FUND FEWER FALIURES. EXECUTE MORE WINNERS. STRATEGIC INTELLIGENCE THAT FINALLY SCALES
KNOWLEDGE ARCHITECTURE THAT PRODUCES DECISION-READY INTELLIGENCE
We've all been there, frustrated, watching initiatives struggle in entirely preventable ways because nobody had the time to ask the hard questions before commitment, not after.
GreenlightIQ is decision intelligence for strategic planning that systematically integrates fragmented expert knowledge across your organization, ensuring strategic decisions are grounded in verified capabilities rather than optimistic assumptions. We draw out what experts know but struggle to articulate surface conflicts before they become failures, and compare proposed initiatives against your organization's actual capabilities—in days, not weeks.
Where traditional AI makes incomplete information look complete, we refuse to synthesize until the foundation is solid, forcing the evidence-based assessment that prevents costly failures caused by committing to approaches teams can't execute.
The result: Stop funding initiatives that aren't ready, and before budgets and timelines are wasted. Accelerate the ones that are, with confidence they'll succeed. Catch portfolio risks while they're still addressable, not after they've become expensive failures.
THE PROBLEM WITH PORTFOLIO DECISIONS
Portfolio decisions take too long and still go wrong, not only because organizations lack information, but because they lack systematic ways to handle complexity.
Leadership makes decisions based on analyses they can't fully trust. Teams present initiatives using inconsistent frameworks that make meaningful comparison impossible. Smart people interpret the same strategic direction differently, creating an illusion of alignment that unravels during execution. Assumptions hide inside confident projections because there's no structured way to surface them. Organizational capability gaps go unexamined because nobody's asking whether the company can actually execute what's being proposed.
These problems compound: weak analytical rigor plus hidden assumptions plus capability mismatches equals predictable failures nobody flagged upfront. Decisions get delayed while teams scramble for more data that doesn't address the underlying issues. When initiatives finally get approved, months get wasted reverse-engineering what was actually committed to because common understanding was never established.
The post-mortems always reach the same conclusion: "We should have seen this coming."
WHY I BUILT THIS
I've spent my career in senior leadership roles introducing new technologies to market, product launches, system implementations, market expansions. And across decades, across multiple companies and industries, I kept watching the same failure patterns play out.
The Pattern I Couldn't Escape
Leadership would make a decision, launch an adjacent product, enter a new market, upgrade a core system, expand into enterprise. Everyone would nod. Everyone thought they understood. The initiative would get approved, timelines would be set, teams would be assigned.
Then months into execution, the same problems would surface: everyone had walked away from that decision with completely different understandings of what we'd committed to. Engineering thought we were doing X. Product thought we were building Y. Operations had planned for Z. Nobody had defined in concrete terms what the strategic decision actually meant for implementation.
Infrastructure issues we'd assumed were simple turned out to be major blockers. Staffing requirements we thought were adequate weren't, we'd never properly scoped what skills were actually needed. Performance concerns we knew about from customer feedback didn't get taken seriously because we'd relied on anecdotal evidence instead of rigorous analysis.
And here's what made it so frustrating: these weren't isolated incidents. This happened repeatedly. Smart, experienced professionals reasonably interpreting the same strategic direction differently because we'd never forced the critical details into the open.
The Aha Moment
I started studying how humans actually behave in corporations, how decisions get made, how information flows, how expertise stays trapped in people's heads. The patterns were consistent across industries, across types of initiatives. There were universal questions that needed answering, regardless of whether you were launching software or manufacturing products.
Then I realized: AI was exceptionally good at exactly what organizations were terrible at. Understanding goals. Probing assumptions. Synthesizing large, disparate sets of information into coherent analysis. Making tacit knowledge explicit.
The pieces clicked: if I could structure the right questions based on how humans actually execute complex work, AI could systematically capture strategic intelligence that had never been accessible before, then turn it into the decision-ready business cases and risk analyses leadership actually needs.
Building What Should Have Existed
That's why GreenlightIQ exists: to create the knowledge architecture organizations need to capture strategic intelligence systematically.
It's like the technology approach used to analyze supply chains, financial networks, and intelligence data, applied to the strategic decisions that shape your portfolio. Universal questions grounded in human behavior. AI that probes deeper, synthesizes completely, and produces execution-ready content.
So we stop wasting months discovering problems that were knowable from day one.
Steve Tyrrell, Founder & CEO, GreenlightIQ
WHAT CHANGES WITH GREENLIGHTIQ
Challenge: Lack of Common Understanding
Leadership approves an initiative. Everyone nods. But months later, you discover Engineering, Product, and Operations were working toward meaningfully different interpretations of what was decided.
After GreenlightIQ: Teams document what they know, what they're assuming, and what capabilities are required in structured, explicit terms. Leadership sees exactly what's being committed to, scope boundaries, infrastructure needs, underlying assumptions, creating genuine common understanding, not just nodding agreement. Misalignment surfaces during planning, not months into execution.
Challenge: Inconsistent Analytical Rigor
Some teams deliver 50-page detailed analyses. Others deliver 10-slide optimistic projections. Leadership can't compare them fairly because every team uses different frameworks, different assumptions, different levels of detail.
After GreenlightIQ: Every initiative goes through the same structured evaluation process. Teams distinguish verified facts from stated assumptions from acknowledged unknowns using consistent frameworks. Leadership can finally compare initiatives on equal footing, seeing which have solid foundations and which are built on hope, making informed trade-offs based on substance, not presentation quality.
Challenge: Hidden Assumptions
Teams build analyses on assumptions they haven't made explicit, often because they've internalized them as facts or because surfacing uncertainty feels like admitting weakness. "The platform can handle it." "Customers will adopt quickly." "The vendor will deliver on time." These assumptions get embedded in timelines and financial models, treated as givens. Nobody challenges them because nobody realizes they're assumptions until reality proves otherwise.
After GreenlightIQ: The structured evaluation process forces assumptions into the open before they can hide. Teams must explicitly state what they're assuming and why. AI guidance helps identify where claims lack evidence or where reasonable interpretations could diverge. Leadership sees not just the plan, but the assumptions holding it together—and can validate critical ones upfront or consciously accept the risk, rather than discovering assumption failures during post-mortems.
Challenge: Organizational Capability Mismatches
A Scaling company proposes enterprise sales. A team with basic data infrastructure commits to real-time analytics. Nobody asks whether the organization can actually execute what's being proposed. The initiative gets funded. Then fails because capabilities didn't exist.
After GreenlightIQ: Organizational readiness gets assessed before every initiative. We evaluate whether your current capabilities, infrastructure, talent, processes, data systems, match what the initiative demands. Capability gaps get flagged explicitly: "This requires Enterprise-level data infrastructure; you're currently Scaling-level." Leadership can address gaps first or accept the risk knowingly, not discover it during failure.
OUR SOLUTION
Our Approach to AI: Augmenting Human Judgment, Not Replacing It
GreenlightIQ's AI doesn't generate your analysis, it challenges your thinking, demands evidence, then synthesizes your quality inputs into coherent business cases.
Most AI initiatives fail because they try to automate human judgment or generate content from weak inputs, and people accept polished AI output without critical evaluation. Generative AI is seductive: it makes weak ideas sound confident, lacks your specific context, and can be convincingly wrong. We've designed GreenlightIQ differently. Our AI never generates your analysis, you do. It functions as a sage advisor, helping you think more rigorously by asking better questions, offering different perspectives, and evaluating the quality of your reasoning. You must distinguish what you know from what you assume, provide evidence for claims, and remain accountable for every assertion. The AI won't let you be lazy or delegate your thinking, it demands rigor and rewards honesty. Only after you've generated quality information does AI do what it genuinely excels at: pulling together complete, coherent business cases and risk analyses by synthesizing the structured, high-quality content you've provided.
Why This Architecture Works
Guided AI ensures your thinking is sound, then synthesizes across initiatives to surface portfolio-wide patterns and risks humans can't detect.
Traditional business planning tools are templates that accept whatever you put in them. Traditional AI tools generate content that sounds good but often isn't. We combine structured human accountability with AI-powered synthesis. During intake, our AI guides you through systematic evaluation, forcing explicit statements about evidence, assumptions, and unknowns while assessing answer quality across multiple dimensions. This ensures the source material is reliable before any synthesis happens. Then AI does the heavy lifting: integrating your organizational capability assessment with your initiative analysis to identify patterns, surface compound risks, and create comparable portfolio views. The result is business cases built on verifiable thinking, not polished guesswork, where every claim traces back to human judgment that was systematically evaluated, and AI synthesis reveals insights no human could spot manually across dozens of initiatives.
A System Designed for Human-AI Synergy
Humans bring judgment and context, AI brings systematic evaluation and pattern recognition, we combine both to produce consultant-grade strategic intelligence at software speed.
We built GreenlightIQ from the ground up around a fundamental insight: human judgment and AI capabilities have complementary strengths that most tools either ignore or try to replace rather than combine. Humans bring situational knowledge, nuanced judgment, and accountability, but struggle with systematic evaluation at scale and often miss critical details under time pressure. AI excels at consistent evaluation, pattern recognition across initiatives, and relentless questioning—but lacks context and can't make judgment calls. Our architecture maximizes both. The AI asks deep, probing questions that draw out important details teams typically overlook, then evaluates answer quality across multiple dimensions while humans provide all content and remain accountable for every claim. We go deep on initiative specifics and assess organizational capabilities systematically, ensuring the information foundation is solid. Then AI synthesizes across that high-quality base—identifying compound risks, surfacing patterns, creating portfolio comparisons, producing the level of analytical rigor and completeness previously reserved for expensive strategy consulting teams. This isn't AI automation replacing human work; it's intelligent architecture that gets the best from each.
HOW IT WORKS
1. Baseline Your Organization (One Time)
Complete our Enterprise Capability Survey to establish how your organization actually functions, your processes, systems, talent, and capacity. This becomes your readiness baseline, used to assess every future initiative against reality, not hope.
2. Capture Initiative Intelligence (Per Initiative)
For each initiative, answer our strategic questions designed from decades of studying how humans execute complex work. AI probes deeper based on your responses, systematically capturing what you know, what you assume, and what's still uncertain.
3. AI Analysis Against Reality
Your initiative intelligence meets your organizational baseline. AI analyzes the match: Is your organization actually ready? Where are the gaps? What's being underestimated? Every claim is evaluated against both your stated plans and your proven capabilities.
4. Decision-Ready Output
AI generates your complete business case and risk analysis, what's strong, what's weak, what's ready, what's not. Fully documented, grounded in your organization's actual capability, comparable across initiatives. Delivered in days, not weeks.
WHO THIS IS FOR
Built for Organizations that Intend to Learn From Failure
You'll succeed with GreenlightIQ if:
Post-mortems often conclude "we weren't organizationally ready" or "the risks were visible early but not surfaced"
Leadership asks "what are we missing?" not just "will this work?"
You value preventing bad investments over preserving team optimism
Culture treats intellectual honesty as strength, not weakness
You're willing to change how decisions get made, not just how documents look
This isn't the right fit if:
Admitting capability gaps is culturally threatening
Surfacing uncertainty is interpreted as lack of commitment
Political dynamics require appearance of certainty regardless of reality
Speed-to-decision always trumps quality-of-decision
You need better templates, not better thinking
Beta launches January 2026. Limited design partner spots available.
We're selecting organizations that have experienced costly initiative failures and are committed to structural improvement, not just faster documentation.