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AI and digital adoption

Examples checked

In one minute

Using software, data and AI to do work faster, cheaper or better.

The big idea: Digital and AI projects pay only when they change a measure that matters: cost per transaction, sales per customer, losses avoided. Counting hours saved is not enough; savings are real only when the work, the headcount or the results actually change.

Works when
When the work is high volume and repeated, the data is good, and the company changes the process, not just adds a tool.
Fails when
When tools are bought without changing the work, quality drops and customers leave, or costs of running the models grow faster than the savings.
Check this number first
Volume of the task and cost per unit today. Sets the size of the prize.
In the worked example
Net gain a year: USD 6.20 (USD millions a year). See it add up

What it is

Digital adoption means moving work onto software: online channels, automated processes, data in one place. AI adoption means using models that predict, sort, write or answer, for example to handle customer questions, spot fraud or suggest products. Most value comes from a few high-volume uses, not from many small pilots.

  • Automate. Software or AI does a repeated task: answering questions, checking documents, booking orders.
  • Assist. AI helps staff work faster: drafting, coding, searching.
  • Decide better. Models improve decisions: pricing, credit, fraud, stock levels.
  • New products. Digital channels or services customers pay for.

Why companies do it

The economic logic, most important first. A move usually rests on one or two of these.

  • Scale. Once built, software handles extra volume at almost no extra cost.
  • Margin capture. Better decisions on price, credit or stock raise profit per transaction.
  • Customer relationship. Faster, always-on service can lift satisfaction and sales.

When it creates value, and when it destroys it

Creates value when

  • The use case is high volume and repeated, with clear before and after measures.
  • The process and roles change, so the time saved turns into cost saved or more sales.
  • Data is clean and the model's errors are caught before they reach customers.

Destroys value when

  • Many pilots, none scaled; costs rise and no measure moves.
  • Poor answers drive customers away or create legal and regulatory problems.
  • Running costs (software fees, computing, people to check the models) grow faster than savings.

The numbers to check

Ask for these, in this order, before you recommend the move.

  1. Volume of the task and cost per unit today. Sets the size of the prize.
  2. Share of the work the tool really takes over. Often lower than vendors claim.
  3. Running costs: licences, computing, oversight. They recur every year.
  4. Quality measures: error rate, satisfaction, complaints. Savings mean little if customers leave.
  5. One-off build cost and payback. Compare with other uses of the money.

Worked example (illustrative)

Rounded numbers for a made-up business, shaped like real ones. Positive lines add to profit; negative lines are costs.

A bank has 1,000 customer service agents costing USD 40,000 a year each. An AI assistant would handle 30 percent of contacts. The bank has USD 400 million of revenue from these customers at a 40 percent margin, and expects 0.5 percent of it to be lost through poorer answers.

Worked example for AI and digital adoption, in USD millions a year. Illustrative figures.
LineUSD millions a year
Agent cost saved: 1,000 agents x 30 percent x USD 40,000USD 12
AI software and usage feesminus USD 3
Team to run, check and improve the modelminus USD 2
Profit lost from poorer answers: USD 400 million x 0.5 percent x 40 percentminus USD 0.80
Net gain a yearUSD 6.20

Check: the lines above add up to the total.

The numbers that decide it

Payback of a USD 8 million build
1.3 years
Net gain if only 15 percent of contacts move to AI
USD 0.20

So what: The assistant nets about USD 6.2 million a year and pays back in about 1.3 years, but only if it really takes 30 percent of the work and agent numbers fall to match. At 15 percent the gain all but disappears, because the running costs stay.

Real examples

Companies that made this move, by region, with what happened and a source checked on the date shown.

How to recommend it in a case

Answer first, then the reasons with numbers, then the risk and the next step. Three sentences, said out loud.

I recommend rolling out the AI assistant for the simplest 30 percent of contacts, because it nets about USD 6.2 million a year and pays back its USD 8 million cost in about 1.3 years.

The saving comes from needing fewer agents for routine questions, after USD 5 million a year of software and oversight and a small loss from poorer answers.

The risk is that it handles fewer contacts than planned, since at 15 percent the gain all but disappears; next I would pilot it on one contact type and measure resolution rate and satisfaction weekly.

The numbers to quote: Share of work automated; Cost saved per year; Running costs; Payback; Quality measures.

The figures in the answer come from the illustrative worked example above. In a case, use the client's own numbers.

Classic interview traps

  • Counting hours saved as money saved. It is money only when headcount, overtime or outside spending actually falls, or sales rise.
  • Ignoring running costs, which recur every year.
  • Forgetting quality and regulation: wrong answers to customers can cost more than the savings.

Where it is common

Sources

Every source was opened and the example confirmed on the date shown. Worked examples are illustrative and use no company's figures.

  1. 1.DBS Bank, Annual Report 2024, "Innovating impactful solutions for customers" (opens in a new tab)Checked
  2. 2.Klarna Group, annual report on Form 20-F for 2025 (US SEC), filed 26 February 2026 (opens in a new tab)Checked
  3. 3.JPMorgan Chase, Annual Report 2025, letter from the chief executive of Consumer and Community Banking (opens in a new tab)Checked
  4. 4.ADNOC, "$500 Million in Value Generated by ADNOC through Deployment of AI Solutions in 2023", March 2024 (opens in a new tab)Checked
  5. 5.Tata Consultancy Services, fourth quarter 2025-26 results press release (NSE filing), 9 April 2026 (opens in a new tab)Checked

Practise it

Where this move comes up in cases

Related moves