Agentforce that earns its place in your business
We help you find the right Agentforce use cases, prepare the data and processes behind them, and build with clear guardrails and measurable outcomes.
Agentforce is most useful when it has a proper job to do
Agentforce can summarise, recommend, generate, classify and take action, but capability alone is not a business case. The real question is where Agentforce can remove friction, improve a decision or give customers and employees a better experience.
Art of Cloud helps organisations assess, introduce and improve AI use cases within Salesforce. We begin with the work people are trying to complete, the data Agentforce can rely on and the points where a human must stay in control.
We prioritise valuable use cases, test them against real scenarios, put practical governance in place and measure whether the result is good enough to expand.
What you can expect
A controlled route from the 'idea' of AI to working capability, with value, risk and user experience considered together.
An honest AI readiness view.
We assess the quality and accessibility of relevant data, knowledge, processes, permissions and ownership. If the foundations need work, we say so and show you what to fix.
Clear instructions and guardrails.
We define what Agentforce can do, which information it may use, when it should stop and when a person needs to review or take over.
Measurement and continuous optimisation.
We agree practical measures such as completion, containment, response time, quality, adoption or time saved, then use real performance to improve the solution.
How can we help with Agentforce?
The right capability depends on your Salesforce products, licences, data and use case. Our work can include...
- AI opportunity workshops and use-case prioritisation
- Agentforce readiness assessments
- Data, knowledge and process preparation
- Agentforce agent design, actions, instructions and escalation
- AI-assisted sales, service and internal workflows
- Summaries, drafting and productivity use cases
- Prototypes, proofs of concept and controlled pilots
- Security, permissions, governance and human oversight
- Scenario testing, quality review and launch criteria
- Adoption, training, measurement and ongoing optimisation
Our AI Optimisation process
1.
Assess. We review the business problem, current Salesforce setup, data, users, controls and available AI capability.
2.
Prioritise. We rank use cases and select a starting point with a clear owner, outcome and measure of success.
3.
Prepare. We improve the relevant data, knowledge, process, permissions and escalation routes before relying on them.
4.
Build and test. We configure a controlled solution and test both normal and difficult scenarios with the right users.
5.
Launch carefully. We introduce the capability to an agreed audience, monitor performance and retain appropriate human oversight.
6.
Optimise. We use evidence from real usage to refine instructions, actions, data, controls and the wider roadmap.
Still have questions?
Let’s answer them
Do we need Agentforce to use AI in Salesforce?
Not necessarily. Salesforce offers AI capabilities across different products and licence types. We start with the outcome and your current platform, then identify whether Agentforce or another Salesforce capability is the right fit.
Does our Salesforce data need to be perfect first?
No dataset is perfect, but the data used by an AI solution must be suitable for the decision or action involved. We identify the important gaps and controls rather than making ‘clean all data’ an endless prerequisite.
How do you keep Agentforce and AI in Salesforce safe and controlled?
Controls can include permissions, trusted data sources, scoped actions, clear instructions, testing, monitoring, auditability, escalation and human approval. The design should reflect the sensitivity and consequence of the use case.
Is there a risk that could AI replace your team?
Our focus is on removing repetitive work, improving access to information and helping people complete tasks more effectively. We design the role of AI and the role of people explicitly, including where judgement or approval must remain human.
How would you measure return on Agentforce?
We agree a baseline and measures linked to the specific job, such as time saved, response time, completion rate, quality, adoption or avoided manual effort. A pilot should produce enough evidence to decide whether to improve, expand or stop.