# Sales and Marketing Agents Without the SaaS Bill > Enterprise · AI Course · ENT-9 > Source: https://ibl.ai/solutions/enterprise/course/sales-marketing-agents-without-saas-bill > Last updated: 2026-08-25 **Build revenue-team agents on infrastructure you own — research, outreach, competitive briefs, and content — instead of paying per seat for four separate tools.** ## The Short Answer **Revenue teams pay per seat for four tools that each hold a slice of the same CRM data. ibl.ai consolidates them into agents grounded in your own systems with no per-seat pricing, running where you own all the code and the data — so prospect and customer data is never enriched into a vendor's shared dataset.** On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network. [Request Access](https://ibl.ai/contact) · [Explore Enterprise](https://ibl.ai/solutions/enterprise) ## Course facts - **Level:** Intermediate - **Duration:** 5.5 hours across 8 modules - **Format:** Cohort workshop with a build lab - **Modules:** 8 - **Catalog code:** ENT-9 - **Frameworks covered:** CAN-SPAM, GDPR, FTC endorsement guides ## What is this course about? Revenue teams accumulate point tools that each charge per seat and each hold a slice of the same data. This course audits that stack, rebuilds the highest-value functions as agents grounded in your own CRM, and covers the consent and attribution work that determines whether the result is usable — including where personalization crosses into deception. ## Who is this course for? - Revenue operations leaders - Sales enablement and marketing operations - Demand generation and content teams - CROs evaluating the go-to-market stack ### What do I need before starting? - Familiarity with your CRM and current martech stack - No technical background required ## What will I be able to do afterwards? - Audit the revenue stack for overlapping capability and per-seat spend - Build account research agents grounded in your CRM rather than a vendor's dataset - Maintain competitive briefs without a subscription - Identify where outreach personalization crosses into deception - Attribute agent contribution to pipeline defensibly ## What does each module cover? ### Module 1 — What does your revenue stack actually do? The capability audit that reveals four tools doing three things with the same data. _(40 min)_ **Objectives** - Inventory tools by capability rather than by vendor category - Identify overlapping capability - Compute per-seat spend across the stack **Topics:** Capability inventory · Overlap identification · Per-seat spend · Data duplication **Activity:** Build the capability matrix for your stack and total the per-seat spend. ### Module 2 — How do you build account research on your own data? Research agents grounded in your CRM history rather than a vendor's enrichment dataset. _(50 min)_ **Objectives** - Ground research in CRM history and past interactions - Combine internal and public information appropriately - Produce briefs a seller actually reads **Topics:** CRM grounding · Internal history · Public source combination · Brief format **Activity:** Build an account research agent and test the output with three sellers. ### Module 3 — How do you keep competitive briefs current? Maintaining competitive intelligence without a per-seat subscription. _(45 min)_ **Objectives** - Build a monitoring and update pipeline - Verify claims before they reach a seller - Handle competitor claims fairly and accurately **Topics:** Monitoring pipeline · Claim verification · Fair characterization · Update cadence **Activity:** Build a competitive brief pipeline with a verification step before publication. ### Module 4 — Where does personalization become deception? The line between relevant and manufactured, and why crossing it damages more than it wins. _(45 min)_ **Objectives** - Identify personalization that implies false familiarity - Set standards for what an agent may claim - Design disclosure of automated outreach **Topics:** False familiarity · Claim standards · Automation disclosure · Brand consequence **Activity:** Audit ten real outreach messages against the standard you set. ### Module 5 — What are the consent rules when an agent sends? CAN-SPAM, GDPR, and the obligations that apply regardless of who composed the message. _(45 min)_ **Objectives** - Determine consent requirements by channel and jurisdiction - Implement opt-out that propagates across systems - Maintain records adequate for an inquiry **Topics:** CAN-SPAM · GDPR consent · Opt-out propagation · Record keeping **Activity:** Audit your outreach process for consent and opt-out propagation gaps. ### Module 6 — How do you produce content that is not generic? Content workflows grounded in proprietary knowledge rather than the model's priors. _(45 min)_ **Objectives** - Ground content in proprietary expertise and data - Detect and reject generic output - Maintain a consistent voice at volume **Topics:** Proprietary grounding · Generic detection · Voice consistency · Editorial workflow **Activity:** Produce content two ways — grounded and ungrounded — and have a customer compare. ### Module 7 — How do you attribute agent contribution to pipeline? Attribution that survives a CRO's scrutiny rather than claiming everything it touched. _(45 min)_ **Objectives** - Design attribution that isolates agent contribution - Build a comparison condition - Report honestly including null results **Topics:** Attribution design · Comparison conditions · Touch inflation · Honest reporting **Activity:** Design an attribution study with a holdout group for one agent. ### Module 8 — Replacing one paid tool with an owned agent The build module: a full replacement with a measured comparison. _(55 min)_ **Objectives** - Replace one point tool with an owned agent - Compare output quality against the incumbent - Compute the cost difference honestly **Topics:** Tool replacement · Quality comparison · Cost comparison · Migration **Activity:** Replace one tool and run a blind quality comparison with the team that uses it. ## What is the capstone project? **Revenue stack consolidation plan with one replacement built.** Audit the revenue stack for overlapping per-seat capability, build one owned agent replacing a paid tool, run a blind quality comparison, and produce a consolidation plan with honest cost and quality figures. _Deliverable:_ A consolidation plan plus one working agent with blind comparison results. ## How are learners assessed? - Blind quality comparison run with the team that actually uses the tool - Consent audit findings with remediation for every gap - Attribution design reviewed for whether it could report a null result ## What ships with the course? - **Facilitator guide.** Session-by-session running order, discussion prompts, and the questions that reliably derail a room. - **Learner workbook.** Exercises, checklists, and the templates each module's activity produces. - **Hands-on lab environment.** A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots. - **Assessment bank.** Scenario questions and rubric criteria mapped to each stated learning outcome. - **Source bibliography.** Every primary regulation and standard cited on this page, linked and dated. ## Which AI agents does this course use? - [Sales Enablement Agent](https://ibl.ai/solutions/enterprise/agent/sales-enablement-agent) - [Marketing Agent](https://ibl.ai/solutions/enterprise/agent/marketing-agent) - [Customer Support Agent](https://ibl.ai/solutions/enterprise/agent/customer-support-agent) - [Data Analysis Agent](https://ibl.ai/solutions/enterprise/agent/data-analysis-agent) ## Where does the course material come from? Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time. - [Business guidance](https://www.ftc.gov/business-guidance/blog) — Federal Trade Commission. Advertising, endorsement, and AI claim guidance for Modules 4 and 6. - [General Data Protection Regulation](https://gdpr-info.eu/) — GDPR text. Consent and legitimate interest analysis in Module 5. - [Ideas Made to Matter](https://mitsloan.mit.edu/ideas-made-to-matter) — MIT Sloan. Research on go-to-market technology consolidation. - [Course structured data](https://developers.google.com/search/docs/appearance/structured-data/course) — Google Search Central. Reference for the content and SEO workflow material in Module 6. ## Delivery notes Binding guidance for anyone preparing and delivering this course: - Module 4 is an ethics module in a sales course and will be resisted. Ground it in brand consequence and reply-rate data rather than moral argument, and it lands. - The blind comparison in Module 8 must be genuinely blind and run with actual users. Teams building the replacement always rate it higher than its users do. - Do not name and attack specific martech vendors. The audit is of capability overlap, and vendor attacks make the cost analysis look motivated. - Module 7's attribution work should acknowledge that most marketing attribution is weak. Overclaiming rigor here undermines the credibility of the honest parts. - Cost comparisons must include the engineering time to build and maintain the replacement. A consolidation case that ignores build cost is not a case. ## Why run AI training on a platform you own? - **You own the course, not a licence to it.** Course content, learner data, and the platform run inside your perimeter — you own all the code and the data. - **Model-agnostic delivery.** Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime. - **No per-seat training licences.** Usage-based or self-hosted, so cost tracks actual use rather than headcount. - **Deploy anywhere.** Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools. ## Frequently asked questions ### What does the Sales and Marketing Agents Without the SaaS Bill course cover? Revenue teams accumulate point tools that each charge per seat and each hold a slice of the same data. This course audits that stack, rebuilds the highest-value functions as agents grounded in your own CRM, and covers the consent and attribution work that determines whether the result is usable — including where personalization crosses into deception. It runs 5.5 hours across 8 modules across 8 modules, at intermediate level, and closes with a capstone: Revenue stack consolidation plan with one replacement built. ### Who should take Sales and Marketing Agents Without the SaaS Bill? It is written for Revenue operations leaders, Sales enablement and marketing operations, Demand generation and content teams, CROs evaluating the go-to-market stack. Prerequisites: Familiarity with your CRM and current martech stack; No technical background required. ### Can we run this course on our own infrastructure? Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for enterprise teams that cannot send work to a public AI tool. ### How do we get access to Sales and Marketing Agents Without the SaaS Bill? Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter. ### How much does AI training for enterprise cost on ibl.ai? There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University. ## More Enterprise courses - [Agentic AI for the Enterprise: From Chatbot to Workforce](https://ibl.ai/solutions/enterprise/course/agentic-ai-for-the-enterprise): What separates an agent from a chatbot — tools, memory, autonomy — and the orchestration patterns that let agents finish multi-step work without supervision. - [RAG on Enterprise Knowledge: Architecture, Chunking, Evals](https://ibl.ai/solutions/enterprise/course/rag-on-enterprise-knowledge): Production retrieval over enterprise content — chunking strategy, hybrid search, permission-aware retrieval, and the eval harness that proves it works. - [The Enterprise AI Cost Model: Per-Seat vs Token vs Owned](https://ibl.ai/solutions/enterprise/course/enterprise-ai-cost-model): Model AI spend across pricing shapes at real headcount — where per-seat licensing breaks, what tokens actually cost, and when owning the stack wins. - [AI Security: The OWASP LLM Top 10 in Production](https://ibl.ai/solutions/enterprise/course/ai-security-owasp-llm-top-10): Securing deployed LLM systems — prompt injection, data leakage, supply chain, and excessive agency — with the controls and tests for each. - [AI Governance in Practice: NIST AI RMF, ISO 42001, EU AI Act](https://ibl.ai/solutions/enterprise/course/ai-governance-nist-iso-eu-ai-act): Operationalize three overlapping frameworks into one governance program — inventory, risk classification, controls, and the evidence auditors ask for. - [Building an LLM Eval Harness That Ships](https://ibl.ai/solutions/enterprise/course/llm-eval-harness-that-ships): Move from vibes to measurement — task-specific eval design, LLM-as-judge and its limits, regression gates, and production monitoring.