INBOX INSIGHTS: When Is a Deal Dead, AI Software Clean Rooms (2026-10-21)

INBOX INSIGHTS: When Is a Deal Dead, AI Software Clean Rooms (2026-10-21) :: View in browser

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When is a deal dead?

Despite all the advances with AI, I feel like I am forever behind on everything. And with a smaller company, that feeling can compound fairly quickly. Like a lot of us right now, I’m trying to figure out how to increase our revenue without burning people out. I decided to do some deep research on the members of the C-suite that I don’t have alongside me, but that I have to show up and be, regardless. I started with the three that seemed to make the most sense for me right now: Chief Revenue Officer, Chief Growth Officer, and Chief Marketing Officer. I wanted to know what I was missing, and what those roles could bring to the table, and what gaps they would fill.

After going back and forth with Claude for a couple of hours, we focused in on what the CRO would say, and the CRO unsurprisingly was focused on my CRM data. I’ve talked previously about how we chose a platform we could afford (at the time) instead of doing longer-term thinking about what we actually needed. Fast-forward to almost a decade later, and I’m still trying to “make it work” without having to do a large data migration.

The virtual CRO narrowed in on one specific data point we had developed in our sales funnel: parked deals. To us, a parked deal is a possible customer who is interested, maybe has a scope to review, but can’t make it happen right now. In theory, it sounds reasonable. When you’re trying to do projections, it’s not super helpful.

The question the CRO asked was a fair one: at what point should a parked deal be considered dead? I didn’t have an answer. Nobody at Trust Insights had an answer because, in two years of parking deals, none of us had ever written the rule down. We dealt with each parked deal as we had time.

When I really looked at what was happening, it was less about the pipeline and more about the fear of closing it out. Why? We close out deals all the time; what was the big deal with these ones? Because closing them out meant they were really done. (Are they ever really, though?) It meant one more loss on the board, one more lost dollar that we’d never see.

I took the weekend to pick it all apart, write down everything I was thinking without needing to take action, and just let it sit. On Monday, I compiled all that information and realized the answer was fairly simple. I needed some rules. Writing the rule took about twenty minutes, which means that I spent two years with a large part of our data missing.

You can’t hand an agent a decision you never made

Every time one of us looked at a parked deal, we made a judgment call. That call was based on our history with the prospect, not on rules that we could actually measure. That meant that the data that I was trying to use to forecast revenue wasn’t good. We were able to get away with it because we would tell ourselves we’re a small boutique firm, we know our numbers, and we have personal relationships. Guess what you can’t build a business around? OK, you can, but not well.

I thought my next step was an agent that would fix these issues for me. Well, an agent doesn’t have that context, which leaves it two options. It can stop and ask you, which is what mine did, and which is the good outcome because it fails loudly and you find the gap on day one. Or it can guess. When a model guesses, it treats the guess as the rule and applies it to every deal in the pipeline with complete confidence, and you won’t find out until a prospect you were sure was still warm gets a breakup email.

That second outcome is the one I’d worry about, because nothing about it looks broken.

The question isn’t whether an agent can watch your pipeline. It can. The question is whether you can tell it what it’s watching for.

Platform was never the hard part

This is where I’d send you back to the 5P Framework by Trust Insights™ (Purpose, People, Process, Platform, Performance). The tool I built this weekend is Platform, and it took the least thought of anything involved. The rule about when a deal is dead is Process: what actually happens today, step by step, including the steps that only ever happened in somebody’s head.

Most teams building agents right now are starting with Platform because that’s where the excitement is, and the demos are genuinely impressive. Gartner predicted that more than 40 percent of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. I can’t tell you how many of those are this exact problem. I’d be surprised if it were none of them, since “unclear business value” is often what an unwritten rule looks like from the outside.

Writing the rule down didn’t require new technology. It required twenty minutes of you and your team agreeing on something that had been decided case by case for two years. AI is really strong at pattern recognition, and that’s what mine did for me. It found the pattern of what we were doing with the parked deals and gave me enough information to define the rules for the humans.

What to do next

List the decisions, not the tasks. Before you build an agent for any workflow, write out every point where a person currently decides something: is this lead qualified, is this invoice late enough to chase, is this deal dead. Tasks are easy to document because you can watch someone do them, while decisions are invisible until a machine asks you to explain one.

Ask the people who make the call today, separately. You’ll almost certainly get different answers, and the disagreement is the finding rather than a problem to smooth over. You’re looking for the true answer, not the right one, because the true answer tells you what your pipeline actually looks like right now.

Write the threshold in a sentence a new hire could follow. A good rule names a time, a condition, and an action, so that two different people reading it would make the same call on the same deal. We will start enacting the 90-day rule for a parked deal. If it doesn’t move back to open in 90 days, it goes into the lost pile. It sounds simple, but it took two years to get there.

Decide who owns changing it. Rules go stale, and an agent will apply a stale rule perfectly for as long as you let it. Put a name next to the rule and a date to revisit it, so that when your sales cycle shifts, someone notices before the agent does.

The twenty minutes were easy; the two years were the expensive part.

What’s the rule your team has been making up one deal at a time?

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This week, let’s talk about clean rooms. If you’ve never heard the term, it’s a software development term that refers to building new software based on the outcomes of an existing piece of software but with none of the proprietary code.

Think of it as a form of reverse engineering where you might go and taste a recipe at a restaurant and give your best approximation of the ingredients. Then, combined with your own culinary knowledge, you might recreate the dish at home using none of the original dish’s recipe.

When generative AI first made its appearance on the scene, a lot of folks in the open source community were afraid that commercial companies were going to make clean room versions of popular free and open source software and charge money for it.

But the reverse is happening – open source authors are using their knowledge of software development and the outcomes of popular commercial software packages to make clean room versions of those packages.

Why? A big part of the reason is the en-SaaS-ification of software. Back in the 1990s and early 2000s, when you bought a copy of Adobe Photoshop, you owned a license to the software and a physical disc, and no matter how many releases Adobe made afterwards, you could still use the copy you purchased as long as your computer could run it.

But beginning in the early 2000s, software companies pivoted to subscriptions for everything, requiring rental instead of ownership, as a way of driving predictable recurring revenue. Salesforce led this charge with their “No Software” campaigns, advertising lower complexity by having everything in the cloud and the browser and requiring no local software installs.

For a time, most folks liked this. Prices were reasonable, and companies saved money on maintenance, servers, and other on-premise IT costs. But the hidden side of SaaS is that once a company holds your data hostage, leaving is very difficult.

All that changed once generative AI’s most powerful use case became apparent: it’s a phenomenal coder. It writes software better than any human, properly prompted and managed like a highly skilled senior developer with amnesia and ADHD.

Combine that with increased concerns about data privacy in SaaS software and hundreds or thousands of developers all pooling their usage quotas for generative AI tools, and replicating commercial software becomes trivial.

Is this legal? DISCLAIMER: We are not lawyers and cannot give legal advice. Contact a qualified attorney in your jurisdiction for legal advice specific to your situation. Under most copyright law, the tangible expression of an idea is copyrighted. That is to say, the code that makes a piece of software is fully copyrighted and protected. Ideas cannot be copyrighted. No one can copyright an idea for a photo editor or a video editor. So as long as open source software developers do not mimic the exact layouts with byte-level precision, their creations are not violations of copyright. UIs themselves are hard to copyright, as evidenced by Microsoft winning its 1990s case that Apple sued them over the GUI itself.

Additionally, most open source software projects are free of financial cost. So in a court case, no commercial motive can be ascribed by the copyright holder.

Here are just a few of the packages that have made their debut recently:

Concat: A CapCut clean room implementation that AI tools can control
ArtCraft: A suite of clean room implementations of major Adobe products like Photoshop, Premiere, and others
Wordcraft: Clean room implementations of Microsoft Office

As generative AI and agentic AI get better and better at software development, expect more and more clean room implementations. If your company makes a piece of proprietary software, the ability to charge for the software by itself is diminishing by the day. Only those companies that have proprietary data will be able to keep monetizing their software. Software itself has become a very cheap commodity.

If you are looking to save a bunch of money and not lose major functionality in your business, take a look at these clean room implementations of software, particularly those that are promoted heavily by the open source development community. Combine that with your existing AI coding tools, which provide all the tech support for this software, and you might never pay a subscription again.

Almost every AI course is the same, conceptually. They show you how to prompt, how to set things up – the cooking equivalents of how to use a blender or how to cook a dish. These are foundation skills, and while they’re good and important, you know what’s missing from all of them? How to run a restaurant successfully. That’s the big miss. We’re so focused on the how that we completely lose sight of the why and the what.

This is why our capstone course, the AI-Ready Strategist, is different. It’s not a collection of prompting techniques or a set of recipes; it’s about why we do things with AI. AI strategy has nothing to do with prompting or the shiny object of the day — it has everything to do with extracting value from AI and avoiding preventable disasters. This course is for everyone in a decision-making capacity because it answers the questions almost every AI hype artist ignores: Why are you even considering AI in the first place? What will you do with it? If your AI strategy is the equivalent of obsessing over blenders while your steakhouse goes out of business, this is the course to get you back on course.

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