Build vs Buy: Proposal AI Decision Guide
Building your own proposal AI has never been easier to start
In 2026, a capable engineer can connect a general-purpose AI assistant to your content library in a few weeks. McKinsey found that 32% of organizations have decided against buying at least one software product because they could build it in-house with agentic coding tools.¹
Starting a build is easy. Getting value from one is harder. In the same survey, only 37% of organizations attribute any EBIT impact to AI.¹ Research from MIT’s NANDA initiative found that purchasing AI tools from specialized vendors or partnering with them succeeds about two-thirds of the time, while internal builds succeed only about one-third as often.²
Buyers are responding. Menlo Ventures found that 76% of enterprise AI use cases were purchased in 2025, up from 53% in 2024.³
This guide is for proposal leaders and the IT teams who support them. It covers:
- What building looks like today
- The full cost of each path
- The extra requirements federal contractors face
- A checklist to help you decide
What “building” means in 2026
There are three ways to build, and none of them stop at launch
When people say they’ll build their own proposal AI, they usually mean one of three things:
| Path | What it looks like | Who owns it | What you maintain |
| Configure a general-purpose assistant | A tool such as Microsoft Copilot, ChatGPT Enterprise or Claude, connected to your content library, with custom prompts | Proposal ops and IT | Prompts, content hygiene, user training |
| Build a custom retrieval app | Your own application on a model API, searching your past proposals | Internal engineering | Code, retrieval pipeline, output testing, security, model updates |
| Commission a custom platform | A systems integrator builds a proposal system for you | Integrator and IT | Contract, roadmap, hosting, compliance evidence |
All three paths can produce a working demo within weeks. The real differences show up between months 3 and 24. That’s when you find out who keeps the tool accurate, secure and compliant as models, staff and RFP requirements change.
The case for building
When building makes sense, and where it breaks down
Building can be the right call. It fits best when:
- You have engineers with spare capacity beyond your core product work
- Your proposal process is unusual enough that no product fits it
- A small team needs a light drafting aid, and a general-purpose assistant already covers it
Where builds break down
Pilots stall before production
Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025. The reasons it gave were poor data quality, inadequate risk controls, escalating costs and unclear business value.⁴ It also predicts that over 40% of agentic AI projects will be canceled by the end of 2027.⁵
Proposals need more than a chat window
Compliance matrices, requirement traceability, color team reviews, version control and governance of approved content each become a separate item to build.
Ownership outlasts the builder
When the engineer who built the tool moves on, the knowledge of how it works often leaves too.
Models change underneath you
Each model update means re-testing prompts and outputs before the next deadline.
Your proposal team becomes a product team
Writers end up specifying features, testing releases and reporting bugs instead of writing.
The case for buying
Purpose-built software trades some control for speed and reliability
Advantages of buying
Faster to production
Menlo Ventures found that ready-made AI solutions reach production faster and show value sooner, which is a key reason buying overtook building in 2025.³
Higher success rate
MIT NANDA found that purchased AI tools succeed about two-thirds of the time, roughly three times the rate of internal builds.²
Proposal workflows built in
Compliance checks, content libraries and review cycles come ready to use instead of sitting on a build backlog.
Security is largely handled for you
The vendor maintains the security controls and compliance evidence for the platform. You still own the controls it assigns to customers, such as user access, data marking and incident reporting.
A roadmap you share:
The whole customer base funds improvements, so you benefit from features other teams asked for.
What you give up, and how to manage it
| Tradeoff | How to manage it |
| Less control over the roadmap | Ask for roadmap visibility, APIs and configuration options |
| Dependence on one vendor | Check data portability and exit terms before you sign |
| Integration effort | Confirm connectors for the systems where your content lives |
| Ongoing subscription | Compare it against the full cost of building |
The full cost of each path
The license fee is the easy part to compare
It’s tempting to compare a vendor subscription against the cost of a few engineers. The real comparison has more lines. Use this table to make sure your business case includes all of them.
| Cost line | If you build | If you buy |
| Initial build or setup | Engineering time to design, build and test | Onboarding and configuration with the vendor |
| Ongoing engineering | Permanent headcount to maintain and improve the tool | Included |
| Model and API usage | You pay usage directly and absorb spikes | Usually included |
| Hosting and security | You run infrastructure, monitoring and pen testing | Included |
| Compliance evidence | You produce and maintain it | Vendor provides attestations; you verify them and implement any controls assigned to you |
| Output quality testing | You build test sets and re-run them after each model change | Vendor tests across its customer base |
| Training and adoption | You create materials and run sessions | Vendor-led training and customer success |
| Proposal team time | Writers help specify, test and report bugs | Writers focus on proposals |
| Time to value | Months before the tool supports live RFPs | Weeks, depending on configuration |
| Exit cost | Sunk build cost if the project stalls | Data export and contract notice period |
Tip: Ask your finance team to model at least three years. In year one, building often looks cheaper. Maintenance, staff turnover and compliance work usually change that picture by year three.
For Federal Contractors
If you handle CUI, compliance shapes the build-or-buy decision
For government contractors, the build vs. buy question is also a compliance question. Proposal libraries for defense work can include controlled unclassified information (CUI), such as technical data, program details or export-controlled information carried over from contract work. Any tool that stores or processes that content falls within your compliance scope. The same is true for federal contract information (FCI), which is covered by FAR 52.204-21 and CMMC Level 1.
CMMC is now in contracts, but Phase 2 is on hold
The DFARS rule that puts CMMC requirements into DoD contracts took effect on November 10, 2025. Phase 1 requires CMMC Level 1 or Level 2 self-assessments, with annual affirmations, as a condition of award on applicable contracts. Phase 2, which would have made Level 2 certification by a third-party assessor (C3PAO) a condition of award for most contracts involving CUI from November 10, 2026, was suspended by the Department of Defense on July 13, 2026, pending a program review. No new start date has been announced, and DFARS 252.204-7012 and NIST SP 800-171 obligations are unchanged.⁶ ¹⁰
Cloud tools must meet FedRAMP Moderate or equivalent
Under DFARS 252.204-7012, cloud services that store, process or transmit covered defense information (which includes CUI) must meet the FedRAMP Moderate baseline or an equivalent. A December 2023 DoD CIO memo sets out 100% of the Moderate baseline controls implemented with no open POA&M items, an assessment by a FedRAMP-recognized third-party assessment organization (3PAO), a complete body of evidence made available to the contractor, and continuous monitoring validated annually. The contractor is responsible for verifying this.⁷
What this means for each path
| If you build | If you buy | |
| Scope | Your app, model API, data store and hosting all sit inside your compliance boundary | The vendor’s certified environment covers the platform; you still document it in your security plan and meet your customer responsibilities |
| Evidence | You produce and maintain the security documentation | Check the vendor’s listing on the FedRAMP Marketplace and request its customer responsibility matrix |
| Ongoing effort | Your CMMC assessment, continuous monitoring and any FedRAMP-equivalency evidence for cloud components are yours | You review the vendor’s attestations each year |
Key questions to ask before you build or buy
- Is the tool, and every service it relies on, FedRAMP Certified at Moderate or higher, or FedRAMP Moderate equivalent with a full body of evidence?
- Where do prompts, outputs and uploaded files live, and who can access them?
- Is our content used to train models that other customers use?
- Are CUI and export-controlled data accessed only by U.S. persons, and will the provider support DFARS 252.204-7012 incident reporting within 72 hours?
- Can we export our data and leave without losing our content library?
Make the call
A decision matrix and a five-minute self-assessment
| Factor | Lean toward building if… | Lean toward buying if… |
| Engineering capacity | You have a dedicated team with ongoing capacity | Engineers are committed to your core product |
| Process fit | Your proposal process is highly unusual | Your process follows common RFP and review stages |
| Time to value | You can wait months for results | You have major RFPs due this quarter |
| Compliance | You already run a CMMC Level 2-ready enclave on FedRAMP Moderate (or higher) cloud services | You need CUI-ready tooling without building the evidence yourself |
| Scale | A few writers need a drafting aid | Several teams, business units or regions need one platform |
| Ownership | Someone will own the tool for three years or more | No one has room to own a product long term |
Self-assessment: tick each statement that applies to you.
- We have live RFPs where faster first drafts would make a difference this quarter
- We handle FCI or CUI, or expect CMMC requirements in upcoming solicitations
- Our engineers have no spare capacity for a multi-year internal tool
- More than one team or region writes proposals
- We need review workflows, compliance checks and content governance as well as drafting
- We can’t name a long-term owner for an internal build
If you ticked three or more, buying purpose-built software is likely the lower-risk path.
What to look for in proposal AI
Seven questions to ask any vendor, or your own build team
1. Does it learn from your content?
It should draw on your past proposals, case studies and approved answers, and show where each answer came from.
2. Does it support the whole proposal workflow?
Look for help with RFP analysis, compliance matrices, drafting, review and final edits, as well as generation.
3. Is it secure and compliant for your work?
Check FedRAMP certification class or authorization level and data handling terms.
4. Does it connect to where your content lives?
Look for integrations with your document repositories and collaboration tools.
5. Can you measure the results?
Ask for customer evidence on efficiency and outcomes, with sources.
6. Who keeps it current?
Ask how often it’s updated and how model changes are tested before release.
7. What happens if you leave?
Confirm you can export your data and content library.
Where AutogenAI fits
AutogenAI is purpose-built for proposal teams. It learns from your organization’s past proposals and knowledge assets to produce drafts in your voice. It supports the full workflow, from RFP analysis to final review.
AutogenAI Security
FedRAMP® Certified, Class D (High), via Palantir FedStart
Results
Serco reported an 85% efficiency gain and 5% revenue growth after adopting AutogenAI.⁸ Independent research by MH&A found that AutogenAI customers grew revenue 12.4% from FY23 to FY24, while comparable organizations without it saw a 7.1% decline.⁹
Bring your build plan to a demo
Already scoping an internal tool? Walk us through it. We’ll compare it with AutogenAI on cost, time to value and compliance, so you can make the call with the full picture.
References
- McKinsey & Company. (2026, August 25). The state of AI in 2026: On the road to ROI. Survey of 1,719 respondents, May 4 to June 8, 2026.
- Estrada, S. (2025, August 18). MIT report: 95% of generative AI pilots at companies are failing. Fortune. Reporting on MIT NANDA, The GenAI Divide: State of AI in Business 2025.
- Tully, T., Redfern, J., Das, D., & Xiao, D. (2025, December 9). 2025: The state of generative AI in the enterprise. Menlo Ventures.
- Gartner. (2024, July 29). Gartner predicts 30% of generative AI projects will be abandoned after proof of concept by end of 2025.
- Gartner. (2025, June 25). Gartner predicts over 40% of agentic AI projects will be canceled by end of 2027.
- Cooley. (2025, September 25). DoD releases long-awaited final rule implementing Cybersecurity Maturity Model Certification contract clause.
- Secureframe. FedRAMP equivalency for CMMC: The DoD memo explained. Summarizing the DoD CIO memo of December 21, 2023.
- Ibrahim, M. (2025, May). Comparing the revenue growth of AutogenAI customers with non-AutogenAI using peers. MH&A.
- WilmerHale. (2026, July 20). Pentagon suspends CMMC Phase 2 requirements and launches review of cybersecurity certification program. https://www.wilmerhale.com/en/insights/client-alerts/20260720-pentagon-suspends-cmmc-phase-2-requirements-and-launches-review-of-cybersecurity-certification-program
