SPEED-MC² is the seven-discipline revenue qualification framework built for the deals you're actually trying to close in 2026 — not the ones your playbook was written for in 2018.
SPICED taught your AEs to diagnose before they pitched. Good. MEDDPICC taught your managers to qualify before they forecasted. Also good. If you run a B2B revenue team, you probably have one of them embedded in your CRM, your enablement, your QBRs, and your forecast calls. So do your competitors. So do the prospects you're selling to.
Here's the uncomfortable part. Both frameworks were designed for a market where deals closed on feature parity and died in procurement. Where "is there budget?" was a sufficient qualifying question. Where the competitor on the whiteboard was another logo you recognized. Where the implementation risk you had to manage was whether IT would get the integration done on time.
Look at where your deals are actually dying this year. They're dying in the AI ethics committee you didn't know existed until week twelve. They're dying because the innovation budget that funded the POC doesn't renew in January. They're dying because your champion's CTO ran a weekend experiment with an open-source model and convinced the CEO they can build it in-house. They're dying because the success criteria you agreed on in the kickoff got quietly rewritten after the first model review and nobody told you.
None of those failure modes are in SPICED. None of them are in MEDDPICC. Your team is running qualification plays against a board that changed shape two years ago and has kept changing since.
ValueOrbit runs engagements on whatever methodology your team already trusts. If your AEs are trained on SPICED, we extend SPICED. If your forecast is structured around MEDDIC or MEDDPICC, we work inside those disciplines. If your pipeline still runs on BANT because that's what the CRM was configured for in 2015, we will work with that too, even when we disagree with it. Methodology replacement is not a prerequisite for working with us.
What we recommend, though, is SPEED-MC² — our own proprietary seven-discipline framework, purpose-built for AI-era B2B sales. We built it because the frameworks we just listed all miss the same three things: data governance and AI ethics review, budget origin, and the internal champion test that matters when the real competitor is "let's build it ourselves."
Most engagements start with the client's current methodology. Most end with SPEED-MC² running underneath it. Both are fine. What's not fine is pretending a 2010s framework still covers 2026 deal dynamics. It doesn't — and your close rate knows.
Supported out of the boxSeven disciplines. Proprietary to ValueOrbit. Engineered around the specific deal dynamics that determine close rates in AI, data, and SaaS sales in 2026 — not bolted onto a 2010s framework as an afterthought.
It preserves what SPICED got right: lead with diagnosis, not interrogation. It preserves what MEDDPICC got right: qualify the deal mechanics before you forecast them. It adds what both miss: governance, budget origin, and the champion test that matters in AI deals.
Business context, plus the AI-maturity signals that determine whether the deal is even buildable. Data readiness. Existing stack. Prior POC scars. You're not just qualifying the company — you're qualifying whether they've been burned before, and by whom.
Pain named by the buyer, in the buyer's words, and quantified in euros or dollars per quarter. Not "they're struggling with pipeline visibility." Eight hundred thousand euros of missed forecast per quarter, traced to three specific process gaps. Pain that can't survive a budget freeze isn't pain — it's a preference.
Who signs, and which P&L the money comes from. Innovation budgets expire. CEO discretionary pools shift quarterly. Line-of-business budgets get protected; shadow IT budgets get clawed back. Knowing whose money is funding the project is a stronger survival signal than knowing the budget exists.
Model criteria — accuracy, latency, data residency, explainability — agreed explicitly. And PoV success criteria locked in writing before the first model is trained. The single biggest failure mode in AI sales is a buyer who moves the goalposts mid-POC. This discipline prevents it.
Commercial paper process merged with AI-specific governance into one discipline. Procurement. Legal. Security review. DPIA. Model risk. AI ethics committee. In AI deals, the deal doesn't die in procurement — it dies in one of these. Qualifying all of them in week one, not week ten, is the difference between a closed deal and a forecasted one.
Two clocks running in parallel. Leading metrics — did the PoV hit the criteria you locked in Discipline Four? Lagging metrics — did it produce the business outcome you promised the economic buyer? Vendors who only measure one get surprised. Vendors who measure both forecast accurately.
Your champion isn't fighting another vendor. They're fighting their own CTO, who wants to build it. Their own CFO, who wants to wait for the native GenAI release. Their own board, who just read an article about model commoditization. A champion who can't defend the buy decision against build-vs-wait-vs-buy isn't a champion. They're a contact.
In AI deals, procurement is the easy part. The real blockers are the security review, the DPIA, the model risk assessment, and the AI ethics committee — and any one of them can kill a deal you've already forecasted. SPEED-MC² merges paper process with data governance into a single discipline, which forces your team to surface every reviewer in the first discovery cycle. You either qualify the governance path early, or you find out in week twelve that there was never a path at all.
MEDDPICC asks whether the budget exists. SPEED-MC² asks whose budget it is. Innovation pools that expire, CEO discretionary funds that shift when the CEO changes focus, shadow IT budgets that get clawed back at fiscal year end — these are the funding sources AI projects actually get paid from, and they behave differently from core operating budgets. Qualifying the origin lets you predict, in week three, which deals will still have funding in week thirty.
In traditional enterprise sales, the competition is the vendor on the other side of the shortlist. In AI sales, the primary competitor is almost always internal: the data science team that wants to build it, the CFO who wants to wait for the next model release, the VP of Engineering who read an AI-native stack thread on a Saturday. A champion who hasn't been tested against those three conversations will lose the deal in the final committee meeting — and you'll never know why.
Every AI deal runs a PoV. Every PoV ships with success criteria. The failure mode is universal: criteria agreed verbally in the kickoff, quietly rewritten after the first model review, and used against you at the commercial close. SPEED-MC² requires criteria locked in writing before a single model is trained, and measures two metric clocks in parallel — leading (did the PoV succeed?) and lagging (did the business outcome follow?). Vendors who run this discipline close AI deals. Vendors who don't, forecast them.
SPICED, MEDDIC, MEDDPICC, and BANT are all legitimate instruments for the markets they were designed for. SPEED-MC² is designed for a different market. Here's how they map.
| Dimension | SPICED | MEDDPICC | BANT | SPEED-MC² |
|---|---|---|---|---|
| Built for | Pre-AI SaaS | Pre-AI enterprise | 1960s field sales | AI-era B2B |
| Governance | Not addressed | Late-stage paper process | Not addressed | Week-one discipline |
| Budget qualification | Implicit | Budget existence | Budget existence | Budget origin + P&L |
| Champion test | Omitted by design | Internal advocate | Not addressed | Survives build-vs-wait-vs-buy |
| PoV success criteria | Not addressed | Not addressed | Not addressed | Locked in writing pre-kickoff |
| Consultative DNA | Strong | Moderate | Weak | Preserved |
Your AEs are already running a methodology. It's either SPICED, MEDDIC, MEDDPICC, BANT, something hybrid that your enablement team cobbled together, or — more likely than you'd like to admit — nothing at all beyond whatever their last manager taught them. Whichever it is, it's probably leaking exactly where we said it leaks: in governance, in budget durability, in the champion who can't defend the buy, in the POC that gets rewritten mid-stream.
We'll work with whatever methodology your team runs on today. We'll recommend SPEED-MC² when the deal dynamics warrant it — and for AI, data, and modern SaaS deals, they almost always do. SPEED-MC² is proprietary to ValueOrbit. If you want your team running it, you run it with us.
Twenty years of enterprise GTM leadership. Two billion euros in revenue forecasted. One methodology, purpose-built for the market that actually exists.
SPEED-MC² is a proprietary revenue qualification methodology developed by ValueOrbit AB, Stockholm. Seven disciplines, engineered for AI-era B2B sales. Trusted by revenue leaders across EMEA and MENA.