AI for CMOs – 10 marketing processes where artificial intelligence delivers results fastest

In B2B marketing it is easy to start at the wrong end. Personalisation tools and advanced channel analysis look impressive, but without good data they produce random results. Simple tasks, such as first drafts of texts, bring results within the first few weeks.

The table below puts ten tasks in the order in which they are usually worth implementing. The “When you see results” column is our rough estimate, not a promise. The actual timing depends on the scale of your campaigns and the quality of your company’s data.

The simplest use is a first draft of a text. The marketer prepares a plan: who it is for, what problem it addresses, what examples and data to use. The AI writes a draft, and the marketer edits it and adds knowledge the model does not have. Then the AI turns the finished text into a social post, a newsletter and several ad versions.

The same approach works with recordings. From a one-hour webinar, AI can produce a transcript, a summary, a list of quotes for posts and a draft article. One piece of material then gives you several publications instead of one.

The benefit is easy to calculate: texts per month × hours saved per text × hourly labour cost. Let us hypothetically assume 8 longer texts a month that take 5 hours each instead of 8. That is 24 hours a month – at PLN 80 an hour, about PLN 1,900 a month and PLN 23,000 a year.

A typical mistake is publishing the draft without editing. The text then sounds like thousands of others and contains nothing the reader could not find elsewhere. It helps to give the AI a short description of the brand’s style, a list of banned phrases and a few model texts along with the instruction.

An enquiry from a web form often waits several hours or days before someone calls the customer back. In the meantime, the customer writes to a competitor. This is a common point where marketing and sales lose customers, even though it is easy to fix.

As soon as an enquiry arrives, AI can fill in basic company details from public sources, assign it to the right salesperson and prepare a draft first reply. The salesperson gets a notification in Teams or the CRM and calls already knowing about the customer.

Before you start, measure how long it currently takes from enquiry to first contact. After a month, measure the same thing again. That one figure is usually enough for the management board to see the point of the project.

Classic points-based lead scoring works on fixed rules, e.g. “company with more than 50 employees – 10 points”. AI can learn from history: which enquiries turned into orders in the past and what set them apart. As a result, the salesperson gets a list sorted by the chances of a sale.

There is a strict condition. The CRM must contain at least several hundred enquiries with a recorded outcome: won, lost, abandoned. If salespeople do not close opportunities in the system, the model will learn random patterns.

Scoring concerns people and may amount to profiling within the meaning of the GDPR. Mention it in your privacy notice and leave the final decision to a person. The model should suggest an order of priority, not exclude anyone from contact.

AI speeds up every stage of work on website content: gathering customer questions, planning an article, drafting, suggesting links between texts. Google does not penalise content for being created with the help of AI. It does, however, penalise mass production of pages that add nothing and are only meant to boost rankings.

That is why in B2B marketing it is better to write fewer texts, but ones that contain the company’s experience: examples, figures, answers to customers’ difficult questions. Every text should be checked by an expert, e.g. an engineer or a salesperson. The same principle applies to answers from AI chat tools, which increasingly cite their sources.

Results come slowly, usually after many months. It is an investment worth judging over a longer period, not after the first quarter.

Google’s and Meta’s advertising systems already use AI themselves to choose audiences and bids. Your influence lies in what you feed them: good creatives, a description of the target group and, above all, information about which clicks turned into enquiries. If the system receives wrong conversion data, it optimises for the wrong thing.

AI helps prepare many variants of headlines, copy and graphics for testing. However, it does not replace daily monitoring. A campaign left to its own devices can spend the budget on cheap clicks that will never turn into customers.

Variant testing on the website and in emails works on the same principle. AI will prepare variants faster, but a test result only means something with enough traffic. With a few hundred visits a month, it is better to test less often but make bigger changes.

Personalisation means showing different content to different groups of recipients, e.g. one homepage to a manufacturing company and another to a distributor. In B2B, the simplest place to start is emails and a newsletter split into a few industries. Full real-time website personalisation requires good data and separate tools.

Channel performance analysis answers the question of which channel really brings in customers. In B2B this is difficult, because before a customer buys, they have several contacts with the company: an ad, an article, a trade fair, a conversation with a salesperson. AI can share out the credit between these channels, but only when data from ads, the website and the CRM is connected.

In both cases, get the data in order first and only then choose a tool. In many companies, simply connecting website forms to the CRM provides more insight than an advanced model.

In companies that sell subscription services or have regular buyers, AI can identify customers who are likely to leave. Warning signs include less frequent orders, smaller amounts, more complaints or late payments.

You calculate the value using your own data. Let us hypothetically take a company with revenue of PLN 50 million that each year loses customers accounting for 10% of revenue, or PLN 5 million. If reacting earlier lets it keep a fifth of them, it will retain about PLN 1 million of revenue a year.

A forecast on its own changes nothing. Someone has to act: call, suggest a meeting, solve the problem. Before you build a model, decide who deals with a customer flagged as at risk, and how quickly.

Most uses of AI in marketing are not subject to any special AI Act requirements. However, a few regulations affect the department’s day-to-day work, and it is worth knowing them before a tool reaches the team. We have summarised the most important ones below.

The first uses, such as writing texts and repurposing materials, often require only a licence for an AI tool and a few days of preparing instructions. Tasks involving the CRM and data analysis cost more, because they require connecting systems and getting the data in order.

You will convince the management board with business measures, not with the number of texts generated. Show the cost per enquiry, the time to first contact by a salesperson, the share of enquiries turned into quotes and revenue from new customers. Measure them before you start, because without that you will not be able to prove any improvement.