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Welcome back to another episode of AI in action. I’m Eric Housh, joined as always, by Fast Slow Motion’s Director of AI, Zack Terry. Today we’re diving into the Salesforce Spring 26 release, but we’re not doing a general overview. We’ve already covered the big picture themes on the Fast Slow Motion podcast. If you missed that, that hit the air earlier this month.

This episode is going to be a little bit different today. We’re going to zero in on the five most important AI centered updates in this release. So not flashy headlines. Not feature list. These are updates that actually change how you build governance, measure and scale AI inside your business. Zack, the word that came up on the FSM podcast was maturity.

And that feels right here too. I agree. Yeah, it’s really not a very shiny release in terms of new features and crazy cool things coming out. It’s a lot more about operationalizing things like AI and automation responsibly in your org. So definitely less of the flashy magic and more tools related to control, visibility, sustainability, scalability, observation of what’s going on.

So I think I think AI and automation maturity is a great way to describe it. Absolutely. So let’s start at the top. The one I think everybody’s talking about, AI building flows through natural language. What do we need to know about this? This has been in beta for a little bit, so you’ve probably had a chance to play with it if you’re up to date on beta updates, but now it’s becoming generally available.

And the whole idea here is, you know, about vibe coding. I’m sure you’ve heard about this. Or you have developers that are using things like cursor or cloud code and they’re pair coding with AI. This is similar but for flow. So flow being Salesforce’s declarative automation suite where you can essentially graphically build automations. It’s almost just creating code under the scenes, right.

But you’re using a graphical interface to drag and drop elements and create that logic. So now there is a generally available feature where you can use natural language prompts to describe what you want, when you want to build a flow. And then Agent Force will actually go out and build that flow for you. Now, the caveat there is it’s really more about a first draft.

And so just like you wouldn’t or I should say shouldn’t just do one prompt when you’re coding with AI and just take that and say, yes, this is a finished product. Same idea. You don’t want to just assume it’s building everything right the first time. So I kind of look at it as a way to go from a blank canvas with nothing in front of you to having a really nice first draft, but you still have to proofread that draft.

You still have to validate that it’s working correctly. So it really kind of removes the blank canvas paralysis. If you’ve ever opened up a flow in, you’re like, all right, this is intimidating. Where do I begin? It can accelerate that development time so that you’re not having to worry about going from 0 to 100. It’s going to be more like 0 to 70, right?

You’re going to get to 70% of the way there. And then you’ll probably have to refine some things. There may be some logic that doesn’t work perfectly, or it may have made some assumptions and you know how you prompted it. So the main thing here is the human in the loop is still very important. And we want to make sure that any automations that are being built with this tool aren’t just getting deployed directly to production.

We should still follow the development lifecycle. We should still do proper testing, and we should still have a human and an expert’s eyes on it before it actually goes into production. Because remember, this stuff is is probably touching some pretty critical functions inside of your work. Yeah, that’s what I was just thinking. If somebody, you know, prompts deploys without the proper testing, without the proper validation, that could touch revenue compliance, customer experience could really open up a can of worms.

So crucial. We said this on this podcast dozens of times. Keeping that human in the loop. Exactly. Definitely don’t want to risk introducing something unproven and untested into production. So, kind of paring that back. It really is. It’s about acceleration. You’re still going to go through the development process. Think about it like pair coding with AI.

Except now you’re you’re pair flowing. You’re creating that flow with the help of an AI assistant. But the responsibility is still yours. The human is still responsible for what ultimately gets deployed. And so you want to make sure that you are working with it and validating it and testing it and doing all the things you would do even if you didn’t have this AI tool.

Just use that to help make the process faster. Moving on, Zack, and this is really salient, given a conversation we recently had, every A.I. conversation now is coming down to what does it cost? What’s the value going on here? How are these agents and workflows and AI features actually delivering value to the business? What’s new in the spring 26 release that makes AI consumption a little bit more transparent and easy to see in Salesforce?

AI consumption has always been a little bit of a black box, so these updates are making things a little bit more clear. Now, it may not be as clear up front when you go and actually purchase these products, but in terms of hey, how can I monitor my consumption over time? These tools are designed to give you a more granular breakdown of what’s going on.

So the digital wallet product that Salesforce has to offer, this was introduced a few years ago. It’s a graphical interface to show you all of your variable costs and credits in your org. And what that mostly means is flex credits for AI use and data services credits for Data Cloud. Now, long term, I think we expect these to merge together.

I think flex credit will be the single currency that’s used throughout the system, but right now they’re still using some some different currencies for these two different products. But the idea here is previously the only thing you can really see is, well, what’s my total number of flex credits and how far along am I on consumption. And same thing on data, cloud and data services.

Well this update is going to bring is much more granular control over that usage. And so you’ll be able to see specific agents that are causing this usage. You can go and find tags for different services that are actually incurring consumption. And this is really important because sometimes you may not know that there are certain background processes that have been enabled that are consuming more credits than maybe you thought.

And so the ability to go and dive in and say, you know what, it’s not just I consumed 100,000 credits yesterday. You can say, oh, our users consumed about 10,000 credits actually interacting with AI. But this other service that we enabled is consuming 90% of the day’s usage, right? And so to be able to look at that, it really empowers you to make better decisions, to understand, hey, are we actually getting the ROI that we need to out of these tools?

Do we have the proper number of credits to get us through the next month, the next quarter, the next year? It’s a good improvement, honestly, because it does give you the ability to not only dive into all of those standard features, but you can now create custom tags. And so let’s say you wanted to look at usage across department.

And so you wanted to tag certain roles or certain users with these tags so that you could see, okay, this particular department is using this agent 20% of the time compared to this other department that’s using it 80% of the time. So it gives you a little bit more control to understand where that demand is going to be.

And if we need to get more credits for a certain department, you’re able to go and increase that usage if you need to. So I think it’s really making that I spend more attributable as opposed to just abstract. I just have a number and I have to figure out where that number is going. You can now drive into that quite a bit more than you could before.

So that’s the digital wallet update. And then on license utilization, Salesforce has created a new modern license component. If you’ve ever tried to see your licenses in Salesforce, you know that it typically opens up in an old Salesforce Classic window. And I’ve experienced where I click into that and it doesn’t even load because it’s trying to open it in a lightning environment.

I can’t even see the licenses. And so sometimes you can only see a portion of it that has gone away. So now you have a fully modern lightning enabled component for all of your licenses. You can really easily see all the licenses that you have by name, how many you have in total available, and how many have been assigned.

And then you can see how many you have left. And so it makes it a lot easier to just see in one location which seats have been assigned and which have it, so that you can properly assign those, you know, make sure you’re getting the most out of your contract. And then, of course, when renewal comes around, you can have an idea of, well, maybe you’re not using all the licenses that you thought you were going to use, and so you can kind of ramp that down or move those into other areas.

It’s good stuff because it’s turning AI now from this sort of black box into something more measurable and our leaders that are investing in AI for their businesses, it’s allowing them to expand or pause AI initiatives based on what’s actually happening in versus what they feel might be happening in that data, front and center to allow those good decisions.

Yeah, it’s hard to be confident without understanding how these credits are being used. So adding this additional visibility, I think, will drive more confidence in how credits can be used to enable AI across your organization. And ultimately, that confidence can help drive adoption because your users will, you know, they’ll be they’ll be more ready to use those tools, and you’ll be able to measure it in a way that you can make decisions in terms of what’s best for our organization and where should the spend be allocated.

So moving on, one of the quieter but arguably more powerful updates in this release is scale center and enhanced flow observability. What does that or why does that matter for AI? This is really important because we talked about credits. And if you’re going to scale AI, that means you’re also going to have to scale credits, which ultimately means spend.

You’re going to spend more, right? You’re going to end up spending on those credits. And so to have more tools in place to allow you to measure those and plan for that scale, that’s really important. So Scale Center has been around for a while, but now it’s bringing some some more features. And specifically this enhanced flow analysis capability is really nice because it’s going to give you the ability to see every flow in terms of the individual interview of those flows, how they’re executing, how many times they’re executing, errors that are actually coming across, what’s causing those errors.

Maybe it gives you some better visibility into cascading failures. So maybe I have a flow that’s calling another flow or I’ve got some apex trigger that’s also calling another flow. It can be difficult to track that kind of cascade effect. And so this gives you the ability to see a lot of those errors. And understand how to troubleshoot those errors.

You can plan for higher traffic workflows because you’re able to see kind of everything that’s going on. But really it comes down to being proactive instead of reactive. And so having the tools available to visualize all of this automation and the performance of these automations, as opposed to trying to fix it, just when an error occurs, when an error occurs, you now have the ability to have more information in front of you.

But hopefully this this leads to a more proactive approach where you can stop those errors before they have a chance to really create a problem in the organization. So it’s proactive issue detection, faster root cause analysis. Hopefully that leads to more measurable ROI as your team optimizes or takes the time to optimize those flows and those automations. That’s one thing that’s been pretty difficult in it to to prove the value of time spent optimizing automations.

And so to be able to say, look, a week ago, this was causing us ten hours of downtime. We’ve been able to monitor that, and now we expect it to be less than an hour of downtime. And we’re working to get that down to zero. Just gives you more capability to report to executives to understand this is how this time is being spent and how that translates directly to ROI over time.

So hopefully that also leads to reduced downtime risk. And for scaling organizations, large enterprise organizations, you probably have thousands of flows and automations. And so the ability to be able to see that and proactively address potential issues is really important. Combined with AI’s propensity to potentially create even more automation layers. Observability is is a really big deal. And so seeing more tools come out to give us the ability to observe and optimize automations is critical to being able to scale these system.

It’s that difference between running experiments with AI and actually having AI deployed in production, meaningfully contributing to your business. Definitely. And we’ve seen that shift. We’re still in the early innings, right? This is still a brand new technology in terms of, you know, the whole history of the internet and, and all of these SaaS sort of systems.

But, I think we’re moving from the early days of experimentation more into systems that are actually being deployed in production. And so that visibility into what is going on is even more important than it’s ever been. And so, again, we’re seeing that trend towards less experimentation and more AI and automation maturity as a focus in these releases.

Zack, our next topic, a little subtle but fascinating. Some folks call it answer engine optimization. Salesforce is calling it generative engine optimization. What breakthroughs? What features has Salesforce actually released around this? So let’s talk about generative engine optimization and what that is. You you mentioned answer engine optimization. They’re much the same thing. The whole idea behind this is people are using AI to help them make decisions.

So researching products using AI where beforehand we’re using Google and we’re going and we’re searching through pages of results. And you know, we’re having to actually go and click through them and make those decisions. Maybe we’re taking that information and putting it in a spreadsheet somewhere so we can compare it. Well, now people are more and more turning to AI to do that for them.

And so previously SEOs, the big thing right. Like you want to rank in those search results was the same idea with these AI tools. And so they don’t use the same type of SEO. In fact, when you go and you ask a question to AI and you’re asking it to go, and let’s say you’re researching lawnmowers because you know, you’ve got the spring coming up, you got to make sure you’re prepared for all the new growth.

And, you know, you need a new lawnmower. Well, if you go and you Google that, you’re going to go and parse through a bunch of results and try to figure out a decision your own. If you use AI, the AI is going to formulate its own query, and then it’s going to use that query to go out and actually find those results.

And so the idea behind Geo or Aiteo is all about making sure that your products and your services rank in those AI results, so that when an AI is searching for some keyword or something that you want to rank in, the AI is able to find your product, your service and return that back to the user so that it actually ranks in those results.

And so it’s a different muscle than SEO. It’s a different mechanism than SEO. And so specifically with Salesforce, how are they addressing this. Well right now it’s really limited to experience cloud. But they have a tool and experience cloud now. And it’s really as simple as turning on a toggle. And it’s basically doing that for you.

So it’s analyzing the material that you have on an experienced site. Let’s say it’s a public site, a health portal, or maybe you’re, you know, you’re actually using e-commerce on an experienced site. People are buying products through there that gives the ability to, Salesforce to take that information and optimize it for AI agents to be able to find that information and service it in those results.

So that’s one of the bigger updates. And then, you also have some AI optimized content generation for experience sites. And so, we mentioned the whole geo coming up with that structured metadata enhancement to make sure that is available in AI. But you can also use it to help you create some content. You can use it to help create some CMS assets that you might have in the system.

And so, we’re seeing both sort of this passive, hey, how can I increase the ability of my products and services to appear to AI and then also using AI to help create some of that content? So the big thing there is if I can’t understand your content, it can’t recommend it. So this helps. I understand that content in a way that it can be discovered by those agents, and it helps your specifically Salesforce experience.

Cloud portals stay visible in an AI driven ecosystem. Less about marketing tactics, more about future proofing discoverability. As generative engine optimization becomes sort of the de facto starting point, to problem solving for people using the internet. Very much sort of a forward looking feature. I think this is preparing for the inevitable future. Granted, it has real implications. Now, people are doing this today, and so having the ability to optimize that for today and futureproof it so that, you know, as as this trend continues, you are well positioned to leverage search results inside of these AI engines.

Saving the most sexy topic for last. Retirement’s. Right. These things are never exciting, but we got to talk about them because they are impactful. They are critical, leaders out there focused on AI. What from a retirement perspective, do they need to be paying attention to this release? I’ll start with the general, and then I’ll go into some specifics.

So in terms of retirements, just generally speaking this happens every release. So Salesforce is a massive company with a ton of products a ton of services. And these aren’t going to continue forever. Right? Engineering has new priorities, new technologies to focus on. Different versions are being created and launched. And so naturally things start to be retired over time.

We saw this with process Builder and so Process Builder is no longer supported. You can’t create one. I don’t even think you can edit one anymore. You’ve got to do it in flow now, right? So that’s that’s one of the big examples of hey, this is how a retirement can affect you. You’re going to have to take those process builders and migrate them to flow, and you’re going to have to do it by a certain deadline.

Right. So there’s there’s risks associated with these retirements. You’re going to have unsupported legacy automation tools. And unsupported can mean they continue to work. But Salesforce doesn’t continue to actually offer support for those. So if you have any bugs or issues, you’re kind of out of luck unless you move to whatever new tool is available. Any sort of AI adjacent features, if they lose support and you rely on them.

So, for example, the Legacy Agent Force analytics tool is being retired in favor of the new session tracing model. And so if you’re using Agent Force analytics, probably gonna want to move off of that and move to the session tracing model, which gives you, optimization and observability. You may have experienced cloud components which are public facing your customers or accessing them.

If those components lose support, you’re gonna want to make sure to replace those so that you have business continuity and you’re not having a bad customer experience. And so when these features retire, you lose support from Salesforce. Maintenance on these tools stops altogether. And compatibility will degrade over time. So even though you may be able to continue using it today, if you look at six months, a year or two years, three years down the road, how it’s working today may not at all be the case in the future, right?

So it’s really important to prepare for that. So some of the specifics that we have connected apps. So the ability to create new connected apps is being discontinued entirely in favor of external client apps. And so if you’re still using those legacy connected apps, you’ll want to make sure to to migrate those over to external client apps. There are some lightning component libraries that are being retired and being replaced by a new feature called Lightning Component Reference.

So you want to make sure that you’re up to date on your lightning components. The old chat and live agent is being completely retired. I actually think it’s already retired. That date has passed us. They did that on Valentine’s Day. So happy Valentine’s Day. No more live chat for you. That’s that’s moving over to Enhanced Chat, which is previously known as messaging for in-app and web.

The classic knowledge data model is going to end of support on March 1st. So there are there have been two different knowledge models. If you’ve created a knowledge base in recent history, you’re probably on the newer version, but worth evaluating. If you use knowledge, make sure you’re not on that legacy data model. What’s interesting is data Mask, which has been a feature of the Einstein Trust platform.

The data mask feature is going away on May 1st, and so if you’ve been using Data Mask for Agent Force, you want to make sure to evaluate that and ensure that that’s not going to, mess up with any of your compliance or anything like that. And then, one that’s really interesting is Salesforce to Salesforce. So this is a feature that has been around for a very long time.

And in fact, I think you still have to get the classic to even use it. So it’s not surprising that it’s going away. I’m actually surprised that it’s been around as long as it has been, but that is an old feature that allows you to connect one Salesforce org to another. Salesforce org and pass data back and forth between them.

There’s a lot more modern mechanisms for that now. And so this this is going away. So if you use Salesforce or Salesforce I know we’ve had clients that have done that. We’ve actually implemented that for a number of clients years ago, I might add. But make sure that you’re not relying on that because it cannot be enabled in new orgs starting in spring 26.

Now, what that tells me is it’s not going to break starting in this release, but the support is going away. And so even if it’s not going to disrupt your business today, I would highly recommend planning for a different solution just to make sure that you’re not continuing to operate on legacy technology that’s no longer going to be supported.

Yeah, we talk about maturity, Zack. And in this case, maturity means planning not reacting. So not to give a sales pitch, but this is you know, if it’s been a couple of years since you’ve looked under the hood of your Salesforce instance in a meaningful way with consideration to these upgrades and retirements, a 30 minute conversation, a 40 minute conversation with one of our solution engineers in the context of what’s happening with Salesforce, development can be incredibly useful for helping you look around corners and sort of avoid some emergencies in the future.

Absolutely. We do our best to stay on top of these, and a great example of that is that process builder and flow, right? We have helped so many clients move all of their legacy process builders over to flow, and that’s a major project. Some of these retirements are major, but some of them are relatively small. And you just kind of have to know.

All right. This feature maps to this feature. And that’s something we can absolutely help you with. So, reach out to us if you’re worried about any of these retirements and we can help you walk through what your next steps can be. So is that great episode so far? As we start to wrap it up here, the words that come to mind.

So what? Right. Our executives, our leaders out there listening to this episode, if they’re taking one thing away, what should that be? So we said it at the top. But this release is all about operational maturity and moving from AI experimentation to maturity in AI implementation. And so five sort of key themes that are coming out of this release faster build.

But humans still remain accountable for deployment. So gotta keep that human in the loop. AI spending visibility can really help bring transparency to the AI black box. So having more controls and visibility into how AI is being consumed across your organization. Observability solutions that can assist with growth, scale and ROI reporting. So things like the flow analysis and the analytics that you’re going to get there and scale center.

We also have discoverability in AI driven search. So making sure that we are prepared for both now and the future and ensuring that we are ranking in terms of AI search results and making sure that our products and services are visible and serviceable to those AI tools. And then of course, governance focusing on proactively addressing issues before features retire or before systems fail.

So definitely not as exciting of a release in terms of hey, here’s some flashy new marketing features and brand new tools. But for organizations that rely on Salesforce, this is a really good release because it’s bringing more reliability to the platform and it’s bringing more observability into how you use the system so that you can make better decisions.

And so I think it’s making things like AI and automation running in the background a little bit safer, a little bit more measurable, and certainly more scalable over time. By focusing on how we can create mature workflows and observability patterns in our organizations, as Zack, we call the show AI in action. So let’s make it practical for our leaders.

Listening. What do they need to be doing right now? Top of mind. Just as someone who works with Agent Force a lot, go and check out the changes to Digital Wallet. So if you want to know more than just I have this many credits and here’s how many I’ve consumed, go and check out some of those new features.

There’s a couple things you have to enable, so you’ll have to turn on a couple of features in order to get all of the benefits of this. But I would recommend turning it on, maybe experimenting with some custom tags, you know, see if there’s some additional visibility that, that you want to gain in terms of who is using AI the most or which particular areas inside of our organization are using AI the most.

You can also experiment with the new AI generated flow capabilities. Just make sure to do that responsibly and keep a human in the loop and don’t trust the first output immediately. You can go and turn on and flow embedded analytics so that you can get more observability into your flows and your automations. Get that real time view. Be proactive in terms of when things fail.

Make sure to prepare for that ahead of time. And then if you’re on Experienced cloud, go check out the geo features that have been added. I would say turn it on. It’s really it’s really all you have to do is kind of kind of flip that switch and then allow your, your experience sites to be searchable and optimized for AI tools.

So those would be kind of the big things. And then of course review all the retirements. I mentioned several of them in the show, but we’ll link to the retirements doc in the show notes. And just make sure that you’re staying informed about which features first that you have, and then align those to what’s actually being retired. See if you have any overlap, and then make a plan to go and address those small actions.

Now in terms of retirements can prevent big disruptions later. Wrapping it up at Salesforce Spring 26. It’s not about AI hype, it’s about AI discipline. So you’re serious about scaling AI inside Salesforce? This release is going to give you the guardrails that you need faster builds, clear visibility, better governance across the board. Zack, bring us home with a final word.

So when it comes to I remember that it’s both a mirror and a multiplier. So having those clear processes are going to be critical for successful implementations. If you have a lot of chaos you’re going to get chaos reflected back. So make sure you’re doing the work to define your processes, understand what you’re trying to achieve so that you can measure the results and then plug AI into the process to see how that can help kick things into overdrive.

I, for a lot of leaders out there that can feel a little bit overwhelming. And the good news is you’re not alone. You’ve got some experts you can rely on. Obviously, if you’re partnered up with a Salesforce, implementation partner, have a conversation with them. If you don’t have a partner and you’re looking for some help, some guidance here, to find out if your Salesforce award is truly AI ready, we would love to help.

You can hit fastmotion.com/ai and that gets you directly in contact with us. So I mentioned earlier we’re going to be linking a lot of stuff in the show notes as well. That can be helpful if you want to sort of self guide this AI evaluation that goes along with this spring 26 release. But if you need to help, we’d love to hear from you.

Zack, thanks again for, great episode, man. Appreciate you. And to our audience. We’ll see you next time.