What if your program could
tell you what changed?
SignalPath explores a different role for AI in program management: not another status generator, but an intelligence layer that connects fragmented signals and helps a TPM understand what changed, why it matters, and where to act.
Working prototype · Synthetic data · Private source code · No live enterprise integrations
The information usually exists. The problem is connecting it.
A formal date lives in a ticket. A newer forecast appears in a conversation. A dependency is mentioned in a meeting. The launch plan lives somewhere else. The TPM becomes the integration layer.
Profile API GA
Current formal commitment.
Latest forecast
Confidence in Oct 26 has changed.
Production need-by
Needed to preserve readiness runway.
UAT stub available
UAT does not need the real API.
One program. Four questions. Different kinds of reasoning.
Campaign Personalization is targeting a November 30 production launch. Choose a question to see the kind of conclusion SignalPath is designed to reach from the evidence above.
AI proposes. The TPM governs the change.
The TPM learns: “EU traffic is confirmed in the Nov 30 launch; EU Consent is required and forecast for Nov 15.” SignalPath detects what changed, but never silently rewrites program truth.
UAT
On TrackSecurity + QA
At RiskProduction Readiness
At RiskLaunch · Nov 30
At RiskHistory matters. Current truth matters more.
Oct 26 commitment → ~Nov 15 latest signal
No longer contributes to current launch risk
Preserved as historical evidence
Sources → Evidence → Intelligence → Experience
Calculate what can be calculated
Date variance, dependency traversal and readiness rollups shouldn't need an LLM to guess. Roughly 400 automated checks back them.
Interpret what humans actually say
“We're probably not going to make Oct 26; it may be mid-November” is where semantic interpretation adds value.
Three hard problems
Contradictory evidence
A formal commitment and a newer forecast can both be true.
Permissions & trust
Agents should retrieve only evidence the user is authorized to see.
Adoption
It must save TPM attention, not add another place to maintain.
The biggest opportunity for AI in program management isn't another status report.
It's helping TPMs connect fragmented signals, see what materially changed, and focus human judgment where it matters most. AI doesn't replace the TPM; it moves their time from collecting status to making better decisions.
Agentforce could provide the agent and orchestration layer; SignalPath is the TPM-specific intelligence pattern. Before any of it is real: read-only connectors, a shared audit-trailed backend, identity and permissions, and cost and security controls.
An implementation direction, not a claim that the prototype is deployed in or connected to Salesforce systems.
If you run programs, I'd like your pushback: what would make you trust a tool like this, and what would make you throw it out? Find me on LinkedIn.
An independent exploration of AI-native program management using synthetic demonstration data.
Concept and prototype by Ayush Jain · Source code is private; no code is published here. This page describes an experimental product concept, not a production service or an endorsement by any referenced platform.
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