Board asking what you are doing about AI
Customer data spread across systems that disagree
Repetitive work that really could be automated
An AI pilot that did not survive contact with reality
An agent inherits whatever your data already believes about the customer.
Agentforce and Data Cloud: grounding and guardrails, service and sales agents, unified customer data, escalation to a person and honest measurement.
Where it breaks
The same customer in four systems with three spellings and two owners. An agent given that will answer confidently and wrongly.
AI initiatives launched without a defined task, so success is a feeling and the pilot never graduates.
Teams will not hand work to something they cannot check. Scope that ignores this produces a tool nobody uses.
An agent following a bad process does it faster. Most of the value is in the redesign, not the automation.
What the agent may see, may do, and must escalate — decided after go-live, which is too late.
A proof of concept built outside the platform, with no path to production and no owner.
What we build
Eight steps between an AI idea and something you would let talk to a customer.
An honest read on whether your data and processes can support what you are being asked to deliver.
Unifying customer data across systems so there is one version of a person or an account to reason over.
Choosing tasks narrow enough to succeed and valuable enough to matter.
Connecting agents to your own content so answers come from your business, not from a general model.
What an agent may access, may act on, and must hand to a person.
Deflection and routine resolution, with escalation paths people actually trust.
Qualification, follow-up and research on the parts of selling that do not need a person.
Instrumenting from day one so the value can be shown rather than asserted.
Beyond phase one
Each one came out of delivery work rather than a product roadmap. In most cases the client did not know to ask for it.
Deciding where an agent must stop is more valuable than deciding what it can do. Pricing, legal commitments, anything that creates an obligation. Write the refusals down before the first conversation goes live.
An answer is only as defensible as the record it came from. If a reviewer cannot see which article, case or field produced a response, nobody in your business will stand behind it the first time it is wrong.
Escalation to a person is a success, not a failure, provided the person does not have to start over. The agent should hand across what it already learned, including what it could not resolve.
An agent that answers and a customer who calls anyway is not deflection. Tie the measure to the follow-up contact, not to the session, and the number gets smaller and considerably more useful.
An agent acting on behalf of a user should see exactly what that user sees. This is unglamorous sharing-model work, and it is the thing most likely to stop a pilot from reaching production.
Integrations
Most of this work is connective. These are the categories that determine whether an agent has anything useful to say.
Bringing the company's real data into reach without copying it everywhere.
Order, service and financial context an agent needs to answer properly.
Grounding answers in your documentation rather than in a general model.
Ensuring an agent sees exactly what the person it acts for is allowed to see.
Where the agent meets the customer or the employee.
Watching what the agent actually does, and what it gets wrong.
Salesforce CRM · Enterprise AI · Process Standardization
Service Cloud · AI Assist · Agentforce
Then you get a straight answer on whether your data can support it today, what it would take if it cannot, and whether the first version is worth building before the second.
