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One-off answers
People use ChatGPT for research, copy, or summaries. They supply the context again every time, and nothing carries into the next task.
I teach GTM teams how to set up the company context, shared skills, and tool access to successfully integrate LLMs into their existing workflows. Then we use that foundation to automate research, routing, follow-up, and reporting so people can spend more time with customers and prospects.
Level 1 makes an individual faster. Level 3 gives the company a shared GTM engineering capability that the team can keep extending.
People use ChatGPT for research, copy, or summaries. They supply the context again every time, and nothing carries into the next task.
Individuals save instructions, examples, custom GPTs, or skills. They work faster, but the knowledge and method still live with them.
One workflow has defined context, reusable steps, and access to the right tools. It can run the same job again with review checkpoints.
The team shares one context layer, skills, integrations, permissions, and evaluation. New workflows reuse the same foundation.
Results, exceptions, and human feedback write back into memory. AI proposes improvements; owners decide what becomes a new rule or workflow.
SDR workflows, paid media, customer expansion, and reporting can all run on the same shared context and data layer. The use case changes. The pattern does not.
ICP, positioning, offers, process rules, definitions, and examples of good work.
CRM records, call recordings, product usage, campaign history, and revenue outcomes.
Enrichment, intent, ad platforms, market data, and public web or social signals.
Facts, rules, examples, goals, and memory every workflow can reuse.
Clear definitions, raw records, history, confidence, and ownership.
Reusable instructions, connected tools, permissions, and review thresholds.
Research, score, route, personalize, follow up, and create the next task.
Build audiences, monitor spend, flag anomalies, refresh creative, and recommend changes.
Track health, find expansion signals, prepare renewals, and route the next action.
Agents do the collection, comparison, drafting, and routing. The team sees the evidence, handles exceptions, and spends more time with customers and prospects.
Measure meetings, pipeline, spend efficiency, conversion, time returned, and output quality. Results and corrections update the data, rules, and context used by the next run.
We find where the team loses time, where context disappears, and where the same decision gets made over and over. Then we choose one workflow tied to revenue, customer time, or hours returned.
Your team organizes the company knowledge, tool access, rules, permissions, and ownership that the workflow needs. This becomes the first working version of your AI brain.
Your team puts the first workflow into use with my guidance. They learn how to test it, document it, maintain it, and decide what should come next.
If people are sharing prompts, making custom GPTs, or automating isolated tasks but the company still lacks shared context and standards, email me what you have tried.