Enabling success: disruptive technologies: formula sheet

Key formulas

Sustaining vs disruptive innovation

Sustaining = better products for existing customers; Disruptive = simpler/cheaper/more convenient, starts in low-end or new markets, then moves upmarket

Use this contrast to justify whether a technology in the scenario is really disruptive.

Christensen's pattern of disruption

Entry at low end or new market → performance improves → mainstream customers switch → incumbent loses position

Use it as a sequence to explain how and why the incumbent is at risk.

Typical responses to disruption

Ignore / Defend / Adapt core / Create separate unit / Acquire or partner / Lead the disruption

A checklist of options. Always evaluate them against the scenario, not just list them.

Evaluating a response

Suitability, Acceptability, Feasibility (SAF)

Use SAF to test each response against strategy, stakeholder and resource constraints.

The Vs of big data

Volume + Velocity + Variety (+ Veracity + Value)

Volume, velocity and variety are the core three. Veracity and value are commonly added. Say which list you use.

Four types of analytics

Descriptive (what happened) → Diagnostic (why) → Predictive (what will happen) → Prescriptive (what to do)

Use to show how far a business uses its data. Each stage adds more value and needs more skill.

Data value test

Benefit from better decisions > Cost of collecting, storing, analysing and protecting data

A judgement test, not a calculation. Use it to assess whether big data is worth pursuing.

AI vs ML vs RPA

AI = broad field; ML = AI that learns from data; RPA = rule-based automation of repetitive tasks

Do not treat them as the same thing. RPA does not learn. Many real systems combine them.

Business case test

Net benefit = cost savings + revenue gains + risk reduction − (set-up + running + change costs)

A reasoning frame, not a calculation. Include non-financial factors such as customer trust and staff morale.

Suitability test for RPA

High volume + rule-based + stable process + digital inputs = good RPA candidate

Tasks needing judgement, empathy or frequent change are poor candidates.

Ethics check for AI use

Fairness + transparency + accountability + privacy + human oversight

A memory aid for ethical and governance points. It is not an official ACCA list.

Blockchain in one line

Transactions → block → hash linked to previous block → consensus → added to every copy of the ledger

Use this sequence to explain how it works in two or three sentences.

Blockchain vs cryptocurrency

Cryptocurrency = one application; blockchain = underlying technology

Never use the two terms as if they mean the same thing.

Smart contract logic

IF condition is met THEN action executes automatically

Self-executing code on a blockchain. It still depends on the accuracy of the code and the input data.

Public vs private blockchain

Public = open to all, permissionless; Private = approved participants, permissioned

Businesses usually prefer private or consortium chains for control and privacy.

Evaluation frame

Benefits − limitations/risks, applied to the case, then a recommendation

Use this balance for any 'advise the board' requirement.

Network effect (idea)

More users on one side → more value for the other side → more users

A reinforcing loop. It helps growth but also means a loss of trust can shrink the platform quickly.

Risk assessment

Risk exposure = likelihood × impact

A scoring idea, not an exact calculation. Use it to rank cyber risks and decide which need action first.

Information security aims (CIA)

Confidentiality, Integrity, Availability

Use as a checklist. Ask which of the three a given threat or control affects.

Risk response options (TARA)

Transfer, Avoid, Reduce, Accept

For cyber risk: insurance or outsourcing (transfer), not collecting data (avoid), controls (reduce), tolerate small risks (accept).

Platform revenue sources

Commission, subscription, advertising, data or listing fees

Name the one that fits the case and explain why.

Quick revision

• Disruptive technology changes how an industry works and can displace established firms.
• Big data is often described by volume, velocity and variety, with veracity and value added by some writers.
• Analytics can be descriptive, predictive or prescriptive, and each answers a different question.
• Cloud shifts spending from capital to operating costs and adds flexibility, but brings reliance on the provider and data location issues.
• AI and machine learning use data to find patterns and make decisions, and results can be biased if the data is biased.
• Automation can cut costs and errors but affects jobs, morale and the need for human oversight.
• Blockchain is a shared, tamper-resistant ledger, and smart contracts run automatically when agreed conditions are met.
• Cryptocurrencies are volatile and lightly regulated in many places, which raises risk for any organisation using them.
• IoT devices collect data through connected sensors, and each device can be a security weakness.
• Digital platforms link users and producers, and their value grows with the number of users.
• Cyber risk needs both technical controls and people controls, and the board owns the responsibility.
• In answers, always tie the technology to the case facts, weigh pros and cons, and make a clear recommendation.

Common mistakes

• Treating any new technology as disruptive. Fix: Test it against the definition: simpler or cheaper, entry at low end or new market, then moving upmarket. If it only improves existing products, call it sustaining.
• Describing what the technology does without linking to the business. Fix: Tie every point to a named scenario fact: customers, products, margins, culture or competitors.
• Treating big data and data analytics as the same thing. Fix: Say big data is the data and analytics is the process of drawing insight from it. Use this when a question asks for the difference.
• Listing the Vs without applying them. Fix: After each V, add a fact from the scenario. Marks go for application, not definitions.
• Writing a textbook definition of IaaS, PaaS and SaaS with no link to the scenario. Fix: Use the definition in one line, then spend most of your answer applying it to the organisation's facts.
• Listing only benefits or only risks. Fix: Give both sides unless told otherwise, and end with a judgement on which side outweighs the other for this organisation.
• Writing a generic list of AI benefits and risks. Fix: Use the company's industry, data, staff and customers in every point. Name the process affected.
• Treating AI, machine learning and RPA as the same. Fix: State that RPA follows fixed rules and ML learns from data. Match the right tool to the task.
• Treating blockchain and cryptocurrency as the same thing. Fix: Say that blockchain is the technology and cryptocurrency is one application. Then give a non-currency use that fits the case.
• Writing a generic list of benefits with no link to the scenario. Fix: Tie each point to a named fact in the case. Use phrases such as 'for this company, with many suppliers in different countries...'.

Exam tips

• Name the type of change first: say whether it is sustaining or disruptive, and justify it with scenario facts before going further.
• Link technology to business model elements: value proposition, channels, revenue model and cost base. Examiners reward this over technical description.
• Always evaluate more than one response and finish with a recommendation. A list of options without judgement scores poorly.
• Add a short point on risk, ethics or governance, such as data, cyber, jobs and regulation. This lifts an answer from good to strong.
• Match the format asked for, such as a briefing note or report, and keep points scenario-specific to earn professional skills marks.
• Examiners reward application. Tie every V, use and risk to a fact in the scenario.
• When asked for the difference between big data and analytics, give a clear one-line contrast first, then an example.
• Cover quality, privacy and security whenever risks are asked, then add cost, skills and ethics if marks allow.