Agentforce for Sales
Decide where agents belong in your revenue engine, what they are allowed to do and how the value will be measured.
ExploreAgentforce advisory
Agentforce can take on real work. The harder questions are which work, under whose authority, and how you will know it paid off. We help leadership teams answer those before the first agent goes live.
Where agents work
Each function has different work, different risks and different measures. The discipline is the same: start from the number, then decide what the agent does.
Decide where agents belong in your revenue engine, what they are allowed to do and how the value will be measured.
ExploreSet the strategy for agents in customer service: what they resolve, when people step in and how quality is protected.
ExploreChoose the marketing work agents should take on, set the brand and consent guardrails, and tie the result to revenue.
ExploreIdentify the operational work agents can take on, the controls they must respect and the value that justifies the change.
ExploreAI orchestration
Every agent use case we design follows the same five-step shape. If a step is missing, the use case is not ready.
A customer, a record or an event asks for attention.
The agent reads the context and plans a response.
It acts within its permissions, on the platform.
A person approves what crosses a set threshold.
The result is recorded against a business measure.
A generic pattern. Thresholds and approval rules are set for each use case.
Architecture
Agents are most useful, and easiest to govern, when they sit on the platform that already holds your customer data, your permissions and your processes. We design for that, layer by layer.
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Where customers and employees meet the agent.
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Agents with a defined role, scope and set of actions.
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The rules every agent works within.
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Unified, governed context the agent can rely on.
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The applications where work is recorded.
Governance and trust
An agent should gain freedom the way a new colleague does: step by step, with evidence. We design the controls that make that possible.
What each agent may and may not do, written down before it goes live. Scope, limits and escalation paths that a non-technical leader can read.
Every action traceable to a reason and a record. If you cannot explain what an agent did, it should not have been allowed to do it.
Agents see only the data their task needs. Consent and access rules are treated as design constraints from the first workshop.
Approve before, review after, or handle exceptions only. The oversight model is chosen by risk, and revisited as evidence builds.
We help you write a short, usable policy that sets out how your organisation will use AI agents, who is accountable, and how concerns are raised. It is a working document, not a compliance exhibit.
Outcomes
We agree the measures before the work starts and set a baseline for each one.
We do not publish headline numbers. Targets are set with each client against their own baseline.
How long it takes to move from a shortlisted use case to an agent doing real work.
The share of service contacts resolved without a person, at a held or improved satisfaction level.
How close the forecast lands to the result, quarter after quarter.
The share of a seller’s week spent with customers instead of on administration.
What it costs to handle a customer request from start to finish.
How often people correct or reverse an agent. A measure of trust, tracked over time.
Readiness
Most are ready in some places and not in others. Our readiness assessment scores data, process, people and governance, and tells you where to start.
A first conversation costs nothing and usually clarifies a great deal.