Where should you start with AI?
Start with a task whose current workload and quality you can assess. AI can help draft text, organise feedback or summarise material. It does not automatically improve marketing returns, connect systems or make decisions correct.
This guide covers use cases and a limited trial. Investment calculations are covered separately in the AI ROI article.
Four possible use cases
1. Marketing preparation
AI can draft headline variants, summarise customer questions or help compare messaging angles. The publisher remains responsible for describing the actual service without inventing prices, results or features.
Advertising platform automation is different from AI text generation. Results depend on objectives, measurement, material and the suitability of the offer, among other factors. A budget does not move from Google to Meta simply because AI is in use: that would require a separately designed and supervised implementation.
2. Sales material
AI can help organise meeting notes or customer questions. Only use material appropriate for the chosen tool, and check agreed actions against the original conversation before following up.
Lead scoring and forecasting need information about actual customers. If good and poor enquiries have not been distinguished, another score may create only an appearance of precision. Compare suggestions with sales observations and completed deals.
3. Customer service support
An internal assistant can help find answers in approved guidance. A customer-facing solution also needs boundaries: what may it answer, when does it transfer to a person, and who corrects inaccurate information?
A fast answer is not useful if it promises the wrong delivery time or service. Consider starting with a staff support tool whose answers are checked before broader automation.
4. Organising feedback and information
AI can suggest categories or identify recurring questions. Check a sample against the original material: are the categories accurate, and has anything important been omitted?
A forecast is not knowledge of the future. Its value depends on the data, assumptions and performance in new situations. Treat it as one input to a decision.
Does the task need AI?
Ordinary automation may be sufficient for a task following fixed, precise rules. If the problem is missing information or unclear responsibility, resolve that first. A new tool does not remove either issue.
Choose an AI trial when you can explain the additional value it might offer over the current approach. A long feature list is not a reason to purchase a broad solution.
How to design a limited trial
- Describe the current task. What happens, how often, and who checks the output?
- Record a baseline. Include total time, errors and quality.
- Choose limited material. Establish permissions, settings and data handling before uploading it.
- Compare the same task. Include instructions, checking and corrections.
- Decide what happens next. Expand, change or stop according to the evidence.
Duration depends on the task and number of observations. A calendar month alone does not demonstrate value if only a few relevant events occur.
What determines the cost?
Alongside subscription fees, consider implementation, integrations, training, checking and maintenance. Compare the full cost with benefits over the same period. A low monthly tool fee does not make the project worthwhile on its own.
If you need help assessing your marketing situation, contact us. Our current offering is described on the consulting service page.
When should you postpone a trial?
Postpone if outputs cannot be checked, data use is unclear or nobody owns maintenance. Low repetition may also make manual work the better overall option.
What counts as success?
The agreed task is completed at sufficient quality with better total costs or time use than the baseline. In marketing, this ultimately needs to connect to suitable customers and profitability. Usage counts or the number of texts produced do not establish that.