If you’re in Federal Sales or Business Development, you know how hard it is to pull together a Go-To-Market (GTM) plan that aligns Sales, Marketing, Solution Architects, and Leadership.
Everyone’s busy. Everyone’s got opinions. And yet your VP still wants a “complete AI strategy for federal” by Monday.
But what if you could simulate that entire cross-functional meeting—with every stakeholder giving input—and get a detailed GTM plan in minutes?
With AskArti’s Federal BD Team Simulation Prompt, you can.
Just add three key details—press enter—and the AI acts like your full GTM task force.
Send it to leadership as-is? Bold move.
But with a little polish, you’ll have a plan that’ll make your team think you spent all week crafting it.
Let’s dive in.
What Is the Federal BD Team Simulation Prompt?
In plain terms, it’s a multi-agent prompt that turns any AI (ChatGPT, Claude, Gemini, etc.) into your personal BD war room.
You provide three key inputs—your company, your product, and your target customer—and the AI simulates five experts:
BD Lead, Sales, Marketing, Solution Architect, and Leadership.
Each provides its perspective, and the final output reads like a cohesive GTM plan ready for an executive briefing.
Instead of herding calendars and decks, you get a full strategy in one run.
How to Use the Federal BD Team Simulation Prompt
Step 1: Prepare Your Inputs
Gather these three details:
- Company: Amazon
- Product or Solution: Amazon Bedrock — a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as Anthropic, AI21, and Cohere. Bedrock powers generative-AI use cases and complex agentic applications built on Claude 4’s state-of-the-art reasoning and coding performance.
- Target Customer: [Enter Agency or Sector — for example, Department of Homeland Security]
Step 2: Copy This Prompt into Any AI
Paste the following directly into ChatGPT, Claude, or Gemini.
Replace the bracketed values with your own.
You are now running a **Federal Business Development Team Simulation.**
Simulate a full **OneTeam working session** between Sales, Marketing, Solution Architecture, Partner, Finance, and Leadership personas to produce a **fully executable Go-To-Market (GTM) plan** for a U.S. federal opportunity.
This simulation automatically aligns to **FY26 federal IT priorities, budgets, and procurement mandates.**
Use concrete, current examples and realistic program data. All personas must provide specific, actionable insights.
---
## **SCENARIO INPUTS**
* **Company:** [Insert Company Name]
* **Product or Solution:** [Insert Product or Service Name]
* **Target Customer:** [Insert Federal Agency or DoD Component]
---
## **AUTO-CONTEXT GUIDANCE**
Always reference **FY 2026** context:
* **Budget environment:** Agency appropriations, TMF, IIJA extensions, and modernization funding lines.
* **Policy frameworks:**
* OMB M-24-14 – Cybersecurity Priorities for FY 26
* OMB Circular A-130 – Managing Information as a Strategic Resource
* Zero Trust, FedRAMP High, IL4/IL5, CMMC 2.0, TIC 3.0
* Executive Order 14110 – Safe, Secure AI
* **Procurement trends:** Multi-award IDIQs (Alliant 3, CIO-SP4, SEWP VI), bridge contracts, OTAs.
* **Cultural realities:** Legacy system inertia, small-business participation goals, measurable ROI expectations.
* **Mandates:** AI governance, cybersecurity resilience, workforce automation, climate & sustainability data.
---
## **OBJECTIVE**
Generate a **3-to-6-page executive-ready GTM playbook** that could be used in a BD review.
Every section must include concrete actions, owners, and decision gates. No filler.
---
## **SIMULATION STRUCTURE**
### **Part 1: Agency Overview**
Provide a concise FY26-accurate brief covering:
* Mission and structure (bureaus, commands, labs)
* FY26 IT budget and modernization priorities
* Compliance environment (FedRAMP, IL, CMMC)
* Pain points or Congressional mandates
* Procurement culture and contract vehicles
---
### **Part 2: Stakeholder Perspectives**
Each persona writes **1–2 concise, actionable paragraphs.**
#### **Persona 1: Sales – Field Intelligence & Account Insights**
* Current relationships, champions, blockers
* Active RFPs/RFIs or funding signals
* Competitors and alternatives
* Budget source and timeline
* Key risks or opportunities
#### **Persona 2: Marketing – Narrative, Messaging & Campaign Strategy**
* Tailored mission-aligned value proposition
* “Why now / why us / why not them” framework
* ABM plan: events, exec briefings, thought-leadership content
* Case studies or reference stories
* 90-day content & event calendar
#### **Persona 3: Solution Architect – Technical Validation & Compliance**
* Alignment with Zero Trust, FedRAMP High, CMMC
* Integration requirements (clouds, data fabric, SIEM)
* Compliance gaps and mitigation
* PoC scope, metrics, and resource plan
* Technical risks and mitigations
#### **Persona 4: Partner & Ecosystem Lead – SI/ISV Strategy**
* Incumbent or influential SIs
* Co-sell or subcontracting structure
* Required ISV/hyperscaler integrations (AWS, Azure, ServiceNow, Salesforce)
* Enablement milestones (training, demos, collateral)
* Partner-conflict risks
#### **Persona 5: Finance & Commercial Strategy – Pricing, ROI & Deal Structure**
* Pricing model and rationale
* FY26 budget alignment and appropriation timing
* ROI framework (cost avoidance, FTE savings, mission outcomes)
* Procurement vehicle and contract structure
* Government-specific T&Cs or data-rights considerations
#### **Persona 6: Leadership – Strategic Alignment & Go/No-Go**
* Account tiering (Tier 1/2/3)
* Resource allocation and exec sponsorship
* Go-forward criteria and kill conditions
* Escalation path and VP/C-suite engagement timing
---
### **Part 3: Competitive Intelligence**
| **Competitor** | **Strengths** | **Weaknesses** | **Our Counter-Positioning** |
| ---------------- | ------------------- | ------------------------------ | ------------------------------------------------------- |
| [Competitor 1] | [Advantage] | [Gap] | [Differentiation based on mission outcomes and trust] |
| [Competitor 2] | [Advantage] | [Gap] | [Counter-narrative emphasizing security and compliance] |
| [DIY / In-House] | [Perceived control] | [Hidden costs and talent risk] | [Managed, FedRAMP-ready alternative] |
---
### **Part 4: Unified GTM Plan (BD Lead Integration)**
#### 4.1 Strategic Positioning
* Value proposition mapped to FY26 agency priorities
* Trigger events (budget release, RFP drop, TMF cycle)
* Differentiation through speed, compliance, mission impact
#### 4.2 Entry Points & Stakeholder Map
* Key offices/bureaus to target
* Named champions and buyers
* Engagement sequence and influencer plan
#### 4.3 Partner & Ecosystem Strategy
* SI/ISV alignment and enablement timeline
* Channel Partner Co-sell incentives and delivery ownership
#### 4.4 Commercial Model & Pricing
* FY26 funding justification
* Procurement vehicle choice
* Quantified ROI and cost/mission benefit
#### 4.5 Campaign Execution Plan (Next 90 Days)
Provide a **week-by-week schedule** with actions, owners, deliverables, and decision gates.
#### 4.6 Risk Register & Mitigation**
| **Risk** | **Impact** | **Probability** | **Mitigation Plan** | **Owner** |
| ------------------------ | ---------- | --------------- | ------------------------------------ | --------- |
| Technical non-compliance | H | M | Accelerate FedRAMP High validation | SA |
| Competitive displacement | H | M | Partner alignment + exec sponsorship | Sales |
| Budget delay | M | H | Use TMF bridge or carryover funding | Finance |
#### 4.7 Success Metrics
* **Leading:** qualified meetings, PoC signed, champions secured
* **Lagging:** pipeline $, ARR, reference created
* **Decision Gates:** go/no-go criteria each phase
#### 4.8 RACI**
| **Workstream** | **Responsible** | **Accountable** | **Consulted** | **Informed** |
| --------------------- | --------------- | --------------- | -------------- | ------------ |
| Account strategy | Sales Lead | BD Lead | SA, Marketing | Leadership |
| PoC execution | SA Lead | BD Lead | Sales, Partner | Leadership |
| Marketing campaign | Marketing Lead | BD Lead | Sales, Exec | Partner |
| Pricing & negotiation | Sales Lead | Leadership | Finance | BD Lead |
---
### **Part 5: 90-Day Critical Path**
**Phase 1 – Discovery ( Days 1–30 )**
Validate BANT, map stakeholders, conduct gap analysis.
**Gate 1:** Proceed to PoC if budget and champion confirmed.
**Phase 2 – PoC ( Days 31–60 )**
Execute controlled pilot with metrics; draft case study.
**Gate 2:** Advance if success criteria met and business case approved.
**Phase 3 – Pilot Expansion ( Days 61–90 )**
Finalize contracting, training, and reference build.
**Gate 3:** Enterprise-scale if ROI confirmed.
---
### **Part 6: Post-Sale & Expansion**
* Customer success onboarding and QBR cadence
* Upsell/cross-sell triggers (adjacent bureaus, IL upgrades)
* Reference publication plan
* Expansion ARR targets and review cadence
---
## **FY26 Federal Modernization Quick-Insert Block**
Automatically preload relevant modernization priorities when an agency is named:
### **1. Department of Defense**
* Themes: JADC2, Zero Trust by FY27, software factory expansion, digital engineering.
* Funding: RDT&E and O&M ($14 B+ cyber budget).
* Programs: ZPMO, Cloud One Next, JWCC, CDAO AI initiatives.
* Vehicles: JWCC, OTA consortia (NSTXL, C5, AFWERX), Alliant 3, SEWP VI.
### **2. Civilian Agencies**
* Themes: Zero Trust, AI risk management, cloud modernization, CX per EO 14058.
* Funding: TMF ($1 B+), IRA sustainability funds.
* Programs: Cloud Smart 2.0, AI Policy Implementation, Data Fabric Expansion.
* Vehicles: GSA MAS, SEWP VI, Alliant 3, CIO-SP4, FirstSource III.
### **3. Intelligence Community**
* Themes: IL5–IL7 cloud, AI-assisted analysis, quantum-safe crypto.
* Funding: NIP & MIP appropriations.
* Programs: C2E Next, IC Digital Transformation FY26-28, AI Governance Board.
* Vehicles: C2E TOs, classified IDIQs, NSA E-Commerce Platform.
### **4. Health & Human Services / Public Health**
* Themes: EHR interoperability, data fabric for analytics, AI governance for medical AI.
* Funding: HITECH extensions, CDC Data Modernization, VA Digital Health budget.
* Programs: VA EHRM Reset, NIH STRIDES, CDC DMI Phase 3.
* Vehicles: CIO-SP4, VA T4NG2, NIH LTASC, GSA MAS.
### **5. Energy, Infrastructure & Science Agencies**
* Themes: Grid modernization, AI predictive maintenance, climate modeling at exascale.
* Funding: DOE OCIO funds, IIJA extensions, NASA Transformational Computing.
* Programs: ESnet6, GMLC 2.0, NASA Digital Transformation.
* Vehicles: DOE Energy BPA, GSA MAS, SEWP VI, Lab IDIQs.
---
## **OUTPUT REQUIREMENTS**
* Tone: Executive, concise, federal-savvy.
* Specificity: Include agency names, dates, dollar figures where possible.
* Accountability: Every risk and activity has an owner.
* No filler: Every sentence must advance execution.
* Do not create fictitious placeholder data.
---
## **WORKFLOW**
1. Fill in **Company**, **Product**, and **Target Customer**.
2. Run the simulation.
3. Review each persona output for specificity and realism.
4. Integrate into the Unified GTM Plan.
5. Use the 90-Day Critical Path as execution timeline.
6. Iterate quarterly as budgets and mandates evolve.
---
**End of FY26 One-Shot Prompt**
Step 3: Review and Refine
Once the AI generates the plan:
- Layer in agency-specific programs or RFP data (e.g., DOE AI Innovation Hub, AI CoE).
- Adjust tone and naming conventions for your internal reviewers.
- Drop it into your QBR deck, proposal appendix, or LinkedIn thought-leadership post.
Example Output using Claude Sonnet 4.5
Federal Business Development Team Simulation
OneTeam GTM Plan: AWS Bedrock for DHS
SCENARIO INPUTS
- Company: Amazon Web Services (AWS)
- Product or Solution: AWS Bedrock (Generative AI Platform)
- Target Customer: Department of Homeland Security (DHS)
Part 1: Agency Overview
Department of Homeland Security – FY26 Context
Mission and Structure: DHS encompasses 22 operational components including CISA, TSA, FEMA, CBP, ICE, USCIS, Secret Service, and the Coast Guard. The department’s mission spans cybersecurity, border security, immigration, disaster response, and critical infrastructure protection.
FY26 IT Budget Environment: DHS maintains one of the largest civilian IT budgets in the federal government. The department has historically prioritized cybersecurity modernization, data analytics, and mission system upgrades. IT modernization efforts focus on cloud adoption, Zero Trust implementation, and enhanced data-sharing across components.
Compliance Requirements:
- FedRAMP High authorization required for mission-critical systems
- CISA’s Zero Trust Maturity Model alignment
- NIST AI Risk Management Framework compliance (per Executive Order 14110)
- DHS AI governance and use case approval processes
- Cross-component data security and privacy requirements
Known Pain Points:
- Siloed data across 22 components limiting intelligence sharing
- Legacy system dependencies affecting modernization speed
- Need for AI governance frameworks that balance innovation with risk management
- Workforce challenges in adopting new technologies
- Congressional scrutiny on technology procurement and ROI
Procurement Culture: DHS utilizes various contract vehicles including DHS EAGLE II (Enterprise Acquisition Gateway for Leading-Edge Solutions), FirstSource II, and standard GSA vehicles. Procurement timelines vary by component. The department emphasizes small business participation and measurable mission outcomes.
Part 2: Stakeholder Perspectives
Persona 1: Sales – Field Intelligence & Account Insights
DHS represents a complex multi-stakeholder environment requiring component-level relationship mapping. AWS already maintains FedRAMP High authorization and has existing cloud presence across several DHS components. The key challenge is navigating the AI governance approval process established under EO 14110, which requires impact assessments and use case validation. CISA has expressed interest in AI-assisted threat analysis, while CBP and ICE have requirements around document processing and case management automation. Competition includes Microsoft Azure OpenAI Service and potentially Google Cloud’s Vertex AI once they achieve required authorization levels. Budget authority varies by component, with some modernization funding available through the DHS CIO office and component-specific appropriations. The primary risk is the nascent state of federal AI governance—agencies are cautious about generative AI adoption without clear policy frameworks. The opportunity lies in positioning Bedrock as a controlled, responsible AI platform with appropriate guardrails.
Persona 2: Marketing – Narrative, Messaging & Campaign Strategy
The value proposition centers on “Mission-Ready Generative AI with Built-in Responsibility.” DHS needs AI capabilities that accelerate mission outcomes—threat detection, document analysis, case processing—while maintaining strict security, privacy, and ethical standards. The “why now” is driven by EO 14110 requirements and DHS’s need to modernize data analysis capabilities. The “why us” emphasizes AWS’s existing FedRAMP High authorization, Bedrock’s foundation model choice and customization capabilities, and built-in guardrails for responsible AI use. The “why not competitors” focuses on AWS’s proven federal track record and Bedrock’s architecture allowing agencies to maintain data sovereignty while accessing advanced AI. The ABM approach should target DHS CIO office leadership and component CTOs through executive briefings on AI governance frameworks, participation in DHS AI Working Group discussions, and thought leadership on responsible AI implementation. A 90-day calendar would include: speaking opportunities at DHS-focused events, case study development with non-federal customers demonstrating responsible AI use, technical workshops for DHS component architects, and executive briefing materials on AI risk management alignment.
Persona 3: Solution Architect – Technical Validation & Compliance
AWS Bedrock operates within AWS GovCloud (US) regions with FedRAMP High authorization. The technical validation focuses on demonstrating how Bedrock aligns with NIST AI RMF requirements including transparency, accountability, and safety. Integration requirements include connectivity to existing DHS data environments, which vary by component—some operate in commercial cloud regions while others require GovCloud or on-premises connectivity. The architecture must address data residency requirements and ensure training data never leaves customer control. Key compliance gaps to address include: formal AI impact assessments per DHS AI policy, demonstration of model bias testing and mitigation, audit logging for AI decision transparency, and integration with existing security tools (SIEM, DLP). A proof of concept should focus on a constrained use case—potentially document classification or threat intelligence synthesis—with clear success metrics around accuracy, processing speed, and security compliance. Technical risks include model hallucination in mission-critical contexts (mitigated through retrieval-augmented generation and human-in-the-loop workflows) and integration complexity with legacy systems (mitigated through API-based architecture and phased implementation).
Persona 4: Partner & Ecosystem Lead – SI/ISV Strategy
DHS has established relationships with several large system integrators including Booz Allen Hamilton, Deloitte, Leidos, SAIC, and Accenture Federal Services, many of whom hold positions on DHS EAGLE II. A successful Bedrock deployment will require SI partnership for integration services, change management, and ongoing support. The co-sell structure should identify which integrator has strongest relationships within the target DHS component and align on role clarity—AWS provides platform and foundational AI capabilities while SI provides integration, customization, and mission-specific optimization. Required technology integrations may include ServiceNow for IT service management, Splunk for security monitoring, and existing data analytics platforms. Partner enablement requires technical training on Bedrock capabilities, security and compliance briefings on FedRAMP High requirements, and development of DHS-specific use case demonstrations. A potential conflict exists if competing cloud providers have exclusive arrangements with preferred integrators, requiring early SI alignment and incentive structuring to ensure Bedrock advocacy.
Persona 5: Finance & Commercial Strategy – Pricing, ROI & Deal Structure
AWS Bedrock pricing follows a consumption-based model with costs for model inference and customization. For DHS, pricing should be structured through AWS GovCloud with appropriate federal pricing and potentially volume commitments for predictable budgeting. FY26 budget alignment requires identifying specific component appropriations or DHS CIO modernization funds that can support AI platform investment. The ROI framework should quantify mission outcomes: faster threat analysis (hours to minutes), reduced manual document processing (FTE time savings), and improved decision quality (reduced false positives/negatives). Cost avoidance should calculate current manual processing costs versus AI-assisted workflows. Procurement vehicles include AWS’s presence on GSA Schedule and potential task orders under component-specific IDIQs. Government-specific terms must address data rights (customer maintains ownership of all data and fine-tuned models), security requirements (FedRAMP High compliance), and appropriate use restrictions. A challenge is that AI consumption costs are difficult to predict pre-deployment, requiring pilot pricing with clear scaling economics for production use.
Persona 6: Leadership – Strategic Alignment & Go/No-Go
DHS represents a Tier 1 federal opportunity given agency size, budget scale, and strategic importance. However, the AI governance uncertainty creates execution risk that requires careful resource allocation. This opportunity warrants pursuit if: (1) we can identify a specific component champion willing to pilot, (2) budget authority is confirmed for FY26, and (3) the use case aligns with DHS’s approved AI applications. Resource allocation should include dedicated solution architect support, federal marketing campaign investment, and executive sponsorship at VP level given account significance. Go-forward criteria include securing an initial pilot within 90 days with defined success metrics and budget commitment. Kill conditions would include: inability to navigate DHS AI governance approval process within 6 months, emergence of policy restrictions on generative AI use for the target use case, or competitive displacement by an incumbent with exclusive positioning. Escalation path requires AWS Public Sector leadership engagement with DHS CIO and component leadership, with C-suite involvement if policy barriers require resolution at OMB or White House OSTP level.
Part 3: Competitive Intelligence
| Competitor | Strengths | Weaknesses | Our Counter-Positioning |
|---|---|---|---|
| Microsoft Azure OpenAI Service | Existing Microsoft enterprise agreements across government; familiarity with Microsoft ecosystem; IL5 authorization in progress | Limited foundation model choice (primarily OpenAI models); less flexibility in model customization; newer to federal high-security environments | Emphasize Bedrock’s foundation model choice (Anthropic Claude, Meta Llama, AI21, Cohere, Stability AI), proven AWS GovCloud track record, and architecture enabling greater data sovereignty |
| Google Cloud Vertex AI | Strong AI/ML reputation; integration with Google Workspace potentially used by some DHS staff | Limited federal high-security authorizations; less established federal presence; concerns about commercial data practices | Highlight AWS’s mature FedRAMP High presence, dedicated GovCloud infrastructure, and established DHS relationships across components |
| Palantir Foundry with AI | Strong existing DHS relationships particularly with ICE and CBP; purpose-built for government intelligence analysis | Proprietary platform creates vendor lock-in; higher cost structure; limited foundation model flexibility | Position Bedrock as open, API-based platform avoiding lock-in, with lower total cost of ownership and broader ecosystem integration |
| DIY / Open Source Models | Perceived cost savings; full control over infrastructure | Significant technical resources required; compliance burden; lack of managed security and governance; model performance gaps | Demonstrate managed service value, built-in compliance features, enterprise support, and total cost comparison including hidden staffing and infrastructure costs |
Part 4: Unified GTM Plan
4.1 Strategic Positioning
Value Proposition Mapped to DHS FY26 Priorities: AWS Bedrock enables DHS to responsibly deploy generative AI capabilities that accelerate mission-critical workflows—threat intelligence analysis, document processing, case management—while maintaining strict security controls, data sovereignty, and alignment with federal AI governance requirements. The platform provides choice and flexibility through multiple foundation models, operates within FedRAMP High authorized infrastructure, and includes built-in safeguards for responsible AI deployment.
Trigger Events:
- DHS AI governance policy finalization and use case approval processes
- Component-specific budget releases for modernization
- CISA threat analysis modernization initiatives
- Border security technology refresh cycles
- Disaster response data analytics requirements at FEMA
Differentiation: Speed to deployment through existing FedRAMP High authorization, proven federal operational experience, foundation model flexibility allowing agencies to select appropriate models for specific use cases, and responsible AI features including content filtering and bias detection built into the platform.
4.2 Entry Points & Stakeholder Map
Priority DHS Components for Initial Engagement:
- CISA (Cybersecurity and Infrastructure Security Agency)
- Entry Point: Cybersecurity Division and threat analysis teams
- Use Case: AI-assisted threat intelligence synthesis and vulnerability assessment
- Champion Target: CISA CTO office and Cybersecurity Division leadership
- DHS Office of the Chief Information Officer
- Entry Point: Innovation and AI governance teams
- Use Case: Cross-component AI platform standards and governance framework
- Champion Target: DHS CIO and Deputy CIO for Innovation
- CBP (Customs and Border Protection) or ICE
- Entry Point: IT modernization and case management teams
- Use Case: Document analysis and case processing automation
- Champion Target: Component CIO and mission system owners
Engagement Sequence: Phase 1: Establish dialogue with DHS CIO office on AI governance alignment Phase 2: Identify component-level pilot champion (likely CISA for threat analysis) Phase 3: Conduct technical validation workshops with component architects Phase 4: Develop pilot proposal aligned with approved AI use cases
4.3 Partner & Ecosystem Strategy
SI Alignment: Identify which system integrator has the strongest relationship with target DHS component. For CISA-led opportunities, assess Booz Allen Hamilton and Deloitte positioning. For CBP/ICE, evaluate Leidos and SAIC relationships. Engage SI partner early to align on:
- Role definition: AWS provides platform, SI provides integration and change management
- Revenue sharing and task order structure under DHS EAGLE II
- Joint account planning and pursuit strategy
ISV Integration Requirements:
- Security tools: Integration with existing SIEM platforms (Splunk, others) for AI activity monitoring
- Data platforms: Connectivity to existing data lakes and analytics environments
- Workflow tools: Integration with case management systems and collaboration platforms
Enablement Timeline:
- Month 1: Partner technical training on Bedrock capabilities and federal compliance
- Month 2: Joint use case development and demonstration environment setup
- Month 3: Partner-led customer workshops and technical validation
4.4 Commercial Model & Pricing
Pricing Structure: Consumption-based pricing through AWS GovCloud with potential enterprise discount agreement for volume commitments. Pilot phase should include capped usage to provide budget predictability while demonstrating value.
FY26 Funding Justification: Align pricing to component modernization budgets or DHS CIO innovation funds. Emphasize cost avoidance through automation of manual processes and improved mission outcomes. ROI framework should calculate:
- Current cost of manual threat analysis or document processing (FTE hours × burden rate)
- Projected processing time reduction with AI assistance (e.g., 70% reduction)
- Quality improvements (reduced false positives, faster threat detection)
- Quantified mission value (threats detected faster, cases processed more efficiently)
Procurement Vehicle: Leverage AWS’s GSA Schedule 70 or work through DHS EAGLE II task order with aligned SI partner. Component-specific vehicles may also be available.
Government Terms:
- Data rights: Customer maintains full ownership of all data and fine-tuned models
- FedRAMP High compliance maintained
- Security and privacy controls meeting DHS requirements
- Appropriate use restrictions aligned with DHS AI governance policy
4.5 Campaign Execution Plan (Next 90 Days)
Weeks 1-2: Discovery & Stakeholder Mapping
- Action: Conduct stakeholder analysis to identify DHS CIO contacts and component champions
- Owner: Sales Lead
- Deliverable: Stakeholder map with engagement strategy
- Decision Gate: Confirm access to key decision-makers
Weeks 3-4: Initial Engagement & Use Case Validation
- Action: Executive briefings with DHS CIO office and target component leadership
- Owner: Sales Lead with Solution Architect support
- Deliverable: Use case validation document aligned with DHS AI priorities
- Decision Gate: Component interest confirmed with potential pilot use case identified
Weeks 5-6: Technical Validation & Compliance Review
- Action: Technical workshops demonstrating Bedrock capabilities and compliance alignment
- Owner: Solution Architect Lead
- Deliverable: Technical architecture proposal and compliance mapping
- Decision Gate: Technical feasibility confirmed, no blocking compliance issues
Weeks 7-8: Partner Alignment & Pilot Scoping
- Action: Engage SI partner and develop joint pilot proposal
- Owner: Partner Lead with Sales support
- Deliverable: Pilot scope document with success metrics, timeline, and budget
- Decision Gate: Partner aligned and pilot scope agreed with customer
Weeks 9-10: Budget Validation & Proposal Development
- Action: Confirm FY26 budget availability and develop formal pilot proposal
- Owner: Sales Lead with Finance support
- Deliverable: Pilot proposal with ROI analysis and procurement approach
- Decision Gate: Budget confirmed and proposal submitted
Weeks 11-12: Negotiation & Pilot Authorization
- Action: Address customer questions, refine proposal, secure pilot approval
- Owner: Sales Lead with Leadership escalation as needed
- Deliverable: Signed pilot agreement or task order
- Decision Gate: Pilot approved and scheduled to begin (Gate 1 passed)
4.6 Risk Register & Mitigation
| Risk | Impact | Probability | Mitigation Plan | Owner |
|---|---|---|---|---|
| AI governance approval delays | H | H | Engage DHS CIO office early on governance alignment; select use case with clear mission value and lower risk profile | Sales, Leadership |
| Data residency and security concerns | H | M | Emphasize GovCloud infrastructure, FedRAMP High compliance, and data sovereignty architecture; provide detailed security documentation | Solution Architect |
| Competitive displacement by Microsoft | H | M | Leverage existing AWS relationships; differentiate on foundation model choice and flexibility; accelerate engagement timeline | Sales, Marketing |
| Budget uncertainty or delays | M | H | Identify multiple funding sources; propose pilot with minimal initial investment; demonstrate quick ROI | Finance, Sales |
| Integration complexity with legacy systems | M | M | Conduct thorough technical discovery; use API-based integration approach; phase implementation | Solution Architect |
| Model performance concerns (hallucination, bias) | H | M | Implement retrieval-augmented generation; require human-in-the-loop for mission-critical decisions; conduct bias testing | Solution Architect |
| Lack of AI skills within DHS workforce | M | H | Include training and change management in pilot; partner with SI for knowledge transfer; provide ongoing support | Partner Lead, Solution Architect |
4.7 Success Metrics
Leading Indicators (Pilot Phase):
- Number of qualified stakeholder meetings with decision-makers (Target: 5+ in first 30 days)
- Technical validation workshops completed (Target: 2-3 with component architects)
- Champion identified and actively supporting pilot (Target: by Day 45)
- Pilot scope agreement signed (Target: by Day 90)
Lagging Indicators (Post-Pilot):
- Pilot success metrics achieved (defined per use case – e.g., 70% processing time reduction)
- Production contract value (Target TBD based on pilot scope)
- Reference customer secured (DHS willing to speak about results)
- Expansion opportunities identified (additional components or use cases)
Decision Gates:
- Gate 1 (Day 90): Pilot approved and funded – GO/NO-GO decision on full pursuit
- Gate 2 (Day 120): Pilot launched and initial results positive – decision to plan production deployment
- Gate 3 (Day 180): Pilot completed successfully – decision to expand to production and additional use cases
4.8 RACI
| Workstream | Responsible | Accountable | Consulted | Informed |
|---|---|---|---|---|
| DHS stakeholder engagement | Sales Lead | Public Sector VP | Solution Architect, Marketing | Leadership, Partner |
| Technical validation & architecture | Solution Architect Lead | Public Sector VP | Sales, Security/Compliance | Leadership |
| Marketing campaign & thought leadership | Marketing Lead | Public Sector VP | Sales, Solution Architect | Partner, Leadership |
| Partner alignment & enablement | Partner Lead | Public Sector VP | Sales, Solution Architect | Leadership |
| Pilot scoping & pricing | Sales Lead | Public Sector VP | Finance, Solution Architect | Leadership |
| Contract negotiation | Sales Lead | Public Sector VP | Legal, Finance | Leadership |
Part 5: 90-Day Critical Path
Phase 1 – Discovery & Validation (Days 1-30)
Objectives: Validate opportunity, map stakeholders, identify pilot use case
Key Activities:
- Research DHS AI governance policies and approved use case frameworks
- Map component-level decision-makers and technical stakeholders
- Identify existing AWS relationships within DHS to leverage
- Conduct initial executive briefings on Bedrock capabilities
- Validate budget availability for FY26 pilot initiatives
- Assess competitive positioning and incumbent relationships
Deliverables:
- Stakeholder engagement map
- Target component and use case identified
- Initial technical requirements documented
- Competitive intelligence brief
Gate 1 Decision Criteria:
- Access confirmed to component-level decision-makers
- Pilot use case identified that aligns with DHS AI policy
- Budget source identified (not necessarily committed)
- No blocking technical or compliance issues discovered
- GO Decision: Proceed to technical validation phase
- NO-GO Decision: Park opportunity until governance clarity or budget availability improves
Phase 2 – Technical Validation & Pilot Design (Days 31-60)
Objectives: Demonstrate technical feasibility, design pilot scope, align partner
Key Activities:
- Conduct technical workshops with DHS component architects
- Demonstrate Bedrock capabilities in controlled environment
- Map integration requirements with existing DHS systems
- Develop compliance documentation (FedRAMP, NIST AI RMF alignment)
- Engage and align SI partner on pilot delivery
- Design pilot scope with clear success metrics and timeline
- Develop ROI analysis and cost model
- Create pilot proposal document
Deliverables:
- Technical architecture and integration plan
- Compliance alignment documentation
- Pilot scope with success metrics
- Partner engagement agreement
- ROI analysis and pricing proposal
- Formal pilot proposal document
Gate 2 Decision Criteria:
- Component champion actively supporting pilot
- Technical architecture validated by DHS architects
- No unresolvable compliance gaps
- Partner aligned and capable of delivery support
- Pilot budget and success metrics agreed with customer
- GO Decision: Submit pilot proposal and enter negotiation
- NO-GO Decision: Reassess if technical gaps or lack of champion support warrant pause
Phase 3 – Pilot Authorization & Launch Prep (Days 61-90)
Objectives: Secure pilot approval, finalize agreements, prepare for execution
Key Activities:
- Submit formal pilot proposal through appropriate procurement vehicle
- Address customer questions and refine proposal
- Negotiate terms and finalize task order or agreement
- Conduct pilot kickoff planning with DHS team and SI partner
- Prepare technical environment and access requirements
- Develop pilot execution plan and success measurement framework
- Plan customer training and change management approach
Deliverables:
- Signed pilot agreement or task order
- Pilot execution plan with timeline and milestones
- Technical environment prepared
- Training and onboarding materials
- Success measurement dashboard design
Gate 3 Decision Criteria:
- Pilot contract signed and funded
- DHS team identified and available for pilot participation
- Technical environment ready for pilot launch
- Clear success metrics and evaluation process agreed
- GO Decision: Launch pilot execution (beyond 90-day scope)
- NO-GO Decision: Identify blocking issues for escalation or opportunity reassessment
Part 6: Post-Sale & Expansion
Customer Success & Onboarding
Pilot Execution Support:
- Dedicated solution architect assigned to pilot for technical guidance
- Weekly pilot status meetings with DHS team and SI partner
- Real-time issue resolution and optimization
- Success metrics tracking and reporting
QBR Cadence:
- Monthly reviews during pilot phase
- Quarterly business reviews post-production deployment
- Executive sponsorship meetings at 6-month intervals
Upsell & Cross-Sell Triggers
Expansion Within Component:
- Pilot success leads to production deployment scaling
- Additional use cases within same component (e.g., CISA expands from threat analysis to vulnerability management)
- Increased usage driving revenue growth
Cross-Component Expansion:
- Reference from initial component enables engagement with other DHS entities
- CBP/ICE document processing use cases
- FEMA disaster response data analysis
- TSA screening technology enhancement
- Coast Guard logistics optimization
Technology Expansion:
- Additional AWS services adoption (data lakes, analytics, ML training)
- Advanced Bedrock features (model customization, fine-tuning)
- Integration with broader AWS AI/ML portfolio
Reference Publication Plan
Timeline:
- Month 3 of pilot: Draft preliminary results (internal only)
- Month 6 post-pilot launch: Develop case study pending DHS approval
- Month 9: Public reference (conference presentation, written case study) if DHS approves
- Month 12: Joint thought leadership on responsible AI in government
Content Types:
- Technical case study for architect audience
- Executive brief on mission outcomes for leadership audience
- Conference presentations at federal IT events
- Joint blog posts or articles on responsible AI implementation
Expansion ARR Targets & Review Cadence
Baseline: Pilot represents initial proof point, not significant ARR
Year 1 Target: Production deployment within initial component
- Success metric: 5-10x pilot scale in production usage
Year 2 Target: Expansion to 2-3 additional DHS components
- Success metric: Multiple production use cases across DHS enterprise
Year 3 Target: DHS-wide AI platform standard
- Success metric: Bedrock adopted as preferred generative AI platform for appropriate use cases across DHS
Review Cadence:
- Monthly: Pilot progress and immediate issue resolution
- Quarterly: Expansion opportunity assessment and pipeline development
- Annually: Strategic account planning and multi-year roadmap
EXECUTIVE SUMMARY
This GTM plan positions AWS Bedrock as DHS’s responsible generative AI platform for mission-critical use cases. Success depends on: (1) navigating DHS’s evolving AI governance framework, (2) identifying a component champion willing to pilot, (3) demonstrating mission value while maintaining strict security controls, and (4) leveraging AWS’s existing federal presence and FedRAMP High authorization.
The 90-day critical path focuses on stakeholder engagement, technical validation, and pilot authorization. Primary risks include AI governance approval delays and competitive pressure from Microsoft. Mitigation strategies emphasize early CIO office engagement, differentiation through foundation model flexibility, and accelerated timeline execution.
Recommended Next Action: Initiate stakeholder mapping within DHS CIO office and CISA to identify pilot champion and validate use case alignment with DHS AI policy.
Why It Works
This pattern compresses a week’s worth of internal coordination into a 10-second simulation.
By modeling how each stakeholder thinks, the AI delivers a plan that feels authentic, cross-functional, and executable.
It’s the fastest path to a polished, multi-department GTM strategy—without the endless meetings.
Now You Try It
Open your favorite AI tool.
Paste the Federal BD Team Simulation Prompt.
Enter your Company, Product, and Target Customer.
Press Enter.
You’ll have a complete GTM plan—agency overview, stakeholder perspectives, and next steps—ready to guide your next federal pursuit.
If this was helpful, share it with a teammate who’s tired of “strategy meetings that could’ve been a prompt.”