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BRANDBUSTERS BRIEF / 03 · TRANSFORMATION

10 AI use cases that make business sense

Don't start with the model, the licence or an impressive demo. Start with the decision, process or cost worth improving — and with the measure that will show whether AI is really creating value.

  • 10 MIN READ
  • GROWTH · PRODUCTIVITY · DECISIONS
  • WITH HUMAN-IN-THE-LOOP

AI is not a strategy. It is part of how work gets done.

Value isn't created because a company “has AI”. It is created when technology helps do repetitive work faster, improves the quality of decisions, makes better use of knowledge or removes costly friction.

Results depend on the task, the quality of the data, the design of the process and human oversight. That's why every use case on this page has four elements: the problem, how AI is used, a business measure and a safety condition.

A good candidate for a pilot:

  • 01concerns a valuable, repeatable process,
  • 02has context or data available,
  • 03has a measurable baseline,
  • 04allows a person to check or approve the result,
  • 05carries a risk proportionate to the potential benefit.

  1. Voice of Customer synthesis

    Problem
    Customer feedback sits in call recordings, tickets, surveys, reviews, social media and sales notes. The company analyses only a small sample or reacts to the loudest signals.
    How AI helps
    It can transcribe, organise and group comments by topic, journey stage, intent and type of friction. It should keep a link to the source material and show example comments, not just a summary.
    Measure
    Analysis time, share of interactions analysed, number of recurring problems and the results of actions taken on that basis.
    Safety condition
    Data anonymisation, access control, sample validation and manual checking of important conclusions against the source material.

  2. Creating and localising communication variants

    Problem
    The team spends time adapting one idea to channels, formats, segments and markets, and pressure for volume weakens brand consistency.
    How AI helps
    It can prepare first drafts of copy, creative variants, localisations, short versions and adaptations to a defined brand tone. People remain responsible for the insight, the brief, selection and publication.
    Measure
    Time from brief to approval, production cost, share of approved materials and the performance of variants in a controlled test.
    Safety condition
    No automatic publishing, fact and claims checking, rules for using protected material and final human approval.

  3. Preparing for sales and client meetings

    Problem
    Salespeople manually gather information from the CRM, previous conversations, the offer, product data and public sources. Some of the context gets missed.
    How AI helps
    It can prepare a pre-meeting brief, summarise the history of the relationship, highlight open topics, suggest questions and draft the next step.
    Measure
    Preparation time, CRM completeness, response time after the meeting and progression to the next stage of the process.
    Safety condition
    Visible sources of information, no invented or unverified facts, limits on profiling individuals and mandatory review by the salesperson.

  4. Supporting customer service agents

    Problem
    Agents search for answers across many systems, write similar messages by hand and document the conversation only after it ends.
    How AI helps
    It can find the right piece of knowledge, suggest a reply, summarise the conversation and prepare a note for the system. It is the agent's assistant, not an autonomous arbiter.
    Measure
    Average handling time, first-contact resolution, CSAT, number of repeat tickets and the share of replies needing correction.
    Safety condition
    Cited sources, clearly defined escalation cases, quality control and no independent decisions on high-risk matters.

  5. Making better use of CRM and the customer lifecycle

    Problem
    The company sends similar messages to the entire database or reacts only once the customer has already left.
    How AI helps
    It can help detect churn risk and choose the next best action, the timing of contact and the right message. Generative AI can also explain the recommendation and draft the content.
    Measure
    Incremental second purchase, retention, churn, margin and results against a held-out control group.
    Safety condition
    Legal basis and consent, no use of sensitive categories, checks for discrimination and the ability to explain why a customer received a particular offer.

  6. Analysing price, offer and promotion scenarios

    Problem
    Pricing and promotion decisions are made across many spreadsheets, rely on averages or fail to consider margin, demand, stock and customer behaviour at the same time.
    How AI helps
    It can speed up scenario preparation, describe the assumptions, detect inconsistencies and make it easier to compare the consequences of different decisions.
    Measure
    Margin, forecast accuracy, incremental sales, cannibalisation and promotional ROI.
    Safety condition
    AI does not set prices on its own. The decision stays with an authorised person. Prohibited or discriminatory use of data must be ruled out.

  7. Product data quality and catalogue management

    Problem
    Descriptions, attributes, classifications and translations are incomplete or inconsistent. Launching a new offer takes a long time and customers struggle to filter and compare products.
    How AI helps
    It can extract attributes from approved materials, suggest a taxonomy, detect gaps, and prepare descriptions and translations in an agreed format.
    Measure
    Time to launch a product, attribute completeness, number of errors, search effectiveness and conversion from product pages.
    Safety condition
    Every piece of information must be based on an approved source. Technical specifications, prices, terms and promises need validation before publication.

  8. Management reporting and decision support

    Problem
    Analysts spend a lot of time copying charts and describing what is already visible instead of investigating causes and options for action.
    How AI helps
    It can write the first version of commentary, identify anomalies, compare periods, generate questions and prepare scenarios for further analysis.
    Measure
    Report preparation time, time from signal to decision, number of corrections and the share of recommendations used in further analysis.
    Safety condition
    Read-only access at the start, visible sources and KPI definitions, full drill-down to the data and never present a hypothesis as a confirmed cause.

  9. An internal knowledge assistant based on RAG

    Problem
    Knowledge sits in documents, emails, presentations, procedures and people's heads. Finding an up-to-date answer takes too long.
    How AI helps
    It can search only approved resources, assemble an answer from several documents and point to specific sources.
    Measure
    Search time, share of answers accepted without correction, number of unanswered questions and frequency of use.
    Safety condition
    Permissions inherited from sources, freshness checks, protection against prompt injection and data leakage, logs, citations and a way to report an incorrect answer.

  10. Agents running bounded multi-step processes

    Problem
    Employees move data between systems, run the same checks and prepare similar materials before every decision.
    How AI helps
    An agent can run a clearly bounded workflow, for example: brief → data collection → analysis → draft → human approval. It does not get unlimited access to the whole organisation.
    Measure
    Cycle time, cost per result, number of human interventions, share of exceptions and errors, and the reliability of the whole process.
    Safety condition
    Minimal permissions, limits on operations and costs, approval of critical actions, full logs, testing, rollback and an immediate kill switch.

BEFORE YOU BUY ANOTHER LICENCE

Decision infrastructure first. Then AI.

  1. 01

    A business owner

    Every use case must belong to the person responsible for the result of the process — not just to IT, innovation or an external vendor.

  2. 02

    Baseline and measure

    Before the pilot, measure current time, cost, quality and error rates. Without a baseline, a demo will never become a business case.

  3. 03

    Approved data

    Define what data may be used, where it is processed, who has access to it and how long it is kept.

  4. 04

    Human-in-the-loop

    Write down when a person checks the result, when they make the decision and which actions may never be carried out autonomously.

  5. 05

    An evaluation set

    Prepare real test cases, expected answers, quality thresholds and examples of situations in which the system should refuse or escalate.

  6. 06

    A pilot with a final decision

    After 30 days, make one of three decisions: scale, improve and test again, or stop. “Let's keep experimenting” is not a strategy.

Don't implement it this way.

  • Don't put confidential data into unapproved tools.
  • Don't let a model make decisions on its own about people, health, credit or other high-risk areas.
  • Don't give an agent broad permissions without limits, logs and approval of critical actions.
  • Don't connect a public chatbot to unrestricted internal knowledge.
  • Don't launch mass autonomous sales communication.
  • Don't scale a solution before it has passed evaluation on real cases.
  • Don't assume that an answer that sounds confident is a true answer.

The greater the system's autonomy and the consequences of its actions, the stronger the oversight, observability and ability to stop it must be.

The best AI use case is usually less impressive than the demo — and far more valuable.

It should concern specific work and have an owner, a baseline, a measure and a clearly bounded risk. Only then can technology become part of an operational advantage rather than another endless experiment.

Don't ask first, “What can AI do?” Ask, “Which decision or piece of work is worth doing better?” Ask: “Which decision or piece of work is worth doing better?”.

Let's choose a first use case you can defend in business terms.

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