Agentforce and AI in the Salesforce environment
AI that is connected to the process.
Not a demonstration detached from work.
Integrating AI into an organization begins with a defined process, reliable information, clear permissions, and human oversight. We help identify suitable uses and integrate them into the Salesforce environment in a gradual and controlled manner.
Who is the service for?
Typical starting points for AI in Salesforce
- Organizations already working on Salesforce and looking to identify where AI truly adds value.
- Service teams with a high volume of inquiries looking to reduce handling times without compromising quality.
- Sales teams looking to save time on preparation, summaries, and follow-ups.
- Managers who encounter difficulty extracting insights from existing organizational data.
- Organizations that require Governance and control before implementing AI in production.
Possible Uses
Where AI adds real value
- Summarizing inquiries and conversations
- Search in organizational knowledge
- Next Best Action recommendation
- Self-Service
- Classifying and routing inquiries
- Creating tasks
- Completing missing information
- Drafting texts
- Preparing agents before a call
- Anomaly detection
- Assisting managers with reports
- Agentforce for defined processes
Problems the service solves
Why AI projects get stuck in POC
Enthusiasm without focus
Many organizations start with the technology instead of the problem. We identify 2-3 Use Cases that occur frequently, are measurable and improvable, and focus on them first.
Data not ready for the agent
AI exposes every data issue — empty fields, duplicates, conflicting sources. We audit the data before the agent is built.
Regulatory and security concerns
What the agent sees, what they are allowed to do, how it is documented. We define a Trust Layer, prompt policies, and audit before going live.
POC projects that do not go into production
A POC without defined success criteria remains a nice demo. We define upfront what constitutes 'passed' and how to measure it.
Working principles
A responsible approach to AI integration
Start with the process
Do not add AI just because the capability exists. Define a problem, user, and desired outcome.
Verify the information
An AI agent cannot compensate for unreliable information, incorrect permissions, or an unclear process.
Maintain human oversight
Actions with business, financial, or service implications should include an appropriate approval mechanism.
Define permissions
The agent is granted access only to the information and actions necessary for their role.
Start with a limited scope
Build one Use Case, measure the result, and only then expand.
Measure quality
Check accuracy, usage, time saved, deviations, and user feedback.
Implementation process
Eight controlled steps
- 01
Use Case selection
- 02
Data and permissions review
- 03
Conversation and actions planning
- 04
Prototype building
- 05
User testing
- 06
Guardrails definition
- 07
Phased rollout
- 08
Monitoring and improvement
Decision Point
A short conversation about the first Use Case
In the conversation, we will identify together the processes that are ripe for AI, and those that should undergo process correction before introducing an agent.
Decision Considerations
Four decisions that determine if AI will go into production
Agentforce vs. Custom Development
When to choose built-in Agentforce Actions, and when dedicated tool development or an external system call is needed.
Agent Autonomy Scope
Deciding how much the agent acts alone, when it suggests human approval, and when it fully transfers a task to a representative.
Pricing and Activation Model
AI uses consume credits. Proper planning of context, prompt length, and usage frequency directly affects operational cost.
Monitoring and Continuous Improvement
Measuring not only the agent's outcome but also user behavior around it — which actions were approved, which were rejected, and what returns to the human team.
Common Mistakes
Patterns to Avoid in AI Implementation
- Building an AI agent before a business process is defined.
- Giving the agent overly broad permissions for development convenience.
- Exposing an unchecked information source and trusting the model to 'figure it out.'
- Skipping human approval for actions with financial or service impact.
- Launching without logs and monitoring — there's no way to learn and improve.
Frequently Asked Questions
Agentforce and AI — Frequently Asked Questions
The Next Step
A Conversation about Agentforce and AI
We will collaboratively examine which processes are ready for the first Use Case, and what infrastructure is required.