Big platform shifts lead to massive changes to the way we work and the tools we use. The move from on-premise to cloud sticks out because of Salesforce, who bet on the cloud as the future of software, setting themselves up to take advantage and define a generation of tools. As everyone moved from on-premise software to cloud, a flood of new companies were founded to capitalize on that innovation. Eventually, companies all began building their new tech stacks in the cloud.
We are in the midst of a colossal platform shift with AI. Incumbents are adding new AI tooling and new entrants are launching every week.The result is a bit of chaos for everyone. It’s not clear which tools you should use for which use cases. Some AI is helpful, some AI is a solution in search of a problem to solve.
If you’re confused, don’t worry, you’re not alone… so is everyone else trying to decipher the GTM x AI landscape. Over the next two weeks I'm going to try to demystify the new GTM tech stack and break down where AI is a valuable tool versus a toy.
First, I'm sharing Pocus' philosophy around AI to provide some context on how we think about building new features and tools that use this tech. Then, next week, I'll dive into AI's maturity and fit for GTM jobs to be done, helping you 10x your sellers.
The Pocus philosophy on AI
Separating tools from toys begins with an understanding of where AI is useful today in GTM. In our view, there are 4 categories where AI provides outsize impact on GTM use cases.
Context synthesis
Gathering research is limited by the amount of time a human has in a day, access to data/systems, and the ability to bring information together into actionable insights. AI on the other hand, can run through volumes of data and research on the internet in seconds to synthesize results.
Automated orchestration
One of the biggest areas of improvement in AI is in the reliable orchestration and automation of workflows powered by AI agents. AI agents can be deployed to automate routine tasks like updating systems with new data, smart routing, or assigning owners.
Hyper-personalization
Recent advances in AI have unlocked a higher bar for personalization. AI can tailor content and write personalized copy that resonates.
Predictive analytics
Although this is not a new area, AI can analyze vast amounts of data to identify patterns that are helpful in domains like lead scoring. Take sales playbooks where you have a measurable workflow and conversion data - AI can analyze the current playbooks and make suggestions for how to tune and iterate.
Even with these guidelines, it's important to remember we're in the early stages of AI development and the best use cases are still being created. Next week, I'll unpack where AI is mature enough to help you 10x your sellers today.
Until then, I'd love to hear your AI philosophy. Where are you seeing impact already? Where are you waiting on layering in AI until it's further developed?
You can message me on LinkedIn -I ready every message!