Should You Use Real-World Examples in the CIMA SCS?
Yes — but Vennlisk must remain at the centre of your answer.
Join our Free SCS Mini Course:
https://learn.keystoneacademia.com/course/cima-scs-nov-26-mini-course
Register for the free live session:
Keystone Academia – CIMA SCS Registration
Join our CIMA SCS WhatsApp community:
Join the Keystone Academia CIMA SCS WhatsApp Community
A common mistake is learning lots of real companies, statistics and business stories and then forcing them into an exam answer.
That doesn’t automatically create strong application.
The purpose of an industry example is to help you demonstrate:
commercial awareness
understanding of how an idea works in practice
and, most importantly,
what Vennlisk can learn from it.
Think of it this way:
Industry example = supporting evidence. Vennlisk = your actual analysis.
The Best Technique: Example → Lesson → Vennlisk → Recommendation
Use this simple four-stage structure:
1. EXAMPLE
Briefly identify what another organisation has done.
2. LESSON
Explain what the example demonstrates.
3. VENNLISK APPLICATION
Connect that lesson directly to Vennlisk’s circumstances.
4. RECOMMENDATION
Explain what Vennlisk’s Board should consider doing.
This prevents your answer becoming a general essay about another company.
Example 1: Vodafone — AI + Human Customer Service
Vodafone provides a useful real-world example of AI in customer service.
Its TOBi virtual assistant has evolved into SuperTOBi, while its SuperAgent technology supports human customer-service employees by helping them access information.
Vodafone reported that its generative-AI customer-service technology improved first-time resolution for certain customer journeys, while difficult queries could still be transferred to human agents. Vodafone
What can Vennlisk learn?
This is particularly relevant because Vennlisk is evaluating AI that could provide information and recommendations to agents.
The lesson is not:
“Vodafone uses AI, so Vennlisk should use AI.”
Instead:
AI can potentially support human agents while automating suitable customer interactions.
For Vennlisk, this could help improve efficiency and First Call Resolution while allowing agents to concentrate on calls requiring judgement or empathy.
However, Vennlisk should test whether AI actually improves its own Customer Satisfaction and FCR before implementing it widely.
Exam-ready application
Vodafone’s use of AI alongside human customer-service employees illustrates how automation does not necessarily require complete replacement of human agents. Vennlisk could adopt a similar hybrid approach, using AI to provide agents with relevant information while retaining human judgement for complex or sensitive calls. A pilot should measure FCR, Customer Satisfaction and Average Handle Time before wider rollout.
Notice how most of the paragraph is about Vennlisk, not Vodafone.
Example 2: Bank of America — AI Supporting Employees
Another particularly relevant example comes from Bank of America.
In July 2026, Bank of America reported that more than 18,000 customer-service employees were using EricaAssist.
The technology provides employees with contextual guidance during client conversations. Bank of America stated that it reduced average call times by nearly one minute per interaction while keeping employees at the centre of the service process. Bank of America
Why is this relevant to Vennlisk?
This is very close to one of the potential AI applications described in the Vennlisk pre-seen: providing agents with useful information and recommendations while they handle calls.
The industry example therefore strengthens the commercial logic.
But Vennlisk should not assume it would achieve the same result.
Its:
- clients
- systems
- agents
- call types
and
- customer requirements
may differ.
Exam lesson
Never write:
“Bank of America reduced call times, therefore Vennlisk will reduce call times.”
Write:
“The experience suggests there may be potential for agent-support AI to improve efficiency, but Vennlisk would need to validate the benefits through its own pilot.”
That distinction demonstrates professional scepticism.
Example 3: HCLTech — Contact-Centre Modernisation
HCLTech provides another useful contact-centre example.
A recent AWS case study reported that HCLTech modernised contact-centre infrastructure using Amazon Connect. The reported outcomes included increased call-handling capacity and reduced average handling time. Importantly, the organisation initially migrated 500 agent seats to test areas including compliance, integration and performance before scaling. Amazon Web Services, Inc.
Vennlisk lesson
The most useful lesson here may not actually be the efficiency improvement.
It is the implementation approach.
Rather than changing everything immediately:
Test → Measure → Learn → Scale
That could be highly relevant if Vennlisk considers a major technology or AI project.
A controlled pilot could help Vennlisk assess:
- technical integration
- employee adoption
- privacy
- customer experience
- FCR
- Average Handle Time
and
- financial benefits
before committing to a wider rollout.
Example 4: Air India — AI Handling High Volumes of Customer Queries
Air India has also deployed generative AI in customer service.
A 2026 Microsoft case study reported that its AI system was handling approximately 40,000 customer queries per day across more than 1,300 types of question. Microsoft
Vennlisk lesson
AI can potentially create significant scalability.
This could become relevant if Vennlisk experiences:
- rapid growth
- a major new contract
or
- large fluctuations in call volumes.
However, Vennlisk provides services across different client industries.
That means the complexity and sensitivity of calls could vary considerably.
Automation suitable for a routine enquiry may not be appropriate for a distressed insurance claimant or another emotionally sensitive interaction.
The lesson is:
Automate selectively rather than assuming every customer interaction should be automated.
Industry Examples Aren’t Only About AI
Students should not assume that commercial awareness means memorising technology stories.
Real-world examples can support many Vennlisk themes.
| Vennlisk issue | Useful examples to research |
| AI | Agent assistance, chatbots, automation |
| Customer service | Customer-experience improvements and failures |
| Employees | Training, retention and employee wellbeing |
| Cybersecurity | Data breaches and cyber incidents |
| Privacy | Mishandling of customer information |
| Outsourcing | Companies outsourcing customer-service operations |
| Offshoring | Contact centres operating from lower-cost countries |
| Technology | Cloud-based contact-centre systems |
| Sustainability | Renewable energy and waste reduction |
| Acquisitions | Outsourcing/contact-centre transactions |
This gives you a much broader bank of commercial knowledge.
How Much Detail Should You Remember?
Very little.
You do not need to memorise an entire case study about Vodafone or Bank of America.
Usually you only need:
Company → What happened → Lesson → Vennlisk relevance
For example:
Bank of America
→ AI supports customer-service employees in real time
→ potential efficiency benefits
→ relevant to Vennlisk’s proposed AI support for agents
→ Vennlisk should pilot and measure service + efficiency outcomes.
That’s enough.
Don’t Force an Example Into Every Paragraph
Industry examples can improve an answer.
Too many can make it worse.
Imagine a requirement asking you to advise Vennlisk’s Board on an AI proposal.
Your answer should primarily discuss:
- Vennlisk’s strategy
- Vennlisk’s employees
- Vennlisk’s clients
- Vennlisk’s financial position
- Vennlisk’s risks
and
- the specific unseen information.
A relevant external example can strengthen one or two points.
It should not dominate the answer.
Avoid “Company Name Dropping”
This is weak:
“Vodafone uses AI. Bank of America uses AI. Air India uses AI. Therefore Vennlisk should use AI.”
There is virtually no analysis.
Compare that with:
The adoption of AI-assisted customer service by organisations such as Bank of America demonstrates the potential for technology to support rather than simply replace human agents. This is particularly relevant to Vennlisk because its pre-seen identifies AI-assisted recommendations as a possible way of supporting its 5,000 agents. However, Vennlisk should validate whether such technology improves its own FCR and Customer Satisfaction before committing to a large-scale rollout.
Now the industry example has a purpose.
Be Careful With Numbers
Real-world statistics can change.
A figure you read months before the exam could also be:
- outdated
- misremembered
or
- taken out of context.
So unless you’re confident about a number, don’t rely on it.
Writing:
“A major bank has successfully used AI to support customer-service agents…”
can sometimes be safer than incorrectly stating an exact statistic.
The marks come from the Vennlisk application, not from memorising impressive numbers.
Use Industry Examples to Challenge Proposals Too
Real-world examples shouldn’t always support management’s proposal.
They can also make you more sceptical.
Suppose Vennlisk’s Board is told:
“AI will significantly reduce labour costs.”
A commercially aware candidate might question whether the proposal has fully considered:
- implementation expenditure
- ongoing technology costs
- employee training
- cybersecurity
- human oversight
and
- complex calls that still require agents.
The existence of successful AI implementations elsewhere does not prove that every AI project creates value.
The 70/30 Rule
A useful rule for practice is:
70%+ Vennlisk and unseen application
30% or less external example/theory
This isn’t an official CIMA marking rule.
It is simply a useful discipline to stop students writing generic answers.
Your answer should always sound as though it was written for Vennlisk’s Board.
Five Questions to Ask Before Using an Industry Example
Before adding an example, ask:
- Is it relevant to the requirement?
- What lesson does it demonstrate?
- Why is that lesson relevant to Vennlisk?
- Are Vennlisk’s circumstances different?
- What should Vennlisk’s Board do as a result?
If you cannot answer questions 2–5, the example probably adds little value.
A Simple Exam Formula
Remember:
REAL WORLD → SO WHAT? → VENNLISK → NOW WHAT?
Real World
What happened elsewhere?
So What?
What does it demonstrate?
Vennlisk
Why is this relevant to Vennlisk?
Now What?
What should management do?
This keeps external knowledge commercially useful.
Example of a Complete Applied Paragraph
Suppose Vennlisk is considering AI that assists agents during calls.
A strong paragraph could look like this:
Real-world customer-service organisations have increasingly used AI to support rather than simply replace human employees. Bank of America’s EricaAssist, for example, provides customer-service representatives with contextual guidance during client conversations. This supports the commercial feasibility of the type of agent-assistance technology Vennlisk is evaluating. For Vennlisk, faster access to relevant client information could potentially reduce Average Handle Time and improve First Call Resolution. However, the Board should not assume benefits achieved elsewhere will automatically transfer to Vennlisk, particularly given its diverse client base and privacy obligations. A controlled pilot should therefore measure FCR, Customer Satisfaction, handling time, employee feedback and privacy incidents before wider implementation. Bank of America
That’s commercial awareness with application and judgement.
Final Takeaway
Real-world industry examples can make Vennlisk SCS answers stronger.
But only when they are used correctly.
Don’t think:
“How many companies can I mention?”
Think:
“What does this example teach Vennlisk’s Board?”
Remember the formula:
Example → Lesson → Vennlisk → Recommendation
Keep Vennlisk at the centre, use external examples selectively, and always connect them to the decision the Board actually needs to make.
FAQs
Do I need real-world examples in every CIMA SCS answer?
No. Relevant Vennlisk and unseen application should take priority. Industry examples should strengthen analysis rather than be forced into every answer.
How many industry examples should I learn?
A small collection of flexible examples across AI, customer service, employees, cybersecurity, outsourcing and technology is more useful than memorising dozens of companies.
Can I use Vodafone as an example for Vennlisk?
Yes, where relevant. Vodafone’s use of AI-powered customer-service tools provides useful lessons around automation, agent support and human escalation. Vodafone
Should I memorise industry statistics?
Not necessarily. Understanding the lesson and applying it accurately to Vennlisk is generally more useful than memorising large numbers of statistics.
Can industry examples be used for E3, P3 and F3?
Yes. For example, an AI implementation can support E3 digital strategy, raise P3 implementation/privacy/cyber risks, and create F3 investment and shareholder-value considerations.
Join our Free SCS Mini Course:
https://learn.keystoneacademia.com/course/cima-scs-nov-26-mini-course
Register for the free live session:
Keystone Academia – CIMA SCS Registration
Join our CIMA SCS WhatsApp community:
Join the Keystone Academia CIMA SCS WhatsApp Community
Next: Understand the Numbers That Drive Vennlisk’s Operations
Our next Keystone Academia article looks at five important call-centre measures:
Vennlisk Call Centre KPIs Explained: FCR, CSAT, AHT, Service Level & Call Abandonment
More importantly, we’ll explain why improving one KPI can sometimes make another worse — and what that means for Vennlisk’s Board.