ChatGPT and Gemini for industrial sales and marketing teams

Almost every industrial sales team we have visited over the past year uses ChatGPT or Gemini in some way. The problem is that 80% use it in a way that produces content that sounds generic, technically imprecise, or plainly wrong for the sector. The result is the opposite of what they are after: they come across as less expert, not more.

The difference between a team that gets real value from generative AI and one that does not is not the intelligence of the tool. It is how the prompt is written.

Why generative AI sounds "robotic" in the industrial sector

Generative AI is trained on billions of generic texts from the internet. When you ask it to "write an email for the production manager of a metal components company", the model writes the best email it can based on general patterns for sales emails. The result is formally correct but lacks the technical precision and the direct tone that an industrial professional expects.

The solution is not to switch tools. It is to learn how to give it the right context.

The principle of prior context

Any generative AI prompt for industrial sales use should include three mandatory elements before asking for the content:

1. The role and the experience: "Act as a sales consultant with 15 years of experience in the B2B metal components industry in Spain." Not "write like an expert". Define exactly which expert.

2. The context of the company and the product: Detailed descriptions of the company, the product or service, the specific technical advantages and the profile of the target customer. The more specific, the better the result.

3. The context of the recipient: Who the person it is addressed to is, which company, which role, which problem they are likely to have. If you know it, include it. If not, define the typical profile.

With these three elements, the quality of the generated content improves dramatically.

Specific use cases for industrial B2B sales

Generating prospecting emails

Give the AI: the profile of the target company (sector, size, common challenges), your product and the specific benefit for that sector, and examples of emails that have worked well before. Ask it to generate 3 versions with slightly different tones. Choose the best one and adjust it manually if necessary.

Drafting technical proposals

AI is very useful for structuring and writing the narrative part of a technical proposal. Give it: the customer's problem (in their own words if you have them), the solution you are proposing, the technical specifications, and the expected results. Ask it to write the executive summary and the descriptive sections. The figures and the technical commitments should always be reviewed by a human.

Preparing calls and meetings

Give it public information about the target company (website, LinkedIn, recent news) and ask it to generate: the 5 challenges their sector is likely facing right now, the discovery questions you should ask, and the possible objections and how to respond to them. Preparation time drops from 45 minutes to 10.

Technical marketing content

For LinkedIn, newsletters or blog articles, give the AI the topic, the target audience (a specific industrial profile) and an example of the tone you want. Ask it for a draft. Edit it with your real technical knowledge. Content production time is cut by three.

The mistakes to avoid

• Publishing without reviewing: AI can include incorrect data or technically imprecise claims. Always with human review.
• Prompts that are too short: "Write an email to sell my services" produces useless results. Two minutes of prior context make all the difference.
• Using the same AI for everything: ChatGPT is better for creative writing and structuring. Gemini is better for analysing long documents. Claude is better for tasks that require complex reasoning. Knowing when to use each one is part of the skill.
• Not saving the prompts that work: When you find a prompt that produces excellent results, save it as a template. Do not start from scratch every time.