Daniel sent us this one about writing briefs — not the legal kind, but the kind where you're distilling a fire hose of information into something a busy person can actually use. He's done media monitoring for a fish oil company, he's done periodic news sweeps, and he's noticed something I think is genuinely underrated: the skill of writing a really crisp six hundred word summary is often harder than writing a ten page report.
It absolutely is. Most people treat briefs as just a shorter report. They're not. A brief is a decision-support tool. It's meant to give someone enough context to act, or to decide they don't need to act, without making them do the synthesis work themselves.
Daniel's framing was interesting — he said the foundation remains the process, not the writing. If AI is handling some of the formatting or the first draft, the human's real value is in the thinking that structures the thing. He's picturing something where an AI agent pulls in the raw material and you're the one who verifies, polishes, and shapes it.
Which is exactly the right way to think about it. By the way, quick note — today's script is being generated by DeepSeek V four Pro. So if anything comes out particularly crisp, you know who to thank.
But let's dig in, because brief writing is one of those skills where the gap between mediocre and excellent is enormous, and AI actually makes that gap wider — not narrower — if you don't know what you're doing.
Okay, let's start with the basics. What actually makes a good brief good? Daniel mentioned working with politicians and CEOs, people who learn quickly but have no time. What's the core principle?
There's a framework I keep coming back to. The best briefs answer exactly four questions in the first hundred words. What happened, why it matters, what's likely to happen next, and what you need to decide or do right now. Everything else is supporting material. Most bad briefs bury the "why it matters" in paragraph four, or they never actually state what the reader is supposed to do with the information.
That maps onto what military briefings do. The SITREP format Daniel mentioned — situation, assessment, recommendation. The civilian version is basically the same structure but with less jargon.
The military got there for a reason. When you're briefing a commander, you don't get to be interesting. You get to be useful. The same applies to a CEO or a minister. They're not reading your brief for pleasure. They're reading it because they need to make a decision or because they need to not be surprised later.
Let's talk about where AI fits into this. Daniel's idea is that you'd have an agent pull in the raw material and produce a first draft summary. Then the human checks it, verifies links, polishes the language. That sounds sensible on the surface, but I think there's a pitfall here.
There are several. The biggest one is that AI is really good at producing something that looks like a brief but isn't. It'll give you a competent summary of what happened. It's much worse at telling you why it matters and what to do about it. Those are judgment calls. They require understanding the reader's priorities, the political context, the unstated concerns.
The AI can handle the "what happened" layer, but the "so what" and "now what" layers are still firmly human territory.
I'd put it this way. AI can do the first eighty percent of the work — gathering, sorting, summarizing. That eighty percent is mostly labor. The last twenty percent is where the skill lives. And if you let the AI do the last twenty percent, you get a document that reads smoothly but has no analytical spine.
I've seen this happen with meeting minutes. You feed a transcript to an AI and it produces something that looks like minutes — formatted nicely, grammatically correct — but it misses the thing that was actually decided. It misses the tension in the room, the thing everyone danced around for forty minutes before finally landing on a half-compromise. That's the stuff a good human note-taker captures.
Briefs have the same problem, maybe worse, because the stakes are often higher. If your meeting minutes are bland, it's annoying. If your briefing document fails to flag a regulatory risk that's about to hit your industry, that's a real problem.
Let's get concrete. Daniel mentioned media monitoring specifically. He was reading about Omega three for a fish oil company. What are the best practices for that kind of brief?
Media monitoring briefs have a few specific challenges. First, the volume problem. You're tracking potentially dozens of sources, and ninety-five percent of what you find isn't worth flagging. The skill is knowing which five percent matters. Second, there's the context problem. A negative article in a trade publication means something very different from a negative article in the Wall Street Journal. The brief has to convey that distinction without wasting words. And third, there's the repetition problem. If the same story gets picked up by fifteen outlets, you don't want to list all fifteen.
What's the structure that handles those challenges?
A well-structured media monitoring brief typically has a few standard elements. An executive summary at the top — three or four sentences max. Then a section on tier one coverage — major outlets, things the leadership actually needs to see. Then maybe a tier two section for notable trade press or niche coverage. And then a sentiment summary — is the overall coverage positive, negative, neutral, mixed? And crucially, is the trend changing?
The trend piece is something people often skip, and it's actually one of the most valuable things a brief can provide. A single negative story is noise. Three negative stories in a week from credible outlets is a signal.
That's where the human judgment really comes in. An AI can count the number of positive versus negative articles. It can't tell you that the negative article in the Financial Times matters more than the five positive articles in local papers, because the FT piece signals that a regulatory angle is emerging that hasn't hit the broader press yet.
The AI can do the counting, but the weighting is human.
The weighting is entirely human. And I'd argue that's the core of brief writing. Every piece of information has a weight, and the weight depends on who's reading it. If I'm briefing the head of regulatory affairs, I weight regulatory signals heavily. If I'm briefing the head of marketing, I weight consumer sentiment heavily. Same raw information, different brief.
The brief is shaped by the reader's decision landscape. And that's not something you can automate unless you've built a very detailed model of what that specific reader cares about — which is theoretically possible but practically rare. The human who's been working with that executive for six months probably has a better intuitive model than any AI would.
Let's talk about the writing itself. Daniel mentioned that writing six hundred crisp words is harder than writing ten pages. I think most people don't understand why.
It's because condensing forces you to make choices. In a ten-page report, you can include everything. You don't have to decide what's essential. In a six hundred word brief, every sentence has to earn its place. You're constantly asking: does the reader need this? What happens if I cut it?
There's a great quote — often attributed to Pascal or Mark Twain — "I would have written a shorter letter, but I didn't have the time." The point being that brevity takes more work, not less.
This is where AI can be helpful in a specific way. You can write a longer first draft — get all your thoughts down, include everything that might be relevant — and then use AI to help you compress it. Feed it your draft and say, cut this by forty percent while preserving the key facts and the analytical judgments. It won't do it perfectly, but it'll show you where the fat is.
That's a good workflow. Write long, edit short, with AI helping on the compression pass. But you're still the one making the final calls on what stays and what goes.
I'd add one more thing about the editing process. Reading the brief aloud is a really underrated technique. If you stumble over a sentence, or if it sounds like something you'd never actually say to a colleague, it needs to be rewritten. AI-generated text in particular can sound fluent but unnatural. Reading aloud catches that.
I've been saying that for years. Reading your drafts aloud catches things your eyes skip over.
You have been saying that. And you're right.
So let's talk structure. We've established that a good brief answers what happened, why it matters, what's next, and what to do. But what about the actual architecture of the document? Daniel mentioned working with high-level leadership — the kind of people who might only read the first paragraph before a meeting.
This is where the inverted pyramid from journalism is your friend. Most important information first. If the reader stops after one paragraph, they should still have the essentials. If they read two paragraphs, they get the nuance. If they read the whole thing, they get the full picture. But every layer should be independently useful.
The format should support scanning. Busy people don't read briefs linearly. They scan for what matters to them.
So you want clear section headers, bolded key terms or names, bullet points for lists. But — and this is important — you don't want the document to look like a mess of formatting. The design should be clean enough that the hierarchy is obvious at a glance.
There's also the question of what not to include. One of the most common mistakes in brief writing is including background that the reader already knows. If you're briefing a CEO who's been in the industry for twenty years, you don't need to explain what a key term means or who the major players are. You're wasting their time and signaling that you don't understand your audience.
That's something AI gets wrong constantly. AI defaults to being comprehensive. It'll explain the background, define the terms, provide the context. That's useful for a general audience. It's infuriating for an expert reader who just wants to know what changed since last Tuesday.
One of the human's key tasks in editing an AI-generated brief is stripping out the explanatory padding. The AI's instinct to be helpful actually makes the document less useful for the intended reader.
And that's hard to prompt away. You can tell the AI "assume the reader is an expert," and it helps, but you'll still find sentences that are obviously there for completeness rather than necessity.
Let's shift gears. Daniel mentioned using briefs for internal knowledge management — piping the information into a wiki or a CRM. I think that's an underappreciated use case.
A brief has a shelf life. The immediate version is read today, maybe tomorrow, and then it's stale. But the information in it has longer-term value if it's stored somewhere searchable. Six months later, someone wants to know when the regulatory issue first surfaced, or what the initial industry response was to a competitor's product launch. If your briefs are in a knowledge base, they can find that.
AI can help with that retrieval layer. You dump the briefs into a system that can search them semantically, and suddenly you've got an institutional memory that's actually accessible.
Which connects back to something we talked about before — AI's real power in documentation isn't in writing the perfect document. It's in making the documents findable and usable over time.
Though that only works if the briefs are good in the first place. Garbage in, garbage out.
But that's the whole point of having a process. If you have a consistent format, consistent quality standards, consistent editorial judgment, then your archive of briefs becomes a genuine asset. If every brief is ad hoc, the archive is just noise.
Let's talk about the process. Daniel's envisioning something like: an AI agent pulls in the week's developments on a topic, produces a first draft summary, and then the human verifies links, checks facts, and polishes the output. What would you add to that?
I'd add a step before the AI even touches anything, which is the human defining the scope and the key questions. Before you gather anything, you should write down: what am I trying to understand this week? What would make this brief useful to the reader? What are the specific things I'm watching for?
You're not just saying "give me the news about Omega three." You're saying "I'm watching for regulatory developments in the EU that could affect supplement labeling, I'm tracking competitor product launches, and I'm monitoring consumer sentiment about fish oil purity. Flag anything in those three buckets.
That pre-briefing step — defining the surveillance parameters — is what separates a useful monitoring product from a random collection of headlines. And AI can actually help with that step too. You can ask an AI: "I'm monitoring the fish oil supplement industry for a CEO. What are the categories of development I should be tracking?" It'll give you a decent starting list. You refine it based on your actual knowledge of the business. Then you use that refined list to guide your gathering.
There's a sort of meta-use of AI at the planning stage, before you even get to the writing stage.
And that's the pattern Daniel was getting at when he said the foundation is the process, not the writing. The writing is downstream of good thinking about what you're trying to accomplish.
Let me push on something. Daniel said he thinks AI has a role to play because it's so good at writing. I actually think AI is good at a certain kind of writing — fluent, grammatically correct, well-structured — but that's not the same as being good at the kind of writing a brief requires. A brief requires judgment, compression, and an almost intuitive sense of what the reader needs. Those are not things current AI models do reliably.
I'd frame it slightly differently. AI is good at the craft of writing — the sentence-level fluency, the structure, the transitions. It's not good at the thinking behind the writing. So the division of labor should reflect that. Let the AI handle the prose. Keep the thinking for yourself.
Here's my concern. If you let the AI handle the prose, and the AI's prose is fluent and confident-sounding, it can mask weak thinking. A human reading an AI-generated brief might be less likely to notice that the analysis is shallow because the presentation is so smooth.
That's a genuine risk. And it's why the human editorial pass is non-negotiable. You can't just glance at the AI's output and hit send. You have to read it skeptically, asking: is this actually saying something? Would this help the reader make a decision? If I were the reader, would I feel briefed or would I feel like I just read a book report?
A book report is exactly the right analogy. AI briefs often read like book reports — they summarize accurately but they don't have a point of view. A good brief has a point of view. It's not neutral. It's saying, here's what happened, and here's why you should care, and here's what I think you should do about it. Those last two are inherently subjective.
Which is uncomfortable for some people. It feels presumptuous to tell a CEO or a minister what they should do. But that's what they're paying you for. If they just wanted a summary of the news, they could read the news. They want your judgment.
The judgment gets better over time as you learn what the reader cares about, what decisions they're facing, what their blind spots are. A new analyst writing a brief for a new executive will be less useful than someone who's been doing it for six months. That's not a technology problem. That's a relationship problem.
Which is why I think the AI's role in this is limited, even as the technology improves. It can make the process faster. It can't replace the accumulated understanding of what a specific reader needs.
Let's talk about cadence. Daniel mentioned both daily media monitoring and periodic issue briefs. How does the frequency affect the product?
Daily briefs are about situational awareness. The bar is lower — you're flagging things that might matter, not doing deep analysis. The skill is in not flagging too much. If your daily brief is fifteen items long, you're not doing your job. You're just forwarding the news.
What's the right number?
It depends on the industry and the role, but as a rule of thumb, a daily media brief should have no more than five to seven items, and the top three should be the ones that actually matter. If there are fifteen things worth flagging, you're tracking too broadly or you're not being ruthless enough about what constitutes signal.
The weekly or biweekly brief is different?
The periodic brief should be more analytical. It's looking at patterns, not individual events. The three regulatory stories from this week might seem unconnected in daily briefs, but in a weekly synthesis you can say: there's a pattern emerging, here's what it means, here's what to watch for next week.
That synthesis step is where I think AI can actually add a lot of value. If you've got a week's worth of raw material, feeding it to an AI and saying "identify themes and patterns across these items" can surface connections you might have missed.
The AI is good at pattern matching across a corpus. It might notice that three different stories from three different sources are all pointing toward the same regulatory development, even if none of them states it explicitly. That's useful.
The AI's strength is horizontal — seeing across a wide set of inputs. The human's strength is vertical — understanding what those patterns mean for a specific decision-maker.
That's a really clean way to put it. Horizontal synthesis for the AI, vertical analysis for the human.
Let's get into some specific techniques. Daniel mentioned verifying links. If an AI agent is pulling in sources, how do you make sure they're real and correct?
This is a non-trivial problem. AI models can hallucinate sources, or they can mischaracterize what a source says. So the verification step isn't optional. You need to click through to the original article or document and confirm that it says what the AI says it says, and that it's from a credible source, and that the date is correct.
That's time-consuming. It might actually take as long as writing the brief from scratch.
But here's the counterpoint. If you're doing a daily media brief, you're probably already reading those sources anyway. The AI isn't replacing the reading. It's replacing the note-taking and the first draft. You still read the key articles. You just don't have to write the summary from a blank page.
The time savings come from the drafting, not from the research.
In my experience, yes. And the drafting time savings can be substantial. If you're producing a daily brief, going from a blank page to a polished product might take two hours. With an AI first draft, it might take forty-five minutes. That's an hour and fifteen minutes back every day. Over a year, that's real.
Assuming the AI draft is good enough to be a useful starting point. If it's not, you're spending forty-five minutes fixing it, and you might as well have written it yourself.
That's the threshold question. Is the AI draft saving you time or creating work? And the answer depends entirely on how well you've set up the process. If you've defined your surveillance parameters clearly, if you've given the AI good examples of what a good brief looks like, if you're working with a consistent format, then the draft is probably useful. If you're just saying "summarize the news about fish oil," you're going to get something that's more work to fix than to write from scratch.
The upfront investment in process design pays off in daily time savings.
And this is true of most AI applications in professional work. The people who get the most value aren't the ones using the fanciest models. They're the ones who've thought hardest about how to structure the work so the AI can be useful.
Let's talk about tone. Should a brief be dry and neutral, or should it have some personality?
It depends on the organizational culture and the relationship with the reader. But the default should be clear and direct, not dry. You want the reader to feel like a smart colleague is catching them up, not like a robot is reciting facts. If you can convey judgment without sacrificing clarity, that's usually better than sterile prose.
I think wit is dangerous in a brief. What's witty to you might be flippant to the reader. I'd err on the side of clarity and precision.
I'm not saying you should be cracking jokes. But a brief that sounds like it was written by a human who cares about the subject is more engaging than one that sounds auto-generated. Even if it was auto-generated.
That's an interesting tension. You want the efficiency of AI drafting, but you don't want the output to sound like AI. So part of the human's editorial job is injecting voice.
And I think that's actually one of the most satisfying parts of the workflow. The AI gives you a solid structure and factual foundation. You get to spend your energy on making it sound like you — sharpening the analysis, tightening the prose, adding the sentence that makes the reader go "huh, I hadn't thought of it that way.
The AI handles the commodity writing, and the human adds the distinctive value.
That's the ideal. Whether it works that way in practice depends on the human's skill and the quality of the AI output.
Let's talk about a specific scenario. Daniel mentioned briefing someone on key activities during the week — sort of a personal activity report. How is that different from a media monitoring brief?
A personal activity brief or weekly report is more self-directed. You're not summarizing external events. You're summarizing what you did and why it mattered. The structure is similar — here's what I worked on, here's what got accomplished, here's what's blocked, here's what I'm doing next week — but the sourcing is internal. It's your own work.
The audience is different. A weekly report to a manager is partly about accountability and partly about giving them ammunition. If your manager needs to report up to their manager, your weekly report gives them material. A good weekly report makes your boss look informed.
That most people miss. Your weekly report isn't just for your manager. It's for your manager to use with their manager and with peers. If you write it with that in mind — what would my boss want to share in their own meetings? — it becomes much more valuable.
AI can help with that? Summarizing your own week?
It can, but it needs input. You need to give it your raw notes, your calendar, your email summaries. The AI can synthesize that into a coherent narrative. But again, the judgment calls are yours. You know which project actually mattered, which meeting was significant, which blocked task is worth flagging.
I think the risk with AI-generated weekly reports is that they become generic. "Worked on project X, attended meeting Y, followed up on Z." That's not useful. A useful weekly report says: "We hit a milestone on project X that puts us two weeks ahead of schedule. The meeting with legal surfaced a compliance risk that needs escalation. The follow-up with the vendor is stalled because they haven't responded to our pricing proposal." The specifics matter.
The specifics come from you. The AI doesn't know that the compliance risk is the big story of the week unless you tell it. It'll just list the meeting as an item on the calendar.
The workflow for a weekly report might be: you jot down bullet points at the end of each day — the real highlights, not just what was on your calendar. At the end of the week, you feed those bullets to an AI and say "turn this into a coherent weekly report." Then you edit it for accuracy, emphasis, and voice.
That's a solid process. The daily bullet habit is the hard part. Most people don't do it. But if you do, the Friday afternoon report writes itself.
Because you've already done the synthesis in small increments. You're not staring at a blank page trying to remember what you did on Tuesday.
This is where I think the real productivity gain from AI isn't in the writing. It's in lowering the barrier to good process. If you know that five minutes of daily bullet points plus thirty seconds of AI synthesis gives you a solid weekly report, you're more likely to actually do the daily bullet points.
The AI makes the process feel worth it.
The output is immediate enough that the input feels justified.
Let's circle back to something Daniel mentioned about external stakeholders. He said sending a useful follow-up with outcomes and action items comes across as professional and organized. I think that's true and underleveraged.
After a meeting with an external partner or a client, sending a brief but precise summary of what was discussed, what was decided, and what the next steps are — that's a signal of competence. It says: we're organized, we follow through, you can trust us.
Most people don't do it. Or they do it poorly. They send a rambling email that buries the action items in paragraph three.
The post-meeting brief to an external stakeholder should be ruthlessly simple. Three sections: what we agreed, what we're doing, what you need to do. If the "what you need to do" section is empty, say that explicitly. "No action required from your side at this stage." That's still useful information.
AI can draft that from meeting notes or a transcript. But again, the human needs to verify that the AI correctly identified the decisions and action items. I've seen AI meeting summaries that confidently list action items that were never actually agreed to. They were discussed as possibilities, and the AI flattened that nuance into a commitment.
That's a common failure mode. AI struggles with the difference between "we should think about doing X" and "we agreed to do X." The human ear catches that distinction. The AI often doesn't.
The verification step for meeting follow-ups is especially important because the stakes are high. If you tell a client they agreed to something they didn't agree to, you've created a problem.
If you tell your own team they committed to something they didn't, you've created internal friction. So the editorial pass on AI-generated meeting summaries is not a courtesy. It's a necessity.
Let's broaden the conversation. Are there other types of briefs that benefit from this AI-plus-human workflow?
A few come to mind. Competitive intelligence briefs — what are our competitors doing, what does it mean for us. Policy briefs — what's happening in the regulatory environment, how should we respond. Trip briefs — you're going to meet with someone, here's what you need to know about them and the context. All of these have the same structure: gather, synthesize, analyze, recommend. And all of them benefit from AI handling the gather-and-synthesize steps, with the human doing the analyze-and-recommend.
The trip brief is an interesting one. If you're briefing a senior person before a meeting, you're not just giving them facts. You're giving them a strategy. Here's what the other person wants, here's where they're likely to push, here's what you should ask for, here's what you should avoid.
That strategy layer is intensely human. It requires reading the room, understanding the personalities, knowing the history. AI can give you the bio and the recent public statements. It can't tell you that the person you're meeting with is still angry about something that happened three years ago and will probably bring it up.
Unless that anger was documented somewhere. But usually it's not.
Usually it's in someone's head. Which is why the best trip briefs are written by people who've been around long enough to know the institutional history. AI can supplement that. It can't replace it.
We keep coming back to the same theme. AI is a powerful tool for the parts of brief writing that are about processing information. It's much weaker at the parts that are about judgment, relationships, and strategy.
I think that's a feature, not a bug. The parts AI can't do well are the parts that make the work interesting. If AI could do the judgment part, brief writing would be a fully automatable task, and the human role would disappear. Instead, AI handles the drudgery and leaves the interesting thinking to the human.
That's an optimistic framing. I'll take it.
I'm an optimistic donkey.
Let's talk about what happens when brief writing goes wrong. What are the classic failure modes?
The biggest one is the brief that's too long. It defeats the purpose. If your brief is ten pages, it's not a brief. It's a report. And the busy person you're writing it for won't read it.
Related to that: the brief that doesn't prioritize. Everything is given equal weight. The reader has to figure out what matters. That's the writer outsourcing their judgment to the reader.
Another failure mode is the brief that's all facts and no analysis. It tells you what happened but not what it means. That's a news digest, not a brief.
The opposite failure mode is the brief that's all opinion and no facts. The writer has strong views but can't back them up with evidence. That's an op-ed, not a brief.
Then there's the brief that's written for the wrong audience. It's too technical for a generalist reader, or too basic for an expert. It uses internal jargon that the recipient doesn't know. It assumes knowledge the reader doesn't have, or explains things the reader already understands.
That last one is probably the most common, and the hardest to fix, because it requires understanding the reader's context. You can't fake that.
No, you can't. And it's why the best brief writers tend to be people who've worked closely with the recipient for a while. They've internalized what the person knows and doesn't know, what they care about and don't care about.
Let's talk about AI's failure modes specifically. We've touched on a few — hallucinated sources, flattened nuance, generic prose.
AI can be too balanced. It'll present both sides of an issue even when one side is clearly more credible or more relevant. That's appropriate for some contexts, but in a brief, you often need to take a position. "There's a lot of noise about X, but the credible signal is Y." AI tends to hedge.
It's trained to be helpful and harmless, not to have conviction.
And conviction is what makes a brief useful. If I'm a decision-maker and I read a brief that says "on the one hand this, on the other hand that," I'm no better off than before I read it. I need someone to say "here's what I think is actually happening and why.
Another AI failure mode: it's bad at silence. If something didn't happen, the AI won't necessarily flag that as significant. But in many contexts, the absence of an expected development is the most important thing in the brief. "The regulatory ruling we were expecting didn't come down this week, which suggests it's been delayed or there's internal disagreement at the agency." An AI won't surface that unless you explicitly ask.
That's a really sharp observation. AI is good at summarizing what is in the sources. It's bad at noticing what should be in the sources but isn't. That's a human pattern-recognition skill.
The human's pre-briefing step — defining what you're watching for — serves double duty. It guides the AI's gathering, and it primes the human to notice when something expected didn't happen.
The surveillance parameters are also absence detectors.
Let's talk about one more thing Daniel raised. He mentioned that brief writing is an undervalued skill. Why do you think that is?
I think it's because the output looks simple. A good brief is short, clear, and seemingly effortless. It doesn't look like hard work. So people assume it wasn't hard work. They don't see the hours of reading, the discarded drafts, the editorial choices, the accumulated knowledge that makes the brevity possible.
It's like good design. When it's done well, it's invisible. You only notice it when it's bad.
I think the rise of AI is going to make this dynamic worse. When people see that AI can produce a summary in seconds, they'll devalue the skill of summary-writing even further. They won't understand that the AI summary and a good human brief are different products entirely.
The AI summary is a commodity. The human brief is a craft product. They look similar on the surface, but the craft product contains judgment, context, and strategic intent that the commodity doesn't.
The danger is that organizations will look at the commodity and say "good enough." They'll stop investing in the craft. And then six months later they'll wonder why their leadership is making worse decisions.
Because the information they're getting is shallower.
It's the classic cost-cutting trap. You save money on the brief and pay for it ten times over in decision quality.
The counterargument — if you're a brief writer trying to justify your value — is that good briefs pay for themselves in better decisions.
But that's hard to measure. How do you quantify the decision that didn't go wrong because someone had good information? It's easier to measure the cost of the brief than the benefit.
Which is why brief writing remains undervalued. The benefits are diffuse and long-term. The costs are visible and immediate.
I don't have a solution to that. I just think it's true.
Let's get practical for the last stretch. If someone's listening and they want to get better at writing briefs, with or without AI, what should they do?
First, read good briefs. If your organization has examples of briefs that senior people actually found useful, study them. What's the structure? What's the tone? What's the ratio of facts to analysis? Second, practice compression. Take a long report and try to summarize it in one page, then in three paragraphs, then in one paragraph. Each compression forces you to make harder choices about what's essential.
Third, get feedback from your reader. Ask them: was this useful? What was missing? What was unnecessary? Most people never ask, so they never improve.
Fourth, develop the pre-briefing habit. Before you start gathering information, write down what you're looking for and why. That discipline alone will improve your output more than any writing tip.
Fifth, if you're using AI, treat it as a drafting tool, not a thinking tool. Use it to handle the prose. Don't use it to handle the judgment.
I'd add a sixth. Build a personal knowledge base. The more you know about the domain, the faster you can separate signal from noise. Brief writing gets easier with domain expertise. There's no shortcut.
Domain expertise accumulates over time if you're paying attention. Every brief you write makes the next one slightly easier because you're building a mental model of the landscape.
Which is why consistency matters. The person who's been writing the weekly brief for a year is dramatically more efficient than the person who just started, even if they have the same raw intelligence.
If you're an organization, don't rotate brief writers too frequently. The institutional knowledge that accumulates in the brief writer's head is valuable and hard to transfer.
Though you should document it so it's not entirely lost when the person moves on. Which brings us back to the knowledge management point. If your briefs are in a searchable system, the next person has a running start.
I think we've covered the ground Daniel was asking about. The best practices, the AI workflow, the pitfalls,
