The Go/No-Go Conversation Nobody Wants to Have

Does NYU Have an Architecture Program?

NYU does not operate a dedicated School of Architecture in the way Harvard GSD or Carnegie Mellon does. Architecture-related programs exist, but they live within the Department of Art History, part of the College of Arts & Science¹. The programs are academic and research-oriented— not professional.

NYU's architecture offerings are academic, not professional— the programs focus on history, preservation, and urban design rather than leading to licensure. The MA in Historical and Sustainable Architecture is a research degree requiring an original thesis under faculty supervision². It's not a professional M.Arch, which is the degree most licensing boards require for architectural practice.

If you're evaluating NYU for a professional path in AEC, verify your goals before applying. The programs prepare graduates for careers in historic preservation, real estate development, and graduate study— not for stamping drawings.

What Is a Go/No-Go Decision— and Why It Matters in AEC

In AEC, a go/no-go decision is the difference between a disciplined proposal pipeline and a resource-draining bid machine— a formal evaluation of whether to pursue an RFP or walk away.

A go/no-go evaluation determines one of three outcomes:

• Go— strong strategic and delivery fit; commit proposal resources
• Conditional Go— pursue with clarifications, scope confirmation, or additional intel
• No-Go— low probability or poor commercial fit; decline formally

The business case for formalizing this process is compelling. According to iBeam research³, the average AEC firm wins approximately 25% of what it bids— one win per four proposals. That means 75 cents of every proposal dollar funds losses. Analysis of 1,000+ commercial bids confirmed the same 25% average, with winning bids typically carrying 12-18% margin⁴.

A go/no-go decision process is the mechanism AEC firms use to control their bid-hit ratio— directly influencing their overall win rate. One structural engineering firm improved its win rate from 34% to 78% after implementing organized proposal tracking and qualification criteria⁵. And that's not marginal. Only 40% of AEC firms have a formal go/no-go process— which means most firms are leaving their win rates to chance⁵.

The question isn't whether to have a process— the data makes that case. The question is what to evaluate. Here are the six criteria that consistently separate good pursuits from expensive losses.

The 6 Core Criteria for Your Go/No-Go Evaluation

AEC firms use six core criteria to evaluate go/no-go decisions. While frameworks vary in length (some go to ten criteria), industry consensus clusters around these six essential factors⁶.

1. Strategic Alignment— Does this opportunity advance the firm's 3-5 year goals? If the firm hasn't articulated its strategy, this criterion can't be scored honestly.
2. Capability Fit— Can the firm actually deliver the required services? Honest assessment required— not wishful thinking about what the team could maybe do.
3. Prior Involvement— Was the firm consulted during RFP development? Existing client relationship? Incumbency shifts win probability in ways that proposal quality alone can't overcome— knowing where you stand in the client relationship before committing to a proposal is a better predictor than the proposal itself⁷.
4. Competitive Position— Who else is pursuing this? What's the firm's historical win rate against those specific competitors? External data here is critical.
5. Financial Expectations— Do the pricing terms align with the firm's margin requirements? A project you win at unprofitable rates is a project that drains the firm.
6. Logistics / Capacity— Does the team have bandwidth to produce a quality response on the timeline? Overcommitting on proposals leads to overcommitting on delivery.

Extended frameworks add factors like future opportunity potential, marketing strategy alignment, and risk profile⁸. But mastering these six first is the priority. When your firm has been chasing the same types of opportunities for years without improving its win rate, you can't read the label from inside the bottle— a formal framework gives you the external perspective to see what you've been missing.

Check your AI decision framework for your business as well— the same structured evaluation logic applies to technology investments as it does to project pursuits.

Having the criteria is step one. Knowing how to turn them into a decision— especially when the answer isn't obvious— requires a scoring system.

Three Ways to Score Your Go/No-Go (and When to Use Each)

The three most common scoring systems for go/no-go decisions are color-coded (green/yellow/red), numerical (1-5 scale with weighting), and weighted calculator (17-point evaluation). Each fits a different firm culture and decision complexity.

Color-Coded System⁷: Green = clear go. Yellow = conditional discussion required. Red = no-go. Intuitive, reduces false precision, facilitates conversation. Best for firms formalizing for the first time.

Numerical Scoring (1-5 scale)⁶: Each criterion scored 1-5 with weighted importance applied. Example weighting: Profitability (30%), Strategic Fit (25%), Capacity (20%), Client Relationship (15%), Competitive Position (10%). Produces quantifiable scores that allow comparison across opportunities and data analysis over time.

Weighted Calculator (17-point)⁹: The most rigorous system. Total score maps to Go / Conditional Go / No-Go thresholds, with defined escalation for strategic overrides— reserved for roughly 5% of RFPs, with documented rationale required.

A color-coded system reduces false precision. A numerical system enables data analysis over time. Most firms need to start with one before graduating to the other.

How to choose: if your team hasn't formally scored anything before, start with color-coded. Once leadership sees the patterns and buys in, move to numerical. Decision authority should match project value⁸:

Having a system matters less than using it consistently. Most firms know what a good go/no-go process looks like— they just don't do it. Here's why.

Why Most AEC Firms Still Get This Wrong

Despite clear evidence that formal go/no-go processes improve win rates, only 40% of AEC firms have implemented them⁵. The reason isn't ignorance— it's a cluster of predictable failure modes that appear in firms of every size.

1. Pursuing every opportunity— No qualification filter means all proposal resources get consumed without strategic discipline. Most expensive mistake on the list.
2. Underestimating proposal costs— True cost includes estimating labor, graphics, compliance review, and management time— not just document hours. A lost bid is 100% sunk cost¹⁰.
3. Ignoring the competitive landscape— Pursuing opportunities where a competitor has stronger relationships or incumbency. Low win probability regardless of proposal quality⁷.
4. Missing strategic alignment— Chasing short-term revenue that doesn't advance firm goals. You can win the project and lose the strategy.
5. Overestimating team capacity— Committing proposal resources before honestly auditing availability. Leads to overworked teams and quality decline⁶.
6. Ignoring client history— Clients with payment problems or scope creep patterns turn profitable contracts unprofitable in execution⁷.
7. Ad-hoc override culture— Decision-makers bypassing the process without documented rationale. Undermines discipline firm-wide⁶.

Also worth noting: approximately 15% of proposals get disqualified historically for compliance issues— missing requirements that a go/no-go stage should have surfaced¹¹. But the upstream failure is almost always the same: the evaluation never happened, or someone overrode it without documented rationale.

The most expensive proposals are the ones that never should have been submitted. Understanding the hidden costs of AI projects— or any major investment— requires the same discipline: qualify before you commit. Knowing the failure modes is useful. Knowing how to build a process that holds is the next step.

How to Actually Implement a Go/No-Go Process

Implementing a go/no-go process requires four building blocks: strategic clarity, a scoring system, defined decision authority, and historical tracking. Firms that try to skip the first usually abandon the rest.

1. Start with strategic clarity— Leadership must articulate where the firm is headed before criteria can be calibrated. Without knowing your goals and how you define success, it's impossible to know which projects are worth pursuing⁹.
2. Choose your scoring system— Start simple. Color-coded or basic numerical scoring is enough to create discipline. Match the tool to the culture.
3. Define decision authority— Assign roles by project value tier. Create an escalation path for exceptions— with documented rationale required for any override⁶.
4. Enforce the 48-hour timeline— Complete the evaluation within 48 hours of RFP receipt. Single owner. Calendar deadline⁹.
5. Track historical performance— Maintain a record of go/no-go decisions and their outcomes. After 6-12 months, patterns emerge that sharpen future decisions¹².
6. Validate retroactively— Before rolling out the new framework, score your last 10 RFPs against it. This shows leadership what could have been avoided, builds the case for changing the process, and reveals calibration gaps before they affect live decisions⁹.

One area where firms are starting to see real advantage: using AI to make the go/no-go evaluation faster and more accurate.

How AI Can Sharpen Your Go/No-Go Decisions

AI doesn't replace the go/no-go judgment call— but it can make the inputs faster and more accurate. Fifty-three percent of A&E firms now use AI tools, up from 38% the previous year¹³. But most aren't using it for decision support yet— only 27% report using AI for automation and decision-making¹³.

Four practical AI capabilities worth knowing:

• Pattern recognition— Analyzes historical proposal data to surface win/loss correlates you'd never find in a spreadsheet— which project types, client categories, or fee ranges your firm consistently wins or loses
• Capability gap analysis— Scans project databases to verify relevant experience for proposed work before resources are committed
• Workflow automation— Scores proposals against defined criteria automatically, reduces manual data entry, provides real-time pipeline visibility
• Content assembly— Speeds proposal development, making "can we deliver on this timeline?" easier to assess upfront

Purpose-built platforms like Unanet ProposalAI and ContraVault have AEC-specific features— including SF330 support, credential databases, and compliance tracking (feature availability varies by platform). General-purpose AI tools work well for research and rapid drafting, but lack those specialized capabilities. See how engineering firms use AI for proposals for a deeper look at practical applications.

AI's best role in go/no-go isn't to make the call— it's to cut the research time and surface patterns in your historical data that a spreadsheet would miss. The go/no-go decision still requires human judgment on strategy, relationships, and firm culture. That part doesn't automate.

The go/no-go frameworks are mature and widely accessible. Most AEC firms aren't losing to better competitors on proposal quality— they're losing to firms that decided sooner which opportunities weren't worth chasing. The barrier to a better win rate is adoption, not methodology.

Three things to do this week: (1) Score your last five RFPs against the six criteria above. (2) Identify which failure mode most frequently describes why your firm went for them anyway. (3) Assign a single owner and a 48-hour deadline to your next evaluation.

If your firm is evaluating where AI strategy fits into proposal operations and BD, an experienced implementation partner can shortcut the learning curve. Dan Cumberland Labs helps AEC and professional services firms integrate AI into their core workflows. You can also explore how to start measuring AI success in your operations to track the impact from day one.

FAQ

What is a good win rate for AEC firms?

The industry baseline is approximately 25%— one win per four bids submitted³. Hard competitive bids typically run 10-20%; negotiated or selective bids run 30-50%. Analysis of 1,000+ commercial bids confirmed the 25% average⁴. Firms with formal go/no-go processes consistently outperform the baseline— the 34% to 78% improvement documented in one structural engineering firm is the extreme case, but directional improvement is well-documented.

What is a bid-hit ratio in construction?

A bid-hit ratio measures proposals submitted per contract won. The industry standard is approximately 5:1— one win per five bids¹⁴. Subcontractors bidding public work should not exceed 10:1 or 11:1. Less than 6% of construction companies currently track this metric, despite its direct connection to business development strategy.

How much does it cost to submit an AEC proposal?

Private sector architecture fees typically run 7-10% of construction costs; high-end residential can reach 10-20%¹⁰. Proposal development itself consumes significant labor that is 100% sunk cost if the bid is lost. That's what makes qualification critical— you're not just deciding whether to pursue a project, you're deciding whether to invest real money in a lottery ticket with known odds.

Why do AEC firms fail to implement go/no-go processes?

The most common barriers are lack of strategic clarity (firms haven't defined their goals, so criteria can't be calibrated), sales culture resistance (formal "no" feels like lost business), and manual process friction (spreadsheet-based systems are tedious and lack visibility)⁵. The frameworks are mature and accessible. The barrier is adoption, not methodology.

References

1. New York University, "Urban Design and Architecture Studies — NYU Arts & Science" (2026) — https://as.nyu.edu/departments/arthistory/programs/undergraduate/urban-design-and-architecture-studies.html
2. New York University, "Master of Arts in Historical and Sustainable Architecture" (2026) — https://as.nyu.edu/departments/arthistory/programs/graduate/master-of-arts-in-historical-and-sustainable-architecture.html
3. iBeam, "How Construction Bid Win Rates Are Measured" (2025) — https://www.ibeam.ai/insight/what-is-a-good-construction-bid-win-rate
4. Smart Movers Club, "We Analyzed 1,000+ Commercial Bids: Here's How to Win in 2026" (2025) — https://smartmoversclub.com/we-analyzed-1000-commercial-bids/
5. HSO, "Maximizing Proposal Win Rates for AEC Firms" (2025) — https://www.hso.com/blog/maximizing-proposal-win-rates-for-aec-firms
6. Arphie, "Best Practices Series: The Go/No-Go Decision" (2025) — https://www.arphie.ai/blog/best-practices-series-the-go-no-go-decision
7. Treble Hook, "Mastering the Go No Go Decision in AEC Projects" (2025) — https://treblehook.com/blog/mastering-the-go-no-go-decision-in-aec-projects/
8. CRM Software Blog, "AEC go/no-go automation for better pursuit decision making" (2024) — https://www.crmsoftwareblog.com/2024/01/aec-firms-can-make-smarter-decisions-about-pursuing-work-by-automating-the-go-no-go-process/
9. Unanet, "Expert Advice: How to Make the Right Go/No-Go Decision" (2025) — https://unanet.com/blog/expert-advice-how-to-make-the-right-go-no-go-decision
10. Monograph, "Architectural & Engineering Fee Estimating Guidelines" (2025) — https://monograph.com/blog/architectural-engineering-fee-estimating-guidelines
11. ContraVault AI, "7 Common Mistakes That Kill AEC Proposal Win Rates and How to Fix Them" (2025) — https://www.contravault.com/blog/7-common-mistakes-that-kill-aec-proposal-win-rates-and-how-to-fix-them
12. Monograph, "Proposal Tracking Systems: A Complete Guide for A&E Firms" (2025) — https://monograph.com/blog/proposal-tracking-systems-complete-guide-ae-firms
13. Flowcase, "AI Tool for AEC Professionals: Top Software Guide 2026" (2026) — https://www.flowcase.com/blog/best-ai-tools-for-aec-professionals-in-2026
14. Down To Bid, "Bid-Hit Ratio: Every Construction Company's Drive To Success" (2025) — https://downtobid.com/blog/bid-hit-ratio