Certinia at Dreamforce | Simon Cornwell, Civica

Clips
More from Certinia at Dreamforce

Simon Cornwell

CIO

Civica

play_circle_outline Dreamforce 2026 coverage and industry focus on enterprise AI

play_circle_outline AI's impact on professional services: speed, delivery, time-to-value

play_circle_outline Trust and Sovereignty: Data Residency, Compliance, and AI Adoption in Enterprise and Public Sector

play_circle_outline Shift from probabilistic to more deterministic AI to increase certainty

play_circle_outline AI architecture: multi-model, knowledge graphs, and purpose-specific tools

Info

Simon Cornwell, Civica

Simon Cornwell

CIO Civica

search

Scott Hebner

>> Welcome back to theCUBE's continuing coverage of Dreamforce 2026, where we're focused on the professional services industry, which is clearly a leading indicator of where enterprise AI is heading. I'm Scott Hebner. I'm the principal analyst at theCUBE Research for AI. Appreciate you tuning in today. Today I'm thrilled to be joined by Simon Cornwell. He's the CIO at

Scott Hebner

>> Civica.Thanks very much,

Scott Hebner

>> Scott.Appreciate you being

Scott Hebner

>> here.Yeah, it's great to be

Scott Hebner

>> here.Thank you. It's been a great show, hasn't it?

Scott Hebner

>> Yeah. Dreamforce is always an intense mix of new announcements. We get to see where the products are going. We get to meet great vendors like Certinia. So yeah, good

Scott Hebner

>> show.Yeah, good weather too this time.

Scott Hebner

>> Yeah, it's a bit warm. It's a bit warm.

Scott Hebner

>> Not terrible. And we're in a great venue here with great food. They keep going around with the steaks and lobster. So makes it a great environment. Actually, before we dive into things here. What were your thoughts on Marc Benioff's keynote and where Salesforce is heading in general?

Scott Hebner

>> Yeah, I think it's really interesting. Two years ago, we were all talking about the SaaS-pocalypse, and this is the end of SaaS, we don't need SaaS anymore, we're going to just vibe code it in a weekend. I think what the direction set out in the keynote is really clear that SaaS in a way is a bit dead. SaaS really now becomes the system of record, it becomes where we keep our data, our workflows, our semantics, but actually the place of work where we do our work could be anywhere. It could be in Slack, it could be in Teams or Claude. And so, yeah, I think it's very exciting that they are moving away from a Salesforce-shaped product, which is only Salesforce-shaped.

Scott Hebner

>> And yeah, there's a massive corporate graveyard of companies that resisted the change. And

Scott Hebner

>> it's—yes, it's very indispensable. Yeah, yeah, yeah, yeah.

Scott Hebner

>> Just to say, hey, this is— I know this is what we built our business on, but it's time to change. And yeah, I thought it was very good and very clear. I like the idea of moving from probabilistic AI to increasingly deterministic.

Scott Hebner

>> Yes.

Scott Hebner

>> And all the stuff they're doing to build that stack. So, yeah, I thought it was a very interesting keynote. Okay, so before we start talking a little bit about what you see happening in the industry and talk a little bit about Civica, what you guys do, what your role as a CIO is.

Scott Hebner

>> Okay, yeah. So Civica's been around for a few decades now. It is a company, we provide SaaS and services to predominantly public sector, so local government, central government around the world. Our products and services are powering the interactions of citizens, about 100 million citizens right the way across the world. Typical use cases, it could be anything from how you buy your parking permit to how your kids pay for their school lunch using a contactless payment, or how you arrange social care for a relative. So we have a wide range of software that we deliver out through our professional services.

Scott Hebner

>> And customize, is it customized or is

Scott Hebner

>> it—Yeah, yeah, we— Yes, it's a combination. We have all of our base products, but yeah, we then do professional services to actually implement those, configure, customize, whatever the customer actually needs. And then my role as a CIO, so I own all of the technology that's used internally within Civica, so the corporate space. So that includes things like Salesforce and Certinia and our telephony and our finance system. So all of the internal technology, that comes under me.

Scott Hebner

>> And so what exactly are you doing with Salesforce and Certinia there?

Scott Hebner

>> Yeah, so Civica's actually grown quite successfully over a number of years, but it's grown organically and it's grown through acquisition, and that's left us with a landscape of bespoke systems, disconnected data, fragmentation. So we're going through a transformation at the moment. We've had Salesforce for a number of years. We are in the process of implementing Certinia, and it creates that center of gravity for everything relating to the customer. So all the data is in one place, and the data then flows. Data is captured in sales, flows through to delivery, and we're working from a single source of truth rather than multiple sources of truth.

Scott Hebner

>> And how do you see AI affecting all this? Big change?

Scott Hebner

>> Massively, yeah, yeah, it's going to be a massive change. It's probably a scale, isn't it? Some of it's going to be very incremental, but others are going to be for us groundbreaking in some areas. We're looking at how we can speed up our time to delivery, our time to value, time to revenue recognition. We are looking as well at how do we get the right mix of agentic workflows and people, humans. We're not sure what the answer is yet, but that's something we're navigating right now, but it's very exciting. All of that only works if you've got good operational data, trusted data, trusted source, so that you can trust in the outcome from whatever AI you're using.

Scott Hebner

>> I remember last year, I think it was last year, Marc Benioff did an interview with Dave Vellante at theCUBE, the same setting here, and he had made the comment that he was going to be the last leader at Salesforce to manage a human-only workforce.

Scott Hebner

>> Right.

Scott Hebner

>> Yeah.And so it's interesting your comment about there's going to be digital agents, digital coworkers. Yeah. How that mixes in. You know, my observation from just tracking the industries, I think AI will eventually be a net job grower.

Scott Hebner

>> Yes.Versus it's going to change the nature of jobs, but people are just going to get superpowers, going to be able to do more, and then they're going to want more people to do the high-value things,

Scott Hebner

>> right?Exactly. Exactly. The nature of work may change. It's not going to just do away with everybody's job overnight. I don't believe any of those predictions.

Scott Hebner

>> Or kill everyone over the weekend.

Scott Hebner

>> It's a really interesting debate going on right now around the pace of development and unchecked development. It's an interesting discussion.

Scott Hebner

>> Yeah, now I think that's one of the interesting things that certainly we have to struggle with, and it's a lot more intense than it was in the cloud transformation or client-server or How much do you slow down? How much do you regulate to slow down the technology advancement?

Scott Hebner

>> Yeah.

Scott Hebner

>> When perhaps other parts of the world, that's not going to happen as much.

Scott Hebner

>> Yes.

Scott Hebner

>> Yet you run into risks like, I don't know, I watched the video from OpenAI about how those models escaped and, you know.

Scott Hebner

>> Yes.

Scott Hebner

>> What that was like. They should make a movie out of that.

Scott Hebner

>> Yeah.

Scott Hebner

>> It's like a drama.

Scott Hebner

>> Well, yeah.

Scott Hebner

>> You know, and so it's going to be interesting to see where that all goes.

Scott Hebner

>> Yeah. Yeah.

Scott Hebner

>> And obviously you're in public sector a great deal of your business.

Scott Hebner

>> Yes.

Scott Hebner

>> Which tend to be a little bit more regulated.

Scott Hebner

>> Yeah.

Scott Hebner

>> Than everyday commercial industry. So how do you see AI affecting that world? You mentioned the keyword, which was trust.

Scott Hebner

>> Yeah. Trust is going to be the word that everybody's going to be hearing both at Dreamforce and outside for the next few years. It's just the most important word. We have to be able to trust what the AI is doing, trust the outputs, trust the decisions that it's either making for us or the decisions it's suggesting to us. So I think for our particular line of work where we're dealing with citizen data, absolutely we have to be able to provide that level of trust to our customers. And sometimes the work that we do means that we can't use models which are in a different country, and we have to think about residency and sovereignty and those different aspects of compliance and regulatory compliance.

Scott Hebner

>> That's another big topic these days is sovereign AI.

Scott Hebner

>> Yeah, yeah.

Scott Hebner

>> And it sort of reminds me of the cloud transformation in reverse.

Scott Hebner

>> Yes.

Scott Hebner

>> Right. But I think your data is proprietary, your information, your policies, your people.

Scott Hebner

>> Yeah.

Scott Hebner

>> And there's got to be an element of sovereign capabilities. And I think that's what's going to help to— and I think trust is multidimensional, right? Are the explanations accurate enough that you can rely on it and you feel confident about it?

Scott Hebner

>> Yes.

Scott Hebner

>> There's the human trust. These things are only going to be as good as humans trust them, right? Then there's all the compliance and security of data and do I trust that ROI are going to come? So I agree with you that if I was to summarize all the conversations I get to have in this job with leaders across the industry, the single word I would use to define the state of enterprise AI are that word trust.

Scott Hebner

>> Yeah, yeah.

Scott Hebner

>> Heavy, multidimensional. And I imagine in the environments you're working in, it's even higher because there's fewer degrees of freedom, or you need greater degrees of precision.

Scott Hebner

>> Yes. Yes.

Scott Hebner

>> To do things in those kind of regulated environments.

Scott Hebner

>> Absolutely. Yeah, absolutely. Yeah, yeah, the decisions we make need to be grounded in trust and in truth. And we have to be certain that the models that we use, we understand how they work. We understand where our data are going, how it's going to be used. How do we protect it? Because like you say, our data are proprietary to us. Our ways of working are proprietary to us.

Scott Hebner

>> Yeah, definitely a number one topic. In fact, we're actually running a digital summit on AI trust and cyber resiliency, which I think has been part of the slowdown with all technology ramps up and we've slowed down as an industry a little bit.

Scott Hebner

>> Yeah.

Scott Hebner

>> And I think it's because of that word trust, because as you get into more consequential, it's one thing to have us doing better analysis and all that. But when you start making consequential decisions that have consequence, like real consequence to the business, whether it's financial or reputational.

Scott Hebner

>> Yeah.you got to have that higher level of trust.

Scott Hebner

>> So, yes. Yeah, definitely, definitely.

Scott Hebner

>> Which is, I sort of like the, again, what Marc Benioff was saying about going from probabilistic to more deterministic. That's going to enhance a little bit of trust.

Scott Hebner

>> Yeah.

Scott Hebner

>> So Certinia's message here is be certain.

Scott Hebner

>> Yes.

Scott Hebner

>> Be certain about the actions and trying to drive towards greater certainty, which again is another one of those little corollaries or pillars off of trust.

Scott Hebner

>> Totally. Yeah. And all of that relies on that data foundation. Which leads us back to our transformation and our focus on that center of gravity of Salesforce and Certinia. We need data to flow through the process in a single system. Then we can use that to expose it to AI for agentic AI and use that to help develop insights.

Scott Hebner

>> Right.

Scott Hebner

>> Actionable insights.

Scott Hebner

>> Right.

Scott Hebner

>> Yeah.

Scott Hebner

>> Well that's one thing I'm really impressed with in terms of Certinia doing the— they have built the knowledge graphs and then now they have the ability to maintain context.

Scott Hebner

>> Yeah, right.

Scott Hebner

>> With the Moonnox acquisition. And now they're using that to create, more certainty.

Scott Hebner

>> Yeah.

Scott Hebner

>> Or, confidence in the actions that you take, the system of actions and how they relate and affect each other.

Scott Hebner

>> Absolutely.

Scott Hebner

>> Which ultimately will lead to better outcomes that you can then compound the value over time. So it's an interesting approach to it, which at its core is what you've been saying, is it's trust. It's addressing the trust issue head on.

Scott Hebner

>> Yeah, yeah, yeah. Now being certain, that's one thing which Certinia does give us. It gives us the ability to go from an estimation at the start of a deal process through delivery and understanding that the estimation is based on how we can actually deliver this. And if we improve the accuracy of the estimation at the front end, it improves the success of the project, the implementation, reduces time to market, improves the predictability of onboarding customers. Yeah. So yeah.

Scott Hebner

>> Yeah. And back to your point earlier about it's always going to be a mix. It's going to be a mix of humans and digital agents. Yeah.

Scott Hebner

>> One augments the other.

Scott Hebner

>> Yeah, exactly. And when the trust is improved between both of them, you're more certain about what you're doing.

Scott Hebner

>> Yeah. it makes a good, the word I was struggling with, it just came to me, is the word judgment.

Scott Hebner

>> Yes.

Scott Hebner

>> Because in the end, humans are still going to have to make judgments. The more they can trust the AI agents, the more certain they are that what they're recommending or what they're uncovering, the better judgment you're going to make. The better judgments you make, the better the outcomes. Yes, and less false negatives and going off the wrong path and stuff like that. So, and then you get things done cheaper, more efficiently, better price point, and you can do more, and you can see how it becomes an economic catalyst.

Scott Hebner

>> Yeah, exactly, exactly.

Scott Hebner

>> At least in the commercial market. Now, what additional constraints are you seeing just in the public sector area besides— because ROI doesn't really have the same meaning, does it, as in commercial?

Scott Hebner

>> Well, Yes and no. So I guess public sector aren't beholden to shareholders, but they are beholden to the public. And so they have to be able to demonstrate that they have made good judgment themselves in their purchasing decisions, buying our software and using that to deliver the services they need to the citizen. You know, they have to be able to justify it. So it's still an interesting conversation around ROI.

Scott Hebner

>> Yeah, I think the other thing I've been picking up on too is more and more leaders out there saying the architecture really matters now. It's not as simple as going off and choosing the most capable model.

Scott Hebner

>> No.

Scott Hebner

>> It now is becoming an architectural game where you have multiple elements of AI doing things, not just agents, but you have models and you have knowledge graphs and you got, yeah.

Scott Hebner

>> Yeah, exactly. Yeah, no, probably a year, 18 months ago, we were talking about what's your AI platform, whereas now, that's not even a question anymore because we use the right AI for the right job, the right circumstances.

Scott Hebner

>> Do it your way.

Scott Hebner

>> To do it our way. And that will mean using models from different providers. It'll be using solutions, AI solutions from different providers, but then the right solution for the right purpose and the right bit of our organization rather than blanket everybody gets everything.

Scott Hebner

>> Yeah. And where you put the models and whether they're in a sovereign environment and where the data is.

Scott Hebner

>> Exactly.

Scott Hebner

>> And then it becomes much more of an architectural.

Scott Hebner

>> Yes. Yeah. And we have to think about not just what are the tools that we're putting in place, but how do we govern those tools? What is the fabric or the framework that we are using to manage those and ultimately, should we ever need to stop them from doing what they're doing.

Scott Hebner

>> Yeah. So back to the notion of the headwinds of more regulatory or more constraints, or do you think we're at a point now that there's so much new technology out there that it's not a bad idea to kind of get our hands around it, figure out what you can do with it? Again, part of what I get to do is talk to tons and tons of people, and I get a sense that there's so much new stuff out there that's coming out so quickly that everyone's just struggling to understand it all.

Scott Hebner

>> Oh yes.

Scott Hebner

>> And it just seems like there's enough— there's going to be a while before it's saturated where we have to move on to the next innovation.

Scott Hebner

>> Yeah.

Scott Hebner

>> And it's going to be fascinating to watch the debate coming up about does the industry slow itself down? Now, by the way, these guys that are talking about this, the CEOs of Anthropic and OpenAI and all this, they can certainly just slow things down, right?

Scott Hebner

>> Yeah, yeah, yeah. Yeah, I'm not sure how it's going to go. Perhaps we're reaching this point and having these conversations now because the capabilities are becoming more credible. The ability is there to do more. There are more stories of models that have escaped their guardrails and there's been unintended consequences. And so it's good that we have these kinds of discussions. What the landscape's going to look like in a few years, that's anybody's guess. Are we going to see similar to the cloud technology landscape where it exploded and there were so many new providers, and then it kind of consolidated over time, and things dropped out. We focused then on core capabilities, which are now purely commodity. We don't even think about them anymore. And I think that's where AI is going to get eventually.

Scott Hebner

>> I remember we were all going to die with the year 2000, and all the COBOL developers are gone, and no one knows what they built. And I worked on the Millennium bug, remember that?

Scott Hebner

>> Yeah, yeah.

Scott Hebner

>> So sometimes you have to take these things with a grain of salt. But my sense in professional services at least is there's a lot of new technology that still needs to be tuned.

Scott Hebner

>> Yep.

Scott Hebner

>> Proven.

Scott Hebner

>> Yep.

Scott Hebner

>> A lot of really good use cases and outcomes so far.

Scott Hebner

>> Yeah.

Scott Hebner

>> But there's enough to work with now where maybe as an industry, just not so much the producers of the stuff, but the consumers get that trust level up to where before you start innovating to the next level.

Scott Hebner

>> And yeah, I agree. And I think it's almost incumbent on the consumers have to take a lot of responsibility because how you use a product, what you do with the product, what data you provide that product, that's a choice that you have to make. And there's an education which needs to happen right the way across the world. People need to understand what AI can do and what happens when they interact with it. And we have to take responsibility in our own businesses. About what that means. So for Civica, we consider any new AI tool or products before we introduce it. We think about, as the themes are talked about, where is our data going to be? Is it going to be used for anything other than what we say it's going to be used for? Where's it going to be processed? How many copies are going to be stored? all this kind of stuff. What can it do and how can we trust that it's going to deliver predictable outcomes, whatever that outcome is we're trying to achieve?

Scott Hebner

>> Got to, as an industry, ramp up the responsible AI mantra, which was very, very prevalent a couple of years ago. It kind of has faded back into— you don't hear as much about it anymore, but yeah, maybe the more recent, discussions will get that back up.

Scott Hebner

>> Yes, it's just going to become all our responsibility. we can't simply just let something happen and then say, well, it was the AI that did it.

Scott Hebner

>> Right. That won't—

Scott Hebner

>> I'm responsible. Yeah, yeah, yeah, yeah.

Scott Hebner

>> So last question here before we have to wrap up. So when you talk to a CIO, your cohort out there, what would be one question that they should ask every vendor before they really consider them?

Scott Hebner

>> Yeah.

Scott Hebner

>> If they're going to be like a provider of some sort of AI capability.

Scott Hebner

>> Yeah, so it all comes back to this one word, how can they demonstrate to us trust? How do we know that they understand how their product actually works, what it's doing, what are the implications for us, how will it work with everything else that we've got? Because we will never be a single vendor enterprise. I don't think any organization will be. So yeah, it's demonstrating to us with authenticity and credibility that the service that they're providing is what we need. And trustworthy. Yeah.

Scott Hebner

>> So if you're getting in there with this huge innovation message. Yeah. Needs to be balanced with the trust message.

Scott Hebner

>> Yeah, yeah. It's way more than just saying our product is great. Our product is the best out there. Why is it the best? Why should we trust you with our most valuable assets, our data?

Scott Hebner

>> Especially as it evolves into more consequential business decisions, that trust is the currency of innovation, I think, going forward, from this year on forward. So, everyone's got to double down on that. I think that answer's right on with everything I've heard. All right. Well, it's been great. I appreciate you taking the time to be here. Enjoy the rest of the show.

Scott Hebner

>> Thank you.

Scott Hebner

>> All right. All right. Well, we'll be back with more here from Dreamforce 2026. Hang in there. We'll be back real soon. Thanks.

Scott Hebner

>> Bye.

Simon Cornwell, Civica

search

Scott Hebner

>> Welcome back to theCUBE's continuing coverage of Dreamforce 2026, where we're focused on the professional services industry, which is clearly a leading indicator of where enterprise AI is heading. I'm Scott Hebner. I'm the principal analyst at theCUBE Research for AI. Appreciate you tuning in today. Today I'm thrilled to be joined by Simon Cornwell. He's the CIO at

Scott Hebner

>> Civica.Thanks very much,

Scott Hebner

>> Scott.Appreciate you being

Scott Hebner

>> here.Yeah, it's great to be

Scott Hebner

>> here.Thank you. It's been a great show, hasn't it?

Scott Hebner

>> Yeah. Dreamforce is always an intense mix of new announcements. We get to see where the products are going. We get to meet great vendors like Certinia. So yeah, good

Scott Hebner

>> show.Yeah, good weather too this time.

Scott Hebner

>> Yeah, it's a bit warm. It's a bit warm.

Scott Hebner

>> Not terrible. And we're in a great venue here with great food. They keep going around with the steaks and lobster. So makes it a great environment. Actually, before we dive into things here. What were your thoughts on Marc Benioff's keynote and where Salesforce is heading in general?

Scott Hebner

>> Yeah, I think it's really interesting. Two years ago, we were all talking about the SaaS-pocalypse, and this is the end of SaaS, we don't need SaaS anymore, we're going to just vibe code it in a weekend. I think what the direction set out in the keynote is really clear that SaaS in a way is a bit dead. SaaS really now becomes the system of record, it becomes where we keep our data, our workflows, our semantics, but actually the place of work where we do our work could be anywhere. It could be in Slack, it could be in Teams or Claude. And so, yeah, I think it's very exciting that they are moving away from a Salesforce-shaped product, which is only Salesforce-shaped.

Scott Hebner

>> And yeah, there's a massive corporate graveyard of companies that resisted the change. And

Scott Hebner

>> it's—yes, it's very indispensable. Yeah, yeah, yeah, yeah.

Scott Hebner

>> Just to say, hey, this is— I know this is what we built our business on, but it's time to change. And yeah, I thought it was very good and very clear. I like the idea of moving from probabilistic AI to increasingly deterministic.

Scott Hebner

>> Yes.

Scott Hebner

>> And all the stuff they're doing to build that stack. So, yeah, I thought it was a very interesting keynote. Okay, so before we start talking a little bit about what you see happening in the industry and talk a little bit about Civica, what you guys do, what your role as a CIO is.

Scott Hebner

>> Okay, yeah. So Civica's been around for a few decades now. It is a company, we provide SaaS and services to predominantly public sector, so local government, central government around the world. Our products and services are powering the interactions of citizens, about 100 million citizens right the way across the world. Typical use cases, it could be anything from how you buy your parking permit to how your kids pay for their school lunch using a contactless payment, or how you arrange social care for a relative. So we have a wide range of software that we deliver out through our professional services.

Scott Hebner

>> And customize, is it customized or is

Scott Hebner

>> it—Yeah, yeah, we— Yes, it's a combination. We have all of our base products, but yeah, we then do professional services to actually implement those, configure, customize, whatever the customer actually needs. And then my role as a CIO, so I own all of the technology that's used internally within Civica, so the corporate space. So that includes things like Salesforce and Certinia and our telephony and our finance system. So all of the internal technology, that comes under me.

Scott Hebner

>> And so what exactly are you doing with Salesforce and Certinia there?

Scott Hebner

>> Yeah, so Civica's actually grown quite successfully over a number of years, but it's grown organically and it's grown through acquisition, and that's left us with a landscape of bespoke systems, disconnected data, fragmentation. So we're going through a transformation at the moment. We've had Salesforce for a number of years. We are in the process of implementing Certinia, and it creates that center of gravity for everything relating to the customer. So all the data is in one place, and the data then flows. Data is captured in sales, flows through to delivery, and we're working from a single source of truth rather than multiple sources of truth.

Scott Hebner

>> And how do you see AI affecting all this? Big change?

Scott Hebner

>> Massively, yeah, yeah, it's going to be a massive change. It's probably a scale, isn't it? Some of it's going to be very incremental, but others are going to be for us groundbreaking in some areas. We're looking at how we can speed up our time to delivery, our time to value, time to revenue recognition. We are looking as well at how do we get the right mix of agentic workflows and people, humans. We're not sure what the answer is yet, but that's something we're navigating right now, but it's very exciting. All of that only works if you've got good operational data, trusted data, trusted source, so that you can trust in the outcome from whatever AI you're using.

Scott Hebner

>> I remember last year, I think it was last year, Marc Benioff did an interview with Dave Vellante at theCUBE, the same setting here, and he had made the comment that he was going to be the last leader at Salesforce to manage a human-only workforce.

Scott Hebner

>> Right.

Scott Hebner

>> Yeah.And so it's interesting your comment about there's going to be digital agents, digital coworkers. Yeah. How that mixes in. You know, my observation from just tracking the industries, I think AI will eventually be a net job grower.

Scott Hebner

>> Yes.Versus it's going to change the nature of jobs, but people are just going to get superpowers, going to be able to do more, and then they're going to want more people to do the high-value things,

Scott Hebner

>> right?Exactly. Exactly. The nature of work may change. It's not going to just do away with everybody's job overnight. I don't believe any of those predictions.

Scott Hebner

>> Or kill everyone over the weekend.

Scott Hebner

>> It's a really interesting debate going on right now around the pace of development and unchecked development. It's an interesting discussion.

Scott Hebner

>> Yeah, now I think that's one of the interesting things that certainly we have to struggle with, and it's a lot more intense than it was in the cloud transformation or client-server or How much do you slow down? How much do you regulate to slow down the technology advancement?

Scott Hebner

>> Yeah.

Scott Hebner

>> When perhaps other parts of the world, that's not going to happen as much.

Scott Hebner

>> Yes.

Scott Hebner

>> Yet you run into risks like, I don't know, I watched the video from OpenAI about how those models escaped and, you know.

Scott Hebner

>> Yes.

Scott Hebner

>> What that was like. They should make a movie out of that.

Scott Hebner

>> Yeah.

Scott Hebner

>> It's like a drama.

Scott Hebner

>> Well, yeah.

Scott Hebner

>> You know, and so it's going to be interesting to see where that all goes.

Scott Hebner

>> Yeah. Yeah.

Scott Hebner

>> And obviously you're in public sector a great deal of your business.

Scott Hebner

>> Yes.

Scott Hebner

>> Which tend to be a little bit more regulated.

Scott Hebner

>> Yeah.

Scott Hebner

>> Than everyday commercial industry. So how do you see AI affecting that world? You mentioned the keyword, which was trust.

Scott Hebner

>> Yeah. Trust is going to be the word that everybody's going to be hearing both at Dreamforce and outside for the next few years. It's just the most important word. We have to be able to trust what the AI is doing, trust the outputs, trust the decisions that it's either making for us or the decisions it's suggesting to us. So I think for our particular line of work where we're dealing with citizen data, absolutely we have to be able to provide that level of trust to our customers. And sometimes the work that we do means that we can't use models which are in a different country, and we have to think about residency and sovereignty and those different aspects of compliance and regulatory compliance.

Scott Hebner

>> That's another big topic these days is sovereign AI.

Scott Hebner

>> Yeah, yeah.

Scott Hebner

>> And it sort of reminds me of the cloud transformation in reverse.

Scott Hebner

>> Yes.

Scott Hebner

>> Right. But I think your data is proprietary, your information, your policies, your people.

Scott Hebner

>> Yeah.

Scott Hebner

>> And there's got to be an element of sovereign capabilities. And I think that's what's going to help to— and I think trust is multidimensional, right? Are the explanations accurate enough that you can rely on it and you feel confident about it?

Scott Hebner

>> Yes.

Scott Hebner

>> There's the human trust. These things are only going to be as good as humans trust them, right? Then there's all the compliance and security of data and do I trust that ROI are going to come? So I agree with you that if I was to summarize all the conversations I get to have in this job with leaders across the industry, the single word I would use to define the state of enterprise AI are that word trust.

Scott Hebner

>> Yeah, yeah.

Scott Hebner

>> Heavy, multidimensional. And I imagine in the environments you're working in, it's even higher because there's fewer degrees of freedom, or you need greater degrees of precision.

Scott Hebner

>> Yes. Yes.

Scott Hebner

>> To do things in those kind of regulated environments.

Scott Hebner

>> Absolutely. Yeah, absolutely. Yeah, yeah, the decisions we make need to be grounded in trust and in truth. And we have to be certain that the models that we use, we understand how they work. We understand where our data are going, how it's going to be used. How do we protect it? Because like you say, our data are proprietary to us. Our ways of working are proprietary to us.

Scott Hebner

>> Yeah, definitely a number one topic. In fact, we're actually running a digital summit on AI trust and cyber resiliency, which I think has been part of the slowdown with all technology ramps up and we've slowed down as an industry a little bit.

Scott Hebner

>> Yeah.

Scott Hebner

>> And I think it's because of that word trust, because as you get into more consequential, it's one thing to have us doing better analysis and all that. But when you start making consequential decisions that have consequence, like real consequence to the business, whether it's financial or reputational.

Scott Hebner

>> Yeah.you got to have that higher level of trust.

Scott Hebner

>> So, yes. Yeah, definitely, definitely.

Scott Hebner

>> Which is, I sort of like the, again, what Marc Benioff was saying about going from probabilistic to more deterministic. That's going to enhance a little bit of trust.

Scott Hebner

>> Yeah.

Scott Hebner

>> So Certinia's message here is be certain.

Scott Hebner

>> Yes.

Scott Hebner

>> Be certain about the actions and trying to drive towards greater certainty, which again is another one of those little corollaries or pillars off of trust.

Scott Hebner

>> Totally. Yeah. And all of that relies on that data foundation. Which leads us back to our transformation and our focus on that center of gravity of Salesforce and Certinia. We need data to flow through the process in a single system. Then we can use that to expose it to AI for agentic AI and use that to help develop insights.

Scott Hebner

>> Right.

Scott Hebner

>> Actionable insights.

Scott Hebner

>> Right.

Scott Hebner

>> Yeah.

Scott Hebner

>> Well that's one thing I'm really impressed with in terms of Certinia doing the— they have built the knowledge graphs and then now they have the ability to maintain context.

Scott Hebner

>> Yeah, right.

Scott Hebner

>> With the Moonnox acquisition. And now they're using that to create, more certainty.

Scott Hebner

>> Yeah.

Scott Hebner

>> Or, confidence in the actions that you take, the system of actions and how they relate and affect each other.

Scott Hebner

>> Absolutely.

Scott Hebner

>> Which ultimately will lead to better outcomes that you can then compound the value over time. So it's an interesting approach to it, which at its core is what you've been saying, is it's trust. It's addressing the trust issue head on.

Scott Hebner

>> Yeah, yeah, yeah. Now being certain, that's one thing which Certinia does give us. It gives us the ability to go from an estimation at the start of a deal process through delivery and understanding that the estimation is based on how we can actually deliver this. And if we improve the accuracy of the estimation at the front end, it improves the success of the project, the implementation, reduces time to market, improves the predictability of onboarding customers. Yeah. So yeah.

Scott Hebner

>> Yeah. And back to your point earlier about it's always going to be a mix. It's going to be a mix of humans and digital agents. Yeah.

Scott Hebner

>> One augments the other.

Scott Hebner

>> Yeah, exactly. And when the trust is improved between both of them, you're more certain about what you're doing.

Scott Hebner

>> Yeah. it makes a good, the word I was struggling with, it just came to me, is the word judgment.

Scott Hebner

>> Yes.

Scott Hebner

>> Because in the end, humans are still going to have to make judgments. The more they can trust the AI agents, the more certain they are that what they're recommending or what they're uncovering, the better judgment you're going to make. The better judgments you make, the better the outcomes. Yes, and less false negatives and going off the wrong path and stuff like that. So, and then you get things done cheaper, more efficiently, better price point, and you can do more, and you can see how it becomes an economic catalyst.

Scott Hebner

>> Yeah, exactly, exactly.

Scott Hebner

>> At least in the commercial market. Now, what additional constraints are you seeing just in the public sector area besides— because ROI doesn't really have the same meaning, does it, as in commercial?

Scott Hebner

>> Well, Yes and no. So I guess public sector aren't beholden to shareholders, but they are beholden to the public. And so they have to be able to demonstrate that they have made good judgment themselves in their purchasing decisions, buying our software and using that to deliver the services they need to the citizen. You know, they have to be able to justify it. So it's still an interesting conversation around ROI.

Scott Hebner

>> Yeah, I think the other thing I've been picking up on too is more and more leaders out there saying the architecture really matters now. It's not as simple as going off and choosing the most capable model.

Scott Hebner

>> No.

Scott Hebner

>> It now is becoming an architectural game where you have multiple elements of AI doing things, not just agents, but you have models and you have knowledge graphs and you got, yeah.

Scott Hebner

>> Yeah, exactly. Yeah, no, probably a year, 18 months ago, we were talking about what's your AI platform, whereas now, that's not even a question anymore because we use the right AI for the right job, the right circumstances.

Scott Hebner

>> Do it your way.

Scott Hebner

>> To do it our way. And that will mean using models from different providers. It'll be using solutions, AI solutions from different providers, but then the right solution for the right purpose and the right bit of our organization rather than blanket everybody gets everything.

Scott Hebner

>> Yeah. And where you put the models and whether they're in a sovereign environment and where the data is.

Scott Hebner

>> Exactly.

Scott Hebner

>> And then it becomes much more of an architectural.

Scott Hebner

>> Yes. Yeah. And we have to think about not just what are the tools that we're putting in place, but how do we govern those tools? What is the fabric or the framework that we are using to manage those and ultimately, should we ever need to stop them from doing what they're doing.

Scott Hebner

>> Yeah. So back to the notion of the headwinds of more regulatory or more constraints, or do you think we're at a point now that there's so much new technology out there that it's not a bad idea to kind of get our hands around it, figure out what you can do with it? Again, part of what I get to do is talk to tons and tons of people, and I get a sense that there's so much new stuff out there that's coming out so quickly that everyone's just struggling to understand it all.

Scott Hebner

>> Oh yes.

Scott Hebner

>> And it just seems like there's enough— there's going to be a while before it's saturated where we have to move on to the next innovation.

Scott Hebner

>> Yeah.

Scott Hebner

>> And it's going to be fascinating to watch the debate coming up about does the industry slow itself down? Now, by the way, these guys that are talking about this, the CEOs of Anthropic and OpenAI and all this, they can certainly just slow things down, right?

Scott Hebner

>> Yeah, yeah, yeah. Yeah, I'm not sure how it's going to go. Perhaps we're reaching this point and having these conversations now because the capabilities are becoming more credible. The ability is there to do more. There are more stories of models that have escaped their guardrails and there's been unintended consequences. And so it's good that we have these kinds of discussions. What the landscape's going to look like in a few years, that's anybody's guess. Are we going to see similar to the cloud technology landscape where it exploded and there were so many new providers, and then it kind of consolidated over time, and things dropped out. We focused then on core capabilities, which are now purely commodity. We don't even think about them anymore. And I think that's where AI is going to get eventually.

Scott Hebner

>> I remember we were all going to die with the year 2000, and all the COBOL developers are gone, and no one knows what they built. And I worked on the Millennium bug, remember that?

Scott Hebner

>> Yeah, yeah.

Scott Hebner

>> So sometimes you have to take these things with a grain of salt. But my sense in professional services at least is there's a lot of new technology that still needs to be tuned.

Scott Hebner

>> Yep.

Scott Hebner

>> Proven.

Scott Hebner

>> Yep.

Scott Hebner

>> A lot of really good use cases and outcomes so far.

Scott Hebner

>> Yeah.

Scott Hebner

>> But there's enough to work with now where maybe as an industry, just not so much the producers of the stuff, but the consumers get that trust level up to where before you start innovating to the next level.

Scott Hebner

>> And yeah, I agree. And I think it's almost incumbent on the consumers have to take a lot of responsibility because how you use a product, what you do with the product, what data you provide that product, that's a choice that you have to make. And there's an education which needs to happen right the way across the world. People need to understand what AI can do and what happens when they interact with it. And we have to take responsibility in our own businesses. About what that means. So for Civica, we consider any new AI tool or products before we introduce it. We think about, as the themes are talked about, where is our data going to be? Is it going to be used for anything other than what we say it's going to be used for? Where's it going to be processed? How many copies are going to be stored? all this kind of stuff. What can it do and how can we trust that it's going to deliver predictable outcomes, whatever that outcome is we're trying to achieve?

Scott Hebner

>> Got to, as an industry, ramp up the responsible AI mantra, which was very, very prevalent a couple of years ago. It kind of has faded back into— you don't hear as much about it anymore, but yeah, maybe the more recent, discussions will get that back up.

Scott Hebner

>> Yes, it's just going to become all our responsibility. we can't simply just let something happen and then say, well, it was the AI that did it.

Scott Hebner

>> Right. That won't—

Scott Hebner

>> I'm responsible. Yeah, yeah, yeah, yeah.

Scott Hebner

>> So last question here before we have to wrap up. So when you talk to a CIO, your cohort out there, what would be one question that they should ask every vendor before they really consider them?

Scott Hebner

>> Yeah.

Scott Hebner

>> If they're going to be like a provider of some sort of AI capability.

Scott Hebner

>> Yeah, so it all comes back to this one word, how can they demonstrate to us trust? How do we know that they understand how their product actually works, what it's doing, what are the implications for us, how will it work with everything else that we've got? Because we will never be a single vendor enterprise. I don't think any organization will be. So yeah, it's demonstrating to us with authenticity and credibility that the service that they're providing is what we need. And trustworthy. Yeah.

Scott Hebner

>> So if you're getting in there with this huge innovation message. Yeah. Needs to be balanced with the trust message.

Scott Hebner

>> Yeah, yeah. It's way more than just saying our product is great. Our product is the best out there. Why is it the best? Why should we trust you with our most valuable assets, our data?

Scott Hebner

>> Especially as it evolves into more consequential business decisions, that trust is the currency of innovation, I think, going forward, from this year on forward. So, everyone's got to double down on that. I think that answer's right on with everything I've heard. All right. Well, it's been great. I appreciate you taking the time to be here. Enjoy the rest of the show.

Scott Hebner

>> Thank you.

Scott Hebner

>> All right. All right. Well, we'll be back with more here from Dreamforce 2026. Hang in there. We'll be back real soon. Thanks.

Scott Hebner

>> Bye.