licence cost, integration, training, governance, support, internal effort, consumption and measurement.
Five Possible Outcomes
Pursue Now
Strong value, appropriate readiness and manageable risk.
Pilot
Promising, but assumptions should be validated first.
Investigate
Potential exists, but questions remain around data, technology, cost, governance or process design.
Defer
The idea may be valid, but readiness dependencies need resolution first.
Reject
The value does not justify the cost, complexity or risk.
A rejected use case is not a failed assessment. It is capital that can now be directed somewhere better.
How the AI Use-Case Assessment Works
Where AI Opportunities Can Come From
Finance & FP&A
Planning, forecasting, reporting, variance analysis, scenarios, commentary, EPM and Finance knowledge.
IT
Service management, knowledge access, support, documentation, incident analysis, Copilot and automation.
Operations
Process coordination, exception handling, document processing, workflow and quality support.
HR
Employee information, policy support, onboarding, learning and internal knowledge, with careful governance for higher-impact uses.
Sales & Customer Service
Research, proposal support, knowledge access, service assistance and communication.
Procurement
Supplier information, document review, purchasing analysis and policy guidance.
Enterprise Applications
Microsoft 365, Oracle, EPM, ERP, CRM, workflow platforms and existing SaaS environments.
Balance quick wins with strategic bets
Quick Wins
Lower-complexity opportunities that can demonstrate measurable value quickly.
Foundational Opportunities
Initiatives that establish data, governance or capability needed for future adoption.
Strategic Use Cases
Higher-value opportunities directly linked to business priorities and executive outcomes.
Transformational Opportunities
More significant process, technology or operating-model change requiring stronger evidence and control.
Turn a promising use case into an investment decision
Business Case
Document the current state, business problem, AI role, expected benefit, full cost, risk, adoption requirements, measurement and recommendation.
Pilot Evidence
Test value, accuracy, adoption, data, integration, risk and economics. Conclude with Scale, Modify, Retest or Stop.
Explore AI Risk AssessmentAI or Automation
Choose the simplest mechanism that delivers the outcome. Predictable rule-based work may be better suited to traditional automation.
Delivery Path
Compare existing capability, specialist SaaS, platform configuration, automation, AI agents, custom development and process redesign.
Explore AI Vendor AssessmentApply use-case assessment across Microsoft, Oracle and Finance
Microsoft 365 Copilot
Identify roles with repeatable use cases where Copilot can create measurable value.
Explore Copilot ConsultingOracle AI
Assess opportunities inside the Oracle estate before adding more platforms.
Explore Oracle AI ConsultingAI for Finance
Build a business case the CFO can defend: what decision improves, what work changes, what control remains and how value is measured.
Explore AI for FinanceWhat You Receive from an AI Opportunity Sprint
Opportunity Evidence
An AI opportunity inventory, defined use-case profiles, initial screening and a consistent opportunity scorecard.
Investment Priorities
A prioritisation matrix, recommended classifications, business-case shortlist and preliminary business cases.
Executive Decisions
Pilot recommendations, capability dependencies and an executive decision pack.
The outcome is to stop asking “Where can we use AI?” and start asking “Where should we invest?”
Who Is This For?
Organisations asking:
- We have dozens of AI ideas. Which ones are actually good?
- How do we identify AI use cases for our business?
- Which AI projects should receive funding?
- How do we build an AI business case?
- Which use case should we pilot first?
- Is this really an AI problem?
- Should we use Copilot, an agent or traditional automation?
- How do we calculate AI ROI?
- Where can AI create value in Finance?
- What Oracle AI opportunities exist?
- How do we stop running pilots that go nowhere?
What It Is Not
This is not a workshop designed to sell a predetermined platform. Namandla may recommend:
- Use existing capability
- Buy a specialised product
- Pilot an AI solution
- Automate conventionally
- Fix the process first
- Wait
- Do not spend money on this
Why Namandla?
Use-case prioritisation sits at the intersection of business strategy, technology and product management. Namandla's current learning base includes AI product-management programmes focused on product strategy, roadmaps, stakeholder collaboration, market analysis, machine-learning project management and human factors.
The service is designed to help make better investment decisions before expensive technical delivery begins.
Questions answered
What is an AI use case?
A defined business application of AI intended to improve a specific activity, process, decision or outcome.
How do we identify AI use cases?
Start with business problems, manual effort, information bottlenecks, slow analysis, repetitive knowledge work and poor access to insight. Then determine whether AI is actually the right mechanism.
What makes a good enterprise AI use case?
Meaningful business problem, clear ownership, sufficient data, feasibility, manageable risk, readiness, measurable outcomes and a credible adoption path.
How should use cases be prioritised?
Use a consistent framework based on value, strategic alignment, feasibility, data, organisational readiness, risk, economics and measurability.
How do we calculate business value?
Establish the current baseline and measure potential changes in hours, cost, capacity, process time, customer outcomes, error rates, decisions, risk or revenue.
What should an AI business case include?
Problem, baseline, proposed change, benefits, implementation approach, full cost, risk, dependencies, organisational impact, measurement and time to value.
How do we calculate AI ROI?
Compare measurable benefit with the full cost of licences, implementation, integration, data, governance, training, change, internal time and support.
Which use cases should be piloted first?
Those with meaningful potential value, manageable complexity and risk, and assumptions that can be tested within a controlled scope.
What should a pilot prove?
Value, accuracy, adoption, data, integration, governance and economics.
Pilot vs proof of concept?
A proof of concept focuses on whether something is technically possible. A business pilot tests whether it works in a realistic environment and creates sufficient value.
When should an AI idea be rejected?
When value is weak, cost is disproportionate, risk is unacceptable, data is unavailable, adoption is unrealistic, existing technology solves it better or measurement is impossible.
AI or traditional automation?
Traditional automation is often better for predictable rule-based activities. AI is more relevant for language, unstructured information, interpretation and variable inputs.
When should we use an AI agent?
When a process requires controlled multi-step activities, tool use or enterprise-system interaction and agentic complexity is justified by the business value.
How does data readiness affect the use case?
If the required information is unavailable, inaccessible or inappropriate, a valuable use case may need to be deferred.
How should risk be included in the business case?
Risk should be considered alongside expected value and may include data exposure, security, reliability, financial impact, vendor dependency, operational dependency and human oversight.
How many use cases should we pursue at once?
A manageable portfolio appropriate to organisational capacity, funding, governance maturity and executive attention.
What are AI quick wins?
Relatively low-complexity use cases that can create useful evidence quickly. Easy does not automatically mean valuable.
Can Namandla run a use-case workshop?
Yes. Workshops can form part of the AI Opportunity Sprint, but discovery is followed by definition, scoring and prioritisation.
Who should participate?
Business leadership, process owners, Finance, IT, data, risk, operational teams, application owners and subject-matter experts depending on scope.
Can Namandla assess use cases we already have?
Yes. Existing backlogs can be assessed using a common framework.
Can the service assess Copilot, Oracle or Finance use cases?
Yes. The assessment can focus specifically on Microsoft Copilot, Oracle AI or Finance/FP&A opportunities.
Is this just another brainstorming workshop?
No. The service moves from discovery into definition, screening, scoring, prioritisation, business-case development and executive recommendation.