From writing assistant to working assistant
The first wave of generative AI in marketing was about producing text: blog drafts, emails and social posts. Useful, but limited, and it filled many channels with similar-sounding content.
The current wave is different. AI agents can follow a set of steps on their own: pull data from a CRM or analytics tool, check it against rules, summarise what changed, draft an output and pass it to a person for approval. That makes them useful for the operational work that quietly takes up much of a go-to-market team's week.
Expectations are high, and so is the noise. Industry commentators predict that agents will handle a growing share of early buyer and customer interactions, while others point out that complex B2B purchases still depend on trust and human relationships. Both can be true. The practical question for most teams is narrower: which of our tasks should an agent do, and which should it never do alone?
Which work suits an AI agent
Two questions sort most go-to-market tasks: how repeatable is the task, and how much judgement does it need?
Common mistakes
Automating a broken process
If a process is unclear or the data behind it is poor, an agent will repeat the problem faster. Fix and simplify the process first.
Removing the human review step
Agents make confident mistakes. Anything customer-facing, or anything that changes records, should pass a person before it goes out, at least until the agent has a track record.
Using AI to produce more of the same
Publishing more generic content does not help buyers or search visibility. Agents are better used to research, check and organise than to multiply average content.
No owner and no measure
An agent without an owner drifts. Each workflow needs a named person responsible for it and a simple measure of whether it saves time or improves quality.
A safe pattern: human in the loop
The most reliable go-to-market agents follow the same basic pattern, with a person approving the output before anything is published or changed.
A three-stage roadmap
Questions to ask before you build
| Question | Why it matters |
|---|---|
| What exactly does the task involve today, step by step? | You cannot automate what you have not written down |
| Where does the data come from, and is it reliable? | Poor inputs produce confident, wrong outputs |
| Who reviews the output, and how quickly? | Review is the main protection against errors |
| What data must the agent never see or send? | Customer and company data need clear limits |
| How will we know it is working? | Time saved, errors caught and quality of output |
Key takeaways
- AI agents now carry out multi-step work, which makes them useful for go-to-market operations, not just writing.
- The best starting points are repeatable, rule-based tasks with low risk.
- Keep people in charge of positioning, relationships and anything customer-facing.
- Roll out one workflow at a time, with an owner, a review step and a measure.
Frequently asked questions
What is an AI agent in marketing?
An AI agent is software that uses a language model to carry out a sequence of steps towards a goal, such as gathering data, checking it, drafting an output and handing it to a person, often across several tools.
Will AI agents replace marketing and sales teams?
They change the work rather than replace the team. Agents take on repeatable research, reporting and operations tasks, while people focus on strategy, relationships and decisions that need judgement.
What tools are used to build GTM agents?
Teams commonly use workflow platforms such as n8n, Zapier or Make, enterprise tools such as Microsoft Copilot Studio, and direct access to models from providers such as Anthropic and OpenAI, connected to their CRM and marketing tools.