Part 1: Transforming Work and Document Intake Across the Federal Government

This episode of The Interconnectedness of Things kicks off our five-part series exploring the full lifecycle of work — from initial intake through processing, collaboration, records management, and long-term retention.

We begin at the front door: work and document intake across the Federal Government. Work doesn’t start when a document is filed, it starts the moment a request is made, a form is submitted, or a case is opened. Yet for many agencies, intake remains fragmented, manual, and inconsistent, creating downstream delays that impact mission delivery.

In this conversation, we unpack how standardization, automation, and AI-powered tools are transforming the intake stage into a strategic control point. By structuring submissions, capturing metadata at the source, and intelligently routing work to the right teams, agencies can reduce rework, improve auditability, and accelerate decision-making.

Getting intake right sets the tone for the entire lifecycle of work. When agencies connect people, process, and technology from the very first touchpoint, they create a foundation for greater transparency, compliance, and operational resilience. This episode lays the groundwork for understanding how modern federal systems can truly support the interconnectedness of work from start to finish.

About "The Interconnectedness of Things"
Welcome to "The Interconnectedness of Things," where hosts Dr. Andrew Hutson and Emily Nava explore the ever-evolving landscape of technology, innovation, and how these forces shape our world. Each episode dives deep into the critical topics of enterprise solutions, AI, document management, and more, offering insights and practical advice for businesses and tech enthusiasts alike.

Brought to you by QFlow Systems
QFlow helps manage your documents in a secure and organized way. It works with your existing software to make it easy for you to find all your documents in one place. Discover how QFlow can transform your organization at qflow.com

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Intro and Outro music provided by Marser. 

Transcript

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<v Nava Emily>Welcome back to the interconnectedness of things. I'm your host, Emily Nava. And today is the first episode of a five part series that we're starting where we explore how government work actually moves, especially in a moment when agencies are being asked to do a lot more with less resources. So I wanted to start off our discussion today about work intake with Can federal agencies better leverage AI tools to improve intake internally between agencies and with citizen requests?

00:00:37.169 --> 00:00:45.342
<v Nava Emily>I'm joined here with my cohost, Andrew Hudson, and would love to hear his thoughts on that question.

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<v Hutson>Man. It's a big question. So first, like, when you hear AI tools, what comes to mind?

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<v Nava Emily>I think of chat GBT summarization.

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<v Hutson>Mhmm. Mhmm. So, like, summarizing documents, like, what are some other ways that maybe you use AI tools? Like you use them for first drafts or anything like that?

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<v Nava Emily>Yeah. Content outlines, just some ideation help. Yeah. It's really just me, like, asking a bot a question.

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<v Hutson>Mhmm. Mhmm. Mhmm. I I use it similarly. And I'll tell you, it's it's an enormous investment to go past where you just articulated,

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<v Nava Emily>Mhmm.

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<v Hutson>like, to actually getting work done. This concept of an agent and what the heck even an agent is within the the confines of AI, it is a little bit tough to describe because you can target almost any type of job and you're gonna get some level of quality depending on the model, depending on Well you articulate what's needed.

00:02:07.982 --> 00:02:09.501
<v Hutson>The examples in context you give.

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<v Nava Emily>Mhmm.

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<v Hutson>It's not a simple thing. It's not like a flip the switch AI is now on and everything's okay. So if it's not simple, why would the federal government invest the time to get some benefit back? Well, the from what I've seen in practice, Started to figure this out. And they're gonna get varying results, with how they wanna use it.

00:02:44.081 --> 00:03:21.606
<v Hutson>Like, it can be all the way Roam, can AI predict different scenarios on the fly on the battlefield? To better assess a situation, to can I optimize, crop incentives? For farmers, producers. And then there's just the rudimentary stuff. So that kind of takes me to the second part of your question, intake internally triggering some type of work, something between agencies.

00:03:21.606 --> 00:03:58.841
<v Hutson>We hear a lot in with our customers and our federal government work, how often the DOJ interfaces with other agencies. And how can that be easier? You'd be might be surprised to know that AI tools have nothing to do with it. The the improvements that can be made, really center on adoption of technologies that have been around for a while and proven.

00:03:58.841 --> 00:04:30.942
<v Hutson>And making sure that they are supported, maintained, and organized in a central way. And that takes a lot of vision and coordination, that may be hard with changing initiatives and paradigms and administrations. So I don't know. What do you think? You have some. Some list of things here like digital forms and portals and emails, document uploads.

00:04:30.942 --> 00:04:35.590
<v Hutson>Do you see AI helping with any of that?

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<v Nava Emily>Well, I'm I'm kind of under the impression that people are thinking AI is an easy button, that I don't have the staff I had before. I'm still getting the same amount of work coming in. And so AI just needs to we need to put it in there and organize everything. But that's not right.

00:04:53.702 --> 00:04:55.815
<v Hutson>It's magical.

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<v Nava Emily>And that's just not the reality

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<v Hutson>Yeah.

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<v Nava Emily>of how AI works. Like, you On-It get what you put into it. And so, at these different agencies, there's lots of different ways that people can, put work into the system or start work, like forms or emails or calls or mail paper mail still. So AI can't Take all of those things and organize them for you.

00:05:33.337 --> 00:05:35.089
<v Hutson>It could take a stab,

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<v Nava Emily>It can, yeah, it can definitely try.

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<v Hutson>and maybe that's enough to get somebody started.

00:05:39.169 --> 00:05:42.224
<v Nava Emily>But I think right.

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<v Hutson>But I don't think it's an answer. Like I have and this could be my own bias, but, like, I have a tough time letting something just go and,

00:05:53.424 --> 00:05:55.041
<v Nava Emily>Mhmm.

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<v Hutson>feeling assured that it will get done no matter how much detail I provide, no matter how well organized something is to begin with. However, if you're starting with the most disorganized thing, I think AI can make a dent in that. Yeah. But that depends on how you ask it to. If you got this grand vision and you can't think of step by steps, on how you would tackle the problem and and break it up into chunks, if you're just, here's the whole problem and here's the outcome I want,

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<v Nava Emily>Yep.

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<v Hutson>do it. I've never seen that work at a 100%,

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<v Nava Emily>Right. He needs some kind of status.

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<v Hutson>whether I'm talking to you a human or an AI.

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<v Nava Emily>Right. So they need to be clear about their standards and create a standardized approach

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<v Hutson>Yeah.

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<v Nava Emily>to intake.

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<v Hutson>Structure really helps. And what I mean by structure, when let's take a file system. So a lot of stuff just or or your email inbox. Stuff just comes in. It has some metadata, like who sent it, has a subject line, has a body, has a has a timestamp. If you look at an email, it's got a bunch of other behind the scenes metadata, but most of that isn't relevant to the person looking at it.

00:07:29.816 --> 00:07:31.809
<v Hutson>And it's just, it's just a mess.

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<v Nava Emily>Right.

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<v Hutson>And we've even seen attempts by Microsoft and Google, perhaps even others, those are the two that I've seen, trying to just give basic organization to your files. We've seen them promotion, important, but it gets it wrong. And then you have to go in and you have to say, no, this, this is, this is shouldn't have been hidden. I want this in the main inbox.

00:08:00.150 --> 00:08:03.721
<v Nava Emily>Yeah. Things end up in spam all the time. That's not spam.

00:08:03.721 --> 00:08:09.190
<v Hutson>Right. And not enough ends up in spam that should.

00:08:09.190 --> 00:08:13.081
<v Nava Emily>Exactly.

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<v Hutson>So Roam a starting point framework, it's good. Or if you're 95% done and need some tweaks, it can kind of take the baton from there. And what I've seen is really a knowledge graph needs to be made for the target. To expedite how the agent traverses the system. Because the agent can waste a lot of context just trying to figure out what the heck is going on. And then you have to start over.

00:08:44.189 --> 00:08:53.415
<v Nava Emily>So so that would mean, like, creating a knowledge graph of what you of all of the important information you need. Is that what you mean?

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<v Hutson>Yeah. And I've only used this at a small, as a small scale. So, when we make tools here, we can have a code graph, a knowledge graph get created, which cuts down the time the AI tool needs to get its context for what you're asking by like 80%. So it means it has more space to actually do something that you're asking it to do. All that to be said, how do we tie this back to improving the intake between agencies?

00:09:27.161 --> 00:09:59.321
<v Hutson>Citizen requests. You need to start with a consistent structured methodology. The fewer ways something can be defined, which creates predictability, which then improves the AI tools. Because you have to remember, AI tools don't think they operate based off of probabilities. Every time you start a conversation, it's putting that through multiple transformers.

00:09:59.321 --> 00:10:14.416
<v Hutson>That's the t in chat g p t to figure out what is probably the next best word cluster or character cluster to come next. Well, that's that needs predictability,

00:10:14.190 --> 00:10:15.136
<v Nava Emily>Yep.

00:10:15.136 --> 00:10:46.542
<v Hutson>which Means you're making your own model, or you're making something to support the model you're trying to use. That's hard, and I don't think people get that. So I would start back to the basics. Are you organized? Yes or no? Do you have, a digital form or an email for taking a request? Could you make a digital form?

00:10:46.542 --> 00:11:22.201
<v Hutson>Do you have a portal that everybody goes to, or is it however they can get to somebody is acceptable? When you start narrowing the channels that these requests can come in and standardizing with a level of flexibility, don't get me wrong, on how you take that information in, the greater you create predictability Which increases the likelihood you can get AI tools to do something productive.

00:11:22.201 --> 00:11:23.081
<v Hutson>What do you think about all that?

00:11:22.510 --> 00:11:30.936
<v Nava Emily>Would this mean so this mean cutting down on the ways you can intake information?

00:11:30.936 --> 00:11:40.862
<v Hutson>Oh, it does. But sometimes when you say it like that, it makes me think being rigid. One of my favorite sayings that

00:11:39.375 --> 00:11:41.102
<v Nava Emily>Mhmm.

00:11:41.102 --> 00:11:52.310
<v Hutson>I I always love is you have to assume uniqueness, but you need a standard way to assume uniqueness.

00:11:52.310 --> 00:11:55.697
<v Nava Emily>That seems hard.

00:11:55.697 --> 00:12:05.382
<v Hutson>1924 Henry Ford in creating one of the first production lines. Love to say you can have any color you want as long as it's black. And that's where you start.

00:12:04.645 --> 00:12:06.742
<v Nava Emily>Exactly.

00:12:06.742 --> 00:12:39.121
<v Hutson>Okay. Let me narrow this down to just exactly what's needed. Okay. But a color is a variable. So now I can build that variable in. Now you can select from a list of available colors. Maybe you have a a special need for something that you can't predict, but you can standardize how you collect that special need. To be in a form, has to be in its own free text field, has to have other contexts along with it.

00:12:39.121 --> 00:13:06.830
<v Hutson>And once you start standardizing, then you can get to standardized customization, which is still predictable. But if everything is a snowflake, it's hard for anyone or anything to help know what to do next. Unless you're gonna shovel the snowflakes, which then it doesn't matter that it's a snowflake.

00:13:06.830 --> 00:13:20.777
<v Nava Emily>Right. So then it would almost seem like if you're trying to standardize snowflakes, you're spending just as much time reviewing the intake to make sure it's all standardized.

00:13:20.777 --> 00:13:47.329
<v Hutson>Yeah. It takes considerable forethought, envision for any process. To be adopted. And always, always, always remember what Steve Jobs said. You can only please some of the people some of the time. And if you don't have a steadfast vision for where this needs to be and accept that there will be pushback.

00:13:47.329 --> 00:13:47.976
<v Nava Emily>Mhmm.

00:13:47.976 --> 00:14:18.849
<v Hutson>Where I've seen these initiatives fail is verbally we've identified setbacks could happen. Like in our heart, we we fold. So that when we get pushback from someone, we overemphasize that pushback. We over oh, one of my words I'm trying to think of, like, give it too much importance? I don't know. I thought I was trying to be more articulate.

00:14:18.849 --> 00:14:22.422
<v Nava Emily>I mean, I think that's so it's perfectly.

00:14:22.422 --> 00:14:39.317
<v Hutson>Okay. Thank you. And as a result, people will stop change, roll back, do something different because one person said something. And that can be all it takes.

00:14:38.465 --> 00:14:40.437
<v Nava Emily>Mhmm.

00:14:40.437 --> 00:15:10.230
<v Hutson>And and so it to do something like this, to change your your feedback, it's the people involved that Really are the most important because the people have to be willing to adopt it. They have to understand why it's important on what you're doing and the added benefits you get. And you have to have leadership ready and willing to back it up and not back down.

00:15:10.230 --> 00:15:31.862
<v Nava Emily>And I think that's an important thing too. It is kind of a top down, top down thing if your leaders aren't following the standards and it's would be hard for the rest of the team to, I had a question here. And I lost it.

00:15:31.862 --> 00:15:34.545
<v Hutson>Mhmm. Oh, no. Sure. It's brilliant.

00:15:34.545 --> 00:16:07.281
<v Nava Emily>It was. It had something to do with I guess, getting getting started with something or getting started with working through the chaos of intake, if that is what you're an agency is facing. And so it sounds like starting at the end, creating your ideal end state of the data would be a good place to start.

00:16:07.281 --> 00:16:57.062
<v Hutson>Yeah. It is. Seven habits of highly effective people written by Stephen Covey would say on one of those seven things, begin with the end in mind. And it is good to articulate that summit, that mountain top that you're hiking up to. But be flexible on the path. And I think that's where a lot of these initiatives fail, and not just in federal agencies, everywhere, is that folks are not quite sure how to adapt to a different path or even be able to articulate the path that they would need to follow so everyone involved can understand to get to the same summit together, not just me by myself.

00:16:57.062 --> 00:17:22.609
<v Hutson>And so if, like, I as if you're asking for, like, advice, don't just begin with the end in mind. Think about where you can start as well. What's a win that I can get up front that helps me build upon? And then start building on that. Sometimes it's easy to go from where you want to be and reverse engineer it.

00:17:22.609 --> 00:17:22.742
<v Nava Emily>Mhmm.

00:17:22.742 --> 00:17:35.714
<v Hutson>Sometimes it's okay to say, here's where I wanna go, and I know I gotta start here and let me build on top as I go. I can't say which is best for you, but if you're starting to feel struggles with one method, try the other.

00:17:35.714 --> 00:17:59.302
<v Nava Emily>Yeah. Well, and I just it I'm I guess too, I'm hearkening back on AI just being seemingly this all encompassing solution for any Gaps and resources. And people just like to say, well, we need AI because we don't have the people for it.

00:17:59.302 --> 00:18:30.622
<v Hutson>Yeah. That that is a foolish, and I apologize for being harsh, but, like, that is foolish to think just that I get AI, it does something, and everything's better. That's the thing. The the worst thing. It's almost like, okay. In a world, I want AI to handle 80% of my tasks. Because my workload just grew

00:18:29.209 --> 00:18:31.262
<v Nava Emily>Yep.

00:18:31.262 --> 00:18:33.284
<v Hutson>five X.

00:18:33.284 --> 00:18:35.021
<v Nava Emily>Yep.

00:18:35.021 --> 00:18:36.781
<v Hutson>So you have to start with.

00:18:36.084 --> 00:18:39.364
<v Nava Emily>So what are the tasks? How do you do the task? What do you need for the tasks?

00:18:38.701 --> 00:18:50.209
<v Hutson>That's right. Exactly. Right. How would you get another person to handle it? If you can't answer that, there's no way you could get AI to do it.

00:18:50.209 --> 00:18:52.209
<v Nava Emily>Right. And like we talked about at the very beginning,

00:18:50.747 --> 00:18:57.161
<v Hutson>And there's Mhmm.

00:18:52.929 --> 00:18:58.441
<v Nava Emily>AI means a lot of different things to a lot of different people.

00:18:58.441 --> 00:19:23.990
<v Hutson>So when I'm talking about it, I'm mostly talking about the generative AI for creating summaries and helping with the outlines, like you had articulated before. I'm not talking about workflow automation and adaptability, which is another item that an LLM could provide.

00:19:23.990 --> 00:19:24.681
<v Nava Emily>Mhmm.

00:19:24.681 --> 00:19:43.705
<v Hutson>And I'm not talking about, machine learning and things like predictive analytics that have been around for twenty plus years. However, some people might conflate the two the all those different things into into one.

00:19:43.705 --> 00:19:44.486
<v Nava Emily>Yep.

00:19:44.486 --> 00:20:19.790
<v Hutson>And by the way, they all have the same Roam. You need to be structured and predictable with your structure. There are some things out there that don't force that. So I'm not saying it can't be done without structure. I'm simply saying I I want if you're like me and you want more predictability, reliability from your outputs of the system, you need to have greater organization, standards and habits.

00:20:19.790 --> 00:20:24.990
<v Nava Emily>So the change starts with you and your processes. The change does not start with AI.

00:20:23.601 --> 00:20:31.309
<v Hutson>Man. Wait. Yeah. Yeah. Like there's so much in your control that you can start with for sure.

00:20:31.309 --> 00:20:31.841
<v Nava Emily>Yeah.

00:20:31.841 --> 00:20:44.375
<v Hutson>Maybe that's a mess. That's why I don't get, I don't worry about AI replacing people because it's for most people, it's incredibly difficult to think in those ways.

00:20:44.375 --> 00:21:16.169
<v Nava Emily>Right. So quick question before we wrap up this, this first episode in our five part series. So if an agency leader wants, has their intake process And they're feeling like it's disjointed. They think maybe AI might be something that they can leverage. What is something that they they should audit?

00:21:16.169 --> 00:21:25.462
<v Nava Emily>They should figure out write down structure tomorrow morning if they wanted to go down this road.

00:21:25.462 --> 00:21:36.146
<v Hutson>Mhmm. So if it were me and I was faced with that, I would begin a conversation with an LLM. And it would say here's.

00:21:35.864 --> 00:21:39.827
<v Nava Emily>So open open Claude or open chat g b t.

00:21:39.827 --> 00:21:39.944
<v Hutson>Yep.

00:21:39.944 --> 00:21:40.307
<v Nava Emily>Start a chat.

00:21:40.307 --> 00:22:12.921
<v Hutson>Or whatever I had access to, whether, I would avoid co pilot like the plague. But I would chat GBT, OpenAI directly. Claude has been very predictable. Even Gemini has been helpful, to get a conversation started and outline articulated. So I would first write down everything I knew about the process.

00:22:12.921 --> 00:22:43.022
<v Hutson>Then I would write down every pain point that the process creates, and I would take that and provide it to the AI model and say, here's my process. Here's what isn't working. Give me two to three approaches I can take.

00:22:43.022 --> 00:23:14.089
<v Hutson>This tech has to be this level of access, whatever it is. So you have to put in the constraints in there to the extent that you can make decisions and leverage other tools and then ask it for those two or three approaches and see what it comes back with. And that's a great place to get you started because it's gonna give you direction. Now if you got a Copilot, it's just gonna tell you how to Don't know. Upload a PDF yourself.

00:23:14.089 --> 00:23:16.021
<v Nava Emily>How to open a PDF.

00:23:16.021 --> 00:23:18.422
<v Hutson>Yeah. And send you to a link to go

00:23:17.129 --> 00:23:18.662
<v Nava Emily>Yep.

00:23:18.662 --> 00:23:29.184
<v Hutson>open it and not even talk to you about how to do it in the chat itself. So I view Copilot as just, a good help desk.

00:23:29.184 --> 00:23:48.549
<v Nava Emily>Yeah. So and that too can just kind of give somebody an idea of of how AI could even help or where to AI makes sense in the process. Because we know it can't be fully automated. And then it could also give somebody trying to do this

00:23:47.342 --> 00:23:49.110
<v Hutson>Yeah.

00:23:49.110 --> 00:24:01.277
<v Nava Emily>more context to take to an outsource partner that might specialize in workflow automation, for instance.

00:24:01.277 --> 00:24:05.382
<v Hutson>You mean like us? Yeah. So what Yeah.

00:24:03.625 --> 00:24:06.142
<v Nava Emily>Like us, people systems. Exactly.

00:24:06.142 --> 00:24:39.141
<v Hutson>Yeah. Yeah. Unless I'd be too biased here, but, I mean, we we go and create workflows, and we are adding in a Genentech steps so that people can, deliberately and in a controlled manner, add an AI agent to a specific step. And decide which route to take next. That's incredibly powerful and also incredibly targeted.

00:24:39.141 --> 00:25:11.516
<v Hutson>And that's what you really need is is something I want you to do this and only this, And here's what you're going to get as an input, and here's what I expect one of these three outputs. And be super specific. It'll do it if you're practiced at making it do that. So it can it can be fully automated. Sorry to contradict your earlier statement, but it not easily.

00:25:11.516 --> 00:25:16.797
<v Hutson>And uh-huh.

00:25:11.889 --> 00:25:18.237
<v Nava Emily>Well, with the standard predictable linear process.

00:25:18.237 --> 00:25:49.457
<v Hutson>Even a a multi Directional workflow that can go in different states and directions. Those are still finite, and those are still standard. If you do something like put in a workflow step and say, you know, do what you think is best, which I've seen people do. Sometimes they get a a just a fine output with that. And other times, it's like, what the heck?

00:25:49.457 --> 00:26:03.505
<v Hutson>Like Is is this model taking LSD? What's going on? These hallucinations are wild. So yeah. And then it's your level of comfort with letting the reins go.

00:26:03.505 --> 00:26:04.557
<v Nava Emily>Mhmm.

00:26:04.557 --> 00:26:14.750
<v Hutson>I've said this a lot when when we're using AI internally, which we do. It's an enhancement, not a replacement.

00:26:14.750 --> 00:26:16.681
<v Nava Emily>Yep. Exactly.

00:26:16.681 --> 00:26:25.150
<v Hutson>Just like you would give credit to someone making a spreadsheet to Excel. Right?

00:26:25.150 --> 00:26:25.961
<v Nava Emily>Right.

00:26:25.961 --> 00:26:57.449
<v Hutson>If an LLM creates or populates the Excel file or makes changes, to me, that's just enhancing a a thing that you wanted done anyway. You just didn't wanna spend the time that it would have taken to do it manually. And I I think more and more folks can get on board with that mentality with using this these tools. And also a little perspective based on our scar tissue of, like, when should you apply it and what are the best cases for it?

00:26:57.449 --> 00:27:05.976
<v Nava Emily>Mhmm. Great. And it's not staff replacement. It's just an efficiency tool.

00:27:05.976 --> 00:27:17.289
<v Hutson>Even AI first companies are investing heavily in defining agents. As an enhancement to an existing Roam.

00:27:17.289 --> 00:27:17.461
<v Nava Emily>Mhmm.

00:27:17.461 --> 00:27:30.504
<v Hutson>And the person who's conducting that role is still responsible for it and responsible for improving the agentic performance.

00:27:30.504 --> 00:27:52.101
<v Nava Emily>Right. So what I've learned here is that if you want the rest of your process to go smoothly and get to that dream in in state that you have in mind, you have to start at intake. Is standard before anything else.

00:27:52.101 --> 00:27:56.689
<v Hutson>It will sure help.

00:27:56.689 --> 00:28:28.345
<v Nava Emily>Well, great. Well, awesome. Well, I hope that if if you're listening and you've got a crazy intake process that's just all over the place, I hope that you found a tidbit of information here that that might help you on your journey as you See if AI is is the solution or the tool that you might need next. So next time, we we're gonna talk about once information has entered the system, now what?

00:28:28.345 --> 00:28:39.369
<v Nava Emily>And how do we keep this standardization going? So Yeah.

00:28:34.317 --> 00:28:39.997
<v Hutson>I might I might change my mind by the time we do the next episode, so buckle up and stay tuned because you don't know what I'm gonna say.

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<v Nava Emily>It might not even be about that anymore. So who knows? But thank you again for tuning in to another episode of the interconnectedness of things. If you found this conversation insightful or entertaining, please share with your network, and we'll see you on the next one.