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From Automation to Agents: How Weak Data Forces AI to Guess—and What To Do About It
As organizations plot their path toward 2026, a rapid transformation is underway: classic automation is being replaced—or “agentified”—by AI agents. This shi ft is present across business operations, from routine workflows to core enterprise processes. The integration of agent-based systems promises greater flexibility and autonomy, but it also surfaces new complexities. Key insights from the Everyday AI podcast episode reveal specific dangers and best practices that business leaders should note to avoid costly missteps.
Automations vs. AI Agents: The Impact on Workflow Reliability
In legacy automation, workflows were built with deterministic logic—rigid “if/then” rules. The result? When an input didn’t align (a missing comma, an extra space), the process simply failed, producing no output at all. This rigidity, though limiting, protected organizations from “silent errors” and made it easier to trace root causes.
Agent-based workflows, however, are nondeterministic. Rather than failing outright, an agent often produces outputs under any circumstance, even when underlying data or policy definitions are incomplete or incorrect. Sometimes, this means the agent will “guess,” make unsound assumptions, or even fabricate information. The bottom line: the evolution to agentic systems introduces risks of misleading results if critical foundations are not in place.
Agentification Pros and Cons: Productivity Gains and Data Risks
Transitioning from classic automations to AI agents brings tangible productivity benefits. Human workers can “take their hand off the wheel” for mundane, repetitive tasks, freeing up time for higher-value work. In finance processes, like expense report approvals, agents enable conversational interfaces and adaptive rule interpretation, streamlining submission and approval cycles.
Yet, these advantages come at the cost of increased dependence on data quality. Agentic automations only deliver trustworthy outputs if fueled by robust, well-governed, and up-to-date data. Poor data inputs lead to erroneous outputs—often without immediate human intervention to catch or correct mistakes. This means organizations must shift their focus from simply configuring rules to managing ongoing data quality and governance.
Data Quality in AI Agent Workflows: Concrete Business Impact
The emergence of AI agents places new pressures on foundational datasets. In multi-agent orchestration—where agents hand off tasks to one another—compromised data in any part of the chain can propagate errors, compounding the negative outcomes. For example, disparate systems like Salesforce and ServiceNow may have agents operating over unsynchronized datasets. If these sources diverge, agents may “talk past” each other, leading to operational breakdowns or policy violations.
Organizations must therefore double down on data governance. This includes:
Prioritizing pristine data in core business datasets
Implementing strict access controls so that agents only use authorized data
Continuously maintaining policy documents and business wikis, as agents now interpret these directly
Ignoring these steps can swiftly lead to “bad data in, worse outcomes out,” particularly as human oversight decreases.
Observability, Governance, and Agent Control Towers
With multi-agent workflows, monitoring agent behavior across systems becomes more challenging. Traditional observability tools offer visibility into IT operations; now, specialized AI governance solutions are needed to track agent decisions and catch anomalous actions in real time.
Agent control tower technologies, for instance, provide aggregated oversight across diverse agent platforms (such as Salesforce, AWS Bedrock, and Boomi), monitoring for deviations from standard patterns. If an agent exhibits unexpected behavior, these tools can alert stakeholders and even pause questionable processes, adding another layer of defense against silent errors.
Practical Steps: Connecting AI Innovators and Business Owners
Forward-thinking organizations are proactively bringing their AI innovators together with line-of-business data owners. These collaborations focus not on boiling the ocean of all enterprise data, but rather on identifying the precise data subsets driving high-impact agentic workflows. By orienting data quality initiatives toward the most business-critical outcomes, companies drive faster ROI and avoid resource-draining blanket data projects.
This practical approach enables iterative value creation: starting small with targeted datasets, proving outcome-based improvements, and scaling efforts based on real returns.
Moving Forward: Measure ROI at the Outcome Level
To unlock the full potential of agentic workflows, companies are advised to reverse their project logic. Rather than chasing technology implementations in search of possible business applications, they start by defining the measurable business outcome they wish to achieve (cost savings in expense approval, accelerated customer onboarding, etc.). Required data and AI technologies are then applied to those well-defined endpoints.
This methodology guarantees alignment between technological investment and real business impact, allowing clearer ROI measurement and more strategic data governance investment.
Organizations adopting agentic workflows in place of deterministic automations must fundamentally shift how they manage their data foundations. Sustained ROI derives not from the inherent intelligence of AI agents, but from the strength, governance, and ongoing hygiene of the data infrastructure they depend on. The risks and opportunities here are sharply defined—and should be addressed with deliberate strategy, robust oversight, and targeted collaboration between data stakeholders and AI innovators.
Topics Covered in This Episode:
- Automation vs. Agentic Workflows: Key Differences
- AI Agents: Data Quality and Output Risks
- Agentification Trends in Enterprise Automation
- Pros and Cons: Converting Automations to Agents
- AI Agents Impact on Business Processes
- Importance of Data Governance in AI Agents
- AI Agent Control Towers and Observability
- Measuring ROI: AI Agents and Data Investments
Episode Transcript
Jordan Wilson [00:00:46]:
Here's a very obvious trend as we gear up for 2026. Almost everything's gonna get agentified. Right? Your standard procedures are gonna become agents. Traditional processes, agents, features are now just called agents, and what used to be called automations will be agentified as well. But here's the thing that's important. I think when we think about old school automations, right, those that have been around for a decade or more, if you didn't have the right data or if it wasn't configured correctly, that workflow probably would just fail. And the output, well, you wouldn't get one. But when we are starting to make the transition from automation to actual AI agents, that agent, even if something's not right, the data's something else, it's still probably gonna give you an output.
Jordan Wilson [00:01:40]:
It might straight up lie or guess. So how do you get around that? And how do you make sure that as you are bringing more and more of your business processes into agentic workflows or building AI agents, how can you make sure they're not guessing and hopefully giving you outputs that you can trust and actually use to grow your business? Alright. We're gonna be tackling that and a lot more on today's episode of Everyday AI. What's going on y'all? If you're new here, welcome. My name is Jordan. I'm the host, and we do this every day. This is your daily unedited, unscripted, livestream podcast, and free daily newsletter helping everyday business leaders like you and me not just keep up with all of the AI craziness, but how we can make sense of it to grow our companies and our careers. So if that's you, starts here with the podcast, but make sure you go to our website at youreverydayai.com.
Jordan Wilson [00:02:30]:
Sign up for the free daily newsletter. We're gonna be recapping the highlights from today's show and a whole lot more. If you want the AI news from today, make sure you go read it in our newsletter. Alright. Enough of me chit chatting, y'all. I'm excited for today's guest. So please help me welcome to the show Ed Makoski, the chief product and technology officer at Boomi. Ed, thank you so much for joining the Everyday AI Show.
Ed Macosky [00:02:56]:
Thanks for having me, Jordan. Excited to be here.
Jordan Wilson [00:02:58]:
Alright. Me too. Ed, before we get started, tell everyone a little bit, about your background and what you all do at Boomi.
Ed Macosky [00:03:06]:
Happy to. So I am the chief product and technology officer here at Boomi, which means I run all of product and engineering, for Boomi. And who is Boomi? We are an integration automation and AI activation platform. So we spend every day, working with our customers on automating their businesses through either traditional workflows or now through AI agents, and or activating AI within the business. A little bit more about me, I've been an integrator now for, gosh, twenty four years. So, I've spent my entire career, thinking about integrating and integration and automation within a business, and with the rise of AI and agent technologies. It's like a whole new it's a renewed sense of, you know, pride and excitement around how we can apply these within the business, and, that's what we do every day.
Jordan Wilson [00:03:56]:
And I I I love talking to people with a deep background in tech, specifically in automations. Right? Someone myself, I I I've loved, you know, the marketing automation sector for a long time. I've always experimented with different, you know, workflow. So it's obviously been exciting, you know, over the last two years as we're able to add a little bit of agency to traditional, you know, automations that didn't have those. Can you just maybe catch us up, to where is the space at today? You know, are most, you know, enterprise businesses, are they using, you know, still old school automations? Are they having agentic workflows? Are they having just agents, a little mix? What are you seeing in your role?
Ed Macosky [00:04:38]:
Think a little bit of both, actually. And I act and I think we will see both, deterministic and nondeterministic or agentic workflows in the enterprise forever. The reality is, there are certain use cases where you're just gonna wanna have the performance of a fixed workflow in your business. Where that applies, you wanna be structured on the rails. And the way I think about it, you know, agentic capabilities actually helps you augment and expand more and more capabilities. There's some overlap where we do have agentic workflow workloads that are overtaking some of the traditional workflows, to help with maintenance and how to how we can think about these capabilities and be more robust. And I think that's where businesses are today. They've turned the corner from AI pilots and, yes, individual productivity with AI, that is still a thing.
Ed Macosky [00:05:27]:
But in terms of automating the business, we're starting to see some real results in in in to the tune of saving millions in these, you know, team workflows and whatnot that we're agentifying with our customers.
Jordan Wilson [00:05:40]:
Yeah. I think, agentifying and agentification, right, it's it's like almost like the new buzzword, you know, where it was agents maybe a year ago. But can you kind of walk us through some of the pros and cons. Right? So I'm sure that many companies out there, you know, have been piloting different AI solutions over the past year or year and a half, and they maybe have some, you know, automations that have been the backbone of their business for maybe a decade or more. Can you kind of walk us through some of the pros and cons of as companies move from old school automation to newer school agentification or just AI agents out there accomplishing tasks on their own. What are some of the pros and the cons of kind of converting those older processes to agents?
Ed Macosky [00:06:31]:
Certainly, the pros are, you can kind of as the human or you're humans within organization when you convert some of these workflows into agent based flows, you can take your hand off the wheel a little bit. I like to think about it when you're driving you know, these self driving cars where you've got someone sitting there and running, but getting more to the point of, you know, a Waymo that is is fully self driving. We see that happening. So that's the pro. You can get a lot more out of your workforce. You can let the humans do more and provide more value in the business versus these mundane kind of driving things that they click a button every day and what traditional workflows bring. The cons are you have to build these agents and these automations, these new agentic automations on a strong foundation of data or bad bad data in as worse data out in terms of the activity that you would expect from agents. Right? Whether it be the decisions agents are making or the actual way their ways in which they're wielding tools and actually activating, change or updates within your business, you have to be very, very careful.
Ed Macosky [00:07:35]:
So that that is a big con side, but we're here to make that better and healthy.
Jordan Wilson [00:07:39]:
Yeah. I love that. Bad data in, worse data out. That's a great, takeaway already. You know, one thing that I even remember, you know, personally, you know, way, you know, ten plus years ago working with whether it's RPA software, you know, marketing automation software. I would spend a lot of time building these, these automations. And if one thing wasn't right, right, maybe there's an extra space or, you know, a period instead of a comma or something like that. The automation wouldn't run because it was overly rigid and, you know, obviously deterministic.
Jordan Wilson [00:08:12]:
Can you walk us through maybe for our more nontechnical audience, right, what does this mean, specifically when agents have agency to maybe make their own decisions and what that does for the importance of our data?
Ed Macosky [00:08:29]:
Yeah. Makes it super easy. So, you know, agentic technologies like Boomi, for example, make it very easy to not have to maintain, define, you know, these rigid if then else rules that if you're missing a semicolon or you've got a space in there or something that they go bad or new rules get introduced and data changes. That happens that can throw off these rigid workflows. AI agents and using agents to help make these decisions in a workflow, which could be an AI agent wrapped with a traditional workflow if you'd like. But makes these things much easier and less brittle when implementing them. And then they run and then they can make decisions on your behalf. So you can go in terms of you can do things like just defining your policies within the normal documents that you normally do for your employees and let the agents read the policies like your employees would and agents can adapt and make those decisions in a workflow.
Ed Macosky [00:09:29]:
That is all great. The downside or what could happen when you do these is now you could take your eye off of managing those policies. You would be forced to in an old fixed workflow because you'd have to go to some human who would write the code to say I'm gonna add five lines if it's then else because we added a new policy. It's just shifted to today. You need to make sure that your policies are well defined. You're maintaining your internal wikis or your documents or your PDFs or the things that you have, which you should be doing for normal good business hygiene anyway. But as long as you live and work in the systems you have to find and then you have your agents point into those, you should be fine.
Jordan Wilson [00:10:08]:
Could you maybe give us an example? You know, maybe one of those, tried and true automations that, may be more deterministic in years past. And now with AI agents, so companies that have maybe converted those older automations into, agentified them and, you know, either agentic workflows or AI agents. Can you maybe give us an example of, hey. Here's what that process looks like pre generative AI powered AI agents, and here's what it looks like now with them.
Ed Macosky [00:10:40]:
Yeah. There's a lot of them, particularly around the finance area and different things in the business. But my favorite one is expense reports. I think it's one that everybody can kinda relate to that's that works in a business where you have to file expenses, whether you be the person filing the expenses or the manager or leader in the business that is, approving the expenses. A traditional expense report workflow is I submit some expenses. I may I have a whole bunch of roles that someone has defined that tells me error or not as I'm entering expenses. And then once I click submit and I finally get through all the errors, which I probably faked half of them to get through the errors, it goes to my manager, and then he or she then has to approve and do the same thing from the other side. Now there is frustration on both sides in terms of the people, one who submits and the person who then has to approve.
Ed Macosky [00:11:30]:
And then there's a lot of human inter well, a, there's a lot of humans in the back end coding all of those rules in some way, shape, or form or defining them. Then there's humans on both sides that are doing those activities to either submit or or approve them. In terms of an agentic process, you can make life easier on all three parties. The person who's submitting, you can make it in a you know, we are doing this. You you can have that as a conversational agent to make it easier for the person submitting an expense report to just really give the information and or set the tone with the humans filing the expense. Here's what you need. Here's the only stuff I need. Make it really easy.
Ed Macosky [00:12:05]:
Expense report taken, created. Then for all of the rules that that would be part of that, much less rigid, much more open that these agents can then in using large language models, go out and read all these rules and dynamically adapt and adjust. And then on the approver side, these agents can also learn and only engage approvers when necessary. So if you are submitting expenses to me, Jordan, and I'm approving the $100 bottle of wine every time, even if it's part of if it's not part of the policy, the agent will learn. Ed will approve Jordan's $100 bottle of wine. But if it's a $20,000 bottle of wine, the agent will know, wait. Ed's never approved this. It's outside of the policy.
Ed Macosky [00:12:46]:
That's when I'm gonna engage the human to come in the loop to kind of make that statement. So, and approval. So you can kind of get, you know, best of all worlds by being adaptive and not so rigid in this process, but also the foundation of data underneath maintaining these rules, understanding them, maintaining the who's Jordan's boss so I know who to submit this to, who's this and that. Those are all data quality things underneath that that people have, you know, unfortunately, in the last number of years in enterprise, haven't really paid much attention to because humans have been a crutch. Humans have come in and said, oh, that data is bad, but I know I can approve this and, you know, I can't automate that because of x, y, and z. That's why data quality is becoming more and more critical for these agentic flows.
Jordan Wilson [00:13:33]:
And I wanna dig into that a little bit more. And, also, there goes my, tactic of, you know, expensing things that, you know, 10 x the price. There that goes out the window. But, you know, one thing, and I'm not too big of a dork to admit this. I enjoy reading summarized chains of thoughts. Right? I I I I love, you know, working with the latest, you know, models, whether that's, you know, Gemini or GPT-5 or, the newer Claude, Opus. Right? And I love looking to see, you know, how they take whatever inputs, and then what they do and what they think through. Right? And one thing I've really noticed, is, well, obviously, models can be bold, and they can sometimes be a little too confident.
Jordan Wilson [00:14:20]:
Right? Especially when you throw a ton of data at them all at the same time. And sometimes even just one wrong assumption, maybe the model isn't even wrong, but maybe they just assumed something and it's not fully accurate, they might take that and run with it and go in a completely different direction than you may want. Right? And unless you're a dork like me looking at the chain of thought or, you know, really, paying attention, to what your agents are doing, you might miss that. So with that in mind, right, and as these models become more and more agentic by default and more and more capable, talk a little bit about what the average, even the average everyday business leader needs to do when it comes to working with data inside of large language models.
Ed Macosky [00:15:05]:
Well, there are a couple of things because I see this like a sandwich, based on what you said. At the foundation, business needs to continue implementing tools and make sure that the underlying data is as pristine as humanly possible or technically possible, and not overlook that and put in true data governance capabilities within the organization. And then you need to make sure that your AI agents have a true security layer so that agents are accessing the proper datasets and not overstepping bounds there. That's another way of protecting from an agent, getting some level of data, and then going and stepping over some boundaries. That is at a foundation. So to your point, the littlest data that can be off can have bad results. I said this earlier, bad data and worse data out in terms of the actions. The top layer of the sandwich as I see it is investing in AI governance tools that can oversee AI agents and what they're doing.
Ed Macosky [00:16:05]:
There there's tools in market. You know, there's, observability and telemetry tools that are in market today that you would think about governing and having visibility into just your business operations or IT having those, etcetera. We have actually brought to market, an AI agent control tower. And what that does is we'll monitor the behavior of agents that are running in your business that you've implemented as another backstop for these types of things. So agents are off and running. They don't even have to be Boomi agents. They could be agents in, agent force agents in Salesforce. They can be AWS Bedrock agents, combination Boomi agents, all orchestrating together, and we will monitor those to look for anomalies.
Ed Macosky [00:16:43]:
So using another layer of an agentic layer to oversee the agents that are running so that if you see some anomaly behavior like, hey. It's you know, agents have been doing a, b, and c, but this one looks like it may have stepped out of bounds. It will at least it will alert the business user that, hey. This agent did this. Or you might wanna check on this, or do you want me to stop this agent from behaving here, to help you with that governance? Because as a business user trying to manage these thousands or millions of agents in your organization without using AI to manage them as well, is going to be nearly impossible. So those are the types of solutions and things I think folks should look out for.
Jordan Wilson [00:17:22]:
You kind of led into, the next question that I naturally had is I feel, observability, traceability, right, is maybe a little bit easier with, earlier, scopes, when it comes to AI agents. Right? When there's maybe one AI agent that you completely understand, and it's a little bit easier to, look under the hood, so to speak. But as we start going into these multi agent, workflows and multi agentic orchestration, How does the, kind of compounding impact of good, clean, and accurate data, how does that play out when, yeah, now all of a sudden you might have agents handing things off to each other without a human in between?
Ed Macosky [00:18:12]:
Yes. So even worse data out. So it's important to double down on the governance of your data in that aspect and to double down on having observability in aggregate to these agents working together and what they're doing, what data they're passing, looking for anomalies, without just shamelessly plugging the Boomi technology the whole time. There are other control tower capabilities out there. The different hyperscalers are providing visibility at a control tower perspective to get insights into what the agents are doing in their ecosystems. And not just hyperscalers, but application providers are providing these. A value that Boomi has is helping to aggregate them altogether so that you can see, okay, from an end to end series of agents that can be orchestrating across ecosystems, which isn't a big focus right now. A big focus is to a and communication protocols across ecosystems, but not yet.
Ed Macosky [00:19:04]:
That's where we're going next around the governance across ecosystems and making sure that what agents are doing together are, you know, sponsored and what the businesses want to do, the business wants to do. But when you think about there's two levels of your data when you have multi-agent or multi-ecosystem orchestration. There's the data that resides within the system that the agent is on top of, so agent on top of Salesforce data. Then you can have a ServiceNow agent on top of ServiceNow data. If that data is not in sync and of quality on both sides, you can have disaster those agents can talk past each other and not be sharing data. You need to come up with your strategy for, you know, your foundation of data that is kept in sync or you're leveraging an agent, multi-agent system that is on top of the same dataset. Depending on your organization I know I'm getting a little technical here, but depending on your organization and your maturity, you're gonna wanna think about those those different things. Most companies are probably working with agents within their siloed ecosystems versus on a common dataset.
Ed Macosky [00:20:13]:
But you're gonna based on the use case, you're gonna wanna think about how you, you know, you architect that within your business.
Jordan Wilson [00:20:19]:
Yeah. And I think that it's, you know, well past the foregone conclusion now that you need high quality data in order to fuel AI agents or agentic workflows. Yet, I mean, I've talked to, hundreds of people on this show and, you know, I'd say dozens of, Fortune 500 executives that even themselves, whether they admitted it on the air or not, don't always feel the most confident about their data. So how should those types of business leaders, be looking at this problem? Right? Because data is always evolving. We're getting more and more of it. It's coming in different formats. Right, especially as some of it's being formatted specifically for large language models and maybe humans, don't jive well with that. Right? But how can, those organizations that feel maybe iffy on their data game, how can they still use AI agents, or should they not?
Ed Macosky [00:21:20]:
Yeah. I'm gonna try to give some very, very practical, advice here because this is what we're seeing in today. So it we're in this the the fastest innovation life cycle we've ever seen in IT, frankly. Like, every day. I mean, your podcast is show showcasing a lot of this. It's happening every single day. And what's happening is we have the new wave of technology that's emerging around AI and a lot of people being very innovative there and so on and so forth. But those folks may or may not have all of the context that an enterprise or a business would have around their data and workflows and all those other things that we talked about.
Ed Macosky [00:21:54]:
So the the very practical thing to do, it sounds dead simple, but I'm not seeing enough of it. Pull your AI innovators together with your business owners, those that own the data and your AI innovators, and get them talking and working together. We just a lot of what we're doing is pulling line of business owners that own these datasets together with the AI innovators and facilitating discussions on what they can accomplish together and focus very practical practical on not boiling the ocean of all of your data in your whole organization. Look at the datasets that you need for automating your business or the outcomes that you're looking for and really double down on those. You'll be much more cost effective than some large data project that you wanna do in your organization. Focus on the datasets that that are going to matter for your Agentech workflows. Start small and iterate from there. Prove value very quickly and then build on top of the value you've already, you you've seen in previous cycles.
Jordan Wilson [00:22:50]:
Alright. That was I mean I mean, Ed, you took my last question out of my mouth because I think that's some great, kind of parting words for business leaders. But, I'll go this here. You know, I think, you know, at this point here, rolling into the end of the fourth quarter, a lot of CEOs, business owners are, you know, looking back on their 2025 AI investments and, you know, trying to, look at ROI and plan for 2026 and beyond. What would your advice be, specifically when it comes to, kind of measuring the return on agents, and then also even the return on investing in your data. Because at least for me, I've seen a lot of companies, you you know, putting way more resources into their data in 2025 than they had in maybe the last half decade combined because of this tight correlation, between data quality and agentic output. So, kind of two pronged question there. Where should they be focusing, for showing ROI and then also investing, in their data processes?
Ed Macosky [00:24:01]:
I would great question, and I would flip the script entirely in terms of the way you look at defining a project and its outcome. So in the last couple of years from an AI to to data projects, People have focused on AI technologies or data technologies or data and tried solving those problems which have led to or or addressing opportunities in the business. They what I always refer to as these science projects. They go off, people get fascinated with the technologies, they get fascinated with the data itself, etcetera, but they go and they their outcome that there is clean data. Well, nobody the clean data by itself doesn't mean anything or this really cool AI thing doesn't mean anything. Flip the script. Start with discrete projects where you want to define your ROI in the business process or thing you want to optimize and what is that ROI and work backwards. So from there, you say, okay.
Ed Macosky [00:24:56]:
What are the outcomes that I want or what and what are the returns that I expect from those outcomes? Then apply the technology and the data projects to those, then you can't go wrong. You're consistently focused on the outcomes that you're looking to seek versus kind of working on a working on a solution, hunting for a problem. That's I would just flip the script.
Jordan Wilson [00:25:16]:
I love that. Great great way to, to end today's show by flipping the script and starting with the outcomes, building back from there. Love to hear it, Ed. And thank you so much for taking time out of your day to join the Everyday AI Show. We really appreciate it.
Ed Macosky [00:25:30]:
Thank you. It's great being here.
Jordan Wilson [00:25:32]:
Alright. And if you miss anything that Ed said, don't worry. We're gonna be recapping it all in today's newsletter. So if you haven't already, go to your everydayai.com. Sign up for that free daily newsletter. If you're listening on the podcast, appreciate your support as always. Make sure that you please subscribe, like, and leave us a rating on Spotify or Apple Podcasts. Thanks for tuning in today.
Jordan Wilson [00:25:52]:
Hope to see you back tomorrow and every day for more everyday AI. Thanks, y'all.