In the end, a lot of GTM efforts in B2B came down to spray-and-pray. We all liked to think we did things differently, but let’s face it, our activities were never really targeted in a meaningful manner. The target persona was defined too broadly, outreach lists were built at random. Money was wasted.

You may or may not agree with this statement, but you will find it hard to ignore that the usual way we did Growth can’t continue. AI is changing the game.

Blind outbound is dead

Many GTM teams currently invest in capabilities to automate their email outbound activities. For example: gather account lists at scale, do lead research faster and automatically, have an agent personalise your message.

This may sound intriguing for the moment, but it will be short-lived, though. When the volume of outbound messages increases, and the ratio of these messages which are personalised (think: “Congrats to the new job title”, “I heard your podcast, interesting thought”) increases as well, conversion will go down. Your target persona will just get bombarded by outbound messages, making it harder to get their attention and stand out from the crowd.

AI has lowered the barrier to entry executing a decent messaging outbound campaign significantly, and whatever edge outbound activities had are being evaporated.

If anything, good old (human!) cold calling may retain its edge. Its barrier of entry is roughly the same as in the pre-AI era. Who likes doing cold calls and is good at them? Exactly, there is your barrier of entry.

The same logic applies to traditional ad campaigns. AI makes it easier to generate campaign assets and orchestrate these campaigns. The barrier of entry is lowered here as well and the total volume of ads will increase. Conversion will drop.

The rise of the Intent Signal

Two things will become more important: First, a unique positioning of your value proposition. Second, your ability to identify a buying intent signal of your target persona and act on it in a quick and customised manner. It is this signal we want to focus on here.

Buying intent signals have been around for ages. GTM teams have always asked: how can they reach a prospect right in the moment this prospect feels a pain, or is willing to pay for a solution, and with urgency. Two signals emerged as generally trackable: job changes, funding rounds. Both of them are highly generic and only capture a small fraction of intent.

Intent signals are proxies which indicate a possible willingness and urgency of a given target persona to procure a product, service or software solution. They are related to a pain a buying persona feels. They are inherently connected to your buying persona and your specific value proposition.

Intent signals can be manifold:

  • the announcement of an IT transformation

  • the opening of a new factory

  • supply chain crunches

  • price rises or rises in volatility

  • regulatory changes or triggers

Each of these signals requires a dedicated analysis on where and how these can be picked up. They further need filters on geography, or sometimes have to be cross-referenced.

They are at the top of your funnel, the first of your activities.

Monitoring such signals comes down to writing software code. You need to develop specific applications which, for instance, scrape content of a selection of websites, or download and analyse reports, connect to APIs to gather data points, or combine these datapoints in your own processing engines. For some signals, it may be required to monitor and gather datasets over time.

For a newsletter I run, I am scraping the job sites of more than 9000 companies in DACH every 48 hours. I originally needed this to automate the job section we run. It now enables me to e.g. compute the average time a job is open, identify hiring trends or analyse the ratio of Marketers being hired compared to Engineers over time.

GTM teams need to develop Signal Monitoring Engines

There is no generic way to define what is needed regarding technical capabilities to monitor a signal, same as there is no generic set of signals.

It is possible to say, though, how these technical capabilities will be developed: LLMs.

LLMs excel at writing software code, and have probabilistic capabilities allowing them to make judgements, or interpretations. They are the enabler to pick up intent signals. And this goes far beyond asking Claude to “do a deep research on the latest signals for my persona”. It is about using an LLM as your tool to develop your own applications and agents.

B2B GTM teams will have to become individuals which can wield LLMs like a wizard wields its wand. It will allow them to be there when the buyer is “in the money”.

Obtaining the specific signal for your target persona and value proposition will allow you to initiate an outreach which will timely and customized to the situation this particular lead faces. This will turn a classic cold outreach into a conversation starter which your lead naturally wants to continue with you.

This will be hard. It will need deep knowledge on the situation your specific target persona faces, insights about its industry and dynamics. Equipped with these insights, AI will show you a way out of ever decreasing conversion rates.