In the content team, we see AI the same way we see coffee. It doesn’t write anything useful itself, but it can become absolutely essential to the writing process.

The majority of hype around AI in the context of marketing is related to the automatic creation of finished content. Turn a post-it note into a six-part podcast series. Convert your meeting notes into a white paper.

In our experience, this is far from realistic.

Using AI as your writer is unlikely to produce effective results. Your writer should, however, be using AI to help them write more effectively.

A typical technical B2B content production cycle looks something like this:

1. Discovery work to explore the offering
2. Strategic work to anchor the offering within the context of the brand and buyer targets
3. Concept development to tie offer tangibles to user needs
4. Authoring of various content pieces to populate the campaign
5. Distribution into channels identified at 2
6. Refinement and improvement based on performance and feedback

All of the focus being on stage 4 can be off putting to those that understand the importance of stages 1-3, and the importance of true connection with the reader at stage 4. Early experiments with direct content production are typically disappointing, which can turn people away from AI altogether.

In our experience of using AI in the content production process the true value is elsewhere.

1. Discovery
Structuring of unstructured data
Speeding up transcription and reporting

2. Strategic
Correlation identification
Competitor audit / analysis information gathering

3. Concept
Rapid prototyping to test ideas

4. Authoring
Grammar / Style checking
Reading difficulty scoring

5. Distribution
Audience profiling assists

6. Refinement
Structuring multiple input data sets

Speeding up your existing approach with well targeted AI assists will return far greater benefits to most B2B content projects than attempting to go direct to finished copy from a single AI tool.