Salesforce Solutions by Business Situation
HPI Pro maps the process, the data and the existing systems — and builds a Salesforce, Agentforce and AI path tailored to the organization's situation, maturity level and business goals.
Solution Decision Architecture
The decision on a solution doesn't start with a product list but with the question of where the organization stands today, what data already exists, and what needs to change in the process.
Solution Decision Architecture
Organization's situation
Where the work actually starts
Architectural decision layer
What gets decided before development
Solution path
What actually gets executed
Four starting points
Solutions by department
Every department has its own typical problem, information it needs to work, and an end-to-end process. The examples below are general and don't represent a specific client.
The typical problem: Leads arrive from multiple sources and aren't handled to the same standard, and the pipeline reflects personal judgment rather than a measurable stage.
An organization unifying three lead sources into a single qualification process — a general illustrative example only.
The typical problem: Requests arrive through several channels, there's no single customer view, and no distinction between technical urgency and business urgency.
A contact center separating knowledge requests from fault requests to shorten queues — a general illustrative example only.
The typical problem: Approval and execution processes live in emails and files, so there's no tracking of exceptions and no real load data.
An order approval process that gets a unified status between sales and operations — a general illustrative example only.
The typical problem: Every department presents different numbers for the same question, and the discussion is about the source of the data rather than the decision.
Leadership adopting a single pipeline definition across all units — a general illustrative example only.
The typical problem: Multiple initiatives run in parallel without shared architecture, and each one creates new technical debt.
An organization consolidating CRM initiatives under a single governance framework — a general illustrative example only.
The typical problem: Integrations were built ad hoc over the years, and no one can explain the flow direction or the failure handling anymore.
An IT team replacing three point-to-point interfaces with one interface contract — a general illustrative example only.
Solutions by problem
The matrix translates a familiar business pain into the decision that must precede it, and only then into the system capability that implements it.
| Business problem | Decision that needs to be made | Capability that implements it |
|---|---|---|
| Information scattered across systems | Define a source of truth for every entity before connecting systems | Data model, integrations, data governance |
| Leads falling through the cracks | Define ownership and response time at every stage of lead intake | Lead management, routing, alerts |
| Manual and duplicate work | Identify where duplication comes from process, not the system | Automation, approvals, two-way sync |
| No management visibility | Agree on a single measurement definition for every KPI | Dashboards, data quality, reporting cycle |
| A system that isn't adopted | Check whether the issue is usability, process, or trust in the data | UX simplification, role-based training, adoption metrics |
| Unreliable integrations | Choose an integration pattern by failure tolerance, not convenience | Event-driven, retry, monitoring, reconciliation |
| A project that overran its plan | Stop, diagnose and re-prioritize before continuing to develop | Diagnosis, validated backlog, recovery roadmap |
| AI without a data foundation | Prepare data, permissions and actions before activating an agent | Data readiness, grounding, oversight and audit |
Information scattered across systems
Leads falling through the cracks
Manual and duplicate work
No management visibility
A system that isn't adopted
Unreliable integrations
A project that overran its plan
AI without a data foundation
Decision framework
What is the business process that needs to change?
Without a defined process, any product choice is an expensive guess.
Who uses the system and who makes decisions with it?
A daily user and a decision-maker need completely different information.
What is the source of truth for every piece of data?
A field without a source of truth will become a standing argument between departments.
What value do we want to measure after implementation?
A metric set in advance is the best protection against scope creep.
AI inside the solution
An AI agent operates on the same data, permissions and actions as the rest of the system. If the fields aren't reliable, if there's no maintained Knowledge, and if what the agent is allowed to do isn't defined, the result will be answers that sound good and can't be verified.
That's why the decision on AI comes after four things are defined: what data the agent may see, what actions it may take, when it hands off to a human, and how answer quality is checked over time.
In many cases a Flow or standard automation delivers the same business outcome at lower risk and lower maintenance cost. That's a legitimate conclusion of the evaluation process, not a failure of it.
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Frequently asked questions
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A focused conversation to understand the need, the state of your system and your business goals.
