You can buy an AI license without knowing which workflow it should change. That is the gap forward-deployed AI is meant to close. This article is for business owners and operators who want to understand the work behind an AI implementation: mapping the process, testing the decisions, connecting the tools, and keeping people in control. The starting point is your workflow, not a hiring headline.
Product documentation checked August 31, 2026. Workflow examples are illustrations, not reported client results.
Palantir describes forward-deployed engineering as engineers working close to customer problems while collaborating with core engineering teams to improve the software. The useful distinction is between building a general capability and making it work in a particular operating environment.
Applied to AI, forward-deployed engineering is the practice of embedding with a team, mapping real workflows step by step, deciding where AI belongs (and where it does not), and then deploying it inside the systems the business already runs on: the CRM, the ERP, the inbox, the ticketing queue.
In plain terms: start with the people doing the work, map their decisions, and test where AI and automation belong. We recommend evaluating that fit before committing to an implementation. A model is one part of the system, not proof that the whole workflow is ready.
Our view is that an AI purchase should begin with a deployment question: what work needs to change, and how will you know whether it improved? Model access alone does not specify your inputs, exception rules, permissions, review process, or acceptance test.
Focus on deployment: where, how, and why you apply AI to your specific business. Write down what a correct result looks like and who is accountable for it. Then compare the proposed AI step with a rules-based alternative and the current manual process.
Before a pilot begins, define its scope, test cases, owner, and stop conditions. NIST's AI Risk Management Framework 1.0 calls for context-specific risk assessment, human oversight, and evaluation throughout the system lifecycle. It is voluntary guidance, not a guarantee that a project will succeed.
Our recommendation is to start with judgment about where AI belongs, not an assumption that a different model will solve the problem. That judgment begins with something unglamorous: understanding how the work is really done.
Consider a hypothetical intake workflow whose first documented step is "an email arrives." In your test cases, include a PDF attachment, a screenshot, an incomplete request, and a forwarded thread. Ask the team how it handles each case and record any rules that are missing from the procedure.
Do not automate the written procedure until you have checked those exceptions. Sit with the people doing the work, trace representative cases, and decide when the system should stop and ask for help. Include failed and ambiguous inputs in the test set, not only the happy path.
The most important judgment call in any AI project is separating three kinds of work:
Define the boundary between those three kinds of work before choosing tools. Make the review and escalation rules visible to the people who will use the system.
For AI work, we recommend a four-part planning loop: Discover, Evaluate, Deploy, Improve. Treat it as a way to organize an implementation, not a claim that every engagement has the same scope or deliverables.
The five rows below are illustrative workflow designs to evaluate, not turnkey HubSpot features or documented client deployments. Each needs its own configuration, permissions, subscription check, and test. AI steps may require a separate tool or integration.
| Workflow | Deterministic layer | AI judgment layer | Human control |
|---|---|---|---|
| Lead qualification | Property validation, routing, notifications | Interpret intent and score fit | Sales reviews uncertain leads |
| Service intake | Ticket creation, SLA assignment, record updates | Categorize the issue and summarize context | Agent approves sensitive responses |
| Pipeline hygiene | Required-field checks, stale-deal workflows | Identify risk signals and recommend next actions | Manager approves major stage changes |
| Sales follow-up | Enrollment rules, task creation | Draft account-specific outreach | Rep reviews before sending |
| Data cleanup | Formatting, deduplication, validation | Interpret ambiguous company or contact data | Ops reviews low-confidence matches |
The intended pattern is consistent: conventional software for explicit rules, tested AI assistance for judgment-heavy work, and human control where consequences or uncertainty require it.
One principle we hold firmly: assess the systems you already own before proposing a migration. Sometimes replacement is justified by a specific limitation. Require that limitation to be demonstrated, and compare it with the cost and risk of connecting or improving the existing stack. For work involving CRM architecture and revenue operations, keep data ownership and system boundaries explicit.
As of August 31, 2026, HubSpot's workflow documentation lists conventional actions and AI actions for tasks such as summarizing and categorizing records. Available actions depend on your subscription. AI workflow actions require HubSpot Credits, and custom-code workflow actions require Data Hub Professional or Enterprise. Confirm access, beta status where applicable, and usage costs in your account before choosing an action. These building blocks do not remove the need to design integrations, permissions, and monitoring.
Whether you use an internal team or an implementation partner, look for someone who can explain the process, build the integrations, and show how the system will be tested. Ask who owns the documentation, exceptions, and ongoing maintenance. The original article drew on Greg Isenberg's interview with Vas of Varick Agents. That conversation is useful context for the approach, not independent evidence of salaries or financial returns.
For a discovery exercise, use an AI Workflow Opportunity Sprint: map the current workflow, list its bottlenecks and exceptions, separate AI from conventional automation, and rank possible changes. Record assumptions about revenue, cost, and risk alongside the evidence needed to test them. Agree the scope, deliverables, responsibilities, and fees before work begins. The framework is not a promise of a fixed result or a standard entitlement.
If you want to explore where AI belongs in your business, talk with us about your workflows. Start with the process you want to improve and the systems your team already uses.