CX Strategy

Valuize & Sturdy: Uncover data in your blind spots to maximize customer insights

By
Alex Atkins
December 2, 2022
5 min read
Watch the interview on Sturdy's YouTube channel

Valuize's Chief Client Officer, Emily Ryan, invited our very own Joel Passen to discuss data hiding in plain sight. Hosted on LinkedIn live, the Valuize team has been kind enough to share this excellent content with us.

Hosted by: Emily Ryan, Chief Customer Officer at Valuize

Initially hosted on LinkedIn Live

Interview Annotations

1:15 | Introduction

2:20 | Icebreaker

4:10 | When I say customer success operations, what's the first thing that comes to mind for you?

5:40 | The silo-effect

9:41 | Getting a seat at the table

11:10 | Maximize data

14:00 | The richest source of customer feedback

20:20 | Product usage data

21:21 | Telemetry data + qualitative data = insights hiding in plain sight

22:08 | Rarely are you broken up with in the moment that the breakup happens

23:32 | Taking out the guesswork

24:36 | Structuring data in a consistent and repeatable way

26:30 | We're all on the same team

0:00:08.8 S1: Hi, friends. Welcome back. I feel like it's been a million years since you’ve seen us on CS Operations. See, I don't even remember the title. A conversation with Emily Ryan. I'm Emily Ryan, and I'm so excited to have the opportunity as we have all year, this is actually coming up on a year of episodes, to have the opportunity to nerd out with some of my favorite people talking about one of my favorite topics, CS Strategy and Operations. This LinkedIn Live series aims to help define and defend investment in this critical organization, provide tips and tricks for designing a strategy to scale, and provide subject matter expertise to support this awesome new field. Each session will pose a different topic to a unique guest to help you get the most out of your time with us. If you have any questions or would like to connect with us or with each other, please feel free to engage via the comments. We'll try to leave some time to address questions during the session, but if we don't get to your question today, be on the lookout for future conversations, or you can visit our website to engage with our resources.

0:01:11.0 S1: Let's change the way people work together. I'm so excited to have Joel Passen, and I actually didn't actually ask you how you say your last name, so my apologies.

0:01:21.7 S1: Got it. You nailed it. Cool. We're good. Sweet. He is a SaaS entrepreneur, an investor, and an advisor. He is also the founder of Sturdy, a customer intelligence solution that empowers businesses to leverage unstructured customer feedback from every channel, like email, tickets, chats, meetings, and more. Sturdy uses AI and natural language processing to identify opportunities, reduce risks, and create more durable and profitable customer relationships at scale. I know durable is a huge word coming into the ecosystem right now, macro economy being what it is, but thank you, Joel, for joining me. Welcome. Thanks for having me. I'm glad to be here. Yeah, it's going to be fun. We always start with an icebreaker. Since we in the States, most states anyway, recently saw daylight savings end, it might be the last time that that happens depending on how things go in our government, but does daylight savings time mess with you? Relatedly, what is your favorite time of the day and why?

0:02:38.6 S1: I'm going to answer this in reverse, actually. Favorite time of the day is I'm a morning person. I get to work out and it's quiet in my house. Also, I think I think a little bit better in the morning. This is probably good that this event is in the morning. Morning for me, I'm on the West Coast. In terms of daylight savings messing with me, it does because I have small roommates, a six-year-old roommate and an eight-year-old roommate. They don't necessarily understand, their biological clocks don't necessarily understand daylight savings. It messes with me because it messes with them. Right. Yeah. I have furry roommates who are also like, why have not my meals appeared yet?

0:03:24.2 S2: Yeah. It's the same thing. Mine aren't that furry, but it's kind of same jam.

0:03:30.5 S1: Yeah, exactly. Yeah. I'm also a morning person, which comes in handy these days. I'm actually doing an MBA at the same time. I wake up super early to do homework, which sounds really exciting. Well, you know how to live. I mean, that's amazing. Dream life. Yeah. Learning about applied financial management this term. Go me. Awesome. Well, the second question that we always ask is when I say customer success operations, what's the first thing that comes to mind for you?

0:04:06.1 S1: I think about Rev Ops. I kind of blend all these things together. I'll tell you a quick story and the reason I mentioned this. First of all, I'll tell you, not to ingratiate you or the audience, but I think CS Ops is insanely important. I was at an event recently and somebody asked me a question like, if you're going to hire, you had $3 million in ARR and you had to assemble your team and turn in your budget. We were talking about budgeting. I'm like, what would be your, how would you backtrack the math? And I'm like, okay, first, first, good. You're talking about math. Math is good. And Ops sort of plays into that. And what I told them is I'm like, okay, I would try to figure out some sort of tech touch. Obviously I would have my head count resources. I'd be planning for some attrition in that. But I would talk a lot about in my budget, adding a CS operations or data analyst. And I would also, I think enablement CS enablement is also really, really important. And I think those are often overlooked as early hires. CS Ops, it's important to me.

0:05:16.3 S2: I would hire that person really early in my life cycle.

0:05:19.7 S1: Yeah. And, and to your rev ops point, right? I mean, we, we see sales ops come into play pretty early. We see marketing ops come into play reasonably early to get that scale, to get those touch points. But yeah, to your point, CS ops is just forgotten for a really long time.

0:05:39.7 S1: I also think that there's a little bit of the silo effect too. I mean, you know, we're, we're talking about, we're talking about data that can impact, you know, CX CS and, you know, other folks, we're talking about customer generated data, right? And, and, and the topic of this, and I think it's really important to think about like, you can hire CS ops people. I would really want them tightly aligned to the rev ops org and the business or, you know, the business systems org. I think there are more and more product operations people coming up, but all of this layer needs to sort of funnel into a consistency. And I think that's one of the big opportunities that the industry has. And I think that CS ops people sometimes are on their own Island. And I don't necessarily think that's good because the data that they play with and the data that they're making sense out of can be used by all these other teams and the teams that are using, you know, conversely, like the rev ops and product ops, most people all need to use the same data sets to create new analytical frameworks.

0:06:42.4 S2: So I'd like to see them less siloed. So I might say rev ops would be the first thing I think about because I think it's all kind of revenue operations. I think the next thing I'd say is like, keep them off their Island or out of a cave and put them more in the mainstream.

0:06:57.5 S1: Yeah. Yeah. That's interesting. I don't know if it's an Island or a cave. I feel like, I feel like it might be an Island and it's like just far enough away that you can see other people. You can't actually talk to them. Yeah. They're kind of like, what does that person on that Island do over there? Oh, that's CS ops. I'm like, that's their little, why don't they have a lot of, there's not a lot of area on their Island.

0:07:18.5 S1: You know, we need to also give them a nicer things. I think one of the things that I find in CS ops and you talk to them and these folks, these professionals, it's like, you know, how do you beg, but you know, what do you, how do you get resources? You know, do you have access to Tableau? Are you working or Domo or whatever you're using? And like, how do you, and they're kind of siloed. They're like, Oh, well I'm in our CSP and our CRM. Right. You're like, well, what, there's a broader subset of tools that in tooling that you should have. Well, we don't have the resources for that. Right. And it's the same thing by the way, in HR ops, I come out of the HR tech space, you hear it all the time, like TA ops, HR ops. They're kind of on their own little iceberg Islands in an orbit too. And they kind of look, their islands look and feel like the CS ops islands. Right. Yeah. Just like a person. And sometimes even part of a person.

0:08:10.4 S2: Yeah. Yeah. They've got one or, and they're kind of like trying to find food.

0:08:13.9 S1: Yeah. I, well, and the having nice things too. I think that that's, you know, that's one thing that, that we've spoken about a number of times on the Valuize side is as a CS operations person, learning how to speak in money because the value that you bring to your organization, if you can highlight that in real revenue and profitability terms, now folks are listening, but I think that it's taken a while for, for operations professionals to really get in that head space. Yeah.

0:08:47.5 S1: I would, I would actually add to that and say, as a, maybe, you know, I've been sort of listening at, on the conference circuit for two years and attending stuff like this and like kind of showing up in the conversation and just to listen and absorb and learn more and more. I've been more of a CRO person. I've owned CS twice. And I've always had really been fortunate to have good chief customer officer, VP of CS, CX. I've always had customer operations by the way, early on operations really important. But to your point, like I think one of the things that I've heard a lot of on the circuit to, I think a greater extent, I heard it again this year, it was like, Oh, getting a seat at the table. And there was like, there's all this kind of like talk about getting to see the table. By the way, I've heard all of this in the HR Tech years and years ago, like how does HR get a seat at the table? I'm like, it's our most important asset to have our employees first. And you're like, yeah, it's really interesting how aligned these spaces are because HR talks about the employees getting, you know, if you talk to a CEO, like, and say, what's your most important asset?

0:09:53.6 S2: They're like, Oh, our people. Right. And behind closed doors, they might say our cap structure, right. And I think in customer success, by the way, they're like, we want to see to the table and you go to the CEO, like what's your most important constituencies of people? And of course our employees and our customers, you know, they're equal. But yet I think one of the things that people forget about is thats lip service to a certain extent. And that if you want to see to the table, you got to talk in revenue speak because CROs and the product people, you know, the product people kind of get a pass to no fault of their own. They get, they get nice things. Sales teams get nice things because they own a huge number and somebody thinks they need nice things to make that number. But I think that's trend. We might be moving the needle for CS folks, but I think I really encourage people to think about like, yeah, when you speak in revenue, you wield power. That gets you a seat at the table immediately.

0:10:49.2 S1: Exactly. Exactly. Yeah. So digging into like how, right? So, I mean, early in my software career, I had the opportunity to dive into customer success operations even before it really formally existed as such. And one of the first things I learned in that experience was to maximize data, to gain any level of consistency against delivery. Today, over a decade later, we still come across clients at Valuize who insist that they don't have data. And usually it's because they're focused on like product telemetry specifically, but I know Sturdy aims to debunk this myth by leaning into the rich data sets that commonly go unnoticed and aren't tapped into. What are your thoughts about how companies really, especially B2B, but you can talk about any company, can lean into that rich vein of data and what is this data and why is it important?

0:11:52.7 S1: Well, I think it'd start with a stat. So we've had the really good fortune at Sturdy to analyze 55 million conversations collectively. And these can be things from call transcripts to tickets to in-app chats to customer email. And so when companies, to your point, when they talk about, oh, we just don't have any data or our data is a mess because you hear those two things in, oh, not quite ready for that because our data is a mess. And my answer to them is the first thing is I'm like, you have an enormous, enormous amount of language, like your language and feedback, and it's all stuck in email in a variety of silos. And so you have the data and you've been collecting the data for a very long time. And there's trends in that data. If you just think about the single channel in email, it's amazing. And yes, it is an enormous rat's nest of unstructured data. But the cool thing is there are companies and Sturdy is not the only one. There are others that are starting to make sense out of that and being able to distill that information from these silos, ingest it and restructure it in a very consistent, accurate way.

0:13:01.2 S1: So the, we don't have any data is a tough one these days because you just have a ton of ticketing data. I mean, the backend of your Gong calls or your Zoom calls with your customers, enormously valuable data. So it might not be what you would think of your data, Emily, in the traditional sense, like how many emails have we sent? How many times have we engaged the customer, which is really important stuff, but we all, you know, that's the kind of stuff we probably should have, but that's all telemetry based numbers that we look at in our rears. So, yeah, there's by the way, you know how many, you know, I'll put you on the spot here. You might know the answer to this. I feel like I've switched the thing you're supposed to. It's a dialogue we can talk.

0:13:46.7 S2: Okay, cool. If you don't, I have the answer. So if you don't want the answer, how many, so across B2B SaaS companies, $50 million in revenue or higher, how much do you think in terms of all the channels of communication with customers these days, minus in person, because that's impossible to track unless the calls are recorded. What do you think the richest source of feedback and customer feedback or insights is derived from what channel of communication?

0:14:13.8 S1: Like where is it derived from today? Yeah.

0:14:17.0 S2: Like what, where does the, where does the potential lie? What is the, what, where do you think the treasure trove is?

0:14:23.1 S1: Gotcha. I would say it's just all of the back and forth engagement. So anytime your customer responds to something or reaches out, so I'm going to cheat and say any inbound email from your customer and more specifically support tickets.

0:14:45.2 S2: Yeah. You're right. What we find is over 60% of the back and forth communication between customers and I'm talking users and customers, not just your key stakeholders, over 60% of it is an email across on an average nine inboxes. Yep. So if you think about it, yeah, we don't have the data. You can't say you don't have that. You have a lot of data just unfortunately stuck in all these little pits, these little tar pits that you can't get things out of, you know, conveniently. But there's a lot of really, you know, customers in an, you know, if you look at emails and you analyze emails, it's really like an unabridged, unbiased voice of the customer. They are telling you the answers. Yes. It's just really hard to get at.

0:15:27.9 S1: Right. Well, and to your point, like the nine, I mean, there are nine silos into which those emails dump and there's overlap with different emails. So let me see. I would imagine that's, probably some of that goes directly to your sales person because there's never been a severed relationship there. Not that that relationship should be severed, but it should be re refocused. Right. It's your, whoever your technical kind of onboarding first person that talks to your customers inbox or group inbox, it's your CSM or CSM equivalent. So if you have a pool model or digital model, it's your support, ecosystem. So all of the tickets and all of the rich things there, I would imagine it's any engagement with your community or your marketing, ecosystem. Let's see, that's, that's half.

0:16:23.3 S2: I'll give you, you're, you're an expert, so you're, but hold on, hold on. Yeah. Billing. Oh yes. So when you, when you build accounting, accounting is huge and it's, by the way, it's very literal. Those are very binary exchanges.

0:16:38.2 S1: Can I have a copy of our contract?

0:16:39.7 S1: When is the renewal of our contract? Yes. It's, it's November 15th. When's our auto renew trigger. Right. So, and possibly if you've got, you know, similar like that, linking that information with a support ticket, I'm cheating cause you told me this, but I'm linking that information with a support ticket that says, can you point me to the place where I can extract data from our system? Now you've got turn. Yeah. Well, you've got a couple of different vectors to say like, Hey, this is an issue. By the way, if you get a support ticket, it's kind of interesting for like when is our renewal date and somebody might go into Salesforce and be like, and by the way, in, in trying to do the right thing, you know, in a timely manner to provide an excellent level of service. Hey Emily, it's November 15th. Thank you so much. Is there anything I can help you with? No, there's not. Emily Ryan says, okay, case shut right. I'm moving on. If you blend all that language together or if those things get, escalated to somebody that's like, hold on a second, let's have a conversation with these people.

0:17:42.0 S1: They're asking a question that, you know, 65% of the time leads to a cancellation in the next 12 months. Right. So we need to get this to somebody that has the aptitude, you know, probably an account manager or a CS person that can have a conversation with these people and better understand why they're asking the question. Can you save a couple of customers a year doing that? Yeah. That's using data hiding in plain sight to actually lift net retention or you're just kind of stem this, you know, stem off cancellations. Right. I mean, that's what we're talking about when we're using qualitative data to do this stuff. It's the, they're there, the, the signs, the insights are sitting on there. The other ones, by the way, so billing program management, people that are touching integrations or partnership types things where you're engaging or, you know, maybe you're upset, maybe you're getting upsold by account management. So, anywhere from like seven to 12, right. Seven to twelve touch points at any given time at larger enterprises. It's, it's vast, even bigger, could be marketing, could be advocacy groups, that actually get insights from customers where they're like, I can't give you a customer testimonial right now.

0:18:47.6 S1: We'd love to, but we have this issue. So what does a customer marketing person do? Like they try to solve that. Do they escalate it? Do they have to do data entry somewhere? I mean, there's some really murky things that happen to even with people that are well-trained and have the best interests. Exactly. Exactly. Well, and you know, this is another reason why we, we try to help our, our clients with, with whatever system they have starting to make sure that you are viewing the customer with the same lens. We see a lot of folks silo internally, the customer's information and data set by internal team members. So my CSM has of you, my salesperson has of you, my technical account manager has of you and the views are slightly different. And so one of the things that we do is try to crack that open and even extend it. So not only is your whole post-sale customer team looking at the same set of customer information, but we're making sure that support has a view into that, that professional services has a view into that, et cetera. and it's, this is the same type of motion.

0:19:53.9 S1: It's like these, not just the customer information, but the data around all of those interactions. And to your earlier point, it's not, it's not just about quantitative interactions. How many times or, you know, how many times did my customer user open or click on like that's important data probably, but then what, so what, what else happened?

0:20:20.9 S1: I also think that product usage data, if you're in like the, we have payroll providers as customers, you don't just stop doing payroll or tail off and doing payroll in January 1st when your first payroll starts with your new customer. Yeah. Your usage goes from like a hundred percent to nothing that usage doesn't tell that story. So some of the things that we incorporate, I know that, Valuize does good work with customers around helping them sort of create, maybe more holistic health scoring. And I read a lot, you know, like part of the reason that I think I'm on this, with you today is like, you talked about data centricity and I was like, yeah, that's the, that's the, you know, and some of the value wise content, which I think is really quality. And I've mentioned this to your team before, really quality content for people that are looking to get information. You guys write a bunch of, but data centricity, which isn't a light reading topic, but it's really, really important. So, I mean, I think that's what you're trying to get at without beating your own drum, but it's really, really important.

0:21:21.4 S2: So yes, telemetry data combined with qualitative data is sort of like, if you take the data that's been hiding in plain sight, the stuff that you're collecting, you have reams up, you just need to make actionable and you combine it with some of this telemetry based data. It accelerates or, or, I think enhances the story. Like you kind of get to the, what we're all looking for is like, this is happening. Oh my gosh. Like, and then you have to say why, and someone can be like, this is what we've seen. This is a lot of the topics in their conversations go around these feature requests. And one of the things we talk a lot about is like cancellations. They don't happen in a vacuum. No, it is a compilation of lots of things with all of these different actors in all these different silos. And this is part of the reason that's really hard to get in front of cancellations. It's like it's all over the place and it's death by a thousand cuts.

0:22:08.5 S1: Yep. Yep. And just like any, any relationship, you know, rarely are you broken up with in the moment that the breakup happens, you've been broken up with mentally long ago, right? Yeah. Well, yeah, yes. And you know what we're talking about, we're talking about investing in relationships. So, I mean, that metaphor goes a really long way. And when I, I mean, I'm sure you get out and talk to your customers. I get out and talk to mine. It's like, yeah. I mean, think about your own relationships. There are fractures and fissures. And by the way, sometimes, and to this point, sometimes you have really hard conversations in a relationship, a personal relationship, and it makes you stronger with that person because you get to trust. And I think that confronting these fissures and fractures and client relationships head on and honestly with, with high intellectual honesty, right. Sometimes can create a really strong partnership with that customer. They're going to renewal infinitely, right? They love you. You've provided value. Listen to them and listening to their customers, like kind of how this all starts, because that's kind of key to a relationship. So absolutely. And you know, your, your, the work that you're, you and your team are doing helps enlighten your internal teams broadly about those things that your customer is telling you, right?

0:23:26.3 S1: So you can have, it's, it takes the bias out of things or the guesswork like, Hey Emily, how is a XYZ customer?

0:23:32.6 S1: Are they green, yellow or red? Well, I think, I feel like, I feel like that's how a lot of these conversations start where I'd like to say, I feel like they're a little bit of a yellow because we don't have this particular feature and the buyer, we lost our executive sponsor and the new executive sponsor has bought ADP or workday or whatever the competing product is before. So I think we have a little bit of risk. That's what I know. Now, if I'm a leader and I get that download and I'm like, okay, we need to fly to Topeka and go talk to these people and go, that's a trip that I'm going to budget. Like we need to get in front of these. What's their error? Oh, it's this. Yeah. We should get in front of those folks. Right. Yep. No, it's true. And I think, you know, one of the things that we were talking about the other day was just the kind of, you know, we talked about rev ops, the sales and marketing components of that really have learned how to get a lot of data and structure that data in a really consistent, repeatable way.

0:24:36.2 S1: And customer success and post sales in general seem to continue to lag behind when it comes to truly understanding customers through data. Just as kind of our final moment here, what's your perspective when it comes to this like dichotomy within SaaS businesses and how can companies strive to overcome that?

0:24:58.8 S1: So the first thing that I would say just with few words is that it's a maturity issue. You know, CS ops is relatively new compared to revenue operations, which is I think fairly stable. I mean, any business with over, I don't know, what do you think? I'm like $5 million in ARR? You're going to have a full-time rev ops person or at least somebody who's aspiring to do that role, if not augmented with some sort of consulting, cause you're going to need it. And as the motions grow, it just gets more, you know, those teams bloom. There's a whole industry around it. There are platforms, all kinds of stuff. It's mature. I think in CS it's just not mature. And I think the second point is, you know, how do we get people there? I think that like anything else, like how, how does a good sales person or account manager learn? They learn through osmosis through shadowing and being mentored by someone else. So I think CS ops and it comes full circle. Like you asked me what I think of CS ops. I'm like rev ops because I want my CS people, my CS team to be lockstep with the revenue motions with rev ops so that we have to pair them together and that'll increase the maturity on our mathy and data side of the world in customer success or post sales motions.

0:26:10.9 S2: Yeah.

0:26:11.4 S1: Yeah.

0:26:11.7 S1: Like we have to, we have to get closer by the way. They're too, these teams are too segmented. They're too segmented. Like we're all in the revenue game and we're all in the delighting the customer game, whether it's product sales and customer success, we are all on the same team. And that I think sometimes is lost in fiefdom building. It really is. Yeah. Yep. That's a, that's a, that's a nice little tagline into, I will, I will shamelessly pitch our value experience framework release drives to break down those silos and help teams work together towards a common discipline so that, so that we're really driving towards net dollar retention and delight is a core part of that.

0:26:53.4 S1: Well, let me know if you guys take the show on the road to Topeka. I'm available. They have planes. Sounds good. Sounds good. I feel like it's starting to get to the wrong season to be in Topeka, but It is actually, I've never been to Topeka this time of year, but I would imagine that we probably want to wait until the spring.

0:27:11.4 S2: No offense to Topeka constituents, but yeah. Yeah.

0:27:14.6 S2: I know that, you know, Kansas is a, is a neighbor of ours and man, do they get the weather that just comes right off, but skips Denver and then just goes straight to Kansas. So it's wait until spring. Well, as usual, this time just completely flew. Thank you, Joel. And thank you everyone for joining the discussion today to uncover data in your blind spots and maximize customer insights. Let's change the way people work together. Let's do this again too. Let's do it again.

0:27:49.5 S1: Right. Let's, we have so much to talk about. We can keep talking later. Thank you. Thanks for having me. My pleasure.

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AI & ML

The Context Engine

Joel Passen
May 19, 2026
5 min read
Executive Summary

The Context Engine

The model is not the problem. In every enterprise AI deployment that has hit a production wall in 2026, the failure lives one layer down: in how data is prepared, permissioned, and delivered before the model ever begins reasoning. Model choice has become the wrong question. With Anthropic's Claude surpassing OpenAI in U.S. enterprise adoption (34.4% vs. 32.3%, Ramp AI Index, April 2026), the market has already moved on. The competition has shifted from the Reasoning Engine to the Context Engine.

While nearly every enterprise has deployed frontier models, most are paying a Hallucination Tax they cannot see on their P&L. For an organization with 1,000 knowledge workers, the 4.3 hours per employee per week spent manually verifying AI outputs (Forrester, 2025) equates to approximately $16.8 million in annual salary drain, calculated at a conservative $75 per fully-loaded hour. Multiply that across a global enterprise, and it maps to the $67.4 billion in documented AI hallucination losses recorded in 2024 alone (AllAboutAI, 2025). This is not a failure of the model. It is a failure of architecture.

This paper argues that the next phase of enterprise AI requires a Deterministic Intelligence Layer: infrastructure that normalizes, indexes, and permissions customer data before it reaches the model. Teams replacing token-heavy RAG workflows with deterministic, pre-indexed context are seeing substantial reductions in cost per task while dramatically improving retrieval precision and AI reliability. More importantly, they are crossing the Threshold of Action: the point where AI becomes trustworthy enough to move from surfacing insights to executing workflows.

Section 1

The New Benchmark: Claude's Enterprise Breakout Moment

The AI market just had its crossover moment. As of April 2026, more U.S. businesses pay for Anthropic's Claude than for any other AI model. 34.4% vs. 32.3% for OpenAI, according to the Ramp AI Index, which tracks actual spending across more than 50,000 companies. This isn't a survey about intent. It's purchasing data.

By March 2026, Anthropic was capturing 73% of first-time business AI buyers (Axios, March 2026). A year earlier, one in 25 businesses on Ramp's platform paid for Anthropic. Today, it's nearly one in three.

Enterprise buyers don't switch defaults on a whim. They switch when something is demonstrably working better for the work they actually need done.

The Model Is Not the Problem

Here is the harder truth underneath that adoption story. Despite the crossover, most enterprise AI deployments are not delivering.

Widespread adoption. Widespread underdelivery. Both things are true simultaneously.

The instinct in most organizations is to treat this as a model problem: switch providers, upgrade to the latest version, hire a prompt engineer. None of it moves the needle in any sustained way, because the model is not where the failure lives. Claude is a reasoning engine. A sophisticated one. But a reasoning engine can only reason over what it's given. And in most enterprise deployments, what's given is a mess. Fragments.

The Performance Ceiling

Every technical leader deploying Claude at scale hits the same wall. The demo works. The pilot looks promising. Then it moves toward production, and something breaks. Not catastrophically, but consistently. The AI misattributes an item to the wrong account. It summarizes a customer's history using stale data. It generates an output that sounds authoritative and requires 20 minutes of human verification before it can be trusted.

"Feed a world-class reasoning engine confident, well-structured garbage, and you get the same in return."

This is not a failure of reasoning capability. It is a failure of context architecture. The data required to generate reliable outputs, account history, communications, support activity, call transcripts, and operational metadata typically exists across fragmented systems with inconsistent normalization, disconnected permissions, and no canonical entity resolution layer tying it together.

Context Is the New Infrastructure

The companies pulling ahead in 2026 are not winning because they chose a better model. They are winning because they solved the harder problem underneath it: delivering clean, resolved, permission-aware context before the model ever begins reasoning.

  • IT, Data, and Platform Engineering provide the Engine (Claude): a recurring operating expense. World-class reasoning, rented.
  • RevOps, Data, and AI Teams provide the Map (the Deterministic Data Layer): a long-term asset. Customer intelligence, owned.

Claude is the current catalyst. The model market will keep moving. New releases, new providers, new pricing. What doesn't move is the underlying problem: fragmented, unresolved, improperly permissioned data. Deterministic context is the durable architecture. The organizations building it now will carry that advantage into every subsequent model generation.

Most organizations already have the engine. What they lack is the map.

Section 2

The Hallucination Tax: Why Fragmented Data Kills AI Performance

If the model isn't the problem, why are so many production-grade AI initiatives hitting a performance ceiling? The answer is the Hallucination Tax.

In 2024, hallucinations cost enterprises an estimated $67.4 billion in global losses (AllAboutAI, 2025). By early 2026, the cost has shifted from outright fabrications to "silent hallucinations": outputs that look structurally perfect but are factually untethered from the current state of the business.

For an organization with 1,000 knowledge workers, the 4.3 hours lost per person per week equates to roughly 223,600 hours of wasted annual productivity, approximately $16.8 million in annual salary drain at a conservative, fully loaded rate. It never appears on the P&L as an AI cost. It shows up as underperformance, missed forecasts, and slower deal cycles.

This forces employees to act as "Human Middleware": the bridge between fragmented systems and the AI that was supposed to make them irrelevant. This tax is the direct result of four specific architectural failure modes.

Failure Mode 1: Retrieval Precision (The Token Tax)

Standard RAG is probabilistic. It retrieves semantically similar fragments, not operational truth. When a sales leader asks, "Why did we lose this seven-figure deal?", the system may surface an old QBR deck instead of the pricing objections in email, the procurement concerns buried in Slack, the legal escalation in Jira, and the product gaps discussed in call transcripts that actually determined the outcome.

Because retrieval is imprecise, teams over-index by stuffing the context window with every possible document to ensure the right one is in there. The result: thousands of reasoning tokens spent filtering noise. A world-class reasoning engine doing the work of a search index.

Failure Mode 2: "Lost in the Middle" (Attention Drift)

Research by Liu et al. (TACL, 2024) demonstrated that accuracy on multi-document reasoning tasks drops by more than 30 percentage points when relevant information is buried in the middle of a long context window. This matters enormously in enterprise environments, where critical signals are scattered across support escalations, pricing discussions, call transcripts, Slack threads, and CRM updates. Simply increasing context size does not solve the problem. In many cases, it amplifies it by forcing the model to attend to more noise.

Failure Mode 3: The Identity Crisis (Entity Disambiguation)

In a fragmented environment, identity is a variable, not a constant. "Jane Doe" in a Zoom transcript needs to resolve to the same Jane Doe in Salesforce, Gmail, Zendesk, Slack, and the CRM activity timeline. Without deterministic entity resolution, the model is forced to infer whether those interactions belong to the same person, account, or buying committee.

Without deterministic entity resolution, the model is forced to reconstruct identity probabilistically. A support escalation tied to one stakeholder, a pricing objection raised in a sales call, and an executive concern discussed over email may be incorrectly assembled into the wrong account narrative entirely.

Failure Mode 4: The Permission Ghost (Unauthorized Surface)

This is the silent killer of enterprise AI programs. Most RAG pipelines lack Source-System Parity. If the AI retrieves a snippet from a private executive email because it was "semantically relevant" to an intern's query, the system has failed regardless of whether anyone noticed.

Incidents like EchoLeak show exactly why retrieval-layer permission enforcement matters. In late 2025, researchers demonstrated a zero-click vulnerability in Microsoft 365 Copilot that could exfiltrate sensitive data from Copilot context without user interaction. No prompt injection required. The retrieval layer was the attack surface.

For most organizations, the permission layer isn't just a technical problem. It is an organizational liability that Legal and Security will eventually force you to solve on a deadline, under pressure, after something has already gone wrong.

The Production Wall

These four failure modes create the Production Wall. A curated demo can appear remarkably accurate. But production environments are not curated. They are noisy, fragmented, and constantly changing, with critical signals distributed across emails, calls, support threads, Slack conversations, and operational systems evolving in real time.

"You cannot solve these four problems by tuning the prompt. You have to solve them by fixing the context."
Section 3

The Deterministic Intelligence Layer

To climb over the Production Wall, enterprise architecture must evolve. The solution is not a larger context window or a more complex prompt. It is a fundamental shift in how data is prepared for the model. Enter the Deterministic Intelligence Layer: infrastructure that sits between your raw data silos and Claude, acting as the architectural antidote to the four failure modes in Section 2.

The Four Pillars

1. Precision Indexing (Ending the Token Tax)

Instead of relying on similarity search alone, the context layer resolves entities, removes duplication, and prioritizes high-signal interactions before retrieval. The model receives structured operational context rather than raw fragments competing for attention.

In Sturdy-observed deployments, replacing raw context with pre-indexed, distilled payloads has reduced token consumption by 80 to 90% on comparable workflows. Results vary by source data density and baseline architecture. You stop paying for Claude to be a search filter.

2. Signal Distillation (Solving "Lost in the Middle")

Semantic Pruning strips HTML headers, Slack noise, legal footers, and the RE: FWD: RE: reply chains that bury every actual decision in 40 lines of quoted text, distilling threads into thematic buckets: Bug Reports, Feature Requests, Sentiment Shifts. The most critical insights land at the beginning of the context window, bypassing the 30-point accuracy drop documented in long-context research.

3. Deterministic Entity Resolution (Fixing the Identity Crisis)

A Global Entity Map resolves disparate naming conventions into a single, immutable Customer ID. Claude is no longer guessing whether two conversations belong to the same account. It is being told they do.

4. Parity-Enforced Permissions (Exorcising the Permission Ghost)

The retrieval layer enforces source-system permissions before context assembly, so unauthorized records are excluded from the payload sent to the model. This is not a prompt-level instruction that can be overridden or confused. It is an architectural enforcement point that sits entirely upstream of the model.

Security becomes a structural property of the architecture, not a probabilistic instruction to the model. Incidents like EchoLeak show why this distinction matters: when permission logic lives inside the prompt, the retrieval layer remains an attack surface. When it lives at the data layer, it doesn't.

Reference Implementation: Sturdy + Claude via MCP

While the merits of this architecture are clear, building it internally results in years of maintenance debt (see Section 5). Sturdy leverages the Model Context Protocol to serve as the Context Engine for Claude, normalizing, indexing, and permission-stamping your customer intelligence layer across Salesforce, Gmail, Slack, and Zendesk before Claude ever queries it.

Claude provides the Reasoning Layer. Sturdy provides the Memory and Context Layer. Together, they move an enterprise from AI that reads your business to AI that acts on it.

Section 4

What It Unlocks: From Reading to Acting

In 2026, summarization is a commodity. The competitive advantage lies in moving from AI that reads your business to AI that acts on it. This transition requires a fundamental shift in how leadership views the AI stack and who owns what.

  • IT, Data, and Platform Engineering provide the Engine (Claude): recurring operating expense. World-class reasoning, rented.
  • RevOps, Data, and AI Teams provide the Map (the Deterministic Data Layer): a long-term asset. Customer intelligence, owned, not rented.

When the engine has a perfect map, the Acceleration Gap closes.

RevOps: The Revenue Architect

For the RevOps leader, a deterministic layer turns fragmented operational data into active revenue signals. Instead of building static dashboards that explain why a quarter was missed, RevOps can monitor the commercial signals that actually move deals: pricing hesitation in email, procurement delays, legal friction, competitive mentions, executive disengagement, stalled next steps, and tone changes across active opportunities.

A deterministic context layer resolves those signals to the right person, account, opportunity, and timeline before AI ever reasons over them. That is what turns scattered communication into reliable revenue action.

RevOps stops being a report generator. It becomes the operating system for revenue execution: designing the logic that turns verified commercial signals into coordinated GTM action.

Sales: Instant Account Intelligence

The average sales rep spends roughly 20% of their week on pre-call research. With a deterministic layer, the account briefing is no longer a probabilistic summary. It is a verified snapshot: "The customer's last three support tickets were resolved, but they haven't yet implemented the API update discussed in the March QBR."

Product: The Automated Feedback Loop

Product managers are often the most data-rich but insight-poor employees in the company. A deterministic layer moves PMs from reading feedback to querying insights. Claude analyzes 60 days of feedback across Slack and Zendesk and, with a single prompt, generates a high-fidelity Jira ticket including exact customer quotes, impacted account IDs, and revenue at risk.

Customer Success: Proactive Triage

In CS, latency is the enemy. A deterministic layer allows Claude to perform live triage. When a customer sends a frustrated email, the AI checks contract terms and recent product usage logs before the CSM has finished reading the subject line. It presents a Context-Aware Response ready to send, grounded in verified account data.

"The model you license today is rent. The customer intelligence layer you build is equity. One gets replaced. The other compounds."

Every account signal normalized, every entity resolved, every permission enforced. That accumulates. The organizations building this layer now are building institutional memory that makes every model they run on top of it better.

Section 5

The Build vs. Buy Reality

The instinct for most sophisticated IT and data teams is to build. It is a legitimate impulse. The stack looks deceptively simple: a few API connectors, a vector database, and some chunking logic. In the demo phase, an internal build often feels like the most cost-effective path.

The Four Hidden Engineering Hurdles

1. The Normalization Treadmill

Building a connector to Salesforce is straightforward. Maintaining the logic layer that resolves entity names across Salesforce, Slack, and Zendesk as those systems' schemas evolve is a full-time engineering job. This is Semantic Drift: hundreds of developer hours consumed by maintenance rather than innovation.

2. The Permission Mapping Paradox

Mapping row-level permissions from source systems into an AI context window is one of the most complex security challenges in modern software. Most internal builds rely on prompt-level security, which fails under the weight of incidents like EchoLeak. This isn't a technical trade-off. It is an organizational liability waiting to be forced into crisis.

3. The Latency Wall

A custom RAG pipeline often takes 5 to 10 seconds to fetch and clean data. In Sturdy-observed deployments, pre-indexed deterministic retrieval consistently operates under 1 second on production data volumes, but reaching that benchmark requires specialized search infrastructure expertise that is rarely the core competency of a generalist data team building from scratch.

4. The Token Optimization Tax

Without signal distillation, internal builds routinely pass 3x to 5x more tokens than necessary. Teams save on build costs only to spend twice as much on model API costs.

Where Does Your Engineering Dollar Go?

The strategic question isn't "Can we build this?" It's "Should we own the maintenance of this?"

Competitive advantage does not live in the plumbing. No customer chooses a vendor because their AI has a better Python script for cleaning Slack data.

By offloading the Normalization Treadmill to Sturdy, organizations are promoting their engineering teams from Data Cleaners to AI Product Owners, moving their best people away from the maintenance treadmill and toward the high-value work of building AI that drives revenue.

Buy the plumbing. Build the logic. The teams doing this are shipping revenue-generating AI workflows, while their competitors are still debugging entity-resolution scripts.

Section 6

What to Do Now: The 2026 Roadmap

The Acceleration Gap is not a permanent state. It is a choice of architecture. The move is not to wait for a smarter model. The move is to fix the context. Here are four moves for leadership to take in the next 90 days.

Move 1: Audit Your Retrieval Precision, Not Your Prompts

Most teams spend the majority of their time prompt-tuning errors caused by bad data retrieval. The action: Run a Ground Truth test. Take ten complex customer queries and manually check the data fragments Claude is being fed. If more than 20% of that data is noisy, stale, or misattributed, no prompt engineering will save the deployment. You have a plumbing problem, not a reasoning problem.

Move 2: Isolate a Multi-Source Workflow

The highest ROI for a deterministic layer is found where data is most fragmented. The action: Pick a high-value, closed-loop use case where data lives in at least three systems. For example: the path from customer feedback in Slack and Zendesk to an engineering action in Jira. Solve the context problem here, and you've built a blueprint for the rest of the organization.

Move 3: Enforce Permissions at the Data Layer

Stop treating security as a probabilistic instruction. The action: Move permission enforcement out of the system prompt and into the retrieval infrastructure. Ensure the retrieval layer enforces source-system permissions before context assembly, so unauthorized records never reach the model. The Permission Ghost is exorcised structurally, not instructionally, and the organizational liability is removed before Legal ever has to get involved.

Move 4: Define Where AI Earns the Right to Act

The distance between AI that summarizes and AI that executes is a trust gap, not a technology gap. The action: Build human-in-the-loop approval gates for high-stakes actions. Drafting a renewal contract. Creating a Jira ticket. Sending a support response. Use your deterministic layer to provide the required Confidence Equity. The threshold to target is a sub-5% error rate on AI-generated drafts. That is the point at which approval gates can be safely reduced, and workflows become self-sustaining.

Traditional probabilistic RAG architectures struggle to reach this threshold consistently at enterprise scale. Because probabilistic retrieval introduces entity errors, stale data, and permission noise, error rates on complex multi-source tasks typically stabilize in the 15 to 30% range regardless of prompt quality, even with hybrid retrieval and reranking layers added on top.

A deterministic layer that resolves entities before inference, distills the signal before retrieval, and enforces permissions before the model ever sees the data is the only architecture that makes sub-5% structurally achievable, rather than an occasional lucky outcome.

In Sturdy-observed deployments, teams that reach this threshold have consistently moved to reduced-oversight approval workflows within a quarter. Results depend on workflow complexity and baseline data quality. Reaching the sub-5% Trust Threshold is the definitive signal that an organization has graduated from "AI Experiments" to a Context Engine architecture capable of autonomous action. That is the architectural line between AI that assists and AI that acts.

Conclusion

The Architectural Advantage

Frontier models will continue to improve and commoditize. The durable advantage is no longer the model itself. It is the architecture surrounding it.

The long-term value does not live in another standalone AI interface. Interfaces change too quickly. The durable layer is the operational context infrastructure beneath them.

Organizations that solve deterministic context assembly, entity resolution, permission-aware retrieval, and operational state assembly gain a compounding advantage independent of whichever model, interface, or orchestration layer dominates next year.

Organizations that solve context architecture today are building infrastructure that compounds across model generations. As interfaces evolve and models improve, the operational context layer beneath them becomes increasingly valuable.

"The era of the Context Engine is here. Is your architecture ready for it?"

AI & ML

Your AI isn’t the problem. Your data is.

Joel Passen
May 6, 2026
5 min read

IT leaders may have resisted AI early, but that phase passed quickly. The real concern wasn’t whether to use it. It was how to control it. Governance, security, visibility. In the end, it came down to preventing sensitive work from being done in personal accounts. Reasonable.

So they got comfortable, signed off, and rolled it out. ChatGPT, Copilot, Claude, company-wide, with guardrails.

People are using it. That part worked.

The disappointment

The problem is what revenue leaders are finding now that it’s live.

The data they actually want to use isn’t accessible in any meaningful way. And that matters more than most people realize, because LLMs are only as useful as what you put in front of them. They’re exceptional at reasoning over structured, coherent information. They’re not designed to reconcile fragmented, inconsistent data spread across a dozen systems.

Nobody’s model is.

So instead, people compensate.

They cut and paste. Drop in exports. Upload a batch of emails and call transcripts, and hope coherence comes out the other side.

It doesn’t. They get fragments. Plausible-sounding ones, but fragments.

The diagnosis

What commercial leaders are running into isn’t a model problem. It’s a data problem.

The data they actually care about isn’t unified. It lives across email, Slack, Zoom, support tickets, calls, and CRM notes. Different systems. Different formats. No shared identity. No relationship context.

Even with connectors. Even with MCPs.

Because underneath it all, the data isn’t organized in a way a model can reason on. There’s no canonical view of the world.

The model doesn’t know that the same person shows up in Zoom, Slack, Zendesk, and Salesforce. It doesn’t understand that those interactions belong to the same thread, the same account, the same moment in a relationship.

So it fills in the gaps.

Not because it’s weak. Because it has to keep trying.

The gap

Meanwhile, the models themselves have gotten amazingly powerful. Reasoning is sharper than it’s ever been and getting better daily.

But the data layer most companies are feeding them? Still immature.

According to MIT’s 2025 State of AI in Business, over 80% of companies have explored or deployed LLMs, but only around 5% are seeing meaningful business impact.

High adoption. Low transformation.

That’s not a model problem.

What’s possible

What it looks like when this actually works is different.

Not dashboards. Not reports. Not exports.

A conversation. Like having the best revenue ops analyst you’ve ever worked with on call, one who has read every email, sat in on every call, and never forgets anything.

You ask: “Which accounts have shown signs of churn risk in the last 90 days?”

And instead of a guess, you get a ranked list. Accounts. ARR. The exact messages where the signal showed up. What changed. What triggered it. What to do next.

So you ask a follow-up: “Which of these are new customers?”

Now you’re looking at onboarding breakdowns. Common threads. Where the process is failing.

So you keep going: “Where are we missing expansion opportunities?”

And it surfaces accounts where someone said, “We’re thinking about rolling this out to another team.” But nothing was logged. No opportunity created. No follow-up.

That’s the shift.

You’re no longer stitching together context. You’re interrogating it.

What changes

What changes when you fix the data layer, when your commercial data is normalized, deduplicated, and accessible, isn’t just speed.

It’s the level of questions you can ask.

These aren’t dashboard queries. They’re judgment calls. The kind that used to require a senior operator spending a weekend in spreadsheets and Salesforce. When your data layer is clean and the model has real context to work with, they become a 90-second conversation.

That’s the difference. Not a better model. A better fuel.

The data infrastructure reality

Most teams won’t get there by accident. The infrastructure problem is real: identity resolution across systems, conversation reconstruction across channels, deduplication, and signal enrichment. It’s six to twelve months of plumbing if you build it yourself.

The companies that crack it first won’t just be more efficient. They’ll be operating with a fundamentally different information advantage. They’ll see churn coming, spot expansion signals, catch friction early, before any of it shows up in the numbers.

At that point, the question changes.

It’s not whether AI works.

It’s whether your data is ready for it.

And whether you’re going to build that layer, or keep working around the absence of it.

This is what we're building at Sturdy.ai. The data layer your LLM actually needs.

Insight Updates

The Moment B2B Sales Teams Forget Everything They Learned During the Deal

Joel Passen
May 6, 2026
5 min read

It’s not the close. It’s not the kickoff call. It’s the 48 hours in between — when the contract gets signed, the champagne (metaphorically) gets popped, and everything the sales team learned over months of conversations, negotiations, and relationship-building quietly disappears.

The delivery team inherits a contract and a few CRM notes. Not the story behind the deal.

This is the handoff problem. And it’s costing companies more than they realize.

Why the Knowledge Dies at the Signature Line

Think about what actually happens during a complex B2B sale.

Over weeks or months, a sales team accumulates an extraordinary amount of institutional knowledge. They learn why the buyer is actually moving now — not the official reason, but the real one. The compliance incident that became a board-level conversation. The internal champion who’s been pushing for change for two years and finally got budget. The exec who’s skeptical and needs to see a specific proof point before they’ll get on board.

They learn who matters and how decisions actually get made, which is almost never what the org chart suggests. They learn what got promised in the final stretch: the SLA clause that got added at the last minute, the integration that’s now contractually locked, the go-live date that the CFO has already presented to her board.

None of that lives in the CRM. It lives in emails, call recordings, Slack threads, and people’s heads.

And the moment the deal closes, the sales team moves on to the next one. That’s their job. That’s how they get paid. But the institutional knowledge they spent months building the context that would let an implementation team start informed, instead of starting over, largely evaporates.

Onto the next pipeline review.

The Cost Nobody Is Measuring

Companies measure churn. They measure NPS. They measure time-to-value.

Most don’t measure the cost of the knowledge gap at handoff — because it doesn’t show up as a line item. It shows up as implementation delays. Escalations. Customers who feel like they have to repeat themselves six months into a relationship that should already be mature.

It shows up as promises made during the sale that nobody on the delivery side knew about. Commitments that surface in month three as a nasty surprise. Expectations that were set in a negotiation conversation that never made it into a system anyone on the CS team can see.

The SaaS industry has spent a decade optimizing the top of the funnel. Sophisticated systems for capturing and qualifying demand. Playbooks for every stage of the sales motion. Entire conferences dedicated to pipeline hygiene.

And then we hand a contract and a prayer to the team responsible for actually delivering the value we sold.

What Good Looks Like

I’ll make this concrete.

We recently ran Sturdy against a real deal, a $190K ACV implementation that had just closed. Board-level compliance incident drove the urgency. CFO was the economic decision-maker: analytical, direct, not interested in being charmed. An integration was contractually locked in Exhibit A. Timeline slippage wasn’t just an ops problem; it would retrigger board scrutiny because of the prior incident.

The implementation team knew all of that before the first kickoff call.

Not because someone wrote a perfect handoff email at 11 pm the night before go-live. Because Sturdy read across the entire deal — emails, calls, negotiations — and surfaced the context that actually matters: why they bought, who really matters internally, what was promised, and where the risk lives.

That’s the brief I show in the video. Notice how specific it is. Notice that it doesn’t just describe what happened, it tells the delivery team what to do with it.

That’s what institutional knowledge looks like when it doesn’t get lost.

The Broader Shift

The handoff problem is really a symptom of something larger.

B2B revenue has always been a team sport — sales, CS, implementation, product, and finance all own a piece of the outcome. But the systems we’ve built treat each function as a silo. Data gets entered into the CRM by whoever remembered to do it. Calls get recorded and filed somewhere nobody looks. Emails pile up in inboxes that get searched only when something’s already on fire.

The signals are there. The context exists. It’s just buried, and it disappears at exactly the moments in the customer lifecycle when it’s most needed.

The companies that figure this out and build systems to capture, preserve, and operationalize institutional knowledge across the revenue lifecycle will have an operational advantage over those still relying on heroic individual effort and the hope that someone wrote a good handoff doc.

This isn’t an incremental improvement. It’s a different way of operating.

The moment a deal closes should be the moment an organization puts everything it learned to work.

Right now, for most companies, it’s the moment they forget it.

That’s the problem Sturdy was built to solve. If this resonates, start at sturdy.ai.

Your customers are already telling you what's going to happen.

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